Book of Abstracts
2019
The main conference on the scientific analysis
of mobile phone datasets
8-10 July 2019 Mathematical Institute,
Oxford University, Oxford, UK
Organisation:
Renaud Lambiotte (Oxford University)
Esteban Moro (Universidad Carlos III de Madrid)
Vincent Blondel (UCLouvain)
Alex 'Sandy' Pentland (MIT)Site:
http://netmob.org/

Session Economic

Economical Segregation of Encounter Networks in cities
Esteban Moro,1, 2, ⇤ Dan Calacci,2 Xiaowen Dong,3, 2, † and Alex Pentland2
1

Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganés, Spain
2

Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
3
Department of Engineering Science, University of Oxford, Oxford, UK

Segregation is one of the most important population processes in cities: in the US one in five city dwellers live in a very
income-segregated community [1]. Social or income segregation is a spatial process and most work has focused mainly inresidential segregation, i.e. on the basis of places of residence.
But due to increases in mobility of people today, segregation
is a process than goes beyond home or work places [2, 4].
Furthermore, as Ray Oldenburg argued [3], third places (nothome or work) in which people mix are important for civilsociety, democracy or civic engagement. But are there stillenough third places in our cities to do that? How large andhow diverse is the fraction of people that we encounter everyday? How is segregated is our mobility?
To answer that question we study a unique database of 3
billion location events of 329k users in the Boston metropolitan area during 6 months. Using those data we identify 10k
di↵erent places where people of di↵erent economical backgrounds mix and construct the bipartite network that explains
the mobility of individuals and places in the network (see figure 1). When we project the bipartite network onto places,
we found that most of the places in which there is a lot of
economical mixing (third places) are related to shopping andleisure activities and that patterns of mixing depend on theactual character of the place and the surrounding area [5]. On
the other side, when projecting on individuals, we were able toconstruct the high-frequency encounter network due to theirmobility. We find that those encounter networks [6] have alarger degree of economical segregation (large assortativity inA)income), but much less that what residential suggests, showing the important role of mobility to explain social segregationin our cities. Beyond the static view of those networks, wetest the resilience of the segregation networks towards the removal of those social mixing (3rd places) finding that, in mostcases, the economical segregation of cities severely depends onthe existence of those places. We discuss the implications of
our results in the context of future development of areas andin the ever-changing evolution of our cities.

⇤†

Corresponding author: emoro@math.uc3m.es
Corresponding author: xdong@robots.ox.ac.uk[1] Sampson, R. J. (2017). Urban sustainability in an age of enduring inequalities: Advancing theory and ecometrics for the
21st-century city. PNAS, 536(7617), 201614433.
[2] Wissink, B., Schwanen, T., & van Kempen, R. (2016). Beyond
residential segregation: Introduction. Cities, 59, 126?130.
[3] Oldenburg, Ray. The great good place: Caf, co↵ee shops, community centers, beauty parlors, general stores, bars, hangouts,
and how they get you through the day. Paragon House Publishers, 1989.
[4] Silm, S., & Ahas, R. (2014). The temporal variation of ethnic
segregation in a city: Evidence from a mobile phone use dataset.
Social Science Research, 47, 30?43.
[5] De Nadai, M., Staiano, J., Larcher, R., Sebe, N., Quercia, D.,& Lepri, B. (2016, March 13). The Death and Life of Great
Italian Cities: A Mobile Phone Data Perspective. arXiv.org.
[6] Sun, L., Axhausen, K. W., Lee, D.-H., & Huang, X. (2013).
PNAS 110(34), 13774?13779.

B)

$$

$
$

$$

wij
$
$$
$
$$$

Average Neighbours
incomeaverageof neighbors
income

C)

$$

$$$

Totally segregated network

150000100000Totally mixed network
50000

50000

100000150000User income

User income
FIG. 1. A) Mobility segregation map around Boston. Each place in which there is at least encounters by 20 people is shown with acolor proportional to the economical segregation (larger values are more segregated). B) Encounter/segregation network obtained from thebipartite individuals-places, where wij is the link weight between two individuals that measures the strength (duration) of the encounter.
C) Average income of the neighbors of an individual as a function of her income. Real data. (black) is compared with the full mixed
network (red, individual neighbors have random income) and the fully seggregated network (green, individual only meets neighbors ofsimilar income).

Netflix or Youtube? Regional income patterns of mobile service consumption
Iñaki Ucar⇤ , Marco Gramaglia⇤ , Marco Fiore† , Zbigniew Smoreda‡ and Esteban Moro⇤⇤ Universidad Carlos III de Madrid † CNR-IEIIT ‡ Orange Labs - SENSE
Introduction: Among the countless use cases that the analysis of the digital footprints created by the widespread adoptionof fully network connected and portable devices allows to
explore, a very promising but yet partially unexplored one is
the study of socio-economic development of the society withrespect to their interaction with the technology.
Motivated also by the pioneering works in [1], [2], in thisone, we try to go one step further in this direction by using
and unprecedented data source that allows us to capture aricher variety of features. As the current digital society isshaped around the Internet and their applications, we believethat a data set containing fine-grained information of mobile
applications usage such as the one analyzed here will help
to unveil currently hidden socio-economic aspects at very
different geographical levels (e.g., city, region, country).
The data set analyzed here records the mobile traffic exchanged by users of the largest mobile network operator ina major European country. The data set consists of around
3.7 billion timestamped records, collected between May andJune 2017, with a temporal granularity of 5 minutes. Data
is aggregated at the Base Station (BS) level, and reports thetotal traffic (for uplink and downlink) for a particular mobile
application. Applications are grouped into 40 categories that
include very popular ones, such as Youtube, Facebook or
Netflix, and others that identify the type of device, such as
Apple Store or Google Play.
Methods and Results: On the one hand, traffic data are gathered per BS, for which we compute the Voronoi tessellation
around them (BS zones). On the other hand, geographical,
economic and demographic data are gathered per statisticalzone. We only consider those for which we have income
data (around 12k zones), i.e., populated urban areas (income
for zones with less than 1000 inhabitants is not publicly
available). Therefore, it is reasonable to assume that the traffic
data generated in each BS zone was approximately evenly
distributed, and thus we use a real weighted interpolation to
map traffic data counts from BS to our target statistical zones.
As our goal is to infer relationship between the medianincome in a given area from the usage pattern of the 40 defined
traffic categories. First of all, we aggregate traffic by hour andperform a hierarchical clustering of the usage for each hourof the day during weekdays. This clustering reveals a clear
structure: hours from 8 to 17, or “working hours”, are highly
correlated. Instead, we are interested in “home hours”, from18 to 7 h, so that we can minimize the effect of people moving
across areas during working hours as demographic information
matches the inhabitants only.
Following this hierarchical clustering, we aggregate downlink traffic per category and area for home hours during
weekdays, and normalize the usage by population for each area
Email addresses: {inaki.ucar,marco.gramaglia,esteban.moro}@uc3m.es,
marco.fiore@ieiit.cnr.it, zbigniew.smoreda@orange.com

Standardized Coef. Magnitudes
Apple Store
News
Mail
iTunes video streaming
SnapChat
Generic web
Facebook video streaming
Generic video streaming
Audio streaming
Adult
downlink_zon
Instagram
Google Play
Facebook
Encrypted web
iCloudGoogle Drive
Instagram video streaming
Twitter
VOIP

sign
NEG
POS

0

100

200

300

400

coefficients

Fig. 1. The GLM standard coefficients

to obtain the activity in bytes/inhabitant. Then, we compute
the Revealed Comparative Advantage (RCA) index of suchactivity, which measures the relative importance of a category
in a certain area compared to the total importance in all areas.
Finally, we re-arrange the data into 41 explanatory variables(RCA per traffic category and the total downlink bytes count)for each zone.
A penalized Generalized Linear Model (GLM) reports an
r2 = 0.42, which suggests that mobile applications usage is avery valuable data source for analyzing and predicting socioeconomic development. Moreover, the analysis of the magnitudes of the standard coefficients show interesting insights.
Our results indicate that the usage of Apple devices (identified
by categories such as Apple Store, iCloud and iTunes video
streaming) has a positive correlation with the consumption ofNews, Mail, Instagram and Generic Web content, and all thesefeatures have a positive impact in the prediction of the medianincome. On the other hand, the usage of Android devices(identified by categories such as Google Play) has a positive
correlation with the consumption of Facebook, SnapChat andAdult content, and these features have a negative impact in
the prediction of the median income.
Limitations and Future Work: Currently, this study is constrained to populated urban areas due to a limitation in the
public availability of income data. We only consider coarse
aggregates, thus losing the fine-grained features that may beavailable in the complete time series. However, the achievedcorrelation is inline with the one obtained by [1], [2]. Furthermore, we are not controlling factors such as tourist areas
and other effects that may be adding noise to the data traffic
gathered in certain zones. Despite this, these results open up
the opportunity to analyze socio-economic development witha rich source of data with a promising predictive power.
R EFERENCES[1] A. Llorente, et al. ”Social media fingerprints of unemployment.” PloS
one 10.5 (2015): e0128692.
[2] L. Pappalardo, et al. ”An analytical framework to nowcast well-being
using mobile phone data.” International Journal of Data Science andAnalytics 2.1-2 (2016): 75-92.

Enhancing financial inclusion with mobile phone data
Marı́a Óskarsdóttir⇤ , Cristián Bravo† , Carlos Sarraute§ , Bart Baesens⇤† and Jan Vanthienen⇤⇤ Faculty of Economics and Business, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium,

Email: {maria.oskarsdottir, bart.baesens, jan.vanthienen}@kuleuven.be† Southampton Business School, University of Southampton, United Kingdom, Email: c.bravo@soton.ac.uk§ Grandata Labs, Argentina, Email: charles@grandata.com

Financial inclusion is a globally recognized problem with large parts of the population having limited access to useful andaffordable financial products and services, such as transactions, savings, credits and insurance [1]. In particular, over 2 billion
people worldwide do not have a regular bank account and hence no means of obtaining credit in the traditional way since
their –nonexistent– banking history cannot be used to assess their creditworthiness. At the same time, cell phone ownershipin developing countries is increasing as reports from developing countries show [2], [3]. In this research, we demonstrate howmobile phone data can be used in credit risk models, that, in some cases, result in more accurate credit scores than models
built with traditional bank history data [4].
We use a combination of data sets consisting of both customer bank data and mobile phone data, which allows us to constructsocial networks based on the exchange of phone calls between people. Thus we have the opportunity to explore the potentialof enriching traditional credit scoring models with social network effects reflecting calling behavior. Our goal is to build credit
scoring models for bank customer applying for credit cards. For each applicant, we extract several features which fall into oneof the following groups:
1) Socio-Demographic: traditional bank history features including bank history, income and debit account behavior.
2) Calling Behavior: aggregated values for number and duration of phone calls made and received on different days andat different times of the day. These features characterize the intensity, diversity and timing of the applicants’ calling
behavior.
3) Link-Based: features that represent the individuals in an applicant’s nearest neighborhood, such as the number of neighborsthat have payment arrears.
4) Influence Score: the scores each applicant obtained after two distinct influence propagation algorithms are applied to the
network. Credit card holders who already defaulted are used in lieu of information source.
Furthermore, we use the bank data to label the credit card applicants as defaulters and non-defaulters, depending on their
payment arrears during the twelve months after receiving the card.
We build credit scoring models with all the extracted features using random forests. Thus, we are able to identify whichfeatures are most important for the model performance. The models are evaluated from a statistical perspective using thecommonly applied AUC measure and from an economic perspective using the Expected Maximum Profit measure for credit
scoring [5]. It has the advantage of considering the expected losses and operational income generated by a loan, and is thus
tailored towards the business goal of credit scoring. Moreover, when applied to credit scoring models, it facilitates computing
the model value and allows us to identify which features are most favorable in terms of profit.
Our results show that combining mobile phone data with traditional data in credit scoring models significantly increases theirperformance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features.
In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic
performance This is an interesting result since it indicates that people’s phone usage can be used as the sole data source when
deciding whether they should be granted a credit. Mobile phone data is a powerful source of information for credit scoring.
In the context of positive information, it has the potential to enhance financial inclusion by increasing the access to financing
to borrowers who would otherwise be out of options.
R EFERENCES[1] World Bank (2018), Financial Inclusion, viewed 27. April 2018, URL: http://www.worldbank.org/en/topic/financialinclusion[2] Pew Research Center, April, 2015,“Cell Phones in Africa: Communication Lifeline”, accessed 27 April 2018, URL:
http://www.pewglobal.org/2015/04/15/cell-phones-in-africa-communication-lifeline/[3] De Luna-Martinez, J. 2017. Financial inclusion in Malaysia : distilling lessons for other countries (English). Washington, D.C. : World Bank Group.
Accessed 27 April 2018. URL: http://documents.worldbank.org/curated/en/703901495196244578/
Financial-inclusion-in-Malaysia-distilling-lessons-for-other-countries[4] Óskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J. and Baesens, B., 2019. The value of big data for credit scoring: Enhancing financial inclusion
using mobile phone data and social network analytics. Applied Soft Computing, 74, pp.26-39.
[5] Verbraken, T., Verbeke, W. and Baesens, B., 2013. A novel profit maximizing metric for measuring classification performance of customer churn prediction
models. IEEE Transactions on Knowledge and Data Engineering, 25(5), pp.961-973.

Session Social Good

Abstract Submission
Netmob 0

FlowKit: An open-source CDR analysis toolkit for humanitarianand development purposes
Daniel Power, Martin Thom, Jonathan Gra , Ma imilian Albert, Chris Brooks, Sophie Delaporte,
Trace Li, James Harrison, Joshua Greenhalgh, Nick Thorne and Linus Bengtsson
Fl mi de F
da iCorresponding author email: flowkit@flowminder.org
Call Detail Records (CDRs) and other mobile phone data can provide a d namic, near real-time pictureof the mobilit and movement of millions of people across entire countries and, in combination withother data sources, can also provide information on people s characteristics, such as thesocioeconomic status of individuals and communities [1]. With the right tools, actionable insights canbe derived from this data and made available to decision-makers in a timel and accessible wa . Whilethe commercial world routinel uses mobile data to do ever thing from targeting food purchases tooptimi ing one s route to work, the humanitarian and development sector lags behind in optimi ing
service deliver with mobile network data. Despite the fact that 5 billion people are currentl connectedto the mobile internet, the data generated from cell phone use is still a novelt for most humanitarianand development organi ations due to polic concerns about user safet and the lack of turnke
product solutions.
To address a ke part of this problem, we have created FlowKit [2]: a suite of software tools that help
humanitarian and development organi ations access and anal e mobile data. FlowKit is targeted at
the distinct needs of humanitarian and development practitioners in three ke wa s:
1.

FlowKit provides a mobile data anal sis toolkit informed b the Flowminder team s e perience
producing insights from mobile phone data to support the humanitarian and development
communities. Because FlowKit has been developed with a strong understanding of the needs
and priorities of collaborating partners
humanitarian and development organi ation and
MNOs it is able to facilitate collaboration and help form strong partnerships. It provides thenecessar features and support to allow all parties to engage in an eas , low-cost manner. The
implementation of this toolkit can be modified and customi ed according to the needs andconstraints of a range of different end users.

2.

Complications around securit and privac tend to be the single biggest blocker to this t pe ofdata being used b humanitarian and development organi ations. FlowKit facilitates secureand GDPR-compliant data access, and also provides processing with built-in qualit assurance,
lowering overhead costs for users and smoothing the path to data anal sis.

3.

FlowKit is released under the MPLv2 open source license [3], thus promoting the transparencof its techniques as well as future sustainabilit b encouraging additional development andcontributions from others working on similar challenges.

FlowKit will continue to grow and evolve with the addition of a modular CDR QA suite, additions andenhancements to the API, stronger connectivit to other data platforms, and support for more data
anal sis environments. We will present an overview of the features FlowKit provides for data accessand anal sis.
[1] J. E. Steele et al., Journal of the Ro al Societ , Interface 14(127), 20160690 (2017)[2] FlowKit, https://flowminder.github.io/FlowKit/[3] Mo illa Public License, version 2.0, https://www.mo illa.org/enUS/MPL/2.0/

Mapping geographical communities using Call Detail Records and
OpenStreetMap
Joanna Wilkin 1, *, Dr Eloise Biggs 2 and Professor Andrew J Tatem 1
WorldPop, Geography and Environmental Science, University of Southampton
School of Agriculture and Environment, University of Western Australia* Correspondence: jw3e15@soton.ac.uk
1
2

Abstract
Mobile phone call detail records (CDRs) form an integral part of the UN’s proposed data
revolution, which aims in part to mobilise the use of data, traditional and innovative, to help
monitor and achieve the recently agreed Sustainable Development Goals. Within thesegoals, the scale of the community features highly; Goal 11, for example, aims to foster
sustainable communities. Despite this focus on the community within sustainable
development, CDR analyses are not yet provided at this scale. Typically, CDRs are spatiallyaggregated at the coverage level of the cell tower or aggregated further to an appropriate
administrative region. This paper explores the possibility of refining the spatial resolution of
CDRs to the community scale, whilst preserving the required anonymity of the users.
Utilising emerging literature on social spatial networks, where communities ‘adhere to theold-school definition encompassing shared area and based on social ties’ (Sarkar et al, 20161),
and building upon kernel density community mapping methods used by Comber et al(2012)2, Gao et al. (2013)3 and Shi et al. (2015)4, this paper tests whether geographicalcommunities can be identified within CDR data. Already, big spatial network datasets haveshown how geography is a compartmentalizing factor, revealing distinct spatiallyembedded social networks identified as communities. The next step, assigning thesecommunities with a geographical reference is yet to be trialled. By integrating CDR-detected
communities with OpenStreetMap data, this paper tests whether they can be associated witha known geographical community. Pilot testing on CDRs from Nepal has indicated thattowns and cities can be geographically matched to specific CDR-detected communities. This
geo-referencing enables each community member’s data to be attributed to that community,
rather than a cell tower. As a result, aggregation can occur at the community level,
providing a more appropriate scale to be used within sustainable development work. For
example, the next step for this research will be to extract social network metrics for eachdetected community to be used in a community resilience index, similar to Cueto et al(2017)5. Further testing of this innovative geo-referencing methodology within four districtsof Nepal is underway and will be complete by the conference.
Reference List
1.
Sarkar, D., Sieber, R. & Sengupta, R. GIScience considerations in spatial social networks. in The Annual
International Conference on Geographic Information Science 85–98 (Springer, 2016).
2.
Comber, A. et al. Exploring the geography of communities in social networks. in
3.
Gao, S., Liu, Y., Wang, Y. & Ma, X. Discovering spatial interaction communities from mobile phone data.
Transactions in GIS 17, (2013).
4.
Shi, L., Chi, G., Liu, X. & Liu, Y. Human mobility patterns in different communities: a mobile phone data-basedsocial network approach. Annals of GIS 21, (2015).
5.
Cueto, D. N., Villalta, I. V. & Bernal, M. N. Resilience to disasters and social capital. Analysis of social
networks in peripheral neighborhoods of the city of Cusco, Peru. Boletín la Asoc. Geógrafos Españoles 74,(2017).

CubicA A recommender system improving information delivery to smallholder farmers through IVR
Ch i ophe Boc

e , K i na Tom ů¹, Lucio Melito¹, Denys Sementsov¹, Jamie Arkin², Leah Newman²,
William Tinzaara³, Vincent Johnson³, Jérôme Urbain¹¹Dalberg Data Insights, ²Viamo, ³Bioversity International

In Uganda, with a population expected to increase
from 40M today to 100M in 2050, agriculture
provides a livelihood to 77% of the population (FAO,2018). However crops are afflicted by several
challenges including unpredictable and remotemarkets, low financial inclusion, pests and diseases,
environmental disasters and natural resource
depletion. To better address those challenges,
smallholder farmers need access to the rightinformation at the right time. Today, an average of
40.000 Ugandan call Viamo 321-Service, aninteractive voice response (IVR) system, every month
to learn about agricultural practices and accessweather forecasts. The service works like a voicemailbox: anyone can simply call into this hotline for freeand listen to the information menu anywhere,anytime, in their local language, on any mobile device.
While the 321-Service continues to expand every
day, IVR systems are built as a one-size-fits-all.
Therefore callers are directed through multiple layersof listen-and-choose steps in order to access one ofhundreds of messages available on the service, which
can be complicated, time consuming, and tedious.
CubicA is an artificial intelligence (AI) and a big data
analytics module developed to improve theinformation delivery of IVR systems ensuring callers
receive the right message at their time of need. The
idea is to shift the current system towards a realsearch engine. Through the integration of machinelearning leveraging previous caller data to develop a
recommender system along with satellite imagery,meteorological data and other data points to identify
underperforming lands for instance, CubicA will (i)build live profiles for callers and (ii) optimize thecurrent structure and content delivery beyond
traditional information stylization. The existing topdown elementary and rather static information
service will be enriched and extended to also produce
bottom up insights to generate targeted content andbetter user experience. We believe that such a tool
will help farmers to both (i) make strategic decisions
as well as (ii) anticipate, identify and monitor pests
and diseases. Eventually, crowdsourced data
produced by the tool should also create value forgovernment ministries & NGOs, input & financeproviders, and producers who strengthen the
agricultural sector and are tasked with guaranteeing
food security.

The team is developing the recommender system that
ill make Viamo e i ing IVR e ice mo e d namico e ime and c omi ed o diffe ene need .
The development of the Recommender System
ep e enhe co ne one of C bicA
echnical
aspect. With more than two years of user data from
Viamo existing IVR system (including 2 million
unique users, 8 million user journeys including over
95 million interactions with database of 39 thousand
items) combined with external big data sources, there
is an abundance of data that can be exploited to infer
people need and an e en befo e he p o ide
the system with this information.
Our work is focusing on two main lines of inquiry:classical Latent Factor Models augmented bycontextual data, and neural networks that are
particularly apt at dealing with sequential
instructions.
x

The former models try to learn lowerdimen ional embeddingof people
preferences, inferred from the implicit
feedback they have provided by listening to aparticular message among the choices they
were presented with. They can be enhanced
by including information not normallyavailable in their standard representation,
like the weather situation their region was
experiencing at the time, or machineunderstandable representations of the text
users listened to.

x

Deep recurrent neural networks, on theother hand, are perfect at capturing temporalcharacteristics implicit in the data (i.e. in its
simplest form message B always being
listened after message A. They are also ideal
for scenarios that might require repeatedrecommendations.

The testing of these models is accompanied by theusage of relevant evaluation metrics (e.g. precision atK, reciprocal rank etc.) and by ablation studies aimed
at understanding the contribution of each component
to the final predictions. Baseline benchmarking
already shows results exciding initial expectations.

https://bigdata.cgiar.org/inspire/inspire-challenge-2018/cubica-the-new-farmer-advisory-app/

Abstract Submission
Netmob 2019Urban poverty mapping in Haiti
Using linked household survey and Call Data Records for high-resolution
estimation
Guilherme A. Zagatti*, Claudio Bosco, Samantha K. Watson, Nancy Lozano-Gracia, Emilie Perge, Sering
Touray, Linus Bengtsson
Flowminder Foundation and The World Bank Group* Corresponding author email: guilherme.zagatti@flowminder.org
According to the World Bank Development Indicators, Haiti is the poorest country in the American
continent with an estimated poverty headcount of 23.5 percent in 2012. While the country has many
development challenges ahead, there is significant deficit in data to inform national and local
policy-making, with the latest nationwide representative household survey only completed in 2012 fromwhich the poverty rate above originates.
Call Detail Records (CDR) present an opportunity for population characteristic mapping estimation, withparticular regards to poverty. Indeed, a number of prior studies have demonstrated the feasibility ofusing CDR for high-resolution poverty mapping estimation. Blumenstock et al.1 uses CDR to predict
poverty rates captured from a cell phone survey representative of the subscriber base, showing that
their model would more accurately predict subsequent DHS surveys than using past ones in Rwanda.
On the other hand, Steele et al.2 shows that even in the absence of respondent-level data it is possible toestimate poverty using CDR.
The present study builds on the promises of these early findings. We conduct a stratified sample
household-survey that captures household characteristics associated with poverty as well as cellphone
usage patterns in the municipality of Cap-Haitien. Poverty is estimated using a selected number ofindicators via the SWIFT methodology developed by the World Bank. With the spartcity of studies
linking individual survey data with CDR, this study innovates exactly on that, providing the opportunity toestimate poverty at the subscriber level and to model individual heterogeneity more accurately. It also
allows us to understand the representativeness of CDR-based estimates with regards to the complete
population. Finally, since the SWIFT indicator attempts to capture income-based poverty, we investigate
whether CDR can estimate consumption-based indicators of poverty as accurately as asset based
ones. Steele et al. reports wide disparities in the accuracy of prediction of asset-based indicators versus
income- and expenditure- ones, with significant higher levels of accuracy for predicting the former.
The research is still ongoing. While the data has already been captured and processed, modelling andanalysis of the main findings are planned to be completed by the end of April 2019. We plan to proceedby estimating home locations following Zagatti et al.3 and to estimate poverty using latent Gaussianmodels within a Bayesian framework. CDR and geospatial covariates of interest are linked to a
structured additive predictor via a link function, while the correlation induced by geography and social
links are modelled, respectively, via a Gaussian process and by extending the classical Besag model tomultigraphs. Latent Gaussian models are simpler to implement, easier to interpret and provide
confidence intervals which are extremely important to evaluate our degree of uncertainty.
This paper is the result of research commissioned by The World Bank Group, and performed jointly by
Flowminder, the World Bank and Digicel.
1Blumenstock et al. 2015. Predicting poverty and wealth from mobile phone metadata. Science 350, 1073–1076.
Steele, J.E. et al. 2017. Mapping poverty using mobile phone and satellite data. Journal of The Royal SocietyInterface 14, 20160690. https://doi.org/10.1098/rsif.2016.0690
3
Zagatti, G.A. A trip to work: Estimation of origin and destination of commuting patterns in the main metropolitan
regions of Haiti using CDR. Development Engineering 3, 133–165.
https://doi.org/https://doi.org/10.1016/j.deveng.2018.03.002
2

Maximum likelihood reconstruction of population densities frommobile signalling data
Jan van der Laan⇤, Edwin de Jonge†
Introduction

Shepp and Vardi (1982). They derive an expectation
maximisation (EM) algorithm to obtain estimates ofthe density, which in our case is equal to:

For policymakers, (local) governments, and emergency
services, it is important to have detailed and timely es(n)timates of the spatial density of the number of persons.
X
x̂j
H y
(n+1)
Therefore Statistics Netherlands started the Real-time
P ij i (n) .
x̂j
=P
(2)
H
ij
i
k Hik x̂
Population Statistics project which aims to get (alimost) real-time estimates of the population densityfrom aggregated and anonymised signalling data. In This is an iterative method that updates the estimate
this paper we will present a method to obtain max- of the population density in each step. The likelihood
imum likelihood estimates of the population density increases with each step.
using signalling data.
Results
Methods

Figure 1 shows the results of applying the method to
We will assume that the geographic area of interest is simulated data. With the EM-algorithm the estimates
divided into a number of subregions j (j = 1, 2, . . . , Np ) are more localised and the two hotspots top left are
which we will call pixels (although they do not neces- better reconstructed. We are currently applying thesarily have to be rectangular and of equal size). The method to actual signalling data and comparing thegoal is to estimate the number of devices xj in each of results to official population estimates. The results ofthe pixels from the number of devices yi connected to these analyses will be presented at the conference.
(generating an event with) antenna i (i = 1, 2, . . . , Na ).
To estimate the number of devices in a pixel we need References
the probability that a device in pixel j connects with Salgado, D. et al. (2018a). Proposed elements for a methodological framework for the production of official statistics withantenna i, Hij . In our case we used an antenna model
mobile phone data, ESSnet Big Data, WP5, deliverable 5.3.
to estimate the signal strength at each pixel (using
Technical report, Eurostat.
properties of the antenna) from which the probability of connecting to that antenna is derived (Salgado Salgado, D. et al. (2018b). Some it elements for the use of mobile
phone data in the production of official statistics, ESSnet Big
et al., 2018a,b). The expected value of yi is given by
Data, WP5, deliverable 5.4. Technical report, Eurostat.
X
Hij xj .
(1) Shepp, L. and Y. Vardi (1982). Maximum likelihood reconstruci =jtion for emission tomography. IEEE Transactions on Medical
Imaging MI-1 (2).

The yi are independently Poisson distributed. This
leads to the same likelihood function as discussed in⇤ Statistics Netherlands, dj.vanderlaan@cbs.nl† Statistics Netherlands, e.dejonge@cbs.nl(a) True(b) Current method(c) Maximum Likelihood - EM

Figure 1: Comparison of the two types of estimators to the true population density (red dots are antennas) fora simulated data set. In this example the MSE is reduced by 30%.
1

AIDA, the Action Insights Data Platform
Rositsa Zaimova, Kristýna Tomšů, Astrid Van Lierde, Jérôme Urbain, Lucio Melito, Christophe Bocquet,
Denys Sementsov, Damien Jacques, Frédéric Pivetta
Dalberg Data Insights, Brussels, Belgium, jerome.urbain@dalberg.com
Introduction. Relevant, accurate and timely data areneeded to track progress towards the Sustainable
Development Goals (SDGs), support policy decisionand promote sustainable development. Yet, collecting
data for official statistics is expensive and available
data sources in developing countries are often
outdated or lacking granularity. The Action Insights
Data Platform (AIDA) developed by Dalberg Data
Insights (DDI) aims to bridge the resulting knowledge
gap by harnessing the unique features of data fromthe private sector (mobile phone data, satellite
imagery, traffic sensors…) to develop and test new
methods that can supplement official statistics indeveloping and emerging countries.
Objective. In partnership with the United Nations
Capital Development Fund (UNCDF) and the Belgian
Government, DDI has been developing technicaltools, operational processes, legal frameworks as wellas business and commercial models to allow public
and private stakeholders to leverage both data andalgorithms in emerging and developing countries.
Partnerships. This approach has required to develop
specific processes ensuring privacy and creating atrust environment. The different stakeholders ofAIDA are Data Providers (both public and private),
Developers (e.g., DDI, technical teams of local
regulators), End-Users and Funders. AIDA is a hybrid
open/closed platform:
x Some data are public and open, while others are
privatex Some algorithms and visualization tools areopen, while others are closed depending on thedevelopment clauses
On the legal side, we have developed first sets ofcontracts, for both the data holders and the endusers, to clarify the processes, commercial model,
responsibilities and scope. AIDA has alreadydemonstrated how to generate economic value andsocial impact during its pilot phase; and developed apricing structure depending on the project goals.
The platform was piloted in Uganda and has now been
deployed in 7 countries. AIDA provides data insights
in various topics such as urban planning, foodsecurity, financial inclusion, energy, youth
unemployment, gender inequalities and public health.
For each use case, we worked together with the endusers not only to define the relevant metrics, but also
to build interactive dashboards to act on the insights.
AIDA hence offers an end-to-end solution, from data
access to data visualization and covers all steps of adata driven policy process including baseline datacollection, decision support and impact assessment
tools. The dashboards are available only to the endusers, through a secured web portal.
Data Access. For each deployment of AIDA, we havesecured a data access agreement with at least one
major private data provider (e.g., a Mobile Network
Operator). The private data access has taken place in
2 forms: a) DDI installed a server within the data
provider’s premises to obtain anonymized data,process and aggregate them on site, behind the data
provider’s firewall; or b) the data provider directlysends aggregated data to AIDA. In any case, individual
data stays where it has been collected; only
aggregated data is securely transferred to thevisualization layer of the platform. As such, AIDA is inline with the latest developments in individual data
access and privacy protection (e.g., the OPAL project).
Algorithms. The base algorithms provided by AIDA
cover i) mobility (number of people, migration
patterns, travel time and traffic estimation, etc.); ii)
mobile money analysis (activity in each area pertransaction type, with the possibility to focus on somegroups of interest); iii) social graphs; and v) infectious
disease risk monitoring. Machine learning algorithms
were leveraged in the platform, to predict a) the
gender and the employment status of mobile phone
subscribers; b) the incidence of infectious diseases;
and c) crop yield from satellite images. Algorithms
have been implemented in python, R and anatella (anETL software with a Graphical User Interface).
Future Work. Next steps for the platform
development are 1) to automate tasks that are
currently manual (e.g., data anonymization ordebiasing); 2) to standardize some pieces of the codeand open them to audit and external contributions.

Exploring the use of mobile phone data for national migration statistics
Shengjie Lai1,2,3, Elisabeth zu Erbach-Schoenberg1,2, Carla Pezzulo1, Nick W Ruktanonchai1,2,
Alessandro Sorichetta1,2, Jessica Steele1, Tracey Li2, Claire A Dooley1,2, Andrew J Tatem1,2
1WorldPop, School of Geography and Environmental Science, University of Southampton,

Southampton SO17 1BJ, United Kingdom
2Flowminder Foundation, SE-113 55 Stockholm, Sweden
3School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministryof Education, 130 Dongan Road, Shanghai 200032, China
Shengjie Lai and Elisabeth zu Erbach-Schoenberg contributed equally to this work.
*Correspondence to Shengjie Lai (email: Shengjie.Lai@soton.ac.uk).

ABSTRACT
Statistics on internal migration are important for keeping estimates of subnational
population numbers up-to-date as well as urban planning, infrastructure development
and impact assessment, among other applications. However, migration flow statistics
typically remain constrained by the logistics of infrequent censuses or surveys. The
penetration rate of mobile phones is now high across the globe with rapid recent
increases

inownershipin

low-income

countries.

Analysingthechanging

spatiotemporal distribution of mobile phone users through anonymized call detailrecords (CDRs) offers the possibility to measure migration at multiple temporal andspatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from
October 2010 to April 2014, we explore how internal migration estimates can bederived and modelled from CDRs at subnational and annual scales, and how precision
and accuracy of these estimates compare to census-derived migration statistics. We
also demonstrate the use of CDRs to assess how migration patterns change over time,with a finer temporal resolution compared to censuses. Moreover, we show how
gravity-type spatial interaction models built using CDRs can accurately capture
migration flows. Results highlight that estimates of migration flows made using mobile
phone data is a promising avenue for complementing more traditional nationalmigration statistics and obtaining more timely and local data.

Employment Demographics of Refugees in Turkey: A Bayesian
Probabilistic Approach using Weak Social Science Models?
Steven Reece1 , Franck Duvell2 , Carlos Vargas-Silva3 and Zovanga Kone3
1

Machine Learning Research Group, Oxford University and the Alan Turing Institute, London, UK, reece@robots.ox.ac.uk
2German Centre for Integration and Migration Research (DeZIM), Berlin, Germany, duvell@dezim-institut.de3
Compas, Oxford University, UK, {carlos.vargas-silva,zovanga.kone}@compas.ox.ac.uk

We utilise mobile telephone data provided by Türk Telecom to investigate the economic activities of Syrian refugees
in Turkey. To do this we develop a probabilistic model that relates mobile phone usage data to manual and non-manual
job classes and use this model to determine the extent, type and spatial dispersion of the refugee labour force in Turkey.
Our aim is to classify each phone caller or texter as manual, non-manual or non-worker. We propose weak rules of
behaviour from intuition and by interviewing several Turkish and Syrian nationals. These rules are informed hunches
and certainly not scientifically proven and hence ‘weak’. We determine the efficacy of these rules using a machinelearning algorithm that establishes their consistency with the Türk Telecom mobile phone data. This data provides
cell tower identifiers used by a group of randomly chosen active users to make phone calls and send texts. The data is
timestamped and a particular group of users are observed for a period of 2 weeks. At the end of the two-week period,a fresh sample of active users are drawn at random. Each sample contains 3% of the refugee base plus equal amountof non-refugee users.
Our weak rules are based on the assumption is that manual workers do not use their phone during working hours,
7am to 1pm and 2pm to 8pm, However, non-manual workers may use their phones for business purposes and thus
more during working hours. Workers’ usage of telephones for private purposes spike around lunch time and after work,
hence after 7pm. Calls between 7pm and 11pm are of a private nature. Manual workers may make a call before 7am
to determine work availability. We note that non-workers do not use their phone as early in the morning as workers
of any kind, and they also use it more evenly across the day. Research suggests that students display specific patterns
and rather use their phones in the afternoon and evenings.
We use an extension of an unsupervised Bayesian data analysis technique, the Independent Classifier Combination
Algorithm (IBCC), to classify mobile phone users from their mobile phone usage. The IBCC learns the relationshipbetween employment rules and the mobile phone data. The IBCC does not require telephone call/SMS data labelled
as manual or non-manual to train it. Instead it uses the assumption that the majority of the rules are accurate. Our
method models both refugee and non-refugee workforces within a single principled Bayesian approach and thus caninfer both refugee and non-refugee labour classifications without loss of information. The approach provides both anestimate of each Turk’s and refugee’s job class, a statement of confidence in the classification as well as the efficacy of
the job classification rules. We note that our approach is not biased by the relative number of rules for manual andnon-manual work. Furthermore, non-employed phone users are deemed to be those whose behaviours do not conformto manual nor non-manual worker rules.
In this study we found that refugee workers are more likely manual than non-manual workers (in contrast to
Turkish workers). Refugees don’t normally have permission to work and only have access to informal employment.
Our results not only provided country-wide statistics of employment but also gave a detailed breakdown of employment
characteristics via heatmaps across Turkey. This information is valuable since it would allow GOs and NGOs to refine
and target appropriate policy to generate opportunities and economic integration as well as social mobility specific toeach area of Turkey. Our findings with regards to probability of refugees not in work suggest that in most places ofhigh concentration of refugees they are also more probably unemployed than Turks. However, there is one exception
and that is the region of Bursa. Here refugees seem much less likely to be unemployed than Turks in the province.
Bursa is an industrial hub and its surrounding are a centre of agricultural activities, notably growing of grapes andolives. Thereby, it o↵ers a favorable opportunity structure for refugees who can find employment in manufacturing
and/or agriculture.
For further details of our approach and results please see, Steven Reece, Franck Düvell, Carlos Vargas-Silva and
Zovanga Kone, New Approaches to the Study of Spatial Mobility and Economic Integration of Refugees in Turkey,
Data for Refugees Challenge Workshop, 2019. Contact Reece for a pdf copy.

?

Steven Reece was supported through a fellowship at The Alan Turing Institute, sponsored by the Programme on Data-CentricEngineering and funded by Lloyds Register Foundation.

Abstract Submission
Netmob 2019

Novel mobility and social network metrics to predict
disaster-driven displacements from call detail records
Tracey Li, Jesper Dejby, Maximilian Albert and Véronique Lefebvre*
Flowminder Foundation*Corresponding author email: ​veronique.lefebvre@flowminder.org
Over 25 million people are forced to leave their homes every year due to environmental disasters. The
majority of these displaced persons are Internally Displaced Persons (IDPs), meaning that they remain
within their home country. IDPs are among the most vulnerable people in the world today, as many
States affected by natural disasters are unable to support displaced citizens and often lack the capacity
to effectively identify and monitor displacements. The Global Humanitarian Overview 2018 showed
there is a growing gap between humanitarian needs and the resources and information available tomeet them. New approaches to identifying, understanding, and predicting internal displacements areneeded in order to better target assistance and make the humanitarian response more proactive.
Call Details Records (CDRs) have been used in numerous studies to estimate population movements indata deficient contexts. We have developed a novel methodology to identify individual disaster-driven
displacements from CDRs, which is described in the abstract 'Identification of disaster-driven internal
displacements from the time series analysis of call detail records'. We then developed novel metrics toextract information on mobility and social contacts from CDRs in order to analyse how displacement
duration and distance are affected by contextual and individual variables.
We present a novel measure of entropy based on meaningful 'stayed' locations and show how it can beused to determine the time at which individual IDPs resettle, either when they return home or resettle ata new location. We then count the number of IDPs who resettle over time (or equivalently the number of
IDPs who remain displaced), which provides an indication of the rate at which the affected communities
recover and adapt, and thus of disaster resilience. We studied CDRs pertaining to 3 natural disasters(Haiti earthquake 2010, Haiti Hurricane Matthew 2016, and Nepal earthquake 2015) and observed thatthe fraction of IDPs remaining displaced decays at an exponential rate over time, and at the same rate
for the 3 disasters studied. If the study of further disasters leads to the observation of similar
exponential decay rates, then it would imply that the number of IDPs at any time can be inferred froman estimate of the initial number of IDPs immediately following the disaster. Alternatively, the method
provides a way to monitor disaster resilience and compare recovery rates across disasters.
Secondly, we studied the impact of social contact locations, and places visited prior to the disaster, on
the 'choice' of displaced location and displacement distance. We find that good predictors ofdisplacements are the fraction of all social contacts residing within a given distance of an IDP
pre-disaster home, and the fraction of all visited places within a given distance of an IDP pre-disasterhome. Individuals living by most of their social contacts are more likely to be displaced close to theirhome than those with a more widespread social network, even in the most severely affected areas.
Similarly we observed that individuals who mostly visit places close to their home are more likely to be
displaced nearby.
Our results indicate that CDRs can significantly contribute to measuring and predicting displacement
durations, distances, and locations of IDPs in post-disaster scenarios. We believe that information andestimates provided by specifically developed CDR analytics, coupled with field data collection andtraditional survey methods, can assist the humanitarian response to natural disasters and thesubsequent resettlement efforts.

Session Mobility

CDR-based descriptors of seasonal sensitivity and mobility
David Pastor-Escuredo
1

1,2,*

3

4

5

Federica Carfagna Zbigniew Smoreda Miguel Luengo-Oroz Pedro J. Zufiria
2

3

4

1

5

Technical University Madrid, Spain. LifeD Lab. African Risk Capacity, WFP. Orange Labs. United Nations Global Pulse*
corresponding: david@lifedlab.org

The use of Machine Learning techniques combined with CDRs enables the profiling of mobility and social
behaviors based on the patterns of mobile phone usage. Here we present the integration of profiling ofmobility and behavioral (Bandicoot) indicators to automatically estimate seasonal behavior and landmarksof population groups using CDR data available from the Data for Development (D4D) Challenge Senegal.
This is an initial step towards a data-driven assessment of vulnerability for resilience building and funding.
We made an analysis at the Senegalese livelihoods level: geographical areas where people sharebroadly the same patterns of access to food and income, and have the same access to markets. The
profiling of the population was done with behavioral Bandicoot descriptors. Here we used six descriptors:
Days-activity, Duration-calls-mean, Duration-calls-std, Entropy-contacts-texts, Entropy-contacts-calls andEntropy-places. Thus, we could relate behavioral patterns with the production system of the country.
We integrated data sources (remote sensing and survey data) to interpret behavioral patterns in terms of
the Bandicoot descriptors. The descriptor time series with a monthly resolution featured relevant
landmarks in relation to the rainfalls (estimated from NASA-TRMM data) and the most relevant months(field preparation and harvest) of the agricultural calendars of livelihoods (FEWS livelihoods report). The
Duration-calls-mean and the Entropy-contacts-texts reached their maximum during the rainy season as
described by the rainfalls, presumably due to communication regarding the fields and the expected yield.
During the harvest month, the Entropy-contacts-calls spiked, presumably indicating communication
regarding trading. The Entropy-places showed two peaks during the field preparation and the harvest
implying local mobility, potentially due to labor and trading mobility. These landmarks were interpreted tobe relevant to monitor seasonal behavior of populations against climate change and droughts.
We introduced a seasonal sensitivity indicator aggregating Bandicoot indicator landmarks into a feature
vector as a behavioral signature of each user. We applied temporal filtering to the Bandicoots time series
to select the most relevant time points for each descriptor as variables. The resulting vector was then
combined using weights for each variable into a single scalar indicator representing the communicationand mobility associated to seasonal dynamics. Each variable affected positively to the indicator so theweights were all positive. Several weight combinations were tested. Thus, the seasonal sensitivity
indicator was designed as a multidimensional aggregation of landmarks maximizing the amount of activity
related to the livelihoods and the environmental conditions. The indicator showed a quasi-gaussian
distribution for the Senegalese population. We segmented the distribution into different population groups
using unsupervised k-means clustering (k=3): low sensitivity, medium sensitivity and high sensitivity.
We measured the mobility of each classified group by observing which livelihoods were visited throughthe year by each user, creating a dynamic census in each livelihood zone for the three population groups.
Thus, we could explore characteristics and differences across livelihoods. Significant patterns wereobserved for the low sensitivity population, mostly in rural zones. We consider that tracking variations andmobility of this group along years will allow understanding the vulnerability to climate conditions,production and market prices. Finally, we compared the mobility of the sensitivity profiles with the mobility
profiles obtained through direct unsupervised clustering of mobility vectors discovering that the mobility of
the low sensitivity population was not captured by the main profiles. This means that the use of Bandicoot
indicators is key for data disaggregation for humanitarian purposes. We foresee this framework will be
relevant for humanitarian agencies to identify beneficiaries and monitor impact of humanitarian aid.

Anomaly detection of urban dynamics in an extreme weather with mobile GPS data
Yutaro Mishima1, Atsunori Minamikawa1
KDDI Research Inc., Tokyo, Japan
yu-mishima@kddi-research.jp, at-minamikawa@kddi-research.jp

ABSTRACT
These days human dynamics data generated from GPS data or CDRs has animportant role in considering urban environments and emergency management
during natural disaster [1]. In particular, with alerts of abnormal states of dynamics, i.e. large-scale change of the volume of people, we can find some kindof important information to make management decision, e.g. evacuation spots
not designated, so that we can minimize the damage caused by natural disaster.
Generally, many anomaly detection techniques, e.g. classification-based, havebeen proposed [2]. Although anomaly detection techniques in human dynamics
data are proposed in related research [3-4], they have challenges. Interpreting“Abnormal state” is difficult in [3] and [4] is not able to recognize the time
when abnormal state ends in real time. In this paper, we present an anomaly
detection method which can detect anomaly of human dynamics in real-timeand indicate that our method enables monitoring wide area (city-level) humandynamics for a months or year and it is useful for emergency management.

DATASETWe use human dynamics data generated from GPS data provided by our mobile
users who agree to our license. Human dynamics data consists of the volume ofstaying people and moving people in 500m x 500m grid-cell in 30 minutes
timeslot. The volume is estimated from the ratio of our mobile users to population in Japan. There are 2387 grid-cells which cover whole metropolitan areain Tokyo. The data contains dynamics from 1 May, 2017 to 31 Jan, 2018. Noticethat the data is generated so that it does not enable to identity specific individual.

abnormal grid-cell ratio (about staying and moving people) for each day. Although there are some peaks on both of the plot about staying and moving people,
almost all of the peaks are caused by vacations, e.g. spring vacation in May,2017. Exceptions are peaks on 22 Oct, 2017 and 22 Jan, 2018. The former iscaused by typhoon and the latter is heavy snow. As shown by Fig. 1, we canquantify the impact which an event causes on human dynamics.
For an example, we show heatmaps below in 22 Jan, 2018, the anomaly ratio
on which is largest except for vacations. Heavy snow struck Tokyo in 22 Jan,2018 and it had a huge impact on many people and public transportation.

Fig. 2 Heatmaps (staying people/moving people) in 22 Jan, 2018 †1
Red colored cell represents abnormal increase and blue cell represents abnormal decrease. Color depth corresponds to the degree of increase or decrease.
Left heatmap represents abnormal state about staying people from 18:00 to
18:30 in 22 Jan, 2018 and right one represents about moving people from 16:00
to 16:30. We can find that many people (probably working people) came homeearlier than usual because of heavy snow. Therefore, we can monitor city-level
human dynamics and see where significant anomaly occurs.

APPROACH
In this section, we introduce the procedure of our anomaly detection method.
1.
Define “normal” dynamics.
i.We divide dynamics data for each grid-cell by date into 8 (2*2*2) day
groups according to whether the day, previous day and next day are
weekday or holiday.
ii. Referring to [5], we cluster day by day data for each day group by kmeans clustering for excluding abnormal dynamics caused by events etc.
We define the cluster the average people volume of which is minimum
as “normal” cluster. Then we define mean and standard deviation of all
day by day data in cluster for each timeslot as “normal” dynamics of thegrid-cell and day group.
2.
Define “abnormality” for each grid-cell and timeslot in the following
equation.
𝑣 , , −𝑚 , ,𝐴 ,, =()
𝑠 ,,where 𝐴 , , and 𝑣 , , are abnormality and volume, 𝑚 , , and 𝑠 , , are meanand standard deviation as “normal” dynamics in the day group d, the timeslot t
and the grid-cell c.
3.
Determine if the state 𝑆𝑇 , , is abnormal or normal in the following.
𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙, 𝑖𝑓 𝐴 , , ≥ 4 𝑎𝑛𝑑 |𝑣 , , − 𝑚 , , | ≥ 1000
𝑆𝑇 , , =𝑁𝑜𝑟𝑚𝑎𝑙, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
We note that the threshold is set temporarily.

EXPERIMENT

Fig. 3 Time-series plot of a certain grid-cell in 22 Jan, 2018
Fig. 3 shows a time-series plot of a grid-cell (contains Ikebukuro which is oneof the major stations in Tokyo) in 22 Jan, 2018. Vertical axis indicates the volume of people. Blue and green solid lines show the volume of staying and moving people, while yellow and red dashed lines show the average volume of staying and moving people in normal dynamics. On the plot the volume of staying
people is largest around 18:00 and actually at that time entering station wasrestricted in Ikebukuro and it was most crowded [6], so we prove that this dynamics trend correctly reflects the actual human dynamics trend even if we gazeat only one grid-cell. Processing time it takes to define normal dynamics fromabout 9 months dynamics data is about 2600 seconds and one it takes to defineabnormality for 1-day data is about 10 seconds with 36 CPUs and 750 GiB of
RAM. That processing time is short enough to construct real-time anomaly detection system because we don’t have to run the process defining normal dynamics frequently (the system will work even if we run it only once a week).

CONCLUSIONFirst, we determine states for all grid-cell and timeslot (from 1 May, 2017 to 31
Jan, 2018) and find out when many abnormal grid-cells exist and why the anomaly occurs. Next, we generate heatmaps and plot the dynamics in a certain gridcell in one day of abnormal days excluding long vacations. Finally, we checkthe difference of dynamics from ones of “normal” days matches with the content of news reported in the day.

In this paper we present an anomaly detection method in human dynamics data
generated from GPS data and show that we can not only monitor wide area
human dynamics for a long period in real-time but also observe the human dynamics properly in a small grid-cell (500m x 500m) with some experiments.
For future work, we consider more appropriate decision rule whether abnormal
or normal and propose more useful information for emergency management,
e.g. priority of rescue, using hazard information generated from sensor data.

RESULTS AND DISCUSSION

REFERENCES

Fig. 1 Max value of abnormal grid-cell ratio for each day
We define abnormal grid-cell ratio as the ratio of number of abnormal grid-cells
to the number of all grid-cells for each timeslot. Fig. 1 shows max value of†1 © OpenStreetMap contributors, https://www.openstreetmap.org/copyright[1] Sagl, G., Loidl, M., & Beinat, E. (2012). A visual analytics approach for extracting spatio-temporalurban mobility information from mobile network traffic. ISPRS International Journal of Geo-Information,
1(3), 256-271.
[2] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing
surveys (CSUR), 41(3), 15.
[3] Kamiya, K & Fuse, T. (2015). STATISTICAL ANOMALY DETECTION FOR MONITORING OF
HUMAN DYNAMICS. ISPRS - International Archives of the Photogrammetry, Remote Sensing andSpatial Information Sciences. XL-4/W5. 93-98. 10.5194/isprsarchives-XL-4-W5-93-2015.
[4] Neumann, J., Zao, M., Karatzoglou, A. and Oliver, N. (2013). Event Detection in Communication andTransportation Data. Proceedings of IbPRIA 2013.
[5] AL Alfeo, MGCA Cimino, S Egidi, B Lepri, & G Vaglini. (2017). An emergent strategy for characterizing urban hotspot dynamics via GPS data. – NetMob 2017, 27-29.
[6] Weathernews “1gatu22nichi, nanganteikiatsunitomonaukanto-chihounoooyuki” (Heavy Snow caused
by winter extratropical cyclone struck Kanto region)- https://jp.weathernews.com/news/21459/ (2019/3/8)

Extracting meaningful mobility flows from mobile phone individual trajectory data for epidemic metapopulation
modelsGiulia Pullano1,2 * , Stefania Rubrichi2, Vittoria Colizza1
1

INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
SENSE/Orange, XDLab , Chatillion, France*Presenter: giulia.pullano@inserm.fr
2

The past few years have seen an increasing interest in the use of Call Detail Records (CDRs), i.e. georeferenced dataobtained from cell phones activities. As they contain attributes on where and when activities are made, CDRs allow theextraction of user's individual trajectories in space and time by interpolating displacements based on two consecutive
activities. Such high-resolution data have been widely used to characterize individual patterns of mobility and wererecently integrated into spatially explicit epidemic models to understand the spread of infectious disease epidemics.
These have been applied to a variety of epidemic contexts including influenza, malaria, cholera, schistosomiasis. Little
attention has however been devoted to the aggregation process aimed at extracting coupling forces between
geographical locations based on individual mobility described by such high-resolution data, and its relevance for spatial
infectious disease modeling. This may become particularly important given the large heterogeneities exposed by the
analysis of trajectory data, with e.g. individuals spending most of their time in few locations vs. others visiting a large
number of locations, or individuals with remarkably reproducible mobility patterns vs. others displaying highly variable
ones.
We considered different aggregating procedures and evaluated the resulting coupling forces between locations as well as
their impact on the modeled epidemic diffusion once they are integrated in a spatially explicit metapopulation scheme.
We selected three commonly used methods at highest, medium, and lowest spatio-temporal resolution that are based on,
respectively: (i) the full temporal sequence of a user’s displacements between any two consecutive calls (displacementbased coupling matrix D); (ii) the number of calls made in a given location (location-based coupling matrix L); (iii) the
daily most visited location computed on the number of calls (commuting-based coupling matrix C). The D coupling
matrix is able to trace the actual spatial path followed by each user through a sequence of consecutive displacements,
however it does not consider the location of residence. The L and C matrices, instead, couple the residence location ofeach user with all her visited locations (in L), or with her most visited location (in C), however loosing the informationon the actual trajectory. We applied our study to 1-year CDRs from Senegal, from January to December 2013, including
9,569,425 users, and integrated the coupling matrices in a metapopulation model accounting for disease dispersal due tovisitors to a location and to returning residents. The model is stochastic, discrete and non-Markovian, and considers
three different synthetic epidemic scenarios – high transmissibility (Ebola-like), moderate transmissibility (influenzalike), and low transmissibility (e.g. when interventions are put in place).
We found that the median coupling probability between any two locations in D is two orders of magnitude smaller thanthe values estimated in C and L. These discrepancies increase with the distance between the locations, such that longrange coupling is less likely to be captured in D compared to the other aggregating approaches. The lower coupling
probability measured by D results in delayed arrival times of the simulated epidemic ranging from few weeks to almost
two years compared to L and C, and depending on the epidemic scenario. The location-based and commuting-based
coupling matrices lead to highly correlated and similar arrival times per location that are consistent with observations
based on commuting flows. Most importantly, differences in estimated coupling lead to substantially different paths ofinvasion of the epidemic in space and time in the country. Such invasion is mainly fragmented into short distance hopsof spatial transmission in D, whereas a more heterogeneous pattern integrating long-range transmission with local-range
dispersal is reproduced by C and L coupling matrices, consistently with observed epidemic patterns of spatial diffusion.
Our findings indicate that preserving the full resolution of the observed trajectory of individual movements may bias thespatio-temporal diffusion of the simulated epidemic in both the timing and pattern of invasion. Aggregating on visited
locations, while loosing all information about their sequence in an individual path, reproduce realistic simulated
patterns, but preserving only the most visited location is already enough to reliably model the disease spread. This alsosuggests that for a range of epidemic contexts leisure activities (i.e. those performed at the various locations except themost visited one) have no significant impact on the spread, and commuting-like mobility is the main driver of diseasediffusion. While addressing a specific methodological problem of reducing data resolution, our findings have important
implications for (i) the identification of the most relevant locations to be targeted for disease prevention and control, andfor (ii) the data and resolution needs in case of an emerging epidemic, a recently much debated topic.

Urban Vibes and Rural Charms: Analysis of Geographic
Diversity in Mobile Service Usage at National Scale
Rajkarn Singh, Marco Fiore† , Mahesh K. Marina, Alessandro Nordio† and Alberto Tarable†

The University of Edinburgh UK, † CNR-IEIIT Italy
Email: r.singh@ed.ac.uk, marco.fiore@ieiit.cnr.it, mahesh@ed.ac.uk, alessandro.nordio@ieiit.cnr.it, alberto.tarable@ieiit.cnr.it
I. P ROBLEM AND RESULTS
As mobile data traffic keeps surging worldwide, knowledgeof where, when, how and why mobile services are consumed
by network subscribers becomes increasingly relevant acrossresearch and technology domains. Still, our comprehension ofmobile service adoption is currently limited and many questions remain unanswered. We focus on one such open question,
namely: “how similar (or different) are demands for mobile
services across a whole country?” We answer by analyzinga real-world dataset of mobile network traffic collected bya major operator that describes the demands for individual
services in 10,000 communes (i.e., administrative areas) inFrance. Our study yields the following key insights.
• The demand for most popular mobile services is fairly
uniform across the whole country, and only a reduced set of
peculiar services (mainly operating system updates and longlived video streaming) yields geographic diversity.
• Just 9 (respectively, 50) service consumption patterns are
sufficient to retain 23% (respectively, 35%) of the overall
usage diversity, implying that a small number of distinct
behaviors is sufficient to characterize the many thousands ofareas in the whole of France.
• The spatial distribution of these behaviors correlates wellwith the urbanization level, ultimately suggesting that the
adoption of geographically-diverse mobile applications islinked to a dichotomy of cities and rural areas.
II. M ETHODOLOGY
In order to derive our results, we model the amount oftraffic generated by mobile services in different communes as
jointly distributed random variables, and adopt an approachthat hinges on information theory. We first assess theglobal geographic diversity of service usages in France, bycomputing the mutual information I(C; S) of mobile service
demands (S) and geographical locations (C). The mutual
information captures how much can be inferred about theconsumed services by knowing the commune, and we findit to be close to zero in our nationwide scenario, implyingthat mobile services demand distributions are homogeneous
across the whole of France.
However, usage distributions are highly skewed, with afew services generating the vast majority of traffic. Thus,
the mutual information primarily captures the (absence of)diversity in the consumption of popular apps. We investigate
if, among less popular services, there exist some that are informative, i.e., are characterized by a non-negligible diversity

Fig. 1. Relative positioning of informative mobile services in the space of
population density and urbanization levels. People living in rural regions ofFrance have a preference to use Windows Mobile devices. Instead, inhabitantsof metropolitan areas prefer Apple iPhones, and display a remarkable tendency
to significantly use WhatsApp and long-lived streaming services like Netflix.

of usage across geographical areas. In information theoreticalterms, this can be formulated as the combinatorial problemof identifying a subset S 0 ⇢ S of all mobile services whose
mutual information with the spatial areas is maximized, i.e.,0
Sopt= arg max
I(C|S 0 ; S|S 0 ).
0S ✓S

An efficient approximation of the solution is provided by the
Blahut-Arimoto algorithm [1], [2], which maximizes I(C; S)
by weighting all services according to their informativeness.
By applying the approach to our scenario, 13 informative
services are singled out. They are listed in Figure 1.
In order to understand where the informative services differ
in usage, we cluster communes in France based on how their
inhabitants consume such services. To this end, we measure
the distance between two communes i and i0 as the loss ofmutual information d(i, i0 ) = I(K; S) I(Ki,i0 ; S) incurred
when they are merged [3]. Here Ki,i0 is the set of (clustersof) communes in K such that i 2 K and i0 2 K are merged.
We feed such distances to a scalable two-phase greedy
hierarchical clustering algorithm, and find that a significant
portion of the system information is captured by a small
number of clusters, i.e., mobile service consumption patterns.
Interestingly, the geographical distribution of these usage
patterns over the French territory is strongly correlated withpopulation density and urbanization levels, as in Figure 1.
R EFERENCES[1] S. Arimoto, “An algorithm for computing the capacity of arbitrary
discrete memoryless channels,” IEEE Trans. on Inf. Theory 18(1), 1972.
[2] R. Blahut, “Computation of channel capacity and rate-distortion functions,” IEEE Transactions on Information Theory 18(4), 1972.
[3] P.S. Chodrow, “Structure and information in spatial segregation,” Proceedings of the National Academy of Sciences 114(44), 2017.

scikit-mobility: an open-source Python libraryfor human mobility analysis and simulation
Luca Pappalardo1 , Gianni Barlacchi2 , and Filippo Simini3
1

ISTI-CNR, Pisa, Italy
FBK and SKIL-Telecom Italia, Trento, Italy
3
Department of Mathematical Engineering, University of Bristol, UK
2

The availability of geo-spatial mobility data (e.g., GPS traces, call detailrecords, social media records) is a trend that will grow in the near future.
In particular, this will happen when the shift from traditional vehicles to autonomous, self-driving, vehicles, will transform our society, the economy andthe environment. For this reason, understanding and simulating human mobility is of paramount importance for many present and future applications, suchas traffic forecasting, urban planning, estimating migratory flows, and epidemic
modeling, and hence for many actors, from urban planners to decision makers
and advertising companies.
scikit-mobility is a Python library for human mobility analysis and simulation. In particular, the library allows the user to: (1) analyze mobility data byusing the main measures characterizing human mobility patterns (e.g., radius of
gyration, daily motifs, mobility entropy, origin-destination matrices); (2) simulate individual and collective mobility by executing the most common humanmobility models (e.g., gravity and radiation models, exploration and preferential return model); (3) compare all these models by a set of validation metrics
taken from the literature. scikit-mobility provides an efficient and easy-touse implementation, based on the standard Python library numpy, pandas andgeopandas, of the main collective and individual human mobility models existingin literature, allowing for both the fitting of the parameters from real data andthe running of the models for the generation of synthetic spatio-temporal trajectories. Among the collective mobility models, i.e., models generating synthetic
fluxes of people between locations on a space, scikit-mobility implements
the gravity model [1, 2], the radiation model [3], and their recent improvements[4]. Among the individuals mobility models, i.e., models generating synthetic
trajectories of desired length for a set of agents, scikit-mobility provides
the recent improvements of the classical Exploration and Preferential Return
model (s-EPR, r-EPR, d-EPR and recency EPR) [5–7] as well as the most accurate spatio-temporal generative human mobility models like DITRAS [8] andTimeGeo [9]. During the talk practical examples on how to use the library on
real-world mobility data and generate synthetic trajectories will be shown.
scikit-mobility is a starting point for the development of urban simulation
and what-if analysis [10, 11], e.g., simulating changes in urban mobility after theconstruction of a new infrastructure or when traumatic events occur like epidemic
di↵usion, terrorist attacks or international events.

Session Methods

MobiSenseUs: Predicting Objective andSubjective Well-Being from Aggregate Mobile
Data
Martin Hillebrand1,2 , Imran Khan1 , and Nuria Oliver1
2

1
Vodafone Research, London, UK
Correspondence: hillebrand_martin@gmx.net

Assessing the socio-economic status and the well-being of a population is of utmost importance for policy and decision makers so they
can design appropriate policies and interventions aimed at improving the quality of life of their citizens. Traditional methods to assess
such measures consist of surveys which are expensive to obtain anddifficult to scale. Hence, the national statistics offices of most developed countries only carry out such surveys every few years.
In the case of the United Kingdom, the Office for National Statistics (ONS) publishes two important metrics: the Index of Multiple
Deprivation (IMD), composed of seven variables (income, employment, health, education, barriers to housing and services, crime andliving environment) and obtained every three to five years; and the
Subjective Well-being (SWB), composed of 4 metrics (life satisfaction, worthwhile, happiness and anxiety) and assessed every year.
In recent years, the wide adoption of mobile phones has led to several research works that estimate socio-economic and well-being indicators from pseudonymized, aggregated mobile data both in developed (1) and developing (2, 3) countries.
The research described in this paper also focuses on the automatic
estimation of socio-economic and well-being from mobile humanbehavioral data, with three key contributions when compared toprevious work: Firstly, our analysis covers a wide spectrum of human behavior by including geographically aggregated communication patterns (e.g. number of calls), spatial mobility (e.g. commuting distance) and app usage behavior (e.g. number of apps)
based on a pseudonymised dataset of more than one million mobile
phone users in the UK over a period of 6 months (Jan-Mar 2017 andJan-Mar 2018). Secondly, we build predictive models of subjective
well-being (SWB) using the mobile behavioral data. And thirdly,
we carry out a longitudinal analysis of SWB by applying in 2018 amodel that was trained with 2017 data. To the best of our knowledge, we are the first to carry out such a longitudinal analysis.
In terms of ground truth, we use the most recent IMD survey whichis from 2015 and the two SWB surveys from 2017 and 2018. Notethat SWB is available with more frequency which allows the use of
the two most recently published datasets. Our spatial granularity is
the UK local authorities (391 local authorities).
Regarding the mobile data, we compute 16 features that characterize the aggregated communication, mobility and app usage behaviors at the local authority level. We train supervised state-of-the-art
machine learning models (Gradient Boosted Trees, Support Vector
Machines,. . . ) to automatically infer SWB and IMD. We only report the results for Linear Support Vector Machines (using 5-foldcross-validation) using 8 weeks worth of data (mid-Jan to mid-Mar2017) as this combination consistently outperformed the other models. Figure 1 shows the four quartile classification performance ofour models when applied to both the Life Satisfaction facet of SWB
and the IMD average score. As seen in the Figure, IMD can be predicted better (Acc = 66.1%) than SWB (Acc = 46.4%). When we
only want to separate the top quartile from the lowest quartile, analogous to the methodology applied in (4), accuracies rise to 99% for

IMD and 84% for SWB.
The better performance for IMD might be due to the fact that economic activities are better reflected in the behavioral patterns captured by the mobile phone data than self-reported subjective wellbeing. Post-hoc partial correlational analyses reveal that third variables like median age and population density of a region accountfor a substantial share of the correlation between phone behavior
features and well-being targets.
While spatial mobility features are particularly important to predict IMD, communication features play the most important role for
SWB. This is consistent with the fact that social relatedness is a key
element to mental well being (5).
Next, we aim to predict SWB from the available mobile data instead of conducting expensive surveys (6) thus assessing the potential value of mobile data to replace or complement existing methodologies. We build a regression model with the mobile features and
SWB data from 2017 and apply the same model to the mobile features from 2018 to automatically infer SWB in 2018. Note that weare unable to carry out the same analysis for IMD as we only have
one sample of IMD for 2015. We obtain a R2 = 18% (compared to
R2 = 27% in 2017), showing that a model trained with data fromthe previous year could be re-used the next year.
Our empirical findings allow a direct comparison between different
aspects of human behavior and their impact on the objective andsubjective well-being of a region. In addition, we show that the correlational stability between mobile features and well-being targets
allows the re-use of trained models across years.

Fig. 1. Accuracy of a four quartiles classification task with different sets of features

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Road crash hotspot identification through bystander Tweets
Guadalupe Bedoyaa, Arianna Legovinia, Robert Martya, Sveta Milushevaa, Elizabeth Resorb, Sarah Williamsc 1
Introduction(similar to Qian 2016). The first step categorizes tweets toRoad injuries are the 8th leading cause of death globally. identify those that relate to a crash based on a dictionary ofThis development challenge is especially prominent in sub- words associated with crashes. This initial dictionary was
Saharan Africa, with Kenya being one of the worst affected developed using a training dataset of tweets identified ascountries. It ranks among the top countries in death rate due relating to crashes. In order to geolocate crash locations,to road traffic crashes (RTC) in the world with 29 deaths per ideally we would use the GPS associated with the tweet, but
100,000. Yet, the available statistics are unreliable: the WHO due to the heavy battery usage and data costs associatedestimates that the number of RTC fatalities is 3.7 times the with turning GPS on, in 99.9% of cases there is no GPS
official registry, a challenge faced in most low-income location. Therefore, in step two, using a dictionary ofcountries. Better data and scientific analysis on where landmarks in Nairobi developed by (1) scraping landmark
crashes happen, and factors associated with crash hotspots names and locations from Google Maps and (2) simplifying
are needed to improve evidence and policy to address the and cleaning landmark names (building on techniques
outlined in Al-Olimat et al. 2017), we identify the landmarks
growing road safety issues in low-income countries.
Leveraging on crowdsourcing, machine learning and a in the tweet that allow us to geolocate the crash. If multiple
unique multi-year geolocated crash police dataset we built landmarks are found in a tweet, we employ a series offor Nairobi, this paper demonstrates the use of mobile heuristics to choose which landmark to geocode.
phone applications for informing road safety. We build on To assess the quality of the data and precision of theexisting literature that geocodes events from tweets (Al- algorithm we (i) built a truth dataset using double manual
Olimat et al 2017, Gelernter and Mushegian 2011, Gelernter entry of tweets reported in an entire year, and (ii) worked
and Balaji 2013, Gu et al 2016, Middleton et al 2014, Qian with a motorcycle delivery mobile phone platform to verify
2016, Zhang and Gelernter 2014)2, innovating by developing crashes identified via Twitter. For the physical verification,
additional techniques to clean a gazetteer of landmarks and tweets were processed through the algorithm in real-timedeveloping heuristics to prioritize certain landmarks over and triggered a “delivery” in the platform that alerted theothers if multiple are found in a tweet. This is the first closest motorcycle driver to go to the location of the crash
application of these tools for road safety in a developing location identified by the algorithm. We compare thecrowdsourced data and police data, for the years theycountry.
overlap. We develop a clustering algorithm to identify
Data
The crowdsourced data comes from Ma3Route, a crashes in the police data and in the crowdsourced data thatmobile/web/SMS platform that crowd-sources transport are the same based on location and timing.
data and provides users with information including traffic Results
and accidents with around 500,000 subscribers in Nairobi. Using crowdsourcing, we identify 5,727 unique crashes from
Individuals can either directly Tweet to Ma3Route or use the 2012-2018. Our verification processes have shown that our
Ma3Route mobile phone application to post when they see algorithm was able to geocode 68% of crash-related tweets,a road traffic crash (or traffic). All 734,795 Ma3Route tweets where 93% of tweets were physically verified to have had afrom 2012-2018 are scraped and used for this analysis. The car crash near the estimated location. Comparing the
Police data consist of paper reports of crashes aggregated crowdsourced data with police data, we find that 33%
from the 14 police stations in Nairobi for 2011-2018. 11,186 percent of crowdsourced cases are new cases not found inpolice reports are manually digitized and geolocated based the police data. Our approach was able to capture manycrashes not identified by administrative data sources. Ouron the location from the report.
study shows how the use of machine learning,
Methodology
We use a two-step machine learning algorithm for crowdsourcing and data analytics can contribute to costidentification of crash locations from crowd-sourcing effective improvements in road crash data.
1

We thank Shin Bin Tan, Andres Achury and Scott Margeson for their work on the first version of the algorithm, and Robert Tenorio for his excellent support in
Nairobi to build the Police dataset and managing the process to physically verify crashes derived from crowdsourcing. a) World Bank b) UC Berkeley c) Massachusetts
Institute of Technology; Contact: smilusheva@worldbank.org; gbedoya@worldbank.org.
2 Al-Olimat H, Thirunarayan K, Shalin V and Sheth A. Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models. CoRR
2017.; Gelernter J and Balaji S. An algorithm for local geoparsing of microtext. GeoInformatica. 2013;17(4) 635-667.; Gelernter J and Mushegian N. Geo-parsing
Messages from Microtext. Transactions in GIS. 2011;15(6): 753-773.; Middleton S, Middleton L, Modafferi S. Real-Time Crisis Mapping of Natural Disasters Using
Social-Media. IEEE Intelligent Systems 2014;29(2), 9-17.; Qian, Z. Real-time Incident Detection Using Social Media Data. The Pennsylvania Department ofTransportation 2016.; Gu Y, Qian Z, Chen F. From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C:
Emerging Technologies 2016;67, 321-342.; Zhang W, Gelernter J. Geocoding location expressions in Twitter messages: A preference learning method. JOSIS 2014;9.

Inactivity prediction using Convolutional Neural Networks
Elisabetta Alfonsetti, Paola Jafrancesco, Francesco Calabrese, Carlo Baldassi, Riccardo Zecchina
Vodafone Research, Bocconi University
Email: {Elisabetta.Alfonsetti, Paola.Jafrancesco, Francesco.Calabrese}@vodafone.com {carlo.baldassi, riccardo.zecchina}@unibocconi.it
Abstract—One of the major challenges of mobile operators
is the rate at which customers go into inactivity state. Thereis not a specific definition on when a customer will become
inactive.Indeed, this is not related to a specific event, like mobile
number portability or deactivation request [1, 2], but it representsa change in customers’ usage behaviour. In particular, in thispaper, we refer to an inactive customer as a customer with nooutbound calls, sms and data traffic for a period of 30 days. As thedays of inactivity increase, it becomes more difficult to reach thecustomers with prevention activities. Hence, knowing whether acustomer is likely to enter into inactivity state is crucial if we wantto prevent it with targeted actions. In this paper we show how toaddress this problem using Deep Convolutional Neural Networks(CNN). In particular, traffic variables related to outbound calls,
sms and data traffic are treated as images to perform inactivity
prediction by leveraging deep learning architectures prominent
in image classification. Experimental results show that CNN
outperform traditional approaches.

Our data corresponds to anonymized usage and profile datafrom a sample of 200k telecom prepaid customers. We structure the training dataset in two set of features: the temporal andthe static ones. The temporal dataset represents information
about user activities like data traffic, voice (outgoing andincoming) and sms usage. In order to encode this information
as an image we collect 84 days of data - corresponding toroughly 3 months - where each column is a day and eachrow is a feature, ending up with a two-dimensional array of
normalised pixels. The static dataset, instead, contains monthly
aggregations of features about customer behaviour such as
top up, balance events, digital channels interactions, marketing
campaigns, to name a few. The label is defined as shown in
Figure 1. Customers who do not perform any outgoing traffic
event in the 30 days observation period are labeled as 1. Inorder to exclude customers who are already partially inactive(and thus difficult to reach with targeted actions), we label as
0 those customers with no traffic events in the last 14 days.
Our network architecture consists of three main components: a set of convolutional layers for temporal features,
an identity layer for static features and a final step of fully
connected layers, as shown in Figure 2. The first part is madeFig. 2.

CNN architecture used for inactivity prediction.

up of 4 convolutional layers, the first layer involves 64 filtersof size 7x3, with padding of 1 and stride of 7, followed by asecond layer with 64 filters of size 7x7 in order to go across
7 days of customer usage. A third layer with 64 filters ofsize 1x4 and a last one of 1x3 are then used in order toanalyse the temporal data in a monthly and then 3-monthly
basis. The output of the convolutional layers is then mergedwith a static inputs layer containing all the static features andboth of them are used as input of a fully connected layerarchitecture, composed by 2 dense layers of dimension 100 and
80, respectively. The output is a softmax node to predict the
final probability of scoring a customer as positive (inactive) or
negative (still active). ReLu activation functions and AdaDelta
optimizer are selected to train the network. We refer to thisentire architecture as CNN-DL, while the part composed byonly static inputs layer and the fully connected layers is namedas DL architecture.
We compared our CNN-DL architecture with an Xgboost
model (trained with both static and dynamic features) and witha deep learning (DL) architecture trained only using staticfeatures. For the evaluation and comparison of models split
the dataset in 80% training 20% test, and use the lift on the
top 10% of the population. Results presented in the table below
show the improvement obtained using the CNN.
Lift top 10%
XGB
DL
CNN - DL

Train
7.03
6.23
9.18

Test
5.48
4.51
7.15

R EFERENCES

Fig. 1. Three different types of customer behavior where the dots represent
outgoing traffic events. The green line represents a customer who is active forall the observation period and is still active in the prediction window, thus hewill be labeled 0; the black line is an example of customer to be removed fromthe training dataset as he is already not doing any traffic 14 days before theprediction window; the red line is an example of customer who will become
inactive in the prediction window, so will be labelled as 1.

[1] M. F. Abdillah, J. Nasri, and A. Aditsania. Using deeplearning to predict customer churn in a mobile telecomunication network. eProceedings of Engineering, 3(2), 2016.
[2] C. Yang, X. Shi, L. Jie, and J. Han. I know you’ll be back:Interpretable new user clustering and churn prediction ona mobile social application. In ACM SIGKDD, pages 914–
922. ACM, 2018.

Measuring the Relationship between Apps and Urban Context
Eduardo Graells-Garrido
Universidad del Desarrollo
Santiago, Chile

Diego Caro

Universidad del Desarrollo
Santiago, Chile

Rossano Schifanella
University of Turin
Turin, Italy

ABSTRACT

Figure 1: Application tra�c during the period of study in
Santiago, Chile. The tra�c has been de-trended and averaged in two di�erent periods: laboral days and weekends.

Figure 2: Correlations between application usage rates andassociation to speci�c modes of transportation per tower.

The adoption of user-generated content and interaction logs has
been prominent in the measurement of how people and places
relate [4]. Understanding this relationship can help to design andinform place and policy design. The ubiquity of smart phones, andthe wide range of activities supported by mobile applications, may
allow to understand how people use their time. However, there is
still a gap in the knowledge of what kind of activities are performed
in di�erent urban contexts, as there is little information about how
such contexts shape mobile application usage.
In this abstract, we propose a study about how the urban context
shapes what people do on their mobile phones. As urban context,
we consider daily activities such as commuting, and physical places
such as Points of Interests (POIs). To analyze these contexts, we rely
on a Deep Packet Inspection data set covering two weeks of IP requests in Santiago, Chile [1]. This data set contains the aggregatednumber of requests per mobile phone tower at the top countrywise 5,000 popular IP addresses by the largest telecommunications
operator in the country. We matched those IPs to app. categories
to characterize their daily tra�c (c.f. Fig. 1), �nding that app. usage varies with respect to laboral days and weekends (e.g., mail ismore used in laboral days), as well as commuting times (e.g., music
and news are correlated with rush hours). Then, using the resultsfrom prior work that associated mobile phone towers to mode(s)of transportation using XDR [2]), we estimated rank-correlationcoe�cients between app. usage rates (adjusted by �oating population) and mode(s) of transportation (bus, metro, and car), at two
times of the day: morning and afternoon rush hours (c.f. Fig. 2).
Results include patterns such as the usage of social networks andgames with metro, and transportation applications with car andbus (e.g., ride hailing and public transport). Finally, we estimatedrank-correlations between app. usage rates and POIs available in
OpenStreetMap (c.f. Fig. 3). Results include that entertainment areas
are more correlated with messaging apps. than others, and thatfood places are inversely correlated with games, among others.
Our results con�rm the intuition that place and time shape what
people do on their phones, in line with theories about time use suchas equipped time [3]. Thus, understanding this behavior may help
to design transportation equipment, context-aware recommended
systems, as well as to measure what people do with their time.

REFERENCES

Figure 3: Correlations between application usage rates andthe availability of several categories of POIs around towers.

[1] E. Graells-Garrido, D. Caro, O. Miranda, R. Schifanella, and O.F. Peredo. 2018. The
WWW (and an H) of Mobile Application Usage in the City: The What, Where,
When, and How. In Companion of the The Web Conf. 2018. Int. World Wide Web
Conf. Steering Comm., 1221–1229.
[2] E. Graells-Garrido, D. Caro, and D. Parra. 2018. Inferring modes of transportation
using mobile phone data. EPJ Data Science 7, 1 (2018), 49.
[3] Juliet Jain and Glenn Lyons. 2008. The gift of travel time. Journal of transport
geography 16, 2 (2008), 81–89.
[4] D. Quercia, L.M. Aiello, and R. Schifanella. 2018. Diversity of indoor activities andeconomic development of neighborhoods. PloS one 13, 6 (2018), e0198441.

Empirical Evidence For Networks Effects Of Urbanisation,
Fertility Transition, And Migration
Tamás Dávid-Barretta,b,c,d*Sebastian Diaza,eLoreto Bravob
Carlos Rodriguez-Sickerta
János Kertészf,g,ha Universidad del Desarrollo, Facultad de Gobierno, CICS, Av. Plaza 680, San

Carlos de Apoquindo, Las Condes, Santiago de Chile, 7610658 Chileb Trinity College, University of Oxford, OX1 3BH, Oxford, UKc Kiel Institute for the World Economy, Kiellinie 66, D-24105 Kiel, Germany
d Population Research Institute, Väestöliitto, Kalevankatu 16, Helsinki 00101,

Finlande Data Science Institute, Universidad de Desarollo, Av. Plaza 680, Las Condes,
7610658, Santiago de Chile, Chilef Central European University, Center for Network Science, Nador u. 9, Budapest,
H-1051, Hungary
g Department of Computer Science, Aalto University School of Science, P.O.Box
15500, 00076 Finlandh Department of Theoretical Physics, Budapest University of Technology andEconomics, H1111, Budapest, Hungary*Corresponding author:
Tamas David-Barrett, tamas.david-barrett@trinity.ox.ac.uk
A recently published paper (David-Barrett Sci Rep 2019) proposed amathematical theory that links demographic processes, such as fertility
transition, urbanisation, and migration, to a rise in norm violations, and thereplacement of traditional social network based norm enforcement withformalised legal institutions. Here we report an empirical test to this theory
using a combined mobile phone database, census data, and macro statistics. The
size of the database is 5.6 million people, from Chile. We found that the data is
consistent with all causal predictions of the theory at a very high level ofsignificance. The reported magnitudes are as follows: urbanisation, fertility, andmigration explains ~20% of the variation of the clustering coefficient amongindividuals, which, in turn, explains 48% of the variation in crime among
administrative units. These results validate the theory, and offer immediate
policy implications.

Multichannel Social Signatures and Persistent Features of Ego Networks
Sara Heydari1 , Sam G.B. Roberts2 , R.I.M. Dunbar3 , J. Saramäki1,4
sara.heydari@aalto.fi, s.g.roberts1@ljmu.ac.uk, robin.dunbar@psy.ox.ac.uk, jari.saramaki@aalto.fi
1) Department of Computer Science, Aalto University, Espoo, Finland2) School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, UK
3) Department of Experimental Psychology, University of Oxford, UK
4)Helsinki Institute of Information Technology HIIT, Aalto University, Espoo, Finland

The structure of egocentric networks reflects the way people balance their need for strong, emotionallyintense relationships and a diversity of weaker ties. Egocentric network structure can be quantified with’social signatures’, which describe how people distribute their communication e↵ort across the members(alters) of their personal networks. Social signatures based on call data have indicated that people mostly
communicate with a few close alters; they also have persistent, distinct signatures [1].
However, social relationships are shaped and maintained through a diversity of communication channels. People do not use these channels uniformly–rather, the choice of channel depends on many factors.
To examine if the properties of social signatures are generalizable and genuine features of egocentric networks, it is therefore important to look at data from multiple channels of communication, both separately
and together.
Combining information on di↵erent channels can, however, be problematic because of their intrinsic
di↵erences. For example, the number of calls or their total duration is typically used as a proxy fortie strength in mobile telephone call data. But text messages, another common form of communication
via mobile devices, have no duration, and the number of text messages between an ego-alter pair is
not directly comparable to the number of calls between that pair. In this study [2], we develop a wayof constructing comparable call and text-message ego networks as well as mixed ego networks using
both channels (see Fig. 1). We observe that the social signatures made from all these types of ego
networks display persistent individual di↵erences that remain stable despite the turnover in individual
alters. We also show that call, text, and mixed signatures resemble one another both at the populationlevel and at the level of individuals. The consistency of social signatures across individuals for di↵erent
channels of communication is surprising because the choice of channel appears to be alter-specific with noclear overall pattern, and ego networks constructed from calls and texts overlap only partially in termsof alters. These results demonstrate individuals vary in how they allocate their communication e↵ort
across their personal networks and this variation is persistent over time and across di↵erent channels ofcommunication.

calls ego-A

A

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Figure 1: To construct egocentric networks from call and text data, we define for each ego-alter pair thelink weight as the number of 1-hour time-bins that contain at least a communication event of the desired
type. Weights defined this way are better comparable than numbers of calls and texts whose “units” donot match, as one conversation which requires dozens of text messages can be carried out in one call.
For mixed ego-nets, we simply count the number of bins that have at least one event of either type.

References[1] Jari Saramäki, Elizabeth A Leicht, Eduardo López, Sam GB Roberts, Felix Reed-Tsochas, and Robin IM Dunbar. Persistenceof social signatures in human communication. Proceedings of the National Academy of Sciences, 111(3):942–947, 2014.
[2] Sara Heydari, Sam G Roberts, Robin IM Dunbar, and Jari Saramäki. Multichannel social signatures and persistent featuresof ego networks. Applied Network Science, 3(1):8, 2018.

Computable reachability of limited waiting-time processes
in large temporal networks
Arash Badie Modiri1,⇤ , Márton Karsai2,† , Mikko Kivelä1,‡
2

1
Department of Computer Science, School of Science, Aalto University, Finland
Univ. Lyon, ENS de Lyon, Inria, CNRS, Université Claude Bernard Lyon 1, LIP UMR 5668, F-69342, Lyon, France.
⇤
arash.badiemodiri@aalto.fi † marton.karsai@ens-lyon.fr ‡ mikko.kivela@aalto.fi

In many spreading processes a spreading agent may have a limited lifetime t: like in case of transportation
networks with a maximum acceptable transfer time; in social networks where information may become outdated
or forgotten; or in case of diseases where the infectious period ends after a certain amount of time. These problems, concerning limited ( t < 1) waiting time processes, have been previously studied in temporal networks
by simulating the process from a sample of initial nodes and time instances. This approach limits the analysis
to either very small networks, or to average statistics (as opposed to event-level statistics or to statistics of thetails of distributions) [1]. To alleviate this problem, recently the event graph representation has been proposed[2, 3], with weakly connected components (WCC) giving an upper bound on the number of events (resp. nodes)
what a spreading process can follow (resp. reach) [2]. However, as WCCs of event graphs cannot determine theexact reachable set from a node at a given time, the detection of out-components appeared as an open challenge
so far.
In this contribution, we present a set of algorithms based on probabilistic counting that allow us to simultaneously measure the number of nodes and events that can be reached from all di↵erent starting points and times
in a temporal network. Our method works accurately for very large networks, which we demonstrate via theestimation of reachable set of nodes and events from all possible initial conditions in a large mobile phone call
network with ⇠ 320M events (Fig. 1) [2] and in a Twitter mention network of ⇠ 270M interactions [4]. Further,
our method can find the event, which reaches the largest fraction of the network (largest out-component in theevent graph) with high adjustable probability. Note that the reachability without limited waiting time ( t = 1)
appears as a special case here and can be solved as well with our algorithms.
(a)

0.06

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t)

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Figure 1: Statistics of paths of t-connected events calculated for all ⇠ 320M possible source events in the mobile
phone communication network. The sizes of reachable sets are shown as numbers of events. (a) The maximum fractionof reachable events from any starting event ⇢o,E (maximum out-component size in event graphs) with varying limited
waiting time t. The probability of missed detection of the correct initial condition is at most 10 2 . (b) Fraction ofreachable network, so,E , from (all) any event in di↵erent times. One point assigns one source event. A sample of 10% of
points are plotted due to limitations of plotting software. (b inset) Distribution of number of t-reachable events fromall starting events (i.e. the out-component size distribution in event graphs).

Our work has several advantages as compared to the conventional initial condition sampling approach. It
can be used to accurately calculate the tails of the reachability and spreading distributions and it can answer
completely new questions on temporal network data, such as, what is the exact maximum number of nodes thatcan be infected via a spreading process. It can also be used to calculate node/event level statistics, which maylead to new kinds of importance and centrality measures. Further, it opens up a way to analyse percolation
phenomena in temporal networks. For example, instead of resorting to upper-bounds via WCC calculations(and lower bounds via sampling), we can now exactly measure the critical parameters of the temporal network
unfolding as a directed percolation, or a spreading process evolving on the top of it.
[1] Holme, P. (2005). Network reachability of real-world contact sequences. Phys. Rev. E, 71(4), 046119.
[2] Kivelä, M., Cambe, J., Saramäki, J., & Karsai, M. (2018). Mapping temporal-network percolation to weighted, static event
graphs. Sci. Reps., 8(1), 12357.
¯[3] Mellor, A. (2017). The temporal event graph. J. Complex Networks, 6(4), 639-659.
[4] Yang, J., & Leskovec, J. (2011). Patterns of temporal variation in online media. Proceedings of the fourth ACM international
conference on Web search and data mining (pp. 177-186). ACM.

Clean up or mess up: the e↵ect of sampling biases on measurementsof degree distributions in mobile phone datasets
Adeline Decuyper1 , Arnaud Browet1 , Vincent Traag2 , Alexey Medvedev⇤1,3 , Vincent D.
Blondel1 , and Jean-Charles Delvenne1
1 Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique deLouvain, Louvain-La-Neuve, Belgium
2 CWTS, Leiden University, Leiden, The Netherlands
3 Université de Namur, Namur, Belgium

Mobile phone data have been extensively used in the recent years to study social behavior. However, thosestudies are inherently based on partial data, often covering a subset of the population, a specific time frame or asingle country, as mostly provided by a single telecommunication company. Reconstruction of a communication
network from mobile phone data is one of the primary tools for analysis of the client audience. The results does
not show to be universal across all datasets. For example, in one of the first studies on mobile phone data⇤ ,looking at one day of data, it was observed that the reconstructed network corresponded to a random graphmodel with a Power Law degree distribution. However, in a later study † , this time using a longer period of
one month of data, the authors observed that the mobile call graph had a degree distribution corresponding to
a Double Pareto LogNormal (DPLN)‡ . In our work, we point to an observation that the bias due to the limited
coverage in time may have an important influence on the results of the analyses performed.
Additionally, we observe significant di↵erences, both qualitatively and quantitatively, in the degree distribution of the network, depending on the way the dataset is pre-processed. Many researchers start by preprocessing
the data, often called “cleaning”, removing links or nodes that are not active enough. For example, it is common
to remove all links that are non-reciprocated§ or to impose a threshold on activity between a pair of users (e.g.
at least n communications in a t time interval)¶ . These apparently innocuous filtering methods applied before
the analysis of the network generate additional biases that are often overlooked.
In our work, we study two large databases of mobile phone calls in Belgium (6 months) and in Portugal(15 months). We find that by drawing a link between two users as soon as they have communicated at least
once, we obtain a network with a DPLN degree distribution. On the contrary, if we only take into account theusers that remain in the network during the whole observation period, thus removing all users that joined orleft the network during that time frame, the degree distribution changes from a DPLN to a LogNormal. We
validate this observation both in empirical and theoretical scenarios for a wide range of observation periods andpresent a possible stochastic model of degree evolution based on geometric brownian motion with stopping time
T , which corresponds to the time between the first and last observations of activity of the user, representing
the time during which the degree of the user could grow and be observed in our dataset. If the observation ofthis process is stopped at a random time T (which may come from a wide range of distributions), then X(T )follows a DPLN distribution. On the contrary, when T is constant and we observe users over a time window of
fixed length, then we observe X(T ) distributed as LogNormal 3 .
Our observations raise a point about the e↵ects of sampling the datasets and may help in the future tocreate better synthetic datasets o↵ering a closer correspondence with empirical data, as these results reveal
characteristics of mobile phone datasets that may have been overlooked in the past.
0

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degree, kdegree, kdegree, kdegree, kBelgium, no filter

Portugal, no filter

Belgium, filtered

Portugal, filtered

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Figure 1: Degree distributions in empirical networks of mobile phone communications in Belgium and Portugal for allusers and filtered for only active users in the observation window.
⇤ corresponding author, email: an medvedev@yahoo.com⇤ W. Aiello, F. Chung, and L. Lu. A random graph model for massive graphs. In Proceedings of the thirty-second annual ACM
symposium on Theory of computing, pp. 171–180. ACM, 2000.
† M. Seshadri et al. Mobile call graphs: beyond power-law and lognormal distributions. In Proceeding of the 14th ACM
SIGKDD, pp. 596–604. ACM, 2008.
‡ W. J. Reed and M. Jogensen. The double pareto-lognormal distribution - a new parametric model for size distributions.
Communications in Statistics-Theory and Methods, 33(8) pp. 1733–1753, 2004.
§ J.P. Onnela et al. Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of
Sciences, 104 (18):7332, 2007
¶ R. Lambiotte et al. Geographical dispersal of mobile communication networks. Physica A: Statistical Mechanics and its
Applications, 387(21):5317– 5325, 2008.

Going beyond communication intensity for estimating tie strengths in social networks
Javier Ureña-Carrionú , Jari Saramäki, Mikko Kivelä
Department of Computer Science, School of Science, Aalto University, Finlandújavier.urenacarrion@aalto.fi

Construction of social networks from auto-recorded communication data, such as mobile phone calls, has led
to a revolution in our understanding of social systems. In such studies, the strength of a social tie between two
people is almost exclusively calculated as the number or the total time of the contacts. This is due to convention
and convenience rather than a justified choice, and discards all the information on timings of the interactions.
Here, we think of the tie strength as a latent variable which we try to predict based on properties of the time
sequences of the dyadic interactions. Our assumption is that the latent tie strength is expressed as the structural
overlap in the social network (independently of interaction times), as formulated by the Granovetter’s hypothesis[1, 2]. With this assumption, we can use the time sequences to predict the overlap instead of the latent tie
strength—the better the predictor is for overlap, the better it is for the latent tie strength.
We analyze a mobile phone call dataset of ≥ 6.5 million people during a period of 4 months, and obtain the
topological overlap with an extended network of ≥ 77 million users. We compute temporal measures that go
beyond intensity features, including variables derived from the inter-event time distribution, daily and weekly
patterns, and temporal stability during the observation window. We find that the topological overlap, measured
both in a static and dynamic manner, is correlated with a myriad of tie-level dynamics. For example, the numberEof bursty cascades [3] between two nodes (Nij) seems to be more suitable than the total number of calls (wij )for overlap prediction. In addition, we found that differences in the daily outgoing call distribution are also
highly correlated to overlap, suggesting behavioral homophily in tie-level dynamics. Our results strongly suggestthat there are better proxies for tie strength than the widely used communication intensity. As some of thesemeasures are simple and easy to interpret, they are ready to be adopted for future research on social networks
based on communication data.
0.12

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Figure 1 – (a, b, c) The average topological overlap for links which have a given value of communication intensity (number of calls,
E.
wij ) and a candidate temporal feature F , ÈOij |wij , F Í. The three temporal features are: (a) Number of bursty cascades F = Nij(b) Jensen-Shannon Divergence for node-level daily activity distributions, F = JSDij , where we compare the fraction of outgoing‡ · ≠¯·ijcalls each individual makes at each hour of the day. (c) The burstiness coefficient F = Bij = ‡ij, a measure of uncorrelated
· +¯
·
ij

ij

bursty behaviour [4]. In all three cases the inclusion of a new variable adds information not available by using only wij . (d) Scaled
mutual information between overlap and top five variables, where wij ranks third. (e) Feature importance for overlap prediction
E and JSD
problem using Random Forests (RF) and Extra Trees (ET). In both cases, Nijij outweight most other variables, includingEcommunication intensity wij , as its effect is diminished when Nij is in the model.
[1] M S Granovetter, The strength of weak ties, Social networks 347-367. (1977)[2] J-P Onnela et al. Structure and tie strengths in mobile communication networks, PNAS 104.18 7332-7336 (2007)[3] M Karsai, el tal. Universal features of correlated bursty behaviour, Scientific Reports, 397 (2012)[4] K-I Goh, A-L Barabási. Burstiness and memory in complex systems, EPL 81.4 48002 (2008)

Session Epidemics

HUMAN MOBILITY SHAPES THE HIV EPIDEMIC IN NAMIBIA
Eugenio Valdano1, Justin Okano1, Vittoria Colizza2, Sally Blower1

1Center for Biomedical Modeling, Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine,

University of California, Los Angeles, CA 90024, USA. 2INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et deSanté Publique IPLESP, F75012 Paris, France.

Namibia has a severe HIV epidemic. The infection is endemic in the general population, andthere is significant geographic variation in prevalence (6%-40% for women, 0%-24% for men).
Along with other countries in sub-Saharan Africa (SSA), Namibia has implemented extensive
interventions aimed at preventing infections. However, all of them are based on the implicit
assumption that all risk is localized: people get infected by members of their own communities.
We argue that, in order to eliminate HIV in SSA (a goal set by the World Health Organization), a
key ingredient is still missing: human mobility. The Namibian population is highly mobile.
Consequently, individuals may be at risk of acquiring HIV both in their home community (fromother residents or from visitors from other communities) or when visiting other communities(from residents of these communities). We quantify the importance of these different types ofrisk in the different geographic areas, and compute the various flows of risk importation andexportation among them. We use the network of mobility among the 106 Namibian
constituencies (administrative areas), inferred from mobile phone CDRs. These data werecollected between October 2010 and September 2011, and comprise 9 billion communications
from 1.19 million unique SIM cards. These data were used to calculate a mobility network basedon the average proportion of time residents spent in each of the 96 constituencies in Namibia
that include cell towers. We couple the mobility network to demographic data from the 2011
census and HIV-testing data from 7,600 participants of the NDHS (Namibia Demographic &Health Survey). We use the coupled epidemic-mobility network to calculate, for eachconstituency, the risk of acquiring HIV that was due to sexual contact with other residents (i.e.,
localized risk), with visitors, or when traveling. Notably, our results show that mobility does notsubstantially change (in any area of the country), the number of new infections that would occur
in the absence of mobility, but it substantially “redistributes” risk. Hence, mobility changes which
individuals become infected and where they acquire infection. Our results demonstrate that it isessential to design specialized interventions that, informed by mobility patterns, reach thegroups who are at highest risk of getting infected, and increase adherence to treatment of theinfected individuals.

Comparing Sources of Mobility Information for Modelling the Spread of Zika in Colombia
Daniela Perrotta1*, Enrique Frias-Martinez2, Miguel Luengo-Oroz3, Daniela Paolotti1, Michele Tizzoni1,
Alessandro Vespignani1,4(1) ISI Foundation, Turin, Italy (2) Telefonica Research, Madrid, Spain (3) United Nations Global Pulse, New York, USA(4) Northeastern University, Boston, USA*Corresponding author for this abstract, daniela.perrotta@isi.it

Human mobility plays a central role in the spatial spread of human infectious diseases. Accurate
data on human mobility is therefore key to properly design epidemic models that allow to timely
assess the spatial propagation of infectious diseases and to evaluate appropriate control
measures and intervention strategies. In this context, mobile phone data, in the form of Call Detail
Records (CDRs), implicitly brings a large ensemble of details on human activity, including human
movements, which can be identified for example whenever the same phone number is handled by
two different mobile phone towers in two consecutive phone calls. Appropriately harnessing such
digital traces left by mobile phone users' activity thus represents a relatively low-cost resource todraw a high-level picture of human mobility patterns at an unprecedented scale.
In this study, we focus on the human mobility patterns relevant to the epidemic spread of Zika
during the recent outbreak occurred in Colombia in 2015-2016. The aim of the study is toinvestigate the potential predictive power gained by integrating the human mobility derived from
CDRs data into an epidemic modelling approach, as well as more traditional methods, suchcensus data and mobility models. Specifically, more than two billion encrypted and anonymized
calls made by around seven million mobile phone users in Colombia have been used toreconstruct the aggregated mobility network describing the flows of people travelling daily
among different locations. Moreover, we apply both the gravity model and the radiation model tosynthetically infer the population movements in Colombia and generate the corresponding
mobility networks.
From the networks’ point of view and by considering the census data as a reference, our results
show that the gravity model strongly underestimates the mobility flows, whereas the mobility
determined by the radiation model and the CDRs data show comparable performances in termsof high correlations and good similarities metrics with census data, thus reproducing well theactual mobility between the 33 administrative units (i.e. departments) in Colombia.
To model the spread of Zika, we employ a metapopulation modelling approach to explicitly
simulate the spatial spread of Zika as governed by the transmission dynamics of the virus throughhuman-mosquito interactions and as promoted by population movements across the country.
Notably, our approach integrates detailed data on the population, the spatial heterogeneity ofthe mosquito abundance and the consequent exposure of the population to the virus due tosocio-economic factors in order to help provide a more realistic representation of the epidemic
progression.
Given the same modelling settings (i.e. initial conditions and parameters), our approach allows to
perform numerical simulations of the spatio-temporal progression of the disease by integrating
one mobility network at a time to ultimately assess their predictive power by comparing thesimulated epidemic profiles with the Zika cases officially reported by the National Institute of
Health in Colombia.

Telecom data in public health: building generic prediction models for transmission risk
Jérôme Urbain1, Kristýna Tomšů1, Jonathan Frisch1,2, Astrid Van Lierde1, Rositsa Zaimova1, Christophe Bocquet1
1
Dalberg Data Insights, Brussels, Belgium, jerome.urbain@dalberg.com
2
Apptweak, Brussels, Belgium
Introduction. Over that last decade, a number ofstudies have shown the relevance of human
movement data extracted from mobile phone records
to estimate the transmission risk of infectious
diseases [1, 2, 3]. With the support of the UnitedStates Agency for International Development(USAID), Dalberg Data Insights has conducted two
projects aiming at 1) making such approaches
available for health authorities combatting zika and 2)
generalizing the proposed methods to various
diseases and locations, with a single, parameterizable
process. We have also explored machine learningmodelling to predict the disease incidence over a few
weeks in the future. In both cases the outputs of themodels were made easily accessible to the end-users
through data visualization dashboards.
Generalization of the risk estimation. The
generalization part was primarily developed in West
Africa, through discussions with local health experts
and USAID staff. Taking Tatem's model as a starting
point, which estimates the risks of transmission froma place A to a place B by looking at visitors from A to
B and visitors from B to A [2], we have generalized it
to also consider any location C where residents from
A and B could meet. Our risk transmission model is:
𝑅𝑖𝑠𝑘𝐸𝑥𝑝𝑜𝑟𝑡A→B = ∑𝛽C 𝛼 NB ∑αAC
αkC, where:• R is the number of areas,• Nk is the population living in area k,• Ik is the incidence of the disease in area k,• 𝛽k is the transmission rate of the disease in area k at time t,• αij is the percentage of time residents of area i spend in area j.

The proportion of time spent at each location isestimated using Call Detail Records. Depending on
the considered disease, the relevant mobility metrics
vary. For instance, for malaria we are interested in thenumber of nights spent at each place, as malaria istransmitted over night. Dengue, on the other hand, istransmitted by mosquitoes that are active during day
time. For other diseases that are transmitted directlyfrom human to human, such as measles or Ebola, all
movements should be considered.
Predicting future incidence. For the predictive part,
we worked with a ministry of health in Brazil on zika
and dengue. In addition to monitoring the evolution of
the diseases and the prevention activities conductedby the ministry, we i) estimated the import and export
risks as explained above; ii) predicted the incidence
over the next 4 weeks. To do so, we trained different
models (random forest, linear regression with Lasso,

linear regression with Ridge). The models were fed
with the past incidence values, population, altitude
and surface area, for the target area, the 10 areas thathave the biggest mobility flows with the target area
and the 10 areas showing the biggest importation risk
to the target area (using the import/export risk
estimation already explained). Models were trained,
validated and evaluated on disjoint data sets of 14, 3
and 4 months, respectively. These models werecombined using simple linear regression, allowing tosignificantly reduce the mean squared error andachieve reliable predictions, as shown on Figure 1.

Figure 1: Prediction of dengue incidence

The approach was well received by the ministry ofhealth as i) the prediction part enables to bettertarget their future interventions; and ii) the model
remains relatively simple to explain. In addition, themodel implicitly integrates time- and location-based
factors (e.g., altitude, season), reducing the need formodel parameters (𝛽 ) that are hard to accurately
estimate, even by experts, beyond trends. The next
step is to extend the training data to 10 years in orderto capture more complex patterns (e.g., “vaccination”
effect when an area was recently hit by the epidemy).
k

BIBLIOGRAPHY[1] A. J. Tatem, Y. Qiu, D. L. Smith, O. Sabot, A. S. Ali and B.

Moonen, "The use of mobile phone data for the estimationof the travel patterns and imported Plasmodium falciparum
rates among Zanzibar residents," Malaria journal, vol. 8, p.
287, 2009.

[2] A. J. Tatem, Z. Huang, C. Narib, U. Kumar, D. Kandula, D. K.

Pindolia, D. L. Smith, J. M. Cohen, B. Graupe, P. Uusiku andothers, "Integrating rapid risk mapping and mobile phone
call record data for strategic malaria elimination planning,"
Malaria journal, vol. 13, p. 52, 2014.

[3] A. Wesolowski, T. Qureshi, M. F. Boni, P. R. Sundsoy, M. A.

Johansson, S. B. Rasheed, K. Engo-Monsen and C. O. Buckee,"Impact of human mobility on the emergence of dengue
epidemics in Pakistan," Proceedings of the National Academyof Sciences, vol. 112, pp. 11887-11892, 2015.

Exploiting Population Activity Dynamics to Predict
Urban Epidemiological Incidence
Gergana Todorova, Lancaster University and Anastasios Noulas, New York University

Changes in lifestyle as well as intensifying
urbanisation and travel not only pose an increased health risk, but add extra pressure to
emergency services that operate day and nightin the world’s largest cities. In the United
Kingdom, the number of health related incidents that require urgent response has beensteadily increasing, resulting to an unprecedented number of calls that require an ambulance response [2]. As a result, during 2015
only 5 out of 11 ambulance trusts operating
in England have met their operational target
to respond to 75% of most urgent calls withineight minutes 1 . Incidents such as cardiac arrest
require immediate attention by paramedic specialists, with ambulance response times being
critical to patient survival rates [5]. Consequently, intelligence on optimally deploying
paramedic crews across geographic areas andalso with appropriate timetabling becomes asignificant challenge for ambulance services
worldwide.
To meet the goal of timely responding tourgent health incidents ambulance organisations over the past decade have been moving
towards integrating computational methods
and data science practices in their planning
operations [3]. Such practices include the recruitment of analysts and data scientists, thedevelopment of data warehousing infrastructure, as well as the use of software and cartography tools that support planning allowing forthe identification of optimal paramedic crewscheduling [4]. However the functionality of
the class of optimisation tools that simulate
emergency event activity is often opaque. They
are being frequently packaged in proprietry
software becoming effectively a black box forend users 2 . In the meantime, scholarly work inenvironmental epidemiology has been focusing
more on accurate statistical modelling of ambulance calls, rather than providing plausible interpretations on the socio-economic and urban
activity factors that may drive those. Models
developed in this setting have used historic frequency information about ambulance calls for
instance [1, 6] ignoring socio-economic or realtime population activity as well as mobility indicators that could also determine the risk for
emergency incidents.
In this work we seek to establish a stronger
link between the prevalence of health incidents
and spatio-temporal population activities, aiming to bridge the aforementioned gap in theliterature. Critically for its novelty, we model
activities of local populations using the digital
traces emitted by mobile users that engage withlocation intelligence services like Foursquare.
We demonstrate how mobile web indicators ofgeographic user activity become a useful proxy
to understanding the times of the day and daysof the week certain urban activities become
prominent across city neighborhoods. Moreover, we show how those align across space
and time with certain types of ambulance inci2 https://www.intermedix.com/solutions/
response-planning-0

1 https://www.bbc.com/news/health-33166204

1

Special Session
Data for Refugees

Optimizing the Access to Healthcare Services in Dense
Refugee Hosting Urban Areas:
A Case for Istanbul
M. Tarik Altuncu m.altuncu@imperial.ac.uk, Ayse Seyyide Kaptaner ayseyyide@gmail.com,
and Nur Sevencan nur.sevencan@trtworld.com

Abstract
With over 3.5 million refugees, Turkey continues to host the world’s largest refugee population.
This introduced several challenges in many areas including access to healthcare system. Refugees
have legal rights to free healthcare services in Turkey’s public hospitals. With the aim of increasing
healthcare access for refugees, we looked at where the lack of infrastructure is felt the most. Our
study attempts to address these problems by assessing whether Migrant Health Centers’ (MHC)
locations are optimal. The aim of this study is to improve refugees’ access to healthcare services
in Istanbul by improving the locations of health facilities available to them. We use the cellular
network usage data provided by Turk Telekom [3] to figure out where optimal locations for MHCs
are. Our location optimization cuts the average distance and travel duration for refugees need totravel.
We limited the study to Istanbul as it is one of the densest refugee hosting urban area in Turkey.
We then followed the following procedure:– Estimation of the residential location of refugees in the data set based on their calling activities
during nighttime– Calculating the number of refugee residents per Voronoi cell region– Clustering Voronoi cell regions to come up more representative scale of regions [1]– Aggregating the number of refugee residents to the new scale– Optimizing the MHC locations using the multi-facility location optimization linear programming model [2] with cost function below:
min
x

n X
n
X
i=1 j=1

ai · dij · xij

where n is the number of residential regions, ai is the number of refugee residents in i( th)region, dij is public transit travel distance or duration between i( th) and j ( th) regions , and xij isbinary decision variable of MHC assignment between regions.
MHC Locations
Travel Distance Travel Duration
Current
5.9
26
Optimized (Distance)
3.6
20
Optimized (Duration)
4.4
18
Table 1. Cost of travel distances and durations for current MHC locations and optimized MHC locations(on the basis of distance and duration). All distances are provided in kilometers and durations in minutes.

References
1. Arthur, D., Vassilvitskii, S.: K-means++: The Advantages of Careful Seeding. In: Proceedings of theEighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. pp. 1027–1035. SODA ’07, Society
for Industrial and Applied Mathematics, Philadelphia, PA, USA (2007)
2. ReVelle, C.S., Swain, R.W.: Central Facilities Location. Geographical Analysis. 2(1), 30–42 (1970)
3. Salah, A., Pentland, A., Lepri, B., Letouzé, E., Vinck, P., de Montjoye, Y., Dong, X., Dağdelen, .: Datafor Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey (7 2018)

Acknowledgements: We thank the rest of the members of TRT World’s team for the Data for
Refugees challenge.

Measuring fine-grained multidimensional integration
using mobile phone metadata: the case of Syrian
refugees in Turkey
Michiel A. Bakker1 , Daoud A. Piracha1 , Patricia J. Lu1 , Keis Bejgo1 , Mohsen
Bahrami1,2 , Yan Leng1 , Jose Balsa-Barreiro1 , Julie Ricard3 , Alfredo J. Morales1 , Vivek
K. Singh4 , Burcin Bozkaya2 , Selim Balcisoy2 , and Alex ’Sandy’ Pentland1
1

MIT Media Lab, Cambridge, Massachusetts, USA {bakker,pentland}@mit.edu
2
Sabanci University, Istanbul, Turkey
3
DataPop Alliance, New York, USA
4
Rutgers University, New Jersey, USA

The Syrian civil war that began in 2011 has had an enormous human cost and impact
on the region. UNHCR has estimated that over 12 million people fled their homes; 6.6
million Syrians are internally displaced while 5.6 million people fled Syria. As the Syrian
conflict has intensified and lengthened, many refugees have faced challenges integrating
into their host societies. Here we introduce and evaluate di↵erent measures of integration extracted from mobile phone metadata in Turkey, the country that hosts the largest
number of Syrian refugees. Ground truth labels revealing the refugee status of a phone’suser allow us to study integration of refugees along three dimensions: (1) social integration (2) spatial integration and (3) economic integration. A strong social network has
proven to be instrumental in finding housing, employment and healthcare. Nonethelessmany prior studies, relying on sparse self-reported data, have failed to accurately measuresocial interactions. This study measures social integration directly from the data as the
refugee to local call volume. Spatial integration, traditionally measured through census
data, is measured for each area using the spread of refugees across close-by cell towers.
For individual refugees, we measure spatial integration by tracking movement throughout
the city while computing likelihood of encountering a local citizen. Finally, even though
we do not have access to ground truth economical integration, we infer signs of formal
employment through a user’s regularity in commuting patterns.
Where previous studies rely on survey data and interviews, we show that integration
can be measured and understood directly and at scale through mobile phone data. For
example, measurements of spatial integration show that refugees in Istanbul live in more
integrated neighborhoods than in Southeastern Anatolia, close to the Syrian border. Additionally, our toolset allows us to observe the important interactions between di↵erent
dimensions of integration. This leads to interesting insights such as a contrast between
strongly correlated social and spatial integration in Istanbul while these same measures
are uncorrelated elsewhere in Turkey. The relationships between measures are an important first step towards a more fine-grained understanding of the dynamics behind
integration, needed to inform policy decisions and interventions. Finally, leveraging theresults from two general elections in Turkey in 2015 and 2018, we confirm earlier findings
concerning the impact of refugee presence on voting behavior, and demonstrate that wecan better explain voting behavior by incorporating integration metrics. On the whole,this work marks one of the first systematic attempts at employing fine grained mobility
to understand refugee integration and impact of integration at scale.

Improve Education Opportunities
for Better Integration of Syrian Refugees in Turkey
Marco Mamei1 , Seyit Mümin Cilasun2 , Marco Lippi1 , Francesca Pancotto1 , and Semih Tümen3
1

University of Modena and Reggio Emilia, Italy
Central Bank of the Republic of Turkey , Turkey
3
Department of Economics, TED University, Turkey
marco.mamei@unimore.it, marco.lippi@unimore.it, seyit.cilasun@tcmb.gov.tr,
francesca.pancotto@unimore.it, semihtumen@gmail.com
2

Turkey is the largest refugee hosting country worldwide
with 3.5M registered Syrian refugees. Considering that Syrians will continue to live in Turkey for many years, and thata significant portion will not turn back to their country, theirintegration in the Turkish society is crucial.
As more than one third of the Syrian population residing
in Turkey is composed of school-age children, schooling andeducation have a major role in the integration process.
However, from the perspective of any hosting society, mass
refugees inflow represents a huge challenge. Refugees generate competition for several resources and many public services get congested. Host countries have limited education
resources (schools and teachers), which makes policy making
and resource re-allocation a major challenge.
One of the main assets to address these complex challenges is to have up-to-date fine-grained information about
refugees and their activities. Call Detail Records (CDRs) aremobile phone data that allow to track activities of refugees
and natives at a fine grained scale, thus representing a natural response to this need.
In this project we use mobile phone data to analyze someof the challenges to integrate Syrian refugees in the Turkish education system and the impact on the Turkish society.
Educational institutions do not simply transmit human capital, they also pass on social capital in the form of social rules

42

and norms. So, investments in education will not only work
toward increasing Syrians’ school enrolment rate, they will
also impact their integration in the society.
We analyze the distribution of refugees across the country and their possible impact on education facilities. We
study logistic obstructions to schooling of Syrian children,and propose and evaluate an optimization mechanism toidentify areas where new education resources are requiredconsidering the needs of both refugees and natives. Figure1(a) illustrates provinces where education services are morecongested. Figure 1(b) shows the results of an optimization
process trying to plan the construction of new schools wheremost needed according to natives and refugees locations asmeasured from CDRs.
We also analyze the relationship between education andsocial integration, and the impact of Syrian refugees’ schooling on social integration (via numbers of calls between natives and Syrians). Figure 1(c) shows the correlation between children and calls between natives and refugees. We
also consider the impact of Syrian refugees on the education
choices of natives and their impact on Turkish economic development
The full version of this report can be found on D4R Challenge web site. http://d4r.turktelekom.com.tr

school congestion
100−300 students/school
300−400 students/school
400−500 students/school
500−600 students/school> 600 students/school

lat

40

38

refugeeschoolnew school

36
30

35

40

long

Figure 1: a) School congestion at province level; b) Refugees, existing schools, and where to build new schools in the
district of Altindag. c) Correlation between number of children and number of calls between natives and refugees.
1Abstract for the netmob19 conference (D4R Challenge Session)
Title:
AROMA_CoDa: Assessing Refugees’ Onward Mobility through the Analysis ofCommunication Data
Authors:
Harald Sterly, University of Vienna, harald.sterly@univie.ac.at
Benjamin Etzold, Bonn International Center for Conversion, benjamin.etzold@bicc.de
Lars Wirkus, Bonn International Center for Conversion, wirkus@bicc.de
Patrick Sakdapolrak, University of Vienna, e-mail: patrick.sakdapolrak@univie.ac.at
Jacob Schewe, Potsdam Institute for Climate Impact Research, jacob.schewe@pik-potsdam.deCarl-Friedrich Schleussner, Climate Analytics gGmbH, carl.schleussner@climateanalytics.org
Benjamin Hennig, University of Iceland, ben@hi.is
Abstract:
Data availability remains one of the greatest challenges in migration and mobility research,especially for internal migration, and in countries with and contexts of data scarcity. We present a
project in which the analysis of mobile phone data (Call Detail Records) has provided insights into
the secondary mobility of refugees in Turkey.
Spatial and temporal trajectories at different scale levels have been derived from an anonymous data
set that maps the talk time, location and duration of more than 50,000 mobile phone subscribers in
Turkey over the course of a whole year, and that was provided in the course of the Data for
Refugees (D4R) Challenge. The major migration trends in Turkey are clearly visible: from South toNorth, from East to West and from rural to urban areas. However, the analysis also provides insightsthat have not yet been clearly established from existing data, such as specific routes and central
nodes in mobility networks; seasonal, cyclical and event-dependent mobility; or the return migrationof refugees to the Syrian border. Cultural mobility motives (e.g. visits during the Ramadan feast) are
more important, and seasonal mobility to regional agricultural labour markets seems less important
than originally expected.
The analysis of CDR - even with a high degree of anonymisation - enables the description ofmobility and migration patterns in an unprecedented richness of detail, both temporally, spatiallyand across scales. However, the complexity of the mobility patterns that emerges in the process
requires even more in-depth analyses. The results do also show that the boundaries of establishedconcepts, such as commuting, seasonal or permanent migration can be much more fluid than is often
assumed.
Keywords: Call data record, mobility, social integration, Turkey, refugees’ movements

Posters

Extensive and Detailed Nationwide Information on Human Mobility
from Mobile Data in the Netherlands
Peter van der Mede, Joost de Bruijn, , Klaas Friso, Stefan de Graaf, Jakob Henckel: DAT.Mobility,
Deventer, Netherlands; pvdmede@dat.nl
John van de Kooij, Mezuro, Weesp, Netherlands;
Marcel Bijlsma, Frank Ophuis: Mobidot, Enschede, Netherlands
Matthijs de Gier, Frans van der Horst, Vincent Kuijpers, , Kantar, Amsterdam, Netherlands
In 2013 we presented a paper as part of the D4D challenge using Ivory Coast CDR data from Orange.
In a proof of concept we showed that it is possible to build a gravity based transport model for awhole country and major cities based on CDR data and the open streetmap (OSM) network.(Nanni
et all, 2013).
In the present paper we will show two new developments in creating extensive and detailed (Dutch)
nationwide information on human mobility, which is based on CDR/EDR and A-GPS mobile phone
data.
First, in co-operation with Mezuro and by using Vodafone CDR/EDR data we have created a national
origin-destination (OD) matrix. This matrix comprises of movements between 1,250 zones, based on
CDR/EDR from 4-5 million mobile phones . After weighing these data to get national figures, there
still are a number of limitations in such a CDR/EDR based OD matrix. The main problem is that the
granularity of this OD matrix lacks detail for transport modelling on lower levels, e.g. regional andlocal roads. Another limitation is that intrazonal movements are lacking in this matrix. Last, due tooverlapping gsm cells movements over distances < 7 kilometers are underrepresented in the matrix.
To overcome these limitations we developed a number of procedures in which we use socioeconomic census data (e.g. inhabitants and work places per zone, and national travel survey data).
Using these procedures we have created a 30,000 zone matrix, which includes intrazonal
movements, estimates the missing short trips, and which does have enough detail for transportmodelling purposes. Next, we assigned this improved OD-matrix to the complete Dutch transport
network (1 million links), and we compared the road volumes resulting from this traffic assignment
to the volumes in current state-of-the-art regional and national transport models. The results ofthese comparisons show that the developed methodology leads to high quality a priory OD-matrices,and thus traffic volumes, which make it much easier to create up-to-date transport models.
Though a distinction between train and ‘other modes’ can be made from the raw CDR/EDR data,
more detailed insights into travel behaviors, including routes, multimodal trips, trip modality andpurpose, travel times and speeds, dwelling time etc. need other measuring methodologies. Suchinformation is highly wanted for all kinds of monitoring and evaluation purposes. Next to actual
information on traffic volumes on all roads we would need better insight in the composition of theseflows, modal splits, trip-chains etc.
So, secondly, in co-operation with Mobidot and Kantar we initiated the Dutch Mobility Panel. This
longitudinal panel is drawn from the 100,000 members panel of whom 20-25% are willing to sharedata on their location for research purposes. Panel members can download the Kantar app (by invite
only). Kantar uses this app for surveying their panel on a multitude of subjects. If a panel member
consents in sharing location data, all outdoor movements are collected from their smart phone (iOSand android types) and stored in a database. Both the demographic profile data and the travel dataof participants are merged into a real-time updated database.
Both the EDR/CDR based traffic volumes and the A-GPS based outdoor movements can be used inunison to create unprecedented detail of human mobility in the Netherlands.

Title: Distribution of technology (Mobile and Wifi) for total population of cities in India
based on the rank size
Author: Jyothi Gupta
Affiliation: UCL, the Bartlett
Email address: ms.gupta.18@ucl.ac.uk
Abstract:
Urban System is explained as a horizon, large city- regions, global city economic relationshipwith international city. It is the connection between larger system, relationship between spaceand society. Gidden’s (1974) explained us the procedure of nature sciences that is Unity ofmethod between natural and social sciences. Cites are market place as an assumption withsystem of cities to minimize the transportation cost. The structure of cities could beconcentric zones or pattern of pigeon holes in different orientations. India have measurable
economic structure and self-sustain process and shares idealism with its religion, pattern ofurban formulation, culture diversification.
SocialSpatialRelation

GlobalEconomic

Distance location

Development growth

Country

CulturePhysical Boundaries

State-wise religious planning

City

SocialScale &Planninginteraction

Division of Labour/Money
Economylocal

structure

Nature- sustainability

Infrastructure

Urban problem could be stated as enquiry of a problem, social science concepts. Based on the
population growth, the world is urbanizing. M.Batty et,(2012) explains Social Media andtexting is the kind of information that can be transmitted through particular network being
Twitter. Movement on public transport in large cities is increasingly and been collected foranalysis and visualisation.
Algorithms are created for mapping the movements between modes and creating a origin anddestination., its framework to master the complexity. Batty have identified seven types of
initiative for smart city movement which is a summary of digitalization.
Defining the Zipf’s law as inversely proportional to its rank in the frequency table, we haveconsidered a first 20 cities in India with its population growth in year 2011. As per the data,
we understand that Delhi being the highest populated city in the country compared to 20
other cities in the country. It is ranked at the higher scale and the lowest being Hugli.
Basically, after providing a relation ratio of coefficient, data is gathered for analysis:• Mobile connectivity in rural area
• Access to Internet growth• Wireless devices and broadband connections with speed
Based on the data breakdown, Rural area growth for about 20 villages have provided thecoefficient value as 0.51 whereas in the Urban population for highest growth of 20 cities havecoefficient value as 0.88 with the regression line being more persistence. These reality gaps isaddressed, and a real understanding of future needs could be prioritised as per the needs andbetter growth insights to create a technology accessible city.ISRO (2018) published the highspeed connectivity in Rural India ranking Asia to have the fastest growth with statistics
predicted in technology and connectivity.

Monitoring mobile money networks across providers and countries
Carolina Mattsson

Shafique Jamal

Soren Heitmann

Guy Stuart

mattsson.c@husky.neu
Northeastern University

sjamal@ifc.org
IFC

sheitmann@ifc.org
IFC

guystuart@mfopps.org
Microfinance Opportunities

Introduction
Mobile money is an innovative digital financial service that has seen
rapid expansion across Africa, South Asia, and Southeast Asia since 2009.1 Providers support a
digital channel for so-called e-money, providing payment processing and transfer services tousers over the cellular infrastructure (ie. via SMS, USSD, or a mobile application) and servicing
conversion with cash via a large cadre of on-the-ground agents who work on commission.2
While mobile money is growing in popularity and impact, providers have struggled to develop
strong and sustainable networks among users of the service. This is due in part to high levels ofinactivity, and to the widespread tendency for customers to withdraw their e-money into cash
straight away rather than to keep it in their accounts or send it onward.3 This is costly forproviders. Servicing a transfer or payment uses scalable digital infrastructure, whereas
servicing a cash-in or a cash-out entails compensating agents and rebalancing stocks of cash
among them. Analysis tools that monitor trends in network use are of immediate interest.
Methods
We introduce two measures of mobile money network strength that are
directly comparable across countries and operators, while remaining interpretable to a nontechnical audience: transactions until exit (TUE) and proportion remaining in network after 72
hours (PRIN-72). These measures capture complimentary aspects of the extent to which emoney continues to circulate once it is in the digital system; we demonstrate using records
from a provider in East Africa covering over 300 million transactions. We calculate TUE andPRIN directly from transaction records by “following” e-money according to last-in-first-out.4
Results
We find that TUE and PRIN-72 discern important network changes. For
example, we see TUE jumped in
response to an intervention by
the provider in September 2016
to address person-to-person fee
evasion, which suppresses aparticular kind of re-transaction.
The intervention targeted largevalue deposits, and appears tohave been successful in raising
the strength of the network
surrounding such transactions.
But, the effect was temporary
elsewhere in the network. This
intervention showed no effecton PRIN-72, confirming that it is
Figure 1: The average TUE for all deposits made on a given day. The darker
a complimentary measure to TUE. line normalizes by the size of the deposit, giving all an equal weight. The
lighter un-normalized line will reflect especially large-value deposits.

GSMA Mobile Money. State of the Industry 2017: Mobile Money. Tech. Rep., GSMA (2017).
2
Cull, R., Gine, X., Harten, S., Heitmann, S. & Rusu, A. B. Agent banking in a highly under-developed financial sector: Evidence
from Democratic Republic of Congo. World Dev. 107, 54–74, DOI: 10.1016/j.worlddev.2018.02.001 (2018).
3
Stuart, G. & Cohen, M. Cash In, Cash Out Kenya: The Role of M-PESA in the Lives of Low-Income People. The Financial Services
Assesment project (Microfinance Opportunities, 2011).
4
Mattsson, C. Follow the money: making sense of payment systems. Under review. (2019)1

1

Inferring Context of Mobile Data Crowdsensed in
the Wild
Rachit Agarwal, Shaan Chopra, Vassilis Christophides, Nikolaos Georgantas, Valérie Issarny
Inria-Paris, France
Email: {rachit.agarwal, shaan.chopra, vassilis.christophides, nikolaos.georgantas, valerie.issarny}@inria.fr

Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal
datasets. Accelerometer’s precision can vary considerably depending on whether the phone is in-pocket or out-pocket, i.e., held
in hand [1]. GPS accuracy can be very low in places like under-ground metro stations [2]. Further, jump-lengths are shorter andhave higher frequency when a person is in-door. Hence, we focus on contexts such as in/out-pocket, under/over-ground, andin/out-door that can be essential for reliably inferring human mobility attributes and properties (e.g., location, jump-length, andmobility activity like walking or driving) from crowdsensed data. Our work is motivated by the fact that most of the publicly
available crowdsensing datasets (e.g. PRIVA’MOV [3] and Beijing taxi dataset [4]) do not include data from specialized sensors
such as light, barometer, etc. considered by state-of-the-art algorithms for detecting the above mentioned contexts. Therefore,
we focus on mining context from the limited features available in the publicly available mobility related crowdsensing datasets.
Moreover, as ground truth is typically not available in these datasets, we pay special attention to minimizing the training or
tuning efforts of the introduced algorithms. Our algorithms are unsupervised binary classifiers with a small memory footprint
and execution time. As the lack of certain features prohibits us to consider state-of-the-art algorithms as baselines, we compare
the performance of our heuristic algorithms against Machine Learning (ML) models built by an AutoML tool [5] using the same
set of features. Our experimental evaluation with a segment of the Ambiciti [6] dataset demonstrates that when compared tothe best baseline ML model w.r.t. balanced accuracy (see Table I), our algorithm for in/out-pocket performs equally well, while
for under/over-ground and in/out-door contexts, for a specific hyper-parameter, our corresponding algorithms are within 4.3%
and 1%, respectively. Concerning memory, our algorithms require 0kB, 4kB, and 0kB, respectively, while they take 0.08sec,
0.17sec and 0.003sec, respectively, for execution. Our algorithms are lightweight enough to be integrated into smartphone
applications. Context information mined onboard thus remains private and can be used to annotate users’ personal trajectories
and incentivize them to participate in crowd-measurement campaigns.
TABLE I
ACCURACY, P RECISION , R ECALL AND F1 SCORE REPORTED BY DIFFERENT METHODS .

In/Out-Pocket
Under/Over-ground
In/Out-door

MethodGaussian NB†
Heuristics
Bernoulli NB†
Heuristics
Bernoulli NB†
Heuristics⌧ uo in m
313
313
313
313

Accuracy in %balanced80-20 split
54
54
74.5
70.2
66
65

Precision
in
out
0.33
0.78
0.19
0.89
0.33
0.97
0.62
0.78
0.42
0.84
0.62
0.68

Recall
in
out
0.19
0.89
0.19
0.89
0.81
0.68
0.62
0.78
0.70
0.62
0.62
0.68

F1 score
in
out
0.24
0.83
0.19
0.89
0.46
0.79
0.62
0.78
0.53
0.71
0.62
0.68

† TPOT reported method

R EFERENCES[1] E. Miluzzo, M. Pap, N. D. Lane, H. Lu, and A. T. Campbell, “Pocket, Bag, Hand, etc.- Automatically Detecting Phone Context through Discovery,” inFirst International Workshop on Sensing for App Phones (PhoneSense) at SenSys’10, p. 21–25, 2010.
[2] K. v. Erum and J. Schöning, “SubwayAPPS: Using smartphone barometers for positioning in underground transportation environments,” in Progress in
Location-Based Services 2016, pp. 69–85, Springer International Publishing, oct 2016.
[3] S. Ben Mokhtar, A. Boutet, L. Bouzouina, P. Bonnel, O. Brette, L. Brunie, M. Cunche, S. D’Alu, V. Primault, P. Raveneau, H. Rivano, and R. Stanica,“PRIVA’MOV: Analysing Human Mobility through Multi-Sensor Datasets,” in NetMob: Book of Abstracts - Posters, pp. 19–21, 2017.
[4] J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang, “T-drive: Driving Directions Based on Taxi Trajectories,” in Proceedings of the
18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’10, (New York, NY, USA), pp. 99–108, ACM, 2010.
[5] R. S. Olson, N. Bartley, R. J. Urbanowicz, and J. H. Moore, “Evaluation of a tree-based pipeline optimization tool for automating data science,” inProceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO ’16, (New York, NY, USA), pp. 485–492, ACM, 2016.
[6] V. Issarny, V. Mallet, K. Nguyen, P. Raverdy, F. Rebhi, and R. Ventura, “Do’s and Don’ts in Mobile Phone Sensing Middleware: Learning from a
Large-Scale Experiment,” in Proceedings of the 17th International Middleware Conference, (Trento), pp. 17:1–17:13, December 2016.

Characterization of cell activity in mobile networks
Angel Cuevas Muñoz

Rubén Cuevas Rumı́nAngel Cuevas Rumı́nUniversidad Carlos III
Madridacuevas@protonmail.com

Universidad Carlos III
Madridrcuevas@it.uc3m.es

Universidad Carlos III
Madridacrumin@it.uc3m.es

This work aims to characterize the activity of the users in
the cells of a large-scale mobile network. To this end, weanalyze a dataset containing data from an operator offering
nation-wide mobile service in a large country.
The questions addressed are simple: How many users areusually connected to each cell? Is there any temporal patternin the usage of the cell? If so, can these patterns be used to
predict the activity in the cell? Do all the cells have the same
temporal usage pattern? Are there well-defined classes of cells
in terms of the activity?
These questions are not just driven by curiosity but respondto basic needs in network planning and operation. Responsibles for planning need to know where the users of the network
tend to be in order to suitably distribute budget. From the pointof view of the operation, knowing the expected level of activity
in each cell is crucial in prioritizing repairing tasks. There aremany other issues for a mobile operator related to the need toknow (and see) where the users are, to have a feel of where
they tend to be, and to have a forecast of where they will belater on.
The starting point of our study is the strong spatial andtemporal regularity that previous research has shown regarding
the mobility of individuals throughout the mobile network.
However, our approach is somewhat different. While mostprevious research focus on the activity of individual users in
the network or in aggregations at a much lower granularity(e.g. a whole city or even a country), we consider a different
granularity scale and analyze activity at the cell level (i.e theoperator’s view).
We are also able to use a different and more detailed dataset
recording each time a user has a transaction while being in any
cell. We define a transaction here as any interaction between
the user and the cell, including calls, but also all the automatic
updates that are continuously requested by mobile applications(e.g. automatic mail check, location updates, chat messaging,etc). Since not all the users connected to a cell are calling all
the time, our dataset is much more detailed than those based
exclusively in call activity (e.g. CDR logs).
The dataset contains information for more than 75.000
cells spreading over a whole country in a period of threemonths between the spring and the summer of 2017. The
construction of the dataset involves extracting, transforming,and synchronizing information coming from several systems
which in turn collect data from probes distributed all over the
network.

Considering a cell as a fixed spatial area watching users
come and by, we conjecture that the regularity observed inindividual mobility should translate in regularity at the cell.
If users spend most of the time between the same home andwork cells, we should see at each cell the aggregate activity ofusers being at home and users being at work nearby the cell. If
the trajectories of individual users are regular and periodic, thecomposition of passing trajectories at each cell should also be
regular and periodic. If the patterns of movement of individuals
are potentially predictable, we should be able to predict theaggregate patterns utilization of cells.
In trying to verify those hypothesis, we first conducted andexploratory analysis using three cells that network engineers
regarded as representative. All three showed strong periodicity
as expected. Then, clustering algorithms were applied thatconsistently yielded a clear classification in three well definedgroups. Every group presented strong regularity, providing thedesired confirmation. In the last step, we tested several timeseries forecasting algorithms over all the cells of the network,in order to see if the periodicity patterns are predictable at thecell level.
The main findings of our study are:
• The strong regularity observed by previous research at theindividual mobility level translates well at the cell level.
We show that the composition of passing trajectories at
each cell is also strongly regular and periodic, and devise
a specific kind of diagram to view weekly evolution ofactivity in the cells.
• There are well-defined types of cells. We find that 40%of them are home cells, another 40% are work cells, andthe remaining 20% form a class of low activity cells.
• Further analysis of this group of low activity cell showsthat most of them are 2G network cells, that have implicit
limitations to achieve higher levels of activity.
• The patterns of movement of individuals are potentiallypredictable, and we show that it is also feasible to predict
the aggregate patterns at the cells, with good precision in
the short term. The mean RMSE (Root Mean Squared
Error) was about 17 over the whole cell population, andthe ME (Mean Error) was about 2, in both cases with arelatively sharp distribution.
• The possibility of predicting activity at the cell level
could help operators in addressing critical issues such as
network planning and field operation prioritizing.

Understanding Post-Disaster Population Recovery Patterns
Takahiro Yabe1 , Kota Tsubouchi2 , Naoya Fujiwara3 , Yoshihide Sekimoto4 and Satish V. Ukkusuri1⇤
1

Lyles School of Civil Engineering, Purdue University, USA
2
Yahoo Japan Corporation, Tokyo, Japan
3
Graduate School of Information Sciences, Tohoku University, Japan
4
Institute of Industrial Science, University of Tokyo, JapanI. INTRODUCTION
Recent large scale disasters have shown the existence ofsignificant variance in recovery trajectories across communities despite similar damage levels [1]. With the increase in
the availability of large mobility datasets (e.g. mobile phone
call detail records), longitudinal observations of mobility
patterns have become possible [3], and many studies haveanalyzed the human dynamics during and after various
anomalous events [4], [5], [2]. Despite such progress, suchstudies are fragmented and there is no general understandingon the population displacement and recovery patterns ofcommunities across heterogeneous disasters and countries. In
order to bridge this gap in the current literature, we analyzed
large scale mobile phone GPS dataset from multiple disastersto 1) unravel the general patterns of recovery after different
disasters across countries and 2) to identify key factors that
explain such recovery patterns.
II. DATA
We collaborated with 3 different companies across US andJapan and collected GPS location data from mobile phones,
and studied the post-disaster movements of more than 2.5
million affected users over a six-month period. We study
five disasters that in total destroyed more than 1.5 million
households, caused power outages in more than 8 million
households, and caused more than $350 billion in economic
loss. The disasters studied are Hurricane Maria (Puerto Rico,
USA, 2017), Hurricane Irma (Florida, USA, 2017), Tohoku
Tsunami (Tohoku area, Japan, 2011), Kumamoto Earthquake(Kyushu area, 2016), and Kinugawa Flood (Ibaraki area,
Japan, 2015). The collection of disasters in this study are heterogeneous in various aspects, including the type of disaster,
location of occurrence, and socio-economic characteristics ofcommunities in the affected regions.
III. RESULTS
We observe that, despite the differences in the type ofdisaster and distinct socio-economic characteristics of theaffected regions (i.e. Puerto Rico, Florida and Tohoku area),
the population recovery patterns after the five disaster cases
all follow a similar recovery pattern. Moreover, through across comparative analysis of population displacement andrecovery patterns of over 250 communities in these five⇤ sukkusur@purdue.edu

Fig. 1. Displacement rate in Puerto Rico after Hurricane Maria.
Population recovery rates were estimated from mobility patterns of over
50,000 mobile phone users. We observe large spatial heterogeneity within
the island, which can be explained by a set of key socio-economic factors.

disasters, we find that the variance in initial and longterm displacement rates can be well explained by a set of
key common factors, which generalize and further extend
the insights obtained from small scale case studies. This
work lays out a foundation for studies on understanding
the population recovery dynamics of disaster affected cities,
which contribute to urban planning efforts for enhancing theresilience of urban systems.
R EFERENCES[1] Daniel P Aldrich. Building resilience: Social capital in post-disasterrecovery. University of Chicago Press, 2012.
[2] James P Bagrow, Dashun Wang, and Albert-Laszlo Barabasi. Collective
response of human populations to large-scale emergencies. PloS one,6(3):e17680, 2011.
[3] Marta C Gonzalez, Cesar A Hidalgo, and Albert-Laszlo Barabasi. Understanding individual human mobility patterns. nature, 453(7196):779,
2008.
[4] Xin Lu, Linus Bengtsson, and Petter Holme. Predictability of population
displacement after the 2010 haiti earthquake. Proceedings of the
National Academy of Sciences, 109(29):11576–11581, 2012.
[5] Takahiro Yabe, Yoshihide Sekimoto, Kota Tsubouchi, and Satoshi
Ikemoto. Cross-comparative analysis of evacuation behavior after
earthquakes using mobile phone data. PLoS one, 14(2):e0211375, 2019.

Utilizing CDR Data in Migration Studies:
Seasonal Labor Migration Among Syrian Refugees and Urban Deep Map for
Integration in Turkey
Sedef Turper Alışık*sturper@ku.edu.tr
Sibel Salman*
ssalman@ku.edu.tr

Damla Bayraktar Aksel*
dabayraktar@ku.edu.tr

Ahmet İçduygu*
aicduygu@ku.edu.tr

Asım Evren Yantaç*aeyantac@ku.edu.tr

Damla Çay*dcay13@@ku.edu.

Lemi Baruh*lbaruh@ku.edu.trİlker Kayı*
ikayi@ku.edu.tr
Ivon Bensason†
ibensason@yahoo.com* Koç University, 34450 İstanbul, Turkey† Attorney at Law, İstanbul, Turkey

The ongoing civil war in Syria in its eighth year has displaced more than 10 million people,
including 5.6 million people seeking safety in third countries. Since the onset of the Syrian
conflict, Turkey adopted a generous open-door policy towards those Syrians fleeing conflictand currently hosts more than 3.6 million registered Syrian refugees under the extended statusof temporary protection. However, the official statistics on Syrian refugees in Turkey arecompiled mainly through registration data, and therefore, these statistics can offer only a limited
capacity to inform well-tuned integration policies that would meet the needs of a dynamicallychanging target population. Addressing the need to identify spatio-temporal service needs ofthe highly mobile refugee population living under temporary protection in Turkey, the current
study utilizes the mobile call data records of Syrian refugees provided by Turk Telekom D4R(Data for Refugees) Big Data Challenge.
This study provides an overview of the data analysis and visualization steered under “An Urban
Deep Map for Integration in Turkey” (UDMIT) project, which uses mobile call data records ofSyrian refugees under temporary protection in Turkey. First, to examine Syrian refugees’
temporal and spatial dimensions of mobility, the study concentrates on refugees’ interprovincial
migration patterns within Turkey. Based on an analysis of these patterns, the study offers
insights on the potential motivations for regular and seasonal interprovincial mobility,
especially regarding access to services and employment opportunities in the formal andinformal labor market. The findings are also complemented by policy recommendations on how
the D4R data can be of use to central and local authorities on providing occupational health andsafety services and on improving refugees’ access to information. Second, the current study
delivers a web-based deep mapping platform that allows generating and reporting a visual
representation of refugee population densities and mobility across Turkey on a real-time basis.
The interface enables examining the spatio-temporal D4R data at three scales (country,
province and district level) together with other layers of data, including (a) demographic
information at the province and district levels, (b) service providers (non-governmentalorganizations, schools and healthcare services), (c) media analytics and (d) public discussion.
Within the scope of this limited study, the deep mapping platform has been developed as an
early-version prototype to demonstrate the potential of opening the data to the use of experts
and public with a multilayered, visual and interactive tool.

Beyond Traffic Congestion Detection
Jungmin Yoo, Dayea Yim, Eunmi Lyu, and Eunsook Lee
SK Telecom, Repulic of Korea{jungminyoo00,dyyim,eunmi.lyu,jen_lee}@sk.com
Abstract—As mobile traffic increases rapidly, it becomes important for network operators to detect and to optimize traffic
congestion by monitoring network traffic in a real-time manner.
Traffic congestion can be estimated with Charging Data Record(CDR) data from Offline Charging System (OFCS). However,since CDR data does not include the usage of resource allocationof the wireless channel, there is an inaccuracy in predicting traffic
congestion. To improve the accuracy of congestion prediction,
this paper suggests a new method that predict congestion status
accurately with the data collected from base-station.
Index Terms—Congestion Detection, Intelligent Traffic Management, Physical Resource Block, Quality of Experience

I. I NTRODUCTION
In a large-scale event such as a festival or a sport, where
massive people want to download and upload an internet
traffic, radio cell of the mobile network is overloaded usually.
If there exists heavy users who monopolize radio resources, theQuality of Experience (QoE) of other users will be degraded.
Therefore, it is important for network operators to monitor
mobile traffic in real-time, to detect congestion in a timely
manner, and to manage QoE so that all users experience good
service quality.
Traffic congestion occurs when the radio cell is overloaded
and the capacity is insufficient. Various studies have been
conducted to predict the congestion and to perform loadbalancing. The conventional method of congestion detection
infers the cell load with CDR data which reflects core-side
network traffic.

TANGO
TANGO-ITM

TANGO-Analytics

Correlation

Real-time
Analysis

CScorenew

Correlate radio
network data withcell configurationdata

Calculate CScorenew

Collect DU Log

Congestion
Detection

Congestion
Control

Determine
Congestion based on
CScorenew

Perform
Cell control or Heavy
user control

Control Notification

In this paper, we suggest a new method to detect actual
traffic congestion of a radio cell by analyzing the PhysicalResource Block (PRB) usage rate and the number of User
Equipments (UEs). This method is applied into SK Telecom’s
Operation Support System (OSS) called TANGO (T-Advanced
Next Generation OSS) [1] which collects big data from thewhole network, analyzes and optimizes the network. Overallarchitecture is depicted in Fig. 1 with two parts of Analytics
and ITM (Intelligent Traffic Management). We define Congestion Score (CScore) which is the indicator of cell congestion.
II. C ONGESTION D ETECTION : PAST AND N OW
To manage the flow of internet data traffic efficiently, SK
Telecom develops congestion detection algorithms with thedata gathered from the whole network. We have definedcongestion as a state where too many UEs are connected tobase-station and the PRB is insufficient. It is not assumed
as congestion when small number of UEs are occupying thewhole PRB. Congestion occurs, in general, when PRB is notavailable due to a large number of UEs. The data used incongestion detection can be summarized below:• Past: CDR data collected from OFCS (Offline Charging
System) was used in the congestion detection algorithm.
Key indicators of CDR are the number of subscribers
connected to the base-station and data throughput per
subscriber. CDR data could be used to estimate cellcongestion though it does not reflect the cell congestion
exactly.
• Now: Real-time call log generated from the base-station
which includes PRB usage rate and the number ofconnected UEs is collected and analyzed for congestion
detection. Congestion can be measured directly fromthe base-station and analyzed with cell configuration
information.
III. E XPECTATION AND R ESULT
We can improve customer experience by allowing users
to share resources fairly based on the outcome of congestion detection. Load-balancing or changing network policyof heavy users can be triggered for the better experience.
Also, the tracing of congestion area can be used for marketing
data, public safety and energy saving. Detailed result will be
included in the full paper.
R EFERENCES

DU Collector[1] https://www.telecomasia.net/content/skt-expanding-use-tango-aiplatform, ”SKT expanding use of TANGO AI platform”
Fig. 1. Overall architectureDynamic approach of spatial segregation: a framework with mobile phone data
Lino Galiana, Insee, Département des Etudes Economiques
Benjamin Sakarovitch, Insee, SSP-lab
François Sémécurbe, Insee, SSP-lab
Zbigniew Smoreda, Orange Labs
We propose a framework to bring together mobile phone and geocoded tax data to shed a new light on segregation in
urban areas. Urban segregation is generally measured through the glance of residential segregation. However, mobility
brings together people from different areas and continuously shapes the spatial distribution of income. The effect ofmobility on socioeconomic classes copresence in public spaces should be accounted to produce a complete vision ofsegregation. We propose an infra-day segregation approach that takes into account the effect of individual mobility on
within-day segregation dynamics. We use Call Details Record (CDR) andfor their precise spatio-temporal dimensions
that enable to estimate the composition of a city neighborhood at a given time.
That paper proposes an innovative methodology to study segregation dynamics at fine spatial and temporal granularities
for both low- and high-income groups. We build infra-day segregation indexes using individual estimated position frompseudonymized mobile phone data. We adopt a Monte-Carlo procedure to estimate phone users' likelihood of belonging
to low- or high-income groups based on their likely residence. We estimate infra-day segregation indexes by taking into
account co-presence at 500x500 meters cells. We propose robustness checks and compare results with residential
segregation indexes derived from tax data.

We use September 2007 CDR from Orange mobile network operator in France with information for more than 18,5
millions phone users. It is combined with income information at residence level for every French households. Our
methodology is illustrated with Marseilles and Lyon, French second and third largest cities. Results suggest that
residential segregation represents the acme of segregation. As a consequence of mobility, income groups seem to be lessconcentrated during daytime than during nighttime. Aftercorrecting for the discrepancy in estimated levels between
phone and tax data, we find that segregation is 26% higher
at its pinnacle than during the evening. The difference
between nighttime and daytime segregation is even higher.
We do not find a different pattern between low- and highincome infra-day segregation. An example of low-income
infra-day segregation dynamics in Marseilles is presented.
It shows significant within-day variation during the day.
Nighttime segregation is at a level comparable with tax
data measures.

Complete Trajectory Reconstruction
from Sparse Mobile Phone Data
Guangshuo Chen (Inria), Aline Carneiro Viana (Inria), and Marco Fiore (CNR-IEIIT)
aline.viana@inria.fr
5
Cell displacement

Mobile phone data are a popular source of informationin many recent studies across multiple disciplines, and havelargely contributed to improving our understanding of humanmobility, interactions and habits [1]. Mobile phone data consistof time-stamped and geo-referenced communication events
recorded by network operators, on a per-subscriber basis. They
offer an unprecedented possibility of monitoring populationsof millions of individuals over long periods that span months.
However, due to the uneven processes that govern mobile
communications, the sampling of user locations in mobile
phone data tends to be sparse and irregular in time, leading
to substantial gaps in the individual trajectory information.
As a result, mobile phone data offer a quite partial view ofthe overall mobility of each user, with substantial continuous
periods where the positioning information is entirely absent [2].
In short, such data is usually temporarily or spatially sparse.
We address the problem of sparsity in mobile phone data, andinvestigate how to solve it through trajectory reconstruction,i.e., the completion of individual movement information in thedata by recovering the missing positions of each individual.
To achieve our objective, we take the following steps.
First, we mine a large-scale mobile phone dataset collectedin an operational nationwide network over three months, andprovide a characterization of the severe sparsity that affects
mobile phone data of 1.8 million users. We quantify thephenomenon by means of relevant metrics, including thesampling frequency and a dedicated completeness measure ofindividual trajectories in the data. Our results show that sparsity
can be dramatic: for instance, only 5% of the actual positions
that can be inferred, on average, for each user when working at
a temporal resolution of 15 minutes. An important corollary ofthis result is that pre-processing techniques commonly adopted
in the literature –which perform user filtering via (even mild)
data sparsity thresholds– remove the vast majority of theavailable user population from subsequent analyses, hence
introducing hard-to-estimate potential biases.
Second, we introduce an original approach for the reconstruction of trajectories from sparse mobile phone data. Our
solution leverages well-known features of human mobility(e.g., the temporal prevalence of static phases, the regularityof movements, or the stability of overnight locations) to tailor
and solve a tensor factorization problem that befits our goal.
The proposed methodology allows transforming the highly
incomplete positioning information inferred from mobile phonedata into seamless individual trajectories that cover locationsof all users throughout the full duration of the original dataset.
We validate this strategy by using ground-truth mobile phonedata collected at high frequency for a subset of 1,450 users:

Mean
Median

4
3
2
1
0
0

0.05

0.1

0.2

0.3
Completeness

0.4

0.5

Fig. 1. Cell displacement of locations inferred via tailored tensor factorization
with respect to ground truth, versus the level of completeness of the original
trajectory (which are sparser at lower completeness). Candlesticks highlightthe mean (light blue), median (dark blue), 25th and 75th percentiles (box),
and 10th and 90th percentiles (errorbars). The horizontal line (red) highlights
a one-cell displacement. This effectively means that, in the reconstructed data,
a user is typically placed in the correct cell or in one that is very close to it.
Such a level of accuracy is acceptable for metropolitan-scale analyses (where
the urban surface is typically covered by hundreds of cells) and is excellent
for national-scale studies (as inter-city mobility is perfectly captured).

we accurately downsample the ground truth so as to mimic
the irregular sampling of typical mobile phone data, and thenreconstruct the user trajectories from the downsampled data via
our dedicated tensor factorization. Our evaluation demonstrates
that the approach achieves full reconstruction of individual
trajectories with good accuracy, as exemplified in Figure 1.
Third, we give a demonstration of the importance oftrajectory reconstruction for human mobility analyses. We
revisit three seminal studies by using the complete mobilityof 1.7 million users, instead of the incomplete trajectories ofa small fraction of especially active users as in the original
works. Our investigation yields the following insights: (i) travel
distances still follow a truncated power law [3] in the seamlesstrajectory data of a million-strong user population, yet the cutoff point is shifted away from the radius of gyration identified
in the original work; (ii) the high uniqueness of individual
trajectories [4] is substantially reduced in complete data, whichlets us argue that the uniqueness identified in the original
work was largely caused by the the diverse temporal patternsof the mobile communications of each user, rather than by adistinctive mobility; (iii) the theoretical predictability of human
movements is even higher than thought [5], as considering thewhole user population removes a filtering bias that favoredhighly mobile individuals with harder-to-anticipate locations.
R EFERENCES[1] V.D. Blondel et al., “A survey of results on mobile phone datasets
analysis,” EPJ Data Science 4(1), 2015.
[2] G. Chen et al., “Enriching sparse mobility information in call detailrecords,” Computer Communications 122, 2018.
[3] M.C. Gonzalez et al., “Understanding individual human mobility patterns,”
Nature 453, 2008.
[4] Y.-A. de Montjoye et al., “Unique in the crowd: The privacy bounds ofhuman mobility,” Scientific Reports 3, 2013.
[5] C. Song et al., “Limits of predictability in human mobility,” Science
327, 2010.

Mobile Money: Understanding and Predicting its Adoption and
Use in a Developing Economy
Simone Centellegher1,2 , Giovanna Miritello2 , Daniel Villatoro2 ,Devyani Parameshwar2 , Bruno Lepri1 and Nuria Oliver2
1

Fondazione Bruno Kessler
2
Vodafone Research{giovanna.miritello,daniel.villatoro,devyani.parameshwar,nuria.oliver}@vodafone.com{centellegher,lepri}@fbk.eu
As of today, there are approximately 2 billion unbanked individuals world wide, adults who are notbank account holders or do not have access to a financial institution. Access to financial institutions
is difficult in developing economies and especially for the poor, due to the low penetration of financial
services in such countries. The widespread adoption of mobile phones, has enabled the rise of mobile
money services. Mobile money bridges the gap between the cash and digital economies, enabling those
without access to banks to load cash in a mobile wallet and transact digitally using money transfers,
deposits and withdrawals of money, bill payments, etc. through the mobile phone network. Despite thesuccess of mobile money, there is a lack of quantitative studies that unveil which factors contribute to theadoption and sustained usage of such services [2]. In this work, we describe the results of a quantitative
study that analyzes data from the world’s leading mobile money service, M-Pesa [1], where we analyzed
millions of anonymized mobile phone communications and M-Pesa transactions in an African country.
After analyzing the customers’ usage of M-Pesa and their patterns of behavior, we tried to understandwhether and to which degree past mobile phone usage captures elements of human behavior that are
predictive of future mobile money usage.
We build two machine learning-based predictive models (1) “Predicting M-Pesa usage” and “Predicting M-Pesa expenditure”, that predict future M-Pesa adoption and intensity of usage, using multiple
sources of data, including mobile phone data, M-Pesa agent information, the number of M-Pesa friends
in the user’s social network, and the type of geographic location where the mobile activity took place.
Our models show a competitive performance of AUC=0.691 and AUC=0.619 for each of the previous
tasks. We find that the most predictive features (Figure 1) are related to mobile phone activity, to thepresence of M-Pesa users in a customer’s ego-network and to mobility. Finally we discuss and draw key
implications for the design of mobile money services in a developing country.

Figure 1: Feature importance in Task 1: “Predicting M-Pesa usage” (left), and feature importance in
Task 2: “Predicting M-Pesa expenditure” (right).

References[1] William Jack and Tavneet Suri. Mobile money: The economics of m-pesa. Technical report, National
Bureau of Economic Research, 2011.
[2] Muhammad R Khan and Joshua E Blumenstock. Predictors without borders: behavioral modeling of product adoption in three developing countries. In Proceedings of the 22nd ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining, pages 145–154. ACM, 2016.
1

Cities and the Structure of Social Interactions:
Evidence from Mobile Phone Data⇤
Konstantin Büchel and Maximilian v. Ehrlich⇧
Frist Draft: December 2016
This Version: January 14, 2019
Abstract
Social interactions are considered pivotal to agglomeration economies. We explore aunique dataset on mobile phone calls to examine how distance and population density
shape the structure of social interactions. Exploiting an exogenous change in travel
times, we find that distance is highly detrimental to interpersonal exchange. We
show that, despite distance-related costs, urban residents do not benefit from larger
networks when spatial sorting is accounted for. Higher density rather generates amore efficient network in terms of matching quality, measured via link stability, andinformation di↵usion capacity, measured via clustering.

Keywords: Agglomeration Economies; Social Interactions; Network Analysis; Spatial Sorting.
JEL classification: R10; R23; D83; D85; Z13.

⇤

We benefited from numerous comments by Juan Becutti, Sascha Becker, Aymo Brunetti, Shane
Greenstein, Fabian Gunzinger, Christian Hilber, Blaise Melly, Luigi Pascali, Diego Puga, Esteban RossiHansberg, Elisabet Viladecans-Marsal, Alex Whalley, and Yves Zenou. We further received invaluable
feedback from participants at the Verein für Socialpolitik Meeting in Tellow, the Meeting of the Urban
Economics Association in Minneapolis, the CRED-Workshop in Bern, the SERC Conference in London,
the Applecross Conference, the SSES Annual Congress in Lausanne, the NBER Summer Institute 2017, the
6th Lindau Meeting on Economic Sciences, the 2017 SMU Conference on Urban and Regional Economics,
the Meeting of the Urban Economics Association in New York and research seminars at the Paris School of
Economics, the University of Milan, the IEB Barcelona, and the University of St. Gallen. A very special
thanks is due to Swisscom AG for providing the facilities and data to conduct this research project; we areparticularly indebted to Imad Aad and Holger Müller, who have accompanied the project since its start.
We also want to thank search.ch and comparis.ch for providing data on travel times and usage statistics
of messenger apps.
⇧
University of Bern, Department of Economics and Center for Regional Economic Development,
Schanzeneckstrasse 1, 3001 Bern, Switzerland. E-mail: konstantin.buechel@vwi.unibe.ch, maximilian.vonehrlich@vwi.unibe.ch.

1Modeling Dynamic Influence of Customers to Predict Churn:
An Application with Similarity Forests for Time Series ClassificationaLaura Calzada-Infantea*, María Óskarsdóttirb, Bart Baesensb,cEngineering School, Campus de Gijón, University of Oviedo, 33204 Gijón, Spain
Email: calzadalaura@uniovi.esbFaculty of Economics and Business, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgiumc
Dept. of Decision Analytics and Risk, University of Southampton, United Kingdom

Telecommunication companies are interested in implementing effective retention campaigns fortheir potential churners, due to the fact that retaining a customer is cheaper than attracting a
new one, and that long-term customers are more profitable (1). Multiple research has usedbinary classification methods to predict potential churners, and there are new methods toconsider the influence of the customers' relationships (1,2).
Customers’ social relationships influence the decision of changing the operator. Network
analysis techniques allow the representation of these relationships and provide a new tool todetect customer behavioral patterns and define accurate models to predict potential churners(3). Call Detail Records (CDR) of telco’s customers are the most accessible information thattelco companies have about the customer’s behavior and influence.
We propose a novel method to extract the dynamic influence of each customer using Social
Network Analysis techniques, together with binary classification methods. A temporal network is
built based on the CDR to extract behavioral patterns with respect to position of churners in the
network. The dynamic influence of each customer is determined by applying different centrality
metrics and diffusion propagation methods over a sliding window using aggregated and timeorder networks.

Figure 1. Representation of churners’ influence in a customer’s social relationships network.
Black nodes are churners.
The resulting time series are classified by a recently proposed binary classification method
called similarity forests(4). In addition, a comparison to other methods evaluates the accuracy of
predicting further in time and the possibility of designing a method that is capable of detecting
potential churners both in short and long term.
References
1. Óskarsdóttir, M., Bravo, C., Verbeke, W., Sarraute, C., Baesens, B., Vanthienen, J. Social network
analytics for churn prediction in telco: Model building, evaluation and network architecture. Expert
Syst Appl. 2017;85:204–20.
2. Kim, K., Jun, C., Lee, J. Improved churn prediction in telecommunication industry by analyzing alarge network. Expert Syst Appl. 2014;41:6575–84.
3. Óskarsdóttir, M., Calster, T. V., Baesens. B., Lemahieu W, Vanthienen J. Time series for early churndetection: Using similarity based classification for dynamic networks. Expert Syst Appl.
2018;106:55–65.
4. Sathe, S., Aggarwal, C.C. Similarity Forests. Proc 23rd ACM SIGKDD Int Conf Knowl Discov Data
Min. 2017;395–403.

Comparison and Analysis of Migrant Identification Methods
V. Frias-Martinez1, L. Hong1, J. Wu1, A. Villareal3, E. Frias-Martinez2(1) iSchool, UMIACS, University of Maryland, College Park, MD, USA, lzhong@umd.edu ,(2) Telefonica Research,
Madrid, Spain. (3) Department of Sociology, University of Maryland, College Park, MD, USA.

Internal migration refers to the migration of individuals from one region to another within the same
geopolitical entity, typically within the same country. Considerable attention has been given to the study of
migration using spatio-temporal data passively generated, for example cell phone records or social media
data. Such type of data enables to carry out large-scale analyses of migration flows as well as the microlevel view necessary to analyze individual behaviors [1]. At its core, research on migration behaviors firstrequires to identify the internal migrants in the dataset. Methods to identify migrants are based ondetermining home location changes i.e., a person that was living in a location, changes her homepermanently or temporarily within the same country. Several methods have been developed to identify
home location using spatio-temporal data [2] and some of these methods have been applied to identify
volumes of internal migrants [3]. However, no work has looked into analyzing the impact that the choiceof a home location algorithm might has in the identification of migrants. In this paper, we use an 8-monthwindow cell phone dataset from Mexico to identify and compare internal migrations.
We define as internal migrants the individuals who have a consistent home location for at least three months
and then move to another place, where they also stay for at least three months. With this definition, theinternal migrants we identify can be either long-term or short-term (circular) migrants, depending onwhether they go back or not to their original location after our data collection period finishes [4]. The census
data we use for validation measures the internal migration flow at the municipality level. As a result, we
use four different state-of-the art methods to detect home location at a municipality level: (1) most visited
municipality at night, between 6pm to 6am; (2) a shorter period of night time, from 10pm to 6am- the main
motivation to do this is that a tighter temporal range might help to reduce the noise in the set ofcommunication towers considered as potential home location; (3) home location as the municipality where
the center of gravity across all visited cellular towers is located, weighted by the cell phone activity in eachcell and (4) a combination of the temporal window approach (10pm-6am) with the center of gravity
approach- the assumption for this approach is that the activities during night hours tend to be closer to one’s
home location.
We used these methods to identify potential internal migrants and evaluated the accuracy of each methodby comparing the computed migration matrix with official census data. Additionally, we looked into thebiases introduced by each method, measured as the differences in accuracy when different types of urban
and rural flows are approximated via cell phone data. Overall, results show that: (i) methods that use
temporal ranges to identify internal migrants perform better than center of gravity-based methods; (ii)longer temporal ranges show better accuracy than shorter ranges; (iv) current methods show biases against
rural population; and that (v) those biases decrease when total outbound or inbound flows are considered.
Although we focused our study in Mexico, these results could potentially be valid to the great majority ofdeveloping economies, as they tend to show similar characteristics, namely: (1) high penetration of cell
phones; (2) uneven distribution of the population between urban and rural areas; and (3) reduced cell phone
activity when compared to developed economies.
References[1] J. E. Blumenstock, “Inferring patterns of internal migration from mobile phone call records: evidence from rwanda,”
Information Technology for Development, vol. 18, no. 2, pp. 107–125, 2012.
[2] S. Isaacman, R. Becker, R. Caceres, S. Kobourov, J. Martonosi, M.and Rowland, and A. Varshavsky, “Identifying
important places in peoples lives from cellular network data,” in Int. Conf. Pervasive Computing, 2011, pp. 133–151[3] S. Isaacman, V. Frias-Martinez, and E. Frias-Martinez, “Modeling human migration patterns during drought
conditions in la guajira, colombia,” in ACM Computing and Sustainable Societies, 2018.
[4] R. D. Bedford et al., New Hebridean Mobility: a study of circular migration. Canberra, ACT: Dept. of Human
Geography, Research School of Pacific Studies, The Australian National University., 2017

Analysis of Post-Migration Mobility and Social Ties of Internal Migrants
V. Frias-Martinez1, L. Hong1, J. Wu1, A. Villareal3, E. Frias-Martinez2(1) iSchool, UMIACS, University of Maryland, College Park, MD, USA ,(2) Telefonica Research, Madrid, Spain. (3)Department of Sociology, University of Maryland, College Park, MD, USA.

Migrations have been studied using macro-level and micro-level analysis. Macro-level studies are carriedout using a combination of various survey and census datasets to model large-scale behaviors, howeverthese models fail to provide more nuanced information about the physical or social status of the migrants.
Micro approaches, which use interviews and diaries to provide a window into more individual behaviors,
could benefit from methods to identify novel or under-studied behaviors that should be addressed in the
migration research agenda. In this paper, we present a framework that uses information extracted from cell
phone metadata to reveal internal migration behaviors that complement micro-level migration approaches
working to understand the physical, social and psychological decision processes behind migration. The
proposed framework reveals internal migration behaviors with a focus on immediate post-migration
behaviors and the role of pre-migration activities from two perspectives: spatial dynamics and social ties.
The main objective is to carry out large-scale analyses of internal migration trends to reveal migrant
behaviors that would benefit from further qualitative studies through personal interviews or individual
surveys. Ultimately, we expect our analyses to inform migration researchers of pre- and post-migration
behaviors that would benefit from further qualitative analysis.
Given cell phone metadata from millions of individuals for a given country, the proposed framework
consists of three parts. First, the framework uses features extracted from the cell phone metadata to identify
potential migrants in the dataset. We present a method to identify internal migrants and we evaluate its
accuracy using real census migration data [1]. Second, the framework uses the aggregated migrants tocharacterize immediate post-migration behaviors i.e., we analyze the post-migration spatial dynamics andsocial networks and compare these against behaviors from locals that have not undergone any migration
process. Third, we analyze the role that pre-migration spatial dynamics and social networks might play inthe same post-migration behaviors shown by internal migrants.
We evaluate the proposed framework to study internal migration behaviors in Mexico, using a dataset witheight months of anonymized cell phone metadata from over 48 million subscribers in combination with
open data from the Mexican Statistical Institute (INEGI) [3]. In this context, regarding mobility, we observe
among other findings, that in the immediate post-migration period, internal migrants, visit more
municipalities and have more irregular behaviors than the local community. In fact, immediately after
migrating, migrants have, on average, higher entropy than locals, showing more irregular mobility patterns
and having significantly longer trips. These findings could reveal that individuals make an effort to maintaintheir local connections in their pre-migration municipalities either because of work or personal reasons.
Similar findings have been qualitatively reported in other countries [4]. Regarding social ties, migrants
communicate with a similar volume of calls than locals, but with a smaller number of contacts, and show
lower entropy in their cell phone-based social networks than locals. Also in this case, similar results havebeen shown in the context of international migrations [5].
References[1] S. Isaacman, V. Frias-Martinez, E. Frias-Martinez. 2018. Modeling Human Migration patterns during Drought
Conditions in La Guajira, Colombia. ACM SIGCAS[2] L. Hong, J. Wu, E. Frias-Martinez, A. Villareal, V. Frias-Martinez, 2019. Characterization of Internal Migrant
Behavior in the Immediate Post-Migration Period using Cell Phone Traces. 10th ACM ICTD
[3] Mexican Statistical Institute INEGI. 2015. National Survey of Demographic Dynamics 2014.
http://www.beta.inegi.org.mx/proyectos/enchogares/especiales/ enadid/2014/default.html.
[4] Cati Coe. 2011. What is the impact of transnational migration on family life? Women’s comparisons of internal andinternational migration in Ghana. American Ethnologist 38, 1 (2011), 148–163.
[5] Steven Vertovec. 2004. Cheap calls: the social glue of migrant transnationalism. Global networks 4, 2 (2004).

Understanding Tourist Travel Pa�erns using Mobile Signaling
Data ∗
Qiwei Han

Nova School of Business andEconomics
Carcavelos, Portugal
qiwei.han@novasbe.pt

1

Margarida Abreu Novais

Gri�th Business School
Gold Coast, Australiam.abreunovais@gri�th.edu.au

INTRODUCTION

Understanding how tourists move to reach and travel around withina destination is of paramount importance for tourism decisionmakers. Given its signi�cance, the investigation of tourist spatiotemporal patterns represents a critical stream of tourism behavior
research. Notwithstanding existing contributions, there are stillchallenges in fully capturing tourist behavior due to the ine�cient
data collection approaches currently in use. This paper puts forward high-time-density data from mobile network operators as
a powerful data source to investigate tourism spatial behavior in
Tuscany, Italy, a region known for its extensive tourism resources,
especially when combined with other data sources.
We investigate tourist travel patterns using unsupervised learning at individual level and municipality level, respectively. First,
we create tourist personas that represent tourist’s archetypes, byclustering individual tourists’ spatio-temporal behavior. Second,
we propose a latent embedding model Geo2Vec, an approach originally developed for natural language processing tasks to learn
the sequences of tourists’ locations and produce a vector space ofmunicipalities. Geo2vec model is able to reveal similarity between
municipalities in terms of tourist’s travel preferences beyond geographic proximity. Our results demonstrate the advantages of using
mobile signaling data to understand tourist mobility in tourism
management research, compared to traditional mobile phone data
analysis that relies on call detail records.

2

DATA

A large-scale mobile signaling dataset for this study has been provided by a European mobile network operator (referred to hereafter
as EURMO). It includes pre-processed logs of anonymized signaling
traces of mobile devices with foreign SIM cards connected to the
EURMO in Tuscany between May 2017 and February 2018 for over
9.6 million international tourists, including visitor ID, nationality,
timestamp and geo-coordinates of the connected cell towers. The
data is available at the resolution of minute level when tourist’s location is changing (i.e. switching to di�erent towers) and at hourly
level if the visitor remains at the same location (i.e. connecting
to the same cell tower). Meanwhile, we retrieve geo-spatial information about terrain categories (e.g. forest, water, coast, park andcity) and tourism attractions from shape�les and Tuscany o�cial
tourism website, respectively. As such, we can infer tourist’s travel
preferences towards diverse touristic activities in di�erent seasons.
We further clean the dataset and select a subset of 2.95 million∗ We thank Toscana Promozione Turistica and EURMO for their support and domain

expertise. We also thank colleagues from Data Science for Social Good Europe whohave contributed to this work.

Leid Zejnilovic

Nova School of Business andEconomics
Carcavelos, Portugal
leid.zejnilovic@novasbe.pt

tourists from top 6 nationalities (Germany, United States, France,
United Kingdom, Netherlands and China).

3

PERSONA CLUSTERING

By aggregating the signaling data at the individual level, a list of
42 features has been generated, to characterize each tourist. Thesefeatures describe di�erent aspects of tourists’ behavior, such as duration and number of locations spent in Tuscany, duration spent indi�erent types of landscape and attractions and summarized spatial
coordinates. Then, K-means clustering has been performed acrossseasons (pre-summer, summer, fall and winter) and nationalities.
The tourists are segmented into 4 groups, where each groupexhibit distinct travel patterns: 1) Florence Visitors consisting nearly
40% of the tourists that tend to focus their visit on city of Florence
within a short time during non-summer season; 2) Coast Lovers, representing 28% of the tourists mainly from Germany and France thatspend most of their activities along the coast during the summer; 3)
Fast Trippers consisting 21% of all the tourists from non-European
countries such as China and United States that travel beyond Tuscany to visit more places across Italy in a short period; 4) Explorers,
representing 11% of all tourists that tend to enjoy a long holiday
to explore Tuscany in a wide range of activities. Therefore, our�ndings provide insights to designing more personalized travel
marketing campaigns catering to di�erent tourist segments.

4

GEO2VEC

Similar as Word2Vec model that aims to learn the latent embeddingof each word from the vocabulary, Geo2Vec performs feature learning through a shallow, two-layer neural network that is trained toreconstruct contexts of locations through tourist’s travel sequences.
More speci�cally, we �rst create an ordered list of visited municipalities for each tourist and train a neural network to learn distributed
representations for each municipality. Each municipality is represented as an multidimensional vector in a latent space, known asembeddings, such that municipalities that share common trips arelocated in close proximity to one another in that space.
Geo2Vec model is able to detect both short and long-range similarity between municipalities directly from the dataset, insteadof manually generating features subject to domain context. Moreinterestingly, Geo2Vec can identify adjacent municipalities that are
geographically disparate in terms of distances in the latent space,
due to their common tourists’ travel preferences. Therefore, we
can leverage the latent embeddings of municipalities to segment
Tuscany into groups of sub-regions beyond geographic proximity. This would allow us to design a data-driven tourism resource
distribution strategy according to tourist travel patterns.

A novel measure of clustering, with application to a mobile
money network§

Geoffrey Canright*, Rich Ling
*Telenor Research, Snarøyveien 30, 1331 Fornebu, Norway
Email: geoffrey.canright@telenor.com§
Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
Email: riseling@gmail.com
Abstract—Inspired by studies of a mobile money transaction network, we have defined ameasure Γ of clustering of closure links in an egonet. Γ is 0 when the closure links have nonodes in common, is 1 when all closure links form a clique (max clustering), and always
obeys 0≤ Γ≤1. Our data set is 6 months of p2p transaction data (only), furnished by a mobile
money provider. We study recruitment of new users to this network, and—using a fairly
strict definition of “recruitment” and “new users”—find that (i) good recruiters have (onaverage) a much higher egonet Γ value than non-recruiters. This result holds even if one
corrects for other measures of activity. In contrast, the standard egonet clustering coefficient
cc is only weakly, and negatively, correlated with recruitment (Figure 1).
We have also defined a whole-graph (WG) measure of Γ, analogous to the whole-graph cc.
ΓWG is zero when all triads are isolated, and 1 when all triads form a single clique. We show
the evolution of ΓWG and ccWG in Figure 2. We see that ccWG is flat over the 6 months, andsmall (around 1%), while ΓWG grows monotonically to a rather high value (0.77—cf theaverage egonet Γ, which is 0.16 in month 6). Furthermore, we can define clusters of triads as
sets of triads connected by 0 or one hop. With this definition, we find a largest triad clusterthat includes about 200,000 nodes—about 20% of the entire network—in month 3. In short,
we find (ii) strong growth of Γ-clustering over time. This cannot be explained in terms of“crowding” of the closure links, since this growth is observed at constant (low) density ccWG .
We offer plausible explanations for these results. (i) We postulate that new users tend to be
recruited into one of their geographically and socially local circles, which has previous
adopters, and dense closure links. This is supported by the fact that Γ for the recruiters
continues to grow (faster than for non-recruiters) after the recruiting event(s). (ii) We believethat long-range links (socially and/or geographically) are needed to give the observed largescale structure. We know however that such links do exist in the network, in the form oftransactions between agents—who work for the provider, and participate in a hierarchicalnetwork of transactions.
In short—we find that measurement of Γ-clustering gives unique insights into both therecruitment process, and the long-range structure, for a mobile money network. We believealso that the Γ-clustering measure can be useful in a broader range of network studies.

Figure 1. Average cc (blue) and Γ(red) for non-recruiters (left), R=
1 recruiters, and R > 1 recruiters(right)Figure 2. ccWG (lower curve) andΓWG (upper curve) over 6 months

Refugee Mobility: Evidence from Phone Data in Turkey
Michel Beine1 , Luisito Bertinelli1 , Rana Cömertpay1 , Anastasia Litina2 ,Jean-François Maystadt3 , Benteng Zou1
1

) University of Luxembourg, Avenue de la Faı̈encerie 162A, L-1511, Luxembourg
michel.beine@uni.lu
luisito.bertinelli@uni.lu
rana.comertpay@uni.lu
benteng.zou@uni.lu
2) University of Ioannina, University Campus, 455 00 Ioannina, Greece
alitina@cc.uoi.gr
3) University of Antwerp, Prinsstraat 13, 2000 Antwerpen, Belgium
Lancaster University, Baggily, Lancaster LA1 4YW, UK
j.maystadt@lancaster.ac.uk

Our paper employs spatially explicit call detail records provided within theD4R challenge and combines it to several other sources to study one of themultiple aspects of integration of refugees, namely the mobility of refugees acrossprovinces in Turkey.
More specifically, we look at the location of one hundred thousand randomly
selected mobile transactions (fifty thousand refugees and fifty thousand nonrefugees) recorded by cell towers to define likely decisions of individuals to move
between provinces in Turkey. Reconstructing bilateral migration flows at themonthly and at the province level, we employ a gravity model to empiricallyestimate a series of determinants of refugee movements.
We employ two main sets of determinants of mobility. First, we use thestandard gravity model controls, i. e., variables that relate to the attractiveness(resp. repulsiveness) of province d (resp. o) for prospective refugees, the so-calledpull (resp. push) factors. These are levels of income at origin and destination
provinces, distances across provinces, network e↵ects at destination provinces
as well as some refugee-specific determinants such as the presence of refugee
camps at origin and destination provinces. The second set of variables that
we construct is aimed to capture policy related issues. Our source dataset is
the Global Database of Events, Language and Tone, which captures world-wide
news media over thirty years, in over one hundred languages and consists of overa quarter billion georeferenced event records in over three hundred categories.
The variables we rely upon are as follows: rally for leadership change; boycotts;
violent protest; economic aid; humanitarian aid and asylum grants. Considering
news as an indicator of policy implemented at the provincial level we gain abetter understanding as to how policy can facilitate refugee mobility and thus
enhance integration.
We find that standard gravity determinants, such as distance, source income
as well as networks apply. Furthermore, policy interventions that are facilitatedwith political stability, asylum granting and economic aid also matter thus suggesting that there is ample room for policy making.
To benchmark our findings, we estimate the same model for the mobility of
individuals with a non-refugee status. The same determinants apply, however
the impact of each of these determinants is stronger for non-refugees.

1

Towards a Reference Methodological Frameworkfor processing Mobile Network Operator data
for Official Statistics
Fabio Ricciato⇤ , Albrecht Wirthmann⇤ ,Martijn Tennekes† , Benjamin Sakarovitch‡ , Roberta Radini§ and David Salgado¶
EUROSTAT European Commission.
CBS, Netherlands. ‡ INSEE, France. § ISTAT, Italy. ¶ INE, Spain.
⇤†

An increasing number of Mobile Network Operators (MNO) are now able to extract signalling data
originating by the continuous interaction of mobile stations with the cellular network. Compared to
traditional Call Detail Records (CDR), signalling data have better spatial/temporal resolution and represent
a more informative data source to analyse human presence and mobility patterns. On the other hand, suchdata are considerably more costly to extract and more complex to interpret than CDR. Furthermore, theyyield a higher degree of heterogeneity across different MNOs, due to proprietary monitoring technologies
and tighter dependencies with the underlying network configuration. Their format and semantics change,
following the evolution of MNO infrastructure. Consequently, it is difficult for statisticians and researchers
without a solid telco engineering background to interpret such data.
In order to overcome these challenges, and thus promote the adoption of MNO signalling data forthe production of official statistics, we propose a general Reference Methodological Framework (RMF)
intended to facilitate the use of signalling data by statisticians and, in general, by non-telco experts. The
RMF is inspired by the principles of modularity, functional layering and the “hourglass model” that lie
at the foundation of modern computer network architectures.
Broadly speaking, the RMF encompasses three functional layers: an intermediate convergence layer (Clayer) decouples the complexity of signalling data at the bottom (D-layer) from the statistical definitions
on the top (S-layer). Decoupling the D-layer and S-layers allows experts from the two domains, namely
MNO engineers and statisticians, to work independently, easing the evolution of both layers. In the
proposed vision, MNO engineers implement low-level data processing functions, collectively called “Dto-C” mapping, that transform raw network data into a sequence of geo-located events in a common
format, along with additional auxiliary information, that represent C-layer data. The proposed model canaccommodate different geo-location methods for individual events, including simplistic Voronoi tessellation(a popular but sub-optimal choice) and more advanced approaches based on cell radio coverage predictions.
From the C-layer data, the statisticians develop estimation methods that are logically placed in the upper
S-layer. In so doing, they might fuse geo-located C-layer event data with external geographical data, e.g.
transportation maps and/or land use maps.
Adoption of a common RMF enables the reuse of algorithms and processing modules developed bydifferent statistical offices and/or research groups across data from different MNO. In this sense, it will
facilitate benchmarking, independent validation and collaborative development of processing algorithms
developed by academic and industrial research groups. When coupled with open-source algorithms, it
helps to achieve transparency, auditability and public scrutiny onto processing algorithms.
The RMF is being developed within the European Statistical System by a joint effort between EUROSTAT and the National Statistical Institutes of some European countries. In this presentation we will
present the general principles underlying the RMF, report on the status of ongoing development and outline
future work.

Branching Out the Babytree: The Effects of Dual Peer Group Membershipon Social Support During Pregnancy in Online Communities
Lingqing Jiang1 and Zhen Zhu⇤2
1 University of Essex, UK

Abstract

2 University of Greenwich, UK

Social support from peers plays a positive and important role in many contexts. Today, the emergence of online social communities
provides possibility to seek social support to maternity from other individuals that is complementary to those from the family andgovernment.
Our paper investigates the potential spillovers of social support between peer groups formed by pregnant women (see Figure 1afor the distinction between intra- and inter-group spillovers). We collect a sample dataset from the largest maternity and parenting
APP in China, Babytree, which contains over 30,000 pregant women in our sample.
A special feature of Babytree is that users can enroll in peer groups based on their estimated due date (EDD). In principal, users
can enroll in any peer groups that are open for enrollment at any given time. A naive comparison between uses enrolling in two
peer groups and users enrolling in a single peer group has self-selection problem, e.g. users who are more active in posting andresponding might be more likely to enroll in two or more peer groups. If this is the case, the native comparison will simply reflect a
positive correlation between enrolling in an additional peer group and users’ overall activeness in their default peer group.
We take the advantage of the fact that the enrollment of peer groups is largely determined by the estimated due date (EDD) and
use it as an instrument to address the above self-selection issue (see Figure 1b). Figure 1b shows the fraction of users enrolling inan additional peer group of previous or next month for each day of their EDD. Indeed, the fraction of users enrolling in the previous(next) month peer group gradually decreases (increases) over time.
We find that additionally enrolling in the previous month peer group increases users’ contribution of social support by 1 post
and 6 outgoing responses in their default peer group, respectively (see Figure 1c). No such spillover effects are found when users
additionally enroll in the next month peer group. The policy implications of inter-group spillovers are not confined to the Babytree
platform but also apply to other fields in which inter-group interaction plays an important role with similar channels, such as theinteraction between the freshmen and the sophomores, the juniors and the seniors, as well as the interdisciplinary research.

Figure 1: (a) Intra- and inter-group spillover effects; (b) Fraction of users additionally enrolling in the peer group of previous/next
month; (c) Regression results using OLS and IV.
⇤ Corresponding author. Email: z.zhu@gre.ac.uk.

1On the Optimal Marketing Aggressiveness Level of C2C Sellers in Social
Media: Evidence from China
Xu Wang1 , Bart Baesens2 , and Zhen Zhu⇤3
1 IMT School for Advanced Studies Lucca, Italy
2 KU Leuven, Belgium

Abstract

3 University of Greenwich, UK

Social media has become a widely used marketing tool for reaching potential customers. Because of its low cost, social media
marketing is especially appealing to customer-to-customer (C2C) sellers. Customers can also benefit from social media marketingby learning about products and by interacting with sellers in real time. However, a seller’s marketing microblogs may backfire on
her for dominating the social space.
Defining the marketing popularity as the average number of likes each seller receives per marketing-related microblog anddefining the marketing aggressiveness level as the proportion of her marketing-related microblogs, this paper empirically explores
the relationship between marketing aggressiveness level in social media and the marketing popularity. We gather the data from
China’s largest microblogging platform, Sina Weibo, and the sellers in our sample are from China’s largest C2C online shopping
platform, Taobao. We use machine learning classification methods to quantify the marketing aggressiveness level as well as estimate
the optimal aggressiveness level to achieve the maximum popularity. We find that the empirical relationship between the marketing
aggressiveness level and the marketing popularity follows an inverted U-shape curve (see Figure 1a), where the optimal level isaround 30% (see Figure 1b and Figure 1c). In addition, we find a saturation effect of the number of followers on marketing
popularity after it reaches around 100,000. Our findings imply that social media marketing should not overlook customers’ social
needs. Our measure of marketing aggressiveness provides a dynamic business metric for practitioners to monitor so as to improvetheir marketing and managerial decision making process.

Figure 1: (a) Regression results indicating the existence of an optimal aggressiveness level; (b) Visualizing the optimal aggressiveness level in the 2014 sample; (c) Visualizing the optimal aggressiveness level in the 2016 sample.

⇤ Corresponding author. Email: z.zhu@gre.ac.uk.

1

HOW PHONE CALL DETAIL RECORDS MAY CONTRIBUTE
TO IMPROVE DEPRESSION ASSESSMENT
IN OLDER POPULATION ?
ADDED-VALUE OF THE PHONE CALL DIRECTION PARAMETER
Timothée Aubourg1,2, Jacques Demongeot1,3, Félix Renard1, Hervé Provost2, Nicolas Vuillerme1,3
1 Université Grenoble Alpes, AGEIS, Grenoble, France
2 Orange Labs, Meylan, France
3 Institut Universitaire de France, Paris, France

Abstract
Background
Analysis of phone call detail records (CDRs) is now recognized to represent a promising approach in contributing to the future of mental health research. However, some of its aspects still require deeper investigation.
Particularly, no clear consensus exists in the use of the phone call direction parameter, which permits to distinguish outgoing from incoming phone calls in CDRs datasets. It is crucial to address this issue in the field of healthmonitoring. Indeed, an imbalance in social interactions between the individual and his social network, that could
be evidenced by outgoing and incoming phone calls, could be representative of particular disruptions, physicalas well as social, occurring throughout his life time. Besides, recent studies that investigate social interactions inmhealth by using only outgoing and incoming phone calls separately have reported divergent results. All in all,new consistent tools are required for harnessing CDRs analysis in health on a consistent and relevant way.
Objective
In this train of thought, whether and how the phone call direction parameter could be explicitly used in
CDRs analysis and then applied in a mental health context need to be investigated. This paper is specificallydesigned to address this issue.
Methodology
To this end, we use a 12-successive-month dataset that combines CDRs and geriatric depressive scales(GDS) results of 26 volunteers older than 65 years. We specifically address two issues: (1) the existence of aparticular phone call habit in the older adult regarding the direction of his phone calls, and (2) the existence of a
relation between such a habit and the depressive health state of the individual.
Results
On the whole, our results show the existence of three clusters of phone call activity named (1) proactive,(2) interactive, and (3) reactive. Then, by introducing two asymmetry indicators, namely (1) the asymmetry coefficient, and (2) the skewness coefficient, that synthesize these three phone call habits, we find significant correlations between each of these two asymmetry indicators and the GDS scores obtained by the older individual.
In particular, we report that older individuals having a reactive phone call habit obtain GDS results significantly
higher than the other ones. On the contrary, individuals obtaining a GDS lower than 10 tend to have asymmetry
indicators with positive values. Taken together, the present findings suggest the existence of relevant healthrelated insights contained in CDRs datasets when the phone call direction parameter is harnessed. We believethat taking this parameter into consideration in future studies by using asymmetry indicators could be valuable
for improving machine learning of health predictive models in mHealth.
Keywords:
Call detail records ; Older population ; Depression ; Geriatric Depressive Scale ; Asymmetry
Address for correspondence
Timothée Aubourg: timothee.aubourg@orange.com

Enabling Socio-Economic Study for Relocating Refugees using Mobile Phone Data andRegional Statistics
M. Saravanan, Researcher, Ericsson Research, Ericsson India Global Services Pvt. Ltd
This study focuses on the necessity of relocating Syrian refugees to affluent prefectures within Turkey based on the inferences fromrefugee’s mobile phone call records and the available regional statistics. Relocation can be expected to improve the socio-economic
background of refugees. The Syrian refugee’s population in Turkey is approximately 8.5 million which has almost 10% of worldlevel refugee’s population. Refugees influx recipient nation require employment and face challenges pertaining to social andeconomic acceptance. Nations accepting refugees tend to balance the induced burden by localizing refugees at bay. This leads to
impoverished and serious survival conditions. In this proposal, we propose a mechanism of redistributing refugees in the recipient
nations based on wellbeing data; ensuring efficient employment for satisfactory survival and coordinating seamless socio-culturalinteraction thereby improving the life of refugees. We have applied machine learning techniques and statistical inferences todetermine the refugee’s distribution in different clusters of cities and carefully hypothesize the relocation rudiments. Related tothis, we observed the mobility and call pattern of refugees between different identified clusters of prefectures. In this study, ourhypotheses are experimentally evaluated with D4R dataset provided by Turk Telecom and suitable discussion were made relevantto this [1]. Data for Refugees (D4R) Turkey is a big data challenge based on refugee tag. Our experimental proofs highlight thesubstantial rationale behind the suggestion of relocating refugees to progressive prefectures in the Turkey region. Regarding this,
we attempted to group the cities of recipient nation based on regional statistics (wellbeing index data) of different prefectures ofturkey region [2] and by running different clustering methods [3] on the extracted social parameters. Based on outcomes, we
identified the incoming refugee’s movements through their calls within relevant cluster and analyzing overlapping community
traits among clusters in terms of event (Voice and SMS) trends, refugee/non-refugee preferences and their interactions to propose
viable relocation alternatives.
The D4R challenge has provided the CDR data, through which we can extract the different levels of mobility pattern ofrefugees and non-refugees in Turkey. The extracted data set will be helpful to identify the movement of refugees from one place
to another place in search of better livelihood. Our cluster models are trained based on the consideration of providing higher weightto the nation wellbeing parameters: safety and security, health, education, unemployment, social integration and segregation,
mobility, and distribution of resources and infrastructure. Also, we compared the refugee and non-refugee call pattern records tounderstand their present placement and discuss the need for improving social integration. We have trained the data with different
combinations of the K-means, PCA, Self-organizing Maps (SOM) and deep clustering algorithm in this process. From the combined
effort of SOM and K-Means clustering results, we identified three unique clusters namely prosperous, less prosperous, andunderdeveloped. With the use of deep clustering, our results are improved by removing outlier samples. To argue our intention of
relocating refugees to prosperous cluster, we proposed three null hypotheses and tested through statistical measures.

(1) There will not be any significant difference between refugee placements in different clusters of prefectures in the regions of Turkey(2) Refugees movement in regions of Turkey will not represent any patterns relevant to their social upgrade.
(3) There is no difference in the refugee call patterns across the developed clusters in comparison with non-refugees towards social
integration.

Most of the refugees are not living in the prosperous area. Only 32% of refugees are living in prosperous. Remaining around
68% of refugees are not living in prosperous areas. Out of 68% refugees, we found only 7% are living in less prosperous and 61%
are living in the underdeveloped areas. Therefore, by rejecting the first null hypothesis, we proved that there is a significant numberof refugees are staying in underdeveloped areas and it clearly necessitates the need for relocation of refugees to the prosperous
cluster. The refugees living in underdeveloped should develop more social integration with non-refugees to create a goodatmosphere for the country development. Using Chi-square statistics, we compared the three cluster samples and proved the factthat for socially upgrading refugees, there is a necessity to move refugees from underdeveloped to the prosperous or less prosperous
clusters. Also, we found evidence that there is a significant difference between the call pattern in refugees and/or non-refugees whoare staying in prosperous and underdeveloped clusters. The same thing is applicable for the calls made between less prosperous tounderdeveloped and less prosperous to prosperous locations. Hence, we tested using t-test statistics between the calls made byrefugees from prosperous locations to others and non-refugees of a similar domain. From the value, it is evident that there is adifference in mean values of their call summary and movement patterns which ensures that the refugees and non-refugees show
the same pattern in the prosperous home clusters towards the social integration. Finally, we assure that this relocation refugee study
can easily be extended to explore in other countries for improving the socio-economic status of their refugees and by considering
the wellbeing status of prefectures attached to that country.
References:
1.
2.
3.

Albert Ali Salah, Alex Pentland, Bruno Lepri, Emmanuel Letouz´e, Patrick Vinck, Yves-Alexandre de Montjoye, Xiaowen Dong, Ozge
Da˘gdelen. Data for Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey, arXiv:1807.00523, July 2018
Well-being index for provinces, under the statistical table, http://www.turkstat.gov./PreTabloArama.do.
Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal. Data Mining: Practical Machine Learning Tools and Techniques,Morgan Kaufmann Series in Data Management Systems, 4th Edition.

Mobile data to analyse ground access and competition at airports
Amir Brudner1 , Riccardo Gallotti2,3 , Filippo Privitera4 , José J. Ramasco3 , Nicole Adler1
School of Business Administration, The Hebrew University of Jerusalem,
Mount Scopus, 91905 Jerusalem, Israel
2
Fondazione Bruno Kessler, Via Sommarive 18, 38123 Povo (TN), Italy
3
Instituto de Fı́sica Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB),
07122 Palma de Mallorca, Spain
4
Cuebiq Inc., 45 W 27th Street, 3rd floor, 10010, New York, NY, USA
1

Ease of access and egress to airports is likely to impact
demand and therefore should be of interest to the aviation
supply chain from the perspective of both short-run andlong-run investment planning. Data is however often limited in accuracy and based on sporadic local surveys and asa consequence, managers and policy makers must take decisions based on partial information. Recent developmentsin the use of Information and Communication Technologies(ICT) provide new, alternative data-sources which may leadto more precise detail with respect to individual mobility at
different spatial scales [1]. ICT data poses some challenges
due to the need to correct potential biases, but equally may
overcome some of the traditional limitations.
In this research, we investigate mobile data which provides a new, comprehensive perspective on door-to-door airport accessibility in the greater London region. By analysing
the data using discrete choice models, we identify airport
catchment areas in a large city served by multiple airports.
The focus on Greater London allows us to showcase the potential of very large data sources to answer the long-debatedquestion as to whether airports serving the same urban area
in fact compete (see Figure).
We analyse the demand of both locals and tourists foreach of the six airports drawing from all MSOAs in England and Wales. In total, the dataset includes 104,158 trips
by locals and 4,580 trips by tourists in the six month period
from January to July 2017. Validating big data is an important element of the analysis [2]. Consequently, we compare
the dataset to the UK Civil Aviation Authorities information from 2017, after removing connecting passengers. Although the demand mapped with mobile phone records represents less than 0.1% of the market, the annual market share
across airports is very similar, with an error of less than 2%with the notable exception of London City and Gatwick. Indeed, we note a slight bias towards coverage of the more European business-oriented passengers than the general public on average, hence Gatwick is slightly under-represented
whilst London City is over represented. Additional validation draws from a comparison of the median measured access times from each MSOA to that of Google maps data.
We utilise the aggregated GPS records generated by theuse of mobile apps by anonymous opted-in users to estimate distance and ground access times to airport facilities.
Our analysis highlights the role of both ground transportation and the socio-demographic background of passengers
in choosing between alternative airports. In support of thisdata-driven perspective, we use rational choice theory [3] tomodel the decision behaviour of passengers when facing theoption of multiple airports.
The results of the logistic regressions suggest that a reduction of 10% in the relative distance between an MSOA
and an airport (for example by improving ground transport

access), increases the odds of serving demand by 17%. Furthermore, a 10% increase in the population of an MSOA (forexample by increasing density) increases the odds by 12%.
Ground transport access is a significant variable both in explaining demand and its magnitude. With respect to car ownership, our results suggest that 10% lower ownership leads
to 4.5% higher demand and this is due to the fact that the
MSOAs with higher population densities lead to lower car
ownership shares.
The quantitative information gathered, integrated witheconomic data, can be used to inform a two-stage Hotellingstyle game. This new approach throws light on the opportunity for collusion between airports and between airports andairlines, and may be used by regulators and infrastructure
planners to improve decision making at the strategic level.

Luton and Stansted catchment areas. We highlight MSOAs located up to an hour from each airport with access time differencesof more than 30 minutes (green and red) and less than 30 minutes(blue). The overlapping region in blue consists of 110 MSOAs andapproximately 800,000 people. Of the passengers analysed in thesezones, 55% chose Luton and 45% chose Stansted.

[1] Vespignani, A. (2012). Modelling dynamical processes in complex socio-technical systems. Nature physics, 8(1), 32. ISO
690[2] Khan, N., et al. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014.
[3] McFadden, D. (1974). The measurement of urban travel demand.
Journal of public economics, 3(4), 303-328.

Elements for an official statistical production with mobile network data
D. Salgado1, L. Sanguiao1, B. Oancea2, M. Necula2, T. Tuoto3, S. Hadam4, A. Keskyla5, S. Williams6, A. Condron7
1

Statistics Spain (National Statistical Office of Spain)2
INS (National Statistical Office of Romania)
3
Istat (National Statistical Office of Italy)
4
DESTATIS (National Statistical Office of Germany)

5

Statistics Estonia (National Statistical Office of Estonia)
Office for National Statistics (National Statistical Office of UK)
7
Central Statistical Office (National Statistical Office of Ireland)
6

Mobile phone data are a promising data source for producing official statistics. They offer the possibility to reachunprecedented scales of spatial and time disaggregation for relevant statistics in different domains (population,
tourism, labour market, etc.) and also, more innovatively, to tackle novel statistical products of social interest relatedwith the newly born network science.
Diverse studies from academia, the research community around Mobile Network Operators (MNOs) and some leading
National Statistical Institutes (NSIs) have clearly shown the potential to produce official statistics using mobile phonedata. The challenge now is to incorporate them in a standardized production system enabling statistical offices to makeuse of these data in many diverse domains of social interest. The European Statistical System (Eurostat and European
NSIs) is constructing a production framework, facing important scientific and technical challenges, and proposing
concrete solutions for each of them:
-

-

-

-

Access to mobile phone data, despite the regulatory support in National and European Statistical Acts, is proving to
be a major challenge entangling many facets. The goal is to find satisfactory partnership models between MNOs
and NSIs. The Reference Methodological Framework (see parallel contribution by Eurostat and others) proposes alayered modular approach in which the notion of event location plays a central role in this respect. Furthermore, tocomply with regulatory demands, we are advancing in the implementation of privacy-preserving computation
methods. All in all, this entails an excellent opportunity to establish long-standing synergic collaborations among
academia, MNOs and statistical offices.
The geolocation of network events is designed to reach functional modularity in the whole process in order todecouple highly technology-dependent stages from the upper-level statistical analysis. Bayesian techniques to deal
with overlapping BTS cells avoiding Voronoi tessellations have been implemented in an R package called mobloc.
As with many other new digital data sources, the traditional survey methodology and design-based inference do
not apply any longer and a new inference framework for Official Statistics is needed. Bayesian hierarchical models,inspired by the species abundance problem in Ecology and some population estimation methods withadministrative data, are under exploration enabling us to integrate data from different sources (population
registers, survey/admin data, mobile technology penetration rates…). Another R package called pestim is also underconstruction.
The quality assurance framework, structured around the European Statistical Code of Practice, needs a revision.
Quality dimensions such as timeliness and opportunity are reinforced but others, like accuracy, needs appropriate
addressing in the new inferential framework. We investigate quality indicators based on posterior point estimates,
credible intervals, and model checking using the posterior predictive distribution. Some of these have been already
implemented in the pestim package.
In this contribution we present details of this ongoing work. Mobile phone data for official statistical production offers
an excellent opportunity to find synergies among academic researchers, telco professionals, and official statisticians.

Apps, Places and People: strategies, constraints and trade-o↵s in
the physical and digital worlds
Marco De Nadai1,2 , Angelo Cardoso2 , Antonio Lima2 , Bruno Lepri1 and Nuria Oliver2
1

Fondazione Bruno Kessler
2
Vodafone Research{angelo.cardoso,antonio.lima,nuria.oliver}@vodafone.com{denadai,lepri}@fbk.eu
Cognition has been found to constrain several aspects of human activity, such as the number of friends
and favorite places a person can maintain stable over time [1, 2]. Do people exhibit similar constraints on
digital devices? We address this question through the analysis of a dataset consisting of pseudonymized
mobility and mobile apps usage data of 400’000 individuals in a European country for 8 months.
In terms of mobility, we compute the stop locations from GPS data. Regarding applications, weanalyze the mobile phone apps used in the foreground. Based on previous definitions in mobility andsocial interactions, we compute the mobile users’ apps and mobility capacity, defined as the set of apps
used at least twice in a time window of 20 weeks and the set of stop locations visited at least twice in 20
weeks, respectively.
Despite the enormous heterogeneity of apps usage, we find that individuals exhibit a conserved capacity of applications they regularly use. This capacity steadily decreases with age, while the mobility
capacity decreases non-monotonically with age (see Figure 1A). We show that the capacity of applications
might be constrained by cognitive features. Next, we identify two profiles of individuals: apps keepers
and explorers, which correspond to those users with stable vs exploratory apps usage behavior. Finally,
we show that the capacity of applications predicts mobility capacity and vice-versa (see Figure 1B). By
contrast, the profiles do not always match the across domains, such that keepers in the apps domain
could be explorers in the physical space and vice-versa.
Our empirical findings provide an intriguing picture linking human behavior in physical and digitalworlds which is relevant to related research in Computer Science, Social Physics and Computational
Social Sciences.

Figure 1: A) The average capacity of applications usage steadily decreases with age, while mobility
capacity increases until around 28 years, then it decreases until 46 where it is constant, to decrease again
starting from 56 years; B) The correlation between individual’s mobility capacity and apps capacity.

References[1] Laura Alessandretti, Piotr Sapiezynski, Vedran Sekara, Sune Lehmann, and Andrea Baronchelli.
Evidence for a conserved quantity in human mobility. Nature Human Behaviour, 2(7):485–491, 2018.
[2] Giovanna Miritello, Rubén Lara, Manuel Cebrian, and Esteban Moro. Limited communication capacity unveils strategies for human interaction. Scientific reports, 3:1950, 2013.
1

In A Society of Strangers, Kin Is Still
Key: Identified Kin Relations In LargeScale Mobile Phone Data
Sebastian Diazaa,bLoreto Bravob
Carlos Rodriguez-Sickerta
Isabel Behnckea
Anna Rotkirchc
János Kertészd,e,f
Tamás Dávid-Barretta,c,g,ha Universidad del Desarrollo, Facultad de Gobierno, CICS, Av. Plaza 680, San

Carlos de Apoquindo, Las Condes, Santiago de Chile, 7610658 Chileb Data Science Institute, Universidad de Desarollo, Av. Plaza 680, Las Condes,

7610658, Santiago de Chile, Chilec Population Research Institute, Väestöliitto, Kalevankatu 16, Helsinki 00101,
Finlandd Central European University, Center for Network Science, Nador u. 9, Budapest,
H-1051, Hungary
e Department of Computer Science, Aalto University School of Science, P.O.Box
15500, 00076 Finlandf Department of Theoretical Physics, Budapest University of Technology andEconomics, H1111, Budapest, Hungary
g Trinity College, University of Oxford, OX1 3BH, Oxford, UKh Kiel Institute for the World Economy, Kiellinie 66, D-24105 Kiel, Germany
Abstract
Recent research has identified a way of detecting kin and peer relationship types
from anonymous mobile communication patterns, based on a combination ofphone call patterns and basic socio-demographic data (age and gender) of thecallers. This new methodology exploited the fact that the average mobile phonecaller has up to six distinct peaks in the histogram of call frequency, as a functionof the alters’ age and gender. Although the new methodology allowed a morerefined differentiation among close kin networks, it had substantial limitations
as these relationships were merely hypothesised. Here, we are able to movebeyond this methodological limitation using a unique, large mobile phone database with information about individual surnames in a population in whichpeople inherit two surnames: one from their father, and one from their mother.
Using this mobile phone database, here we focus on the difference between themost frequently called alters depending whether they are family relationship ornot. We report tests on two hypotheses: (1) within category kin vs. non-kin
relationship drives call frequency and call length; and (2) life-course dependent
variation of call pattern is higher in kin than in non-kin.

UNSUPERVISED MODELING OF CHRONOTYPESIN PHONE CALLS IN OLDER ADULTS.
Timothée Aubourg1,2, Jacques Demongeot1,3, Félix Renard1, Hervé Provost2, Nicolas Vuillerme1,3
1 Université Grenoble Alpes, AGEIS, Grenoble, France
2 Orange Labs, Meylan, France
3 Institut Universitaire de France, Paris, France

Abstract
BackgroundIn the field of complex systems, recent studies have shown relevant results in modeling circadianrhythms of social activity, as well as their chronotype, by means of the analysis of phone call detail records(CDRs) in social networks of young individuals. In the field of health monitoring, this scientific breakthroughoffers the opportunity to reinforce current methods of disease surveillance by including real-time personalizedsocial interaction data obtained on a passive and non-invasive way. Interestingly, this from-complex-system-tohealth application underlines the potential of a project combining the competence of multi-experts as data scientists and health practitioners for addressing the future of health research. However, key questions still require
answers regarding the observed CDRs and their interpretation.
Objective
In particular, whether and to what extent chronotypes could also be observed in phone calls in othertypes of population remain to be investigated. This paper is specifically designed to address this issue regarding
an older population.
Methodology
To this end, we use a 12-successive-month CDRs dataset of 26 volunteers older than 65 years. We specifically implement an unsupervised machine learning algorithm in order to investigate (1) the existence ofchronotypes in phone calls in older adults by modeling their phone call activity with Gaussian mixture models(GMM), and (2) the existence of different clusters of chronotypes in phone users based on the GMMs results,
by means of a K-means clustering approach.
Results
On the whole, our results do evidence about (1) the ability of GMMs to consistently model circadianrhythms of phone call activity in an older population, and (2) the existence of distinct categories of chronotypes
among individuals regarding these rhythms. In particular, it is interesting to note that, by means of GMMs, ourunsupervised algorithm permits to model the daytime bimodality of phone calls histograms in our older population, but it can also catch unusual peaks of activity occurring at nocturnal period. By using a K-means clustering approach, we then are able to characterize the morning or evening preference for phone calls time in older
adult from the GMMs results. Taken together, these findings suggest that older adults do also present chronotypes in phone call activity that could be evidenced by machine learning approaches. We believe that using
these last ones in complement to complex systems methodologies could provide relevant results both in modeling complex human activities, and also in offering innovate health monitoring tools for disease surveillance.
Keywords:
Call detail records ; Older population ; Unsupervised learning ; Clustering ; Gaussian mixture model ; K-means
Address for correspondence
Timothée Aubourg: timothee.aubourg@orange.com

Gender Analytics and Identification Toolkit (GAIT)
Lucio Melito, Kristyna Tomsu, Rositsa Zaimova, Denys Sementsov, Jerome Urbain
Dalberg Data Insights, lucio.melito@dalberg.com
I.
Introduction
Telecom operators often lack accurate gender data necessary to
proactively work towards connective gender parity, especially indeveloping countries. Existing research shows that phone usage
patterns differ between men and women, confirmed by survey
data [1] as well as studies working directly with call detailrecords to predict subscribers’ gender and fill the gender datagap [2, 3, 4, 5]. Commonly reported distinguishing features for
women are longer call duration, generally lower phone usage,and lower credit recharges. Different studies report genderprediction accuracy between 70 and 80%.
In partnership with GSMA Connected Women, Dalberg Data
Insights has developed a Gender Analytics and Identification
Toolkit, first implemented in Bangladesh. This toolkit can
provide any telecom operator with gender-disaggregated data onphone usage patterns and machine learning models to predict
gender for subscribers with no available reliable gender
information.
II.
Methodology and data
Data: 3 months of anonymized CDRs from ca 40 million
subscribers of Robi Axiata in Bangladesh; for 15,000
subscribers complemented by ground-truth gender labels
coming from a phone survey.
Methodology: We computed over 140 indicators summarizing
phone behavior of the phone users in Bangladesh. The indicators
can be divided in the following categories: generic usage (e.g.,number of incoming/outgoing calls per day), social (e.g.,number of distinct contacts), top-ups (e.g., an average top-up
value), bundles (e.g., bundle value per type), mobility (e.g.,radius of gyration).
We used a 15,000 subset of the subscriber base, for which we
had ground-truth gender labels, to train and evaluate (80%-20%
train-test ratio) several machine learning models to predict thesubscribers’ gender based on the log-transformed andnormalized indicators. Through a combination of Grid,
Randomized and Bayesian optimization search we tested
hundreds of hyperparameter combinations for several modeltypes (Linear Support Vector classifier, Support Vector
Machine, K-Nearest Neighbors, Random Forest and XGBoost),
choosing the best set of hyperparameters through 3-fold crossvalidation, after which the model was re-trained on the wholetraining set. The model with the highest accuracy was thenapplied to predict the gender of the rest of the subscriber base.
All the algorithms for data pre-processing, indicator
computation, model training and application are packaged in aneasy-to-use and easy-to-deploy toolkit, implemented in Python
leveraging pySpark API.
III.
Results
Survey: According to the survey, 30% of the subscriber base arefemales. When comparing the survey responses to the gender
information available to the operator from the SIM registration
process, we found out that 78% of female phone users wereregistered as men.

Predictive model: The best scoring model was XGBoost
reaching an accuracy of 84.5% (i.e., out of all predicted labels,
84.5% were correct), precision of 79.4% (i.e., out of all predicted
female labels, 79.4% were correct) and recall of 61.2% (i.e., outof all females in the dataset, 61.2% were correctly identified by
the model).
The top three indicators contributing to the prediction were: i)Average duration of incoming call (women receive on average
longer calls), ii) Number of contacts (women have on averageless contact numbers), iii) Radius of gyration (typical travel
distance) for average active day (women on average travel less).
IV.
Discussion
We demonstrated that gender data gaps in telecom data can be
filled using a small sample with reliable gender labels and an
automated machine learning pipeline. Gender-disaggregated
CDRs can help the operators to better understand the differences
between men and women in their call behavior and usage of
products. This can lead to increased usage of services in the userbase as well as better targeted campaigns to acquire more female
customers, and thus to increase mobile phone penetration among
women, potentially leading to many societal benefits.
Once set up, the packaging of the toolkit allows to run all thecomputations and predictions in one click, enabling to getgender-disaggregated data even in contexts where the operator
doesn’t have the time or technical capacities to estimate thesubscribers’ gender in a different way. The toolkit, including
thorough documentation, is now freely available to more than
750 GSMA Member operators across the world, aspiring to fill
gender data gaps in several places where gender-disaggregateddata is scarce and contributing to the SDG of achieving genderequality.
References:[1] GSMA, "Connected Women: The Mobile Gender Gap Report
2019,"
2019.
[Online].
Available:
https://www.gsma.com/mobilefordevelopment/resources/mobilegender-gap-report-2019/.
[2] J. Blumenstock and N. Eagle, "Mobile divides: gender,socioeconomic status, and mobile phone use in Rwanda,"
Proceedings of the 4th ACM/IEEE International Conference on
Information and Communication Technologies and Development,
p. 6, 2010.
[3] B. Felbo, P. Sundsøy, A. S. Pentland, S. Lehmann and Y. A. de
Montjoye, "Using Deep Learning to Predict Demographics from
Mobile Phone Metadata," arXiv preprint arXiv:1511.06660, 2015.
[4] V. Frias-Martinez, E. Frias-Martinez and N. Oliver, "A GenderCentric Analysis of Calling Behavior in a Developing Economy
Using Call Detail Records," AAAI spring symposium: artificial
intelligence for development, 2010.
[5] J. E, P. R. Sundsoy, J. Bjelland, A. Iqbal, A. Pentland and Y. A. de
Montjoye, "Predicting gender from mobile phone metadata,"
Netmob 2015 Book of Abstracts: Oral, online resource:
http://netmob.org/assets/img/netmob15_book_of_abstracts_oral.p
df, 2015.

Employment and social capital scores
Lucio Melito, Rositsa Zaimova, Kristyna Tomsu, Denys Sementsov, and Jérôme Urbain
Dalberg Data Insights, Brussels, Belgium, lucio.melito@dalberg.com

Employed
Unemployed

Bonding score0.056 ±0.001
0.075 ±0.003

Bridging score13.5 ±0.48
7.1 ±0.39

Table 1. Mean bonding and bridging scores for employed and unemployed users,with associated standard errors.

undirected graph with nodes representing users and an edge
connecting two nodes only if their corresponding users had
shared a call or exchanged a text-message during that month.
Bonding and bridging social capital scores for the subset of
10000 nodes were computed according to the following definitions:Fig. 1. Bonding social capital. Employment rates of different population segments
vs their bonding social capital scores.

Introduction. The concept of social capital has deep his-

torical roots and occupies a prominent place in sociology,
where it is defined as “the aggregate of the actual or potential
resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” (1). At the same time,some of the original quantitative research on social networks
was kick-started by a number of studies in the 1960s and1970s (2), which found that a majority of people who had
recently changed jobs had learned information about theircurrent position through personal contacts. Perhaps more
surprisingly, they described these connections as acquaintances rather than close friends, a finding later known as “thestrength of weak ties”. This and other research contributed
to academia’s understanding of the subject, with a clear dichotomy widely agreed upon: social capital can be of a bonding or of a bridging kind (3). Bonding capital arises from connections within a tightly-knit group, while bridging capital isfrom connections between such groups. Given the previous
points, it seems natural to hypothesise that bridging social
capital would have a sizable positive effect on the employment prospects of individuals, while bonding capital should
do the opposite.
We set to investigate this hypothesis in a East African country, where it could be tested on the dataset of Call Detail
Records (CDRs) of the major telecom operator in the country,
comprising the telephone calls and messages of more than 10
million users. Our research focused on a subset of ten thousand of these subscribers, for whom we had previously gathered ground truth information on age, gender, location andemployment status, through a telephone survey that was also
used to obtain their explicit consent.
Data and methods. The full dataset of interactions for themonth of November 2018 was therefore translated into an• Bonding score: the probability that any two of thatnode’s connections would also have a link between
them, also known as the clustering coefficient.
• Bridging score: the number of shortest paths that passthrough that node, i.e. the betweenness centrality of thenode; due to the computational impossibility of computing all the shortest paths in a graph with more than
10 million nodes, only a small number of vertices wererandomly sampled to compute the score of each node.
Results and discussion. The mean scores and their stan-

dard errors, disaggregated by employment status, are shownin Table 1. The differences are stark, with employed people
showing significantly higher bridging scores and lower bonding scores, confirming our hypothesis. Figure 1 displays theemployment rate of different segments of the population andtheir average bonding social capital score, with a near perfect(>0.95) negative correlation between the two metrics. These
results are also strengthened by the fact that the average numbers of contacts for employed and unemployed persons are
extremely similar, when a difference could have partially explained the discrepancy in social capital scores.
The correlation between employment and social capital metrics does not necessarily imply causation, and it is likely thatboth directions of the arrow of causality are in action: being
employed broadens someone’s social circle and put them intouch with a more diverse set of individuals, while having ahigher bridging social capital score in the first place will increase the chances of hearing of valuable job opportunities
and also increase the probability of landing a job.

Bibliography
1. Pierre Bourdieu. The forms of capital. 1986.
2. Mark S Granovetter. The strength of weak ties. In Social networks, pages 347–367. Elsevier,1977.
3. Robert D Putnam. Bowling alone: America’s declining social capital. In Culture and politics,
pages 223–234. Springer, 2000.

Melito et al.

|bioR‰iv|

March 15, 2019|

1–8

Socio-Economic Factors in News Consumption
Using Mobile Phone Data
S. Vilella1,4 , L. Ferres2,3 , D. Paolotti1 , and G. Ru↵o4
1

2

ISI Foundation, Turin, Italy
Data Science Institute, Faculty of Engineering, Universidad del Desarrollo
3
Telefónica R&D, Santiago, Chile
4
University of Turin, Turin, Italy

Introduction. In previous work, we have been able to show the e↵ects of certain press manipulations byowners of content [BBE+ 18, EFHB18]. These results, based on hypotheses born out of an operationalization of Herman and Chomsky’s Propaganda Model [HC02] and their “filters” give a good idea of how
the media behaves. One particular hypothesis is that the media will manipulate content to target news
to a certain audience. We’ve been able to show this to a certain extent using Twitter [EFS18]. What westudy here is a specialization of that study; namely, how people of di↵erent socio-economic backgrounds
access particular news outlets of certain characteristics at the finest possible level of granularity using
their mobile pphones. The hypothesis is that the more educated the population, the more they will read,but the more specialized they will be (because of audience targetting).
Data and Methods. We obtained a month (between July and August 2016) of anonymized network
interaction (deep packet inspection) through the cellphone network of a company that possesses 31%of the marketshare of mobile phones in Chile. Information was aggregated by antenna and by hour,
without any single user information, making it virtually impossible to de-anonymize news consumers.
The dataset, called dsUsers, includes the number of unique users that connected to that antenna andto an IP address belonging to one specific news outlet out of a curated list at a certain hour (00, 01,
02... 23), so for example, an entry in our database looks like ABCD0120,20160706,11,200.12.26.117,1,
means that on July 7, 2016 at 11am, there was one unique user connected to IP 200.12.26.117 at antenna
ABCD0120. The exact position of each antenna in the city is known, and we then group together all theantennas within a 1.1km radius, obtaining a lattice of about 700 points that will be our new, “fictitious”
antennas. The geo-location of the antennas allows us to cross the data about consumption of news media
content with the socio-demographic features of the users living in the di↵erent areas of the city. Eachantenna is assigned a census label that refers to a census district in which it is located, obtained byrunning a K-means clustering algorithm on the publicly available 2017 Chilean Census data.
Results. By analyzing the access to news media websites in the light of the census label of the zone
from which they are made, we are able to find patterns and preferences of users that in di↵erent sociodemographic contexts, see Figure 1. By looking at the general trends: the consumption of news media
content does not correlate with the education level of the user (which, incidentally, is known to correlatevery well with income distribution and inequality). Indeed, the most eager consumer of news media
content are those living in the lower-middle census areas, with a low education level. By going deeper
into this analysis, they seem to prefer the more generic media outlets, like Biobio or Cooperativa, while
the most educated usually prefer to read more specialised news media, like Diario Financiero, or more
explicitly political oriented media like the conservative El Mercurio or the leftist The Clinic. Amongthese evidences, one constant element stands out: the people accessing these websites from the mostdeprived areas of the city display the lowest activity. Accesses from these areas indeed are always thelowest, in absolute values, regardless of the typology of the news media that we consider.
Conclusions. The intuitive hypothesis that consumption of news media content grows with educationlevel of the user seems to be disproved here. Indeed, it seems that the least educated, those who live in1

the most deprived areas, are also those who access news media websites the least. This suggests us thatpublic data is, alone, a good reference for basic intervention and policy making, in order to facilitate andpromote the access to news media content among particular zones of an urban area, and make a step
forward in levelling out urban inequalities; nonetheless, mobile data proves to be an incredibly valuableasset in such an analysis.

Figure 1

References[BBE+ 18] Jorge Bahamonde, Johan Bollen, Erick Elejalde, Leo Ferres, and Barbara Poblete. Power
structure in chilean news media. PLOS ONE, 13(6):1–18, 06 2018.
[EFHB18] Erick Elejalde, Leo Ferres, Eelco Herder, and Johan Bollen. Quantifying the ecological diversity and health of online news. Journal of Computational Science, 27:218 – 226, 2018.
[EFS18] Erick Elejalde, Leo Ferres, and Rossano Schifanella. Understanding news outlets’ audiencetargeting patterns. CoRR, abs/1806.02155, 2018.
[HC02] Edward S. Herman and Noam Chomsky. Manufacturing Consent: the Political Economy of
the Mass Media. Pantheon Books, New York, NY, 2nd. edition, 2002.

2

Relations between Mobile Phone Indicators and theSocioeconomic Organisation of Cities
Maarten Vanhoofa,b , Clementine CottineaucaCentre for Advanced Spatial Analysis, University College London, London, UKbSENSe, Orange Labs France, Chatillon, Francec
CNRS, Centre Maurice Halbwachs, UMR 8097, Paris, France

Abstract
In this article we explore the convergence between mobile phone data andmore traditional socioeconomic data from the national census in French cities.
We extract mobile phone indicators from six months worth of Call Detail
Records (CDR) data, while census and administrative data are used to characterize the socioeconomic organisation of French cities. We address various
definitions of cities and investigate how they impact the statistical relationships between mobile phone indicators, such as the number of calls or theentropy of visited cell towers, and measures of economic organisation basedon census data, such as the level of deprivation, inequality and segregation.
Our findings show that some mobile phone indicators relate significantly withdi↵erent socioeconomic organisation of cities. However, we show that relations are sensitive to the way cities are defined and delineated. In several
cases, changing the city delineation rule can change the significance and eventhe sign of the correlation. In general, cities delineated in a restricted way(central cores only) exhibit traces of human activity which are less related totheir socioeconomic organisation than cities delineated as metropolitan areas
and dispersed urban regions.
Keywords: cities, mobile phone data, mobility, deprivation, segregation,
inequality

Email addresses: m.vanhoof@ucl.ac.uk (Maarten Vanhoof),
clementine.cottineau@ens.fr (Clementine Cottineau)
Preprint submitted to Netmob

March 15, 2019

Coverage Anomaly Detection with Mobile Analytics
Daniel H. Chae, Jungmin Moon, Jungmin Yoo, Intaek Lee
SK Telecom, Republic of Korea{dani75.chae,jmmoon,jungminyoo00,intae31.lee}@sk.com
Abstract—As mobile internet connection becomes our daily
necessity, it is important for telecommunication company toprovide a stable wireless connection. One way of providing stableconnection is to operate a mobile network efficiently without acoverage outage. However, since the wireless network equipments
such as base-stations are located spreadly over the nation, it is
not easy to detect malfunctions of the whole network nodes.
This paper introduces how we can improve the network stability
by applying mobile data analytics into anomaly detection ofservice coverage. Big data collected from the whole network isanalyzed, some designated network nodes covering a specific area
is analyzed with correlation of network equipment (NE) analytics
and user equipment (UE) analytics. It shows this method is very
efficient in analyzing service quality of massive mobile traffic
zones such as subway and train where NE analytics does not
represent service quality accurately due to an intermittent traffic
pattern.
Index Terms—UE Analytics, Wireless Connection Coverage,
Anomaly Detection

I. I NTRODUCTION
With streams of network big data analytics, there areneeds to improve an operation efficiency in telecom industry. Accurate measurement of customer experience, anomaly
detection and fault recovery of NE are highly required for theoperation efficiency. Network big data analytics is believed
one of solutions for these purposes and it can be applied into
network operations supports system (OSS) for maintaining
stable wireless service.
Conventionally, NE analytics with performance statistics is
widely under practice for network monitoring purpose. As
telecom subscribers want more broadband and more uninterrupted service, however, more detailed analysis of customer
experience, which is beyond the scope of NE analytics, isrequired to satisfy them. Automatic measurement of customers
experience is important in the era of high demanding qualityof-service (QoS). This is reason why there is a need to extend
analytics from NE-level to UE-level by measuring customer’s
experience accurately with UE analytics.
In this paper, by analyzing individual UE data with lesscomplexity, we introduce a method of coverage anomaly
detection in a massive traffic zone where careful network
operation should be exercised, and outcomes of accurate
tracing of service area and its anomaly detection is shown.
This method is useful in providing stable network operation,and it is embedded into SK Telecom’s OSS called TANGO (TAdvanced Next Generation OSS) [1], [2] as shown in Fig. 1.

II. C ORRELATION OF UE AND NE A NALYTICS
In perspective of network operation, network status canbe assured with NE analytics. NE analytics is performed byanalyzing the data provided from NE via northbound interface.
NE data includes thousands of key performance indicators(KPIs) and alarm data representing NE status. Additionally,
UE data, which contains detailed records of control-plane anddata-plane of the subscriber in the network, can be analyzed forassessing subscribers experience though there is a complexity
burden due to an amount of UE data size. To detect a service
coverage anomaly accurately in a massive mobile traffic zone
with less complexity, we introduce a method correlating UE
analytics and NE analytics.
III. E XPECTATION AND R ESULTWith the suggested method, we can improve customer
experience by minimizing anomalies of radio cell coveragein a specific zone efficiently. To find base-stations coveringa specific zone, UE analtytics is performed, and then NE
analytics is followed in detecting any anomalies of the zone.
The method of combining UE and NE analytics can relieve acomputation burden in anomaly detection. Detailed result will
be included in the full paper.
R EFERENCES[1] https://www.telecomasia.net/content/skt-expanding-use-tango-aiplatform, ”SKT expanding use of TANGO AI platform”[2] https://ovum.informa.com/resources/product-content/using-ai-in-cspnetwork-operations-what-operators-have-deployed, ”Using AI in CSP
Network Operations: What Operators Have Deployed”

Fig. 1. TANGO Data Analytics

Towards Official Tourism Statistics - Machine
Learning for Processing Signalling Data
Marc Ponsen, Yvonne Gootzen, Marco Puts, Martijn Tennekes,
Edwin de Jonge, Shan Shah, May Offermans
March 15, 2019
Statistics Netherlands is working on the next step towards official tourism
statistics based on anonymised mobile phone signalling data. The privacy
preserving process consists of four steps: geolocation, feature extraction,
machine learning and aggregation.
Our geolocation algorithm processes information about an antenna network into probabilities that a connecting device is located in a given location.
In the feature extraction step, all observed antennas and their geolocation
probabilities are analysed for a given time period. Multi-class particle filters
utilise the temporal component of these observations. The output of thesemodels is twofold: trajectory estimation and transportation mode detection.
Trajectories describe an estimated location of a device over time. Transportation mode models are used for particle class definitions. The survival
rates of each particle class is interpreted as the similarity between the actual
movement and a theoretical transportation mode definition.
Extracted features for foreign devices are used as input for an unsupervised machine learning clustering algorithm. The resulting clusters are used
to distinguish potentially new tourism developments. This allows for datadriven definitions, rather than analysis influenced by cluster definitions basedon foreknowledge.
Detailed pre-aggregation analysis result in more accurate anonymised
tourism statistics.

Subway Mobile Analytics for Better Experience
Dayea Yim

Keejoo Bae, Jungmin Yoo, Daniel H. Chae

KOTRA, SOUTH KOREA
dyyim@kotra.or.kr

SK Telecom, SOUTH KOREA{kjbae, jungminyoo00, dani75.chae}@sk.com

Abstract—As consumption of mobile network service s suchas video streaming and mobile games is increasing while using
public transit, seamless mobile connection service is highly
necessary. To satisfy customers with better experience, stablewireless connection should be provided in places where a large
number of people gather simultaneously and move quickly.
For example, in case of the subway or the high-speed train
telecommunications companies manage mobile network service
more carefully by continuous monitoring of radio signal quality.
Generally, manual measurement of signal quality is performed
periodically for this purpose. However, this method does notreflect the latest service quality and incurs labor cost of manual
measurement. This paper introduces a new method to measureradio signal quality automatically by analyzing WIFI accesspoint(AP), whose backhaul is LTE connection, in subway whichhelps wireless internet connection. This method will help analyze
service quality accurately by tracing user equipment(UE) in a
cost-effective way.
Index Terms—Wireless Network Quality, UE Analytics, Mobility Analytics

By analyzing UE call-trace data of LTE hotspots, network
operators can automatically detect service degradation of the
network without manual measurement:• Past: Network operators analyze performance data ofbase-stations that cover subway zones. The performancedata of base-stations is aggregate data from all UE,
and there is a limitation to estimate the service quality
accurately. In addition, manual measurement is required
to collect service quality data of each subway line.
• Now: By analyzing call logs of LTE hotspots, we canidentify to which base-stations the LTE hotspots areconnected and can analyze service quality accurately forstable maintenance of subway service. It is useful in detecting anomalies, such as when base-station designatedfor subway area is covering other area. They happen
frequently for unknown reasons.

I. I NTRODUCTION

UE analytics reduces routine-manual measurement in subway and provides more accurate network status information.
Also, analyzing real time logs of LTE hotspots can be used
for optimizing the base-stations that cover subway areas efficiently. Collected moving pattern of each UE in subway canbe used as marketing data. Detailed result will be included in
the full paper.

In places where massive people move simultaneously withhigh speed, such as the subway, customers will experience
network disconnection or degraded connection quality. Since
the subway is underground, it is difficult to maintain service
quality. In other words, the subway environment presents achallenge of having to remove signal outage and to handle
intermittent bursting traffic that causes a bad service quality.
For a seamless mobile connection service, network operators perform manual measurement of radio signal periodicallyto monitor service quality. The routine-manual measurement,
however, incurs labor cost and is not enough to assure service
quality of a specific zone.
In this paper, we introduce a new method of analyzing aservice quality of subway through UE analytics. SK Telecom
deployed LTE hotspot equipment for all cars of the subway toprovide WIFI access via LTE connection. SK Telecom collects
real-time call-logs of LTE hotspot including service quality
data and analyzes each LTE hotspot. This method has been applied to SK Telecom’s Operation Support System(OSS) calledTANGO (T-Advanced Next Generation OSS) [1] to analyze theradio quality of every subway line real-time without manual
measurement.

III. E XPECTATION AND R ESULTR EFERENCES[1] https://www.telecomasia.net/content/skt-expanding-use-tango-aiplatform, ”SKT expanding use of TANGO AI platform”

UE Call
Trace Data

Real-time
Processing

LTE Hot-spot
Call Logs

LTE Hotspot Call
Logs
Mapping

II. S ERVICE M ANAGEMENT IN S UBWAY: PAST AND N OW
The LTE hotspot located in each car of the subway provides
WIFI access to the mobile customers via LTE backhaul
connection, which helps UE connect to the LTE base-station.

Time
Aligning

Fig. 1. Data Processing In Analytics

Service
Quality
Analysis

Sensing Population Mobility in Greater Maputo via
Mobile Phone Big Data Mining
Mohamed Batran
Data Scientist, Rakuten Inc.
mohamedbatran44@gmail.com
I.
ABSTRACTThe rapid growth rate of urban populations may outpace thedevelopment of needed urban infrastructure, such as related totransportation, therefore, resulting to inadequacy of public
transportation services and traffic congestion. Such as the caseof Maputo, the capital and which is considered as an economic
and political hub for Mozambique. Accordingly, in order tocome up with a solution to this traffic congestion problem andalternative urban planning solutions, initially there is need toacquire a better understanding of the urban dynamics andpeople flow in the capital.
The mobility patterns and trip behavior of people areusually extracted from data collected by traditional survey
methods. However, these methods are generally costly anddifficult to implement, especially in developing countries withlimited resources. On the other hand, the wide spread of
pervasive sensors allowed to capture different levels of humanmobility, and to better describe the displacement of people intime and space. Call Details Records (CDR), or mobile phonedata, is a good example where timestamp along with theapproximate location are recorded with any cellular activity
such as a call, a message or a data connection. Such massive
amount of data generated by ubiquitous mobile phone usage
provide researchers with the opportunity to innovate alternative
methods that are inexpensive and easier and faster toimplement than traditional methods. However, CDR are
spatially and temporally sparse and can only represent a sampleof the population who own a mobile phone.
In this work, we leverage hundreds of millions of mobile
phone records in Greater Maputo Metropolitan area ofMozambique to infer various aspects of human mobility as analternative to the expensive traditional methods. This work has
three major contribution:1) We propose a method based on proven techniques toextract the origin-destination (OD) trips from the raw CDR
data of mobile phone users and process the data to capture themobility of those users. The proposed method was applied to
3.4 million mobile phone users over a 12-day period in
Mozambique, and the data processed to capture the mobility ofpeople living in the Greater Maputo metropolitan area indifferent time frames (weekdays and weekends). Subsequently,
trip generation maps, attraction maps, and the OD matrix of thestudy area, which are all practically usable for urban andtransportation planning, were generated. Furthermore,
spatiotemporal interpolation was applied to all OD trips toreconstruct the population distribution in the study area on anaverage weekday and weekend. Comparison of the results
obtained with actual survey results from the Japan International
Cooperation Agency indicate that the proposed method
achieves acceptable accuracy.

2) In addition to analyzing inter-city mobility pattern, we
propose a method to sense population mobility through cityboundary via mobile phone big data. We rely on a validated
approach to filter and scale up subscribers and trips for an
accurate origin destination (OD) estimation. Furthermore, weslightly modify the algorithm to capture during trip records, enroute CDRs, for more accurate route reconstruction and entry
region sensing. The method is applied to Greater Maputo
metropolitan area in Mozambique and the output is validated tothe most recent traffic count by Japan international cooperation
agency (JICA) in 2011.
3) We introduce a method to extract special trip segmentsfrom the OD trips generated in (1) that can sufficiently
represent and monitor travel time in an urban area from mobile
phone data. Our method were able to sufficiently represent anddistinguish between travel time in weekday and weekend inGreater Maputo. The result was validated against travel time
estimated from an ad-hoc high resolution GPS dataset collectedby the study team for that purpose resulting in 87% correlation
estimated from both datasets.
This Work was done as part of M.Sc. thesis at The Universityof Tokyo between Sept. 2016 ~ Aug. 2018 and does not relate
to Rakuten Inc.
This work was previously published here:
Batran, M., Mejia, M., Kanasugi, H., Sekimoto, Y., &Shibasaki, R. (2018). Inferencing Human Spatiotemporal
Mobility in Greater Maputo via Mobile Phone Big Data
Mining. ISPRS International Journal of Geo-Information, 7(7),259.
Batran, M., Kanasugi, H., Kashiyama, T., Sekimoto, Y., &Shibasaki, R. (2018). Sensing Population Mobility throughCity Boundary in Greater Maputo via Mobile Phone Big Data
Mining. In 2018 IEEE World Congress on Services(SERVICES) (pp. 9–10).
Batran, M., Arai, A., Kanasugi, H., Cumbane, S., Grachane,C., Sekimoto, Y., & Shibasaki, R. (2018). Urban Travel Time
Estimation in Greater Maputo Using Mobile Phone Big Data.
In 2018 IEEE 20th Conference on Business Informatics (CBI)(Vol. 2, pp. 122–127).

Synthetic Call Detail Records (CDRs) fromAuthentic CDR Based Subscriber Mobility Profiles
Lasantha Fernando, Viren Dias, Yashothara Shanmugarasa
LIRNEasia, 12 Balcombe Place, Colombo 08, Sri Lanka{lasantha, viren, yashothara}@lirneasia.net

1 Yves-Alexandre De Montjoye et al. “Unique in the crowd: The privacy
bounds of human mobility”. In: Scientific reports 3 (2013), p. 1376.
2 Darakhshan J Mir et al. “Dp-where: Differentially private modeling of
human mobility”. In: 2013 IEEE international conference on big data. IEEE.
2013, pp. 580–588.

500

700

Correlation = 0.995
Gradient = 0.691

300

We modeled the subscriber profiles based on de-identified
CDR data spanning a single month in 2013 for approximately
6 million subscribers from Sri Lanka. Only location-related
attributes were considered since our focus was on eliciting
mobility characteristics.
Our first step was to discern homogeneous temporal categories by which to model the profiles. We made the assumptionthat subscribers’ activities were consistent between weekdays,
as well as between weekends and national holidays. Withinthese day classifications, we made the supplementary assumption that subscribers’ activities were consistent between corresponding times of the day. Consequently, we segmented each
day into octants - 8 contiguous 3-hour segments, resulting in a
total of 16 temporal categories. We determined the probabilityof a record within each temporal category by calculating the
portion of total authentic records in that temporal category foreach subscriber.
Spatial categories were determined based on the locationsof base transceiver stations. Given a temporal category, theprobability of a record within each spatial category was determined by calculating the portion of total authentic records forthat temporal and spatial category for each subscriber. Finally,
the total records for each subscriber were summed up and anerror of upto 10% was introduced. This in conjunction with

Synthetic Subscribers (k)II. M ETHODOLOGY

III. R ESULTS
The synthetic dataset was subject to rudimentary validations
by comparing the number of distinct subscribers aggregated byspatial category with those of the authentic dataset. Despite thehigh correlation evidenced by Fig. (a), the number of distinct
synthetic subscribers was consistently lower by approximately
20%. Almost 3% of the profiles were discarded as they
could not generate records that conformed to the establishedconstraints. However, this does not wholly account for theidentified discrepancy - we speculate that infrequent, longdistance trips are underproduced as their generation necessitates multiple improbable events in succession, as corroborated
by a radius of gyration comparison in Fig. (b).

100

De-identified CDRs have seen increasing utilization as avaluable secondary data source in studying various aspects ofhuman mobility. However, despite de-identification, a significant portion of subscribers can be re-identified given sufficientmatching records1 . This impairs the shareability of thesedatasets. As such, novel methods that transform such datasets
to safeguard privacy, whilst preserving much of the statisticalcharacteristics of the original dataset, are of significant interest.
Prior research has looked into generating shareable CDR
datasets by profiling a region’s population as a whole2 . In thiswork, we propose an alternative methodology that generates
CDRs using the mobility profiles of each individual subscriber,
coupled with random noise to safeguard privacy.

the temporal and spatial probabilities constituted a subscriber’s
profile.
Given a profile, blank records amounting to the total records
were created to initiate the process of generating synthetic
records. The records were assigned temporal categories inaccordance with the probability distribution, as well as timestamps conforming to that category with equal probability.
The records were ordered chronologically, and independently
assigned a spatial category in accordance with the probability
distribution. The speed between the current and previous
records was validated against a predefined constraint, before
proceeding to the next. If the validation failed, the spatial
category was reassigned and revalidated.

0

I. I NTRODUCTION

0

100

300

500

700

Authentic Subscribers (k)(a) Distinct subscribers(b) Radius of gyration

IV. C ONCLUSION
Our methodology generates a synthetic CDR dataset that
preserves the core characteristics of the authentic CDR dataset,whilst safeguarding privacy. Extending this work to larger
time spans would simply require the addition of a seasonal
dimension to the temporal categories. Social characteristics
could be preserved with the inclusion of the probability
distribution of social contacts in a subscriber’s profile.

Confidential sharing of datasets of two mobile network operators:
A case study in cross-roaming analysis
Angela Sahk, Reimo Rebane, Jaak Randmets, Dan Bogdanov, Baldur Kubo – Cybernetica [first.last@cyber.ee]
Marko Peterson, Margus Tiru, Siim Esko, Erki Saluveer – Positium [first.last@positium.com]
Introduction
Timely and accurate statistics on cross-border
tourism can prove difficult to attain due to various
reasons, including privacy and confidentiality
barriers, if roaming information from telecom
companies is used. Indonesia, led by the vision of
the Ministry of Tourism, is one of the first
countries in the world to use data from mobile
network operators for measuring cross-border
tourism activity. Positium has already set up asystem for the Ministry based on data from one of
the mobile operators. The Ministry wanted toestablish a true baseline for roaming market share,
which is hard to estimate due to subscribers crossroaming in the networks of different operators
during a single visit. The challenge was how tocompare data without sharing it.
MethodA complete answer of the nature of cross-roaming
can only be arrived at when unique subscriber
information (IMSI) is compared across several
operators. Because of privacy reasons, this was acomplex task – it requires uniform hashing of IMSIs
over at least two operators.
Sharemind is a secure computing platform createdto specifically reduce the risk of a privacy breachwhen processing confidential data. The data is
encrypted at the source, by the data owner, andonly then sent to the Sharemind service. The hostof the service will not have access to theunencrypted data nor the encryption keys.
Sharemind traditionally protects data at rest and intransit and surpasses state-of-the-art methods
with protecting data in use. It does not remove
data protections even while processing, so data
will remain protected by cryptographic means
during the whole analysis. The Trusted Execution
Environment technology used in Sharemind HI to
implement privacy-preserving data processing is
the Intel® Software Guard Extensions (SGX)
available in modern Intel® processors. The three
key concepts that SGX provides to protect data are
enclaves, attestation and data sealing.
In Indonesia, two mobile network operators
prepared a list of IMSIs roaming in certain areas as
input. The IMSIs were uniformly hashed from the
7th digit onwards, the file encrypted by operators
and uploaded into the Sharemind HI server. The
confidential output provided the combinations of
7-digit IMSIs unique for and common between thetwo operators and correction coefficients toestimates of tourists by country.
Discussion
The project provided the Ministry of Tourism withinformation on roaming counts and roamer
overlap between the two biggest telecom
providers in Indonesia, effectively allowing tocalculate real roaming market share. Combining
this information with other studies provides amore accurate overview of tourism in Indonesia.
The technology is usable in any situation where
two sensitive datasets need to be matched against
each other.

Simple local cloud setup for cross-roaming analysis. Processing takes place in the SGX processor on encrypted input.

Abstract Submission
Netmob 2019

Identification of disaster-driven internal displacements fromthe time series analysis of call detail records
Tracey Li*, Jesper Dejby, Maximilian Albert and Véronique Lefebvre
Flowminder Foundation*Corresponding author email: tracey.li@flowminder.org
Over 25 million people are forced to leave their homes every year due to environmental disasters. The
majority of these people are internally displaced persons (IDPs), meaning that they remain within theirhome country. IDPs are among the most vulnerable people in the world today, as many States affectedby natural disasters are unable to support displaced citizens. New approaches to identifying,understanding, and predicting internal displacements are needed in order to better target assistanceand make the humanitarian response more proactive.
We present a method for analysing mobile phone call detail records (CDRs) to identify anonymous
individuals that are likely to have been internally displaced as the result of a sudden-onset disaster. In
contrast to methods that are typically used to analyse mobility patterns from CDRs, where theregionally aggregated movements of large groups of individuals are analysed, our method offers theadvantage that no assumptions regarding the destination, distance, or duration of displacements arenecessary. As it enables subscribers that are IDPs to be distinguished from the rest of the population, adetailed study of the mobility behaviour of the IDP subset can then be performed.
We first address the problems of low or irregular spatial and temporal resolution of the data, which areespecially relevant in low-income countries. We then detect changes in each individual’s ‘stay’ location
over time by computing the distance-to-home time series of each individual, and modelling this as apiecewise-constant signal. We apply a step-detection algorithm to identify change points (level shifts) in
the signal and assume that individuals whose stay location changed from their ‘normal’ location in thedays immediately following a disaster are IDPs.
We show the results obtained by the method when analysing datasets pertaining to three natural
disasters - Haiti earthquake 2010, Haiti Hurricane Matthew 2016, and Nepal earthquake 2015. The
extent to which we observe each location to be affected by displacement is consistent with what would
be expected based on the local intensity of the disaster, and matches field observations collected by theInternational Organization for Migration (IOM). We observe many very short-distance and short-duration
displacements, which are typically not included when using conventional CDR analysis methods, inaddition to long-distance and long-duration displacements. The inclusion of these movements provides
a more complete picture of the scale of a disaster, in terms of the number of people that have been
affected and who may require assistance. Mobility information about the individuals who have been
displaced, such as the locations of their contacts and frequently visited places, can then be extracted
from the CDR data and used to study displacement behaviour. Analyses of disaster-driven
displacements will be presented in “Novel mobility and social network metrics to predict disaster-driven
displacements from call detail records”.
We believe that our IDP identification method can facilitate advances in the analysis and modelling of
human mobility in post-disaster scenarios, using CDR and other location data. This will provide crucial
information to humanitarian response efforts in contexts where data is often lacking, such as
low-income countries. Such information can be used to complement traditional survey methods toassess the scale and characteristics of disaster-induced displacements in a timely manner.

Explaining urban mobility from urban features and morphology
Gevorg Yeghikyan⇤
Vahan Nanumyan†⇤ gevorg.yeghikyan@sns.it, Scuola Normale Superiore, Pisa, Italy† vnanumyan@ethz.ch, Chair of Systems Design, ETH Zurich, Switzerland
With the rapidly growing complexity of modern cities, understanding the large scale mechanisms
behind intra-urban human mobility and interactions has become of paramount importance forurban planning, management and policy making. The conventional model-driven approaches tohuman mobility [3, 4] have recently been challenged and augmented by machine learning, specifically deep learning techniques [5, 6, 7]. The latter focus on improving predictive power, but
often fail to provide insights into how the observed mobility relates to urban form and socioeconomic dynamics. The existing studies on such relationships between urban functions and mobility [8, 9, 10] are narrowly tailored to a specific question, e.g., quantifying flow between certain
Point-of-Interest types.
In this work, we explore the dependence of mobility flows on various urban structure features, aswell as socio-economic relations between city locations. Our approach is based on principled statistical tools and uses large datasets on fine-grained CDR data and GPS car trajectories spanningseveral cities.
We follow the complex networks approach by employing a recently developed random graphmodel [1] and the related multiplex network regression method [2]. We divide the city into a gridof 500m⇥500m cells. We then represent each cell by a node in the network and the mobility flows
between them as directed edges between the nodes. The graph model considers each unit of flow
as one unweighted edge. This results in many parallel edges between two nodes.
Then, the random graph model provides a statistical ensemble of all possible flow networks under some constraints: preserving in- and out-flows from each node, as well as respecting heterogeneous pairwise flow propensities of nodes. The multiplex network regression considers thesepropensities as latent variables, which it infers from observed predictor features. These features
include road distances, travel time, demographic and economic statistics of the corresponding
urban grid cells (nodes). As opposed to conventional discriminative regression methods, ourmethod intrinsically respects the network constraints. Hence, it allows for stronger interpretation of the modelling results. As with conventional methods, we are able to perform significancetests and model selection, allowing us to judge both the absolute and the relative importance of
the urban features for explaining the interactions.
Exponent Std.Err
p-valuetime-1.021 0.0021 <1e-16 ***speed-0.716 0.0032 <1e-16 ***population-0.252 0.0007 <1e-16 ***distance-1.826 0.0023 <1e-16 ***road distance-0.222 0.0106 <1e-16 ***route factor-0.377 0.0011 <1e-16 ***Figure 1: (Left) The GPS traces of circa 12,000 taxis over the subdivided area of Shenzhen. (Right) Significant
determinants of the mobility flow in Shenzhen.

The figure shows an example result for a limited number of significant predictors for taxi traffic
in one city. For instance, we confirm an expected result that the larger the distance, less is the flow

between locations. But we also see less trivial results, such as a negative relation between the taxi
flow and population density. This is possibly explained by the preference towards public transportation in densely populated areas. In our contribution, we will present the extended analysis
for a larger set of mobility predictors, together with a comparison study between multiple cities.
References[1] Casiraghi, G, Nanumyan, V, Generalised hypergeometric ensembles of random graphs: the configuration model
as an urn problem. arXiv:1810.06495.
[2] Casiraghi, G, Multiplex Network Regression: How do relations drive interactions? arXiv:1702.02048.
[3] Erlander, S, Stewart, N F, The gravity model in transportation analysis: theory and extensions. Vsp, 1990
[4] Wilson, A G, Urban and regional models in geography and planning. John Wiley & Sons Inc, 1974
[5] Sun, L, Axhausen, K W, Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transportation Research Part B: Methodological, 2016[6] Jiang, R, Song, X, Fan, Z, Xia, T, Chen, Q, Chen, Q, Miyazawa, S, Shibasaki, R, Deep ROI-Based Modeling for
Urban Human Mobility Prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and UbiquitousTechnologies, 2018[7] Feng, J, Li, Y, Zhang, C, Sun, F, Meng, F, Guo, A, Jin, D, Deepmove: Predicting human mobility with attentional
recurrent networks. Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018[8] Wang, M, Yang, S, Sun, Y, Gao, J, Human mobility prediction from region functions with taxi trajectories. Public
Library of Science, 2017[9] Zhang, W, Li, S, Pan, G, Mining the semantics of origin-destination flows using taxi traces. UbiComp ’12, 2012[10] Yao, Z, Fu, Y, Liu, B, Hu, W, Xiong, H, Representing Urban Functions through Zone Embedding with Human
Mobility Patterns. IJCAI, 2018

Abstract Submission
Netmob 2019

CDR analytics to support the environmental surveillance of
infectious diseases
Optimisation of number and placement of surveillance sites

Guilherme A. Zagatti​1​, Tracey Li​1​, Samantha K. Watson​1​, Arend Voorman​2​, Vincent Seaman​2​, Linus
Bengtsson​1​ and Véronique Lefebvre​1​*1​Flowminder Foundation
The Bill and Melinda Gates Foundation

2​* Corresponding author email: veronique.lefebvre@flowminder.org
Environmental Surveillance (ES) is the collection and testing of sewage samples for the presence ofenteric pathogens such as poliovirus. The method allows large numbers of individuals to be regularly
tested and has the potential to supplement or replace active clinical surveillance. However, there areuncertainties about the role ES can take in quantifying prevalence and distribution of disease, given
unanswered questions about its sensitivity and the interpretation of the results. As ES sites are only
sensitive to infections among those defecating in the catchment areas of ES sites, which include bothresidents and temporary visitors, estimating the sensitivity of a set of ES sites and optimising theplacement of future sites partly relies on information on population movements and the number of
unique visitors to ES sites over time.
Expansion of environmental surveillance has so far largely been ad-hoc - mostly targeting known
high-risk neighbourhoods - and without supporting data on population movements. Because the total
number of sites will be limited by laboratory capacity and financial resources, health planners needbetter data to guide the expansion of ES globally. We developed an analytical framework using Call
Detail Records to estimate the fraction of the population covered by ES sites over time, and to optimise
the number and placement of new ES sites. We tested our framework in a country with no current
cases of targeted diseases (Haiti), where the role of ES sites is to ensure the routine monitoring of the
maximum number of people, and not to monitor the spread of an ongoing outbreak.
We first counted the number of unique visitors to each existing and potential site over different time
windows (day, week, month, year) to assess the temporal sensitivity of each location, and provide
insights on the best times to take samples.
We then took advantage of the movements of people between locations in order to maximise thenumber of people captured from a given number of locations. This question can be formulated as theclassical maximum coverage problem, and we used a greedy algorithm to approximate its solution. We
first select the location with the largest number of unique visitors, then select subsequent locations
sequentially according to one rule: select the location that was visited by the largest number of visitorsthat have not visited the previously selected locations (i.e. maximising the number of unique visitors). At
each iteration the algorithm provides a set of locations and the maximum fraction of people it cancapture, until all possible locations are selected and 100% of the population has been captured. We
repeated this optimisation for 3 different time windows (unique visitors to each location in a day, aweek, or a month) and found that e.g. 50% of the phone user population can be captured with 45 sites ifsampling on 1 day, 12 sites if sampling on 7 days, and 4 sites if sampling every day for a month. We
also noted that optimised locations differ from what would be expected from a static population density
map, as people’s mobility varies across different towns and regions, and because locations with a highnumber of visitors but low number of residents are selected.
Further work is required in order to make these results operational and considerations pertaining to thefields of hydrology, sewer systems engineering, and chemistry, as well as adjusting for biases present in
CDRs need to be included. Then our analytical framework which provides maximum coverage curves ofthe population as a function of the number of sites, as well as suggestions for site placement, can beused to plan future expansion efforts of the environmental surveillance of infectious diseases.

Quantifying Gender Gaps in Urban Mobility using Mobile Phone Data
Michele Tizzoni​1​ *​​, Laetitia Gauvin1​ ​​, Ciro Cattuto1​ ​​, Leo Ferres​2​ ​​, Simone Piaggesi1​ ​, Andrew Young3​ ​,Natalia Adler4​ ​, and Stefaan Verhulst3​ ​(1) ISI Foundation, Italy, (2) U. Del Desarrollo & Telefónica R&D, Chile (3) NYU The GovLab, NewYork City, USA (4) UNICEF, New York City, USA* Corresponding author for this abstract, ​michele.tizzoni@isi.it
Mobility is rightfully recognized as a gendered issue. Indeed there is a long tradition of gender andmobility research in the social sciences, urban studies, transportation research and geography concludingthat there are differences in the way men and women move about. Nevertheless, most of these studies aremainly based on surveys which might imply relatively few observations over a limited time span, lowspatial resolution or can be prone to errors due to self-reporting. More importantly, how the observed
differences in mobility can be explained by innate sex-related differences, such as physical differences, orby gendered socially constructed factors, such as household roles, remains a highly debated researchissue. Also, long-term trends of gender differences in mobility, driven by global demographic andsocioeconomic trends, are hard to capture, and this is particularly true in urban areas, whose population iscontinuously growing and it is projected to account for almost 70% of the world's population by 2050,
according to the United Nations. Indeed, urbanization offers many possibilities to reduce gender gaps
through a wealth of new opportunities, but also increases inequalities by, for example, geographicalsegregation, especially in developing settings. In urban areas, mobility is a critical factor to access theopportunities that cities offer. Thus, investigating the role of gender in urban mobility is key to better
understand whether women and young girls can fully benefit from such opportunities and realize their
human rights.
In this study, we study urban mobility from a gendered perspective in the greater metropolitan area ofSantiago, Chile. Our study has two main objectives: first, to assess and quantify gender disparities in themobility patterns of Santiago residents in large scale, and, second, to identify socio-demographic factors
and the availability of transport options that are associated to such mobility inequalities.
To this aim, we analyze the mobility traces extracted from the CDRs of a large cohort of anonymized
mobile phone users disaggregated by sex (male or female) over a period of 3 months. Through estimation
statistics, we quantify the effect of gender on different mobility metrics. We find that women visit fewer
locations than men and they are more localized, that is, they tend to distribute their time within a few
preferred locations. We then map indicators of mobility differences between males and females to 51
municipalities of the Santiago Metropolitan Region and we investigate the association between mobility
inequalities and socio-demographic indicators in different areas of the city, as well as their relationshipwith the Santiago transportation network structure, after controlling for users’ activity and gender ratio.
Finally, we add a ``semantic layer" to the mobility patterns of Santiago residents by identifying specificpoints of interests that are more frequently present along women's or men's trajectories in the urban space,thus demonstrating how our approach can identify specific gendered mobility needs.
In conclusion, our study shows how the complex relations between gender, mobility and socio-economic
factors can be unveiled by combining telecommunication data with demographic statistics and public
transportation data.

Multiview socioeconomic mapping of social networks
Jacobo Levy Abitbol1 , Carlos Sarraute2 , Martin Minnoni2 , Márton Karsai1
Univ Lyon, ENS de Lyon, Inria, CNRS, UCBL, F69342, France
Grandata Labs, 550 15th Street, San Francisco, California 94103, USA
Corresponding author: marton.karsai@ens-lyon.fr

Introduction: Despite the pervasive use of mobile devices and the subsequent abundance of data, readily available
fine-grained socioeconomic maps are still rare commodity to decision makers, policy experts or researchers. Althoughthese large data streams are regularly being collected and stored, they are generally proprietary. This ensuing data scarcity
becomes even more severe when one seeks to blend disparate data sources, usually resulting in aggregated coarse-grained
datasets [1] or small-scale fine-grained ones [2]. Earlier socioeconomic status inference methods were based on a variety ofdata sources, like social media [6, 5], call detailed records (CDRs) [2], or satellite imagery [3, 1]. Although combinationsof these datasets [7] have previously been used for similar tasks, large data-driven studies combining high-resolutionsocioeconomic, social network, and satellite data in large populations are still rare.
In this work we address the inference of the socioeconomic status of a large number of users located in a Latin
American country. We build a learning model relying on a large dataset simultaneously recording mobile-phone communications, bank transaction history, and high-resolution satellite imagery of the concerned areas, which enable us to geta fine-grained view of the social structure and living environment of millions of individuals. In doing so, we seek to a)
design a model that can coherently aggregate these data sources and learn an interpretable representation of the input andb) detect which high-order correlations yield the most predictive performance of our model in terms of socioeconomic
status.

Fig. 1. (a) Available satellite tile coverage of a Latin American city; (b) Display of an individual tile resolution (50cm/pixel); (c)schematic presentation of inferred average socioeconomic status of zip codes.

Data Description: Mobile communication data used in our study records the temporal sequence of 8 billion call and
SMS interactions between ⇠ 112 million anonymised mobile phone customers for a period 21 months. Using the provided
CDRs we construct a large social network with users as nodes (whether clients or not of the actual provider), and links
drawn between them if they interacted at least once during the observation period. To remove commercial customers, werecursively filter nodes from the network with zero in- our out-degrees. Additionally, due to a combined bank dataset,socioeconomic indicators and demographic information of 6 million bank-mobile customers are also available. The combined bank-mobile dataset contains a single connected component of 1 million people, with communication events anddetailed bank records available for all of them. For the purpose of our study, we enrich this dataset with high resolution
satellite images collected via the DigitalGlobe Open Data program from 2016-2017. These satellite ties provided a full
spatial coverage for several cities within the country.
Results: We build on previously introduced approaches to construct the finest grained socioeconomic inference framework benchmarked on the studied area to date. To do so (ongoing), we rely on representation learning tools that enable us
to perform a joint embedding of our input where both the satellite tiles and the underlying social network are projected
into a lower-dimension space. In doing so, we provide insights on how communication patterns and living environment
are intertwined and related to socioeconomic status of people.

References
1. Boris Babenko, et al. NIPS Workshops on Machine Learning for the Developing World, 2017.
2. Joshua Blumenstock, Gabriel Cadamuro, and Robert On. Science, 350 (6264):1073-1076, 2015.
3. Ryan Engstrom, Jonathan Samuel Hersh, and David Locke Newhouse. World Bank, 2017.
4. Martin Fixman, et al. ASONAM’16 San Francisco, 2016.
5. Jacobo Levy Abitbol, Márton Karsai, and Eric Fleury. ICDMW’18 Singapore, pp. 1192-1199, 2018.
6. Daniel Preotiuc-Pietro, et al. PLOS ONE, 10:1–17, 09 2015.
7. Jessica E. Steele, et al. J. Royal Soc. Interface 14, 127, 2017.

Understanding Residential Mobility from Long-term Mobile Phone Data
Isabella Loaiza Saa, Samuel Heroy, Esteban Moro, Neave O’Cleary, Alex ‘Sandy’ Pentland
Human Dynamics Lab, MIT Media Lab, Massachusetts Institute of Technology
Mathematical Institute, University of Oxford

We use nationwide mobile phone data to study the relationship between socioeconomic status (SES)
and residential and social mobility in Colombia. Using a 3-years data-set of 15 million lines spanning theentire country, we associate residential moves with permanent changes in users’ nighttime locations. We study
residential moves in each of the country’s five largest cities, validating our results with previous survey-based
estimates from Colombia (where approximately 5% of residents move each year).
To quantify residential mobility, we take into account both the proportion of individuals who move inand move out per stratum zone. Additionally (lacking individual-level socioeconomic information), we
associate users to a socioeconomic status according to the “socioeconomic stratum” of their pre-moveresidence. Stratum designations represent governmentally assigned assessments of residential wealth used forincome-adjusted pricing of basic services and utilities, taking into account the conditions of a resident’s
home/street block. For instance, stratum 1 represents very poor conditions, while stratum 6 represents very richdesignations, and most residents are assigned 2 or 3. Using this (coarse) designation, we compare thefrequency of residential moves for users’ of different socioeconomic status (SES), hence we can begin to
unpack the link between social mobility (the likelihood to move up or down in SES) and residential mobility.
We find that lower SES residents have much hampered residential mobility compared to higher SES residents(in line with survey-based estimates, as explored in Villarage, Sabater, and Módenes, 2014).
In line with previous results, we find that residential mobility in Colombia is low, and there is little
difference between cities. We explore the frequency of residential moves between the various strata, showing
low social mobility (as measured in this way), as lower (higher) SES residents tend to move among low (high)stratum areas.
While there are numerous studies using call detail records (CDR) to better understand human mobility,
our study is novel in its use of CDRs to understand long-term residential moves and social mobility. Because
accurate survey-based assessments of residential mobility are relatively infrequent (every 5-10 years), CDRs
can provide valuable information in almost ​real-time to policymakers and other stakeholders regarding thechanging makeup of cities and various localities as well as which areas in the city promote low or high social
mobility. Given the recent end to a decades long armed conflict, Colombia has in the decade to comeunprecedented potential for economic growth and prosperity. We hope to build on this work to be able to parseout what factors influence the rise in social class both for individuals and for whole communities in Colombia.

Comparative Analysis Between Travel Patterns from
Cellular Network Data and an Urban Travel Demand Model
Nils Breyer1 (nils.breyer@liu.se)
David Gundlegård1 (david.gundlegard@liu.se)
Clas Rydergren1 (clas.rydergren@liu.se)Lars Sköld2 (lars.skold@telenor.se)1

Department of Science and Technology, Linköping University
2
Telenor Sweden
March 15, 2019

Data on travel patterns and travel demand is an important input to traffic planning andforecasting models. Traditionally, travel demand models are based on census data, travel surveys
and traffic counts. Problems arise from the fact that the sample sizes are rather limited andthat it is expensive to collect and update the data. Cellular network data is seen as a potential
large-scale data source to improve the understanding of travel patterns at relatively low cost.
In this presentation we will give a better understanding of the potentials and limitations ofinferring travel patterns from cellular network data.
The process we propose to infer travel demand from cellular network data consists of a trip
extraction step, which identifies trips from the cellular network data and a scaling step, which
extends the data to represent the total population (see Figure 1). To find out which types of
trips can be extracted from cellular network data, we use a small scale cellular data set collectedusing 20 mobile phones. In addition to the cellular network data, the phones have collected GPS
tracks, which allows a trip-by-trip comparison. Using a large-scale dataset of cellular network
data from a Swedish operator for the city of Norrköping, we are able to compare the travel
demand inferred from cellular network data to municipality’s travel demand model as well as
time profiles from public transit tap-ins.
Remote access server in control of the mobile operator
trip extraction

Trips

aggregation, anonymity
checkscaling

OD-matrix(antenna level)

conversion,
aggregation

OD-matrix(TAZ level)

Mobile network data

Figure 1: Process to infer travel demand from cellular network data
The results are analysed on several di↵erent levels of detail, trip-by-trip and by comparing
the results with a traditional travel demand model. Our results show that the recall (tripdetection rate) is just about 50% for trips which are only 1-2km long while it is 75-80% for
trips of more than 5km length. Similarly, the recall also di↵ers by travel mode with more than
80% for public transit, 74% for car but only 53% for bicycle and walking. After aggregating
trips into an origin-destination matrix we find that the correlation to the urban travel demandmodel used by the municipality depends on the aggregation level of the comparison. While thecorrelation is weak (R2 < 0.2) using the original zoning used in the traditional model with 189
zones, we find a correlation of R2 = 0.82 when aggregating to 24 zones.

Exploring Temporally Evolving Communities through the Lenses of Location Semantics
Olivera Novović1,a, Sanja Brdar1,b, Apostolos N. Papadopoulos2,c1
BioSense Institute, University of Novi Sad, SERBIA
2
Department of Informatics, Aristotle University of Thessaloniki, GREECE
anovovic@biosense.rs, bbrdars@uns.ac.rs, cpapadopo@csd.auth.gr
Introduction
Mobile phone service providers collect large volumes ofdata related to telecom traffic generated by users.
Significant information is recorded in these datasets,
and since the data is geo-referenced it has enormous
potential for identification of human connectivity
patterns in spatio-temporal context. On mobile phonedata set covering Milan city [1] we detected
communities from connectivity patterns and exploredtheir time evolving characteristics through the lenses oflocation semantics. In this way we anticipated andexplored dynamic change in communities on a city
scale.
Location semantic

Figure 1. Frequent items generated clusters over selected locationsin Milan city

In urban areas semantics of the location defines its
importance to local community and visitors. With
increasing number of applications related to location
intelligence, there is growing need for data that contain
information about spatial semantics of the location.
Copernicus Land Monitoring Service provides very
detailed land use and land cover data for European
urban areas called Urban Atlas. Urban Atlas coveringcity of Milan with surrounding suburban area contains
more than 20 classes defining land use. Along with thisdata we also extracted points of interest fromOpenStreetMap database.

Daily base offset between frequent items area and areaof the cluster (i.e. community) is an indicator of cluster
stability. As an example, in Fig. 2 we present the
dynamic of change in cluster size for two locations withdifferent semantics. The selected areas are Bocconi
University and Duomo Cathedral in the city centre. From
Fig. 2 we can notice that Duomo has more stable cluster
than Bocconi. Bocconi cluster significantly change incovered area compare to its frequent core.

Frequent communities
The results of our previous work [2] presented how
different areas of the city have different connectivity
patterns. Furthermore we performed community
detection analysis over telecom data using Apache
Spark and Louvain algorithm [3] to identify communities
in the city, defined as strongly connected sets of cell ids.
We discovered that community structure differs
significantly from day to day. Community associated to
specific location varies in shape and size. To detectstable patterns in time evolving communities, across 30
days period, we used FP-Growth algorithm for frequent
itemset mining. In the next step of the analysis weselected interesting locations that have different spatial
semantics. Fig. 1 presents map of Milan city overlaid
with frequent communities detected from telecom dataaround selected points of interest.

Figure 2. Area of clusters generated daily base compared to area of
frequent items cluster

Further systematic analysis of the results across all
selected locations provides insights how semantics ofthe locations impact communities dynamics.
References[1] Barlacchi, G., et al. (2015). A multi-source dataset of urban life in
the city of Milan and the Province of Trentino. Scientific data 2150055.
[2] Novović, O., Brdar, S., & Crnojević, V. (2017). Evolving
connectivity graphs in mobile phone data. In NetMob, The main
conference on the scientific analysis of mobile phone datasets (pp.
73-75).
[3] Truică, C. O., Novović, O., Brdar, S., & Papadopoulos, A. N. (2018).
Community Detection in Who-calls-Whom Social Networks,
In International Conference on Big Data Analytics and Knowledge
Discovery ,(pp.19-33),
Springer,
ChanAbstract Submission to NetMob19
Conference on the Analysis of Mobile Phone Datasets

Algorithm and architecture for the generation and mobile high-speed transmissionof traffic lights information to connected and automated cars in real time
Gómez Castaño, José
Cabrera García, Juan JoséCTO INSPIDE jgcasta@inspide.com
CEO INSPIDE jjcabrera@inspide.com
GIS Specialist Dpto. Astrofísica y CC de la Atmósfera;
Univ. Complutense de Madrid jgomez03@pdi.ucm.es
The use of communications between vehicles and traffic infrastructure, and more recently, between clouds and vehicles, is a field thatis growing and full of challenges. One of them is to be able to take the information from traffic ligths to the vehicle in the shortest
possible time. Until recently, radio communications based on the 802.11p [1] [2] protocol were the most effective means. These havethe disadvantage of being short-range and have a high cost in their deployment.
Taking advantage of the 4G and 5G microlantency networks that are coming, opens the possibilities of using a cloud-basedarchitecture, which complements short-range communications with large ones. The advantage is a cost reduction in the necessary
hardware because the software can be installed in small computers or even mobile phones and the possibility to include more context
information from sources integrated into the cloud and no more radio communication hardware is required.
In this work, a methodology has been developed that allows to receive in a device on board a vehicle, information from a set of traffic
lights in real time beyond the capacity of vision of vehicle’s sensors. This includes the status of each traffic light that affects a vehicle,
and the changes that occur in it, in real time. The mobile network has been exploited and a data model and algorithm for the
distribution of high-speed notifications have been created. This data model integrate geographical, topological and absolute sequence
traffic light information.
The location of the traffic lights is distributed in a grid. In order to optimize communication, an MQTT interface [3] is created into thecloud for a given grid area, taking advantage of the possibility of cascading this interface [4]. Messages from each traffic light are sent
to the corresponding topic where the traffic light is located into the grid. The naming mechanism is combined with the identification of
the reticule in GeoHash format [5] to achieve a higher calculation speed.
The position reported by each vehicle is integrated in this grid and an information zone of NxN tiles is generated around it. As thevehicle moves, its tiles will include one or more traffic lights. If at that moment there is a topological relationship between the vehicle
and the traffic light, being linked by a route of a given length along the streets, the vehicle device subscribes to the MQTT interface ofthe cloud that includes notifications to it. A direct communication link semaphore-cloud-vehicle is created, based on the spatial
distribution of traffic lights and vehicles in real time.
Additionally, the relationship among different traffic light in the network has been implemented on a graph database. This allows topreload the information of the semaphores really related to the one that at that moment is sending information to the vehicle.

Vehicle vs traffic light grid
Madrid Traffic lights distribution
This architecture and transport protocol allows each vehicle to receive the specific information that affects it efficiently and quickly. The
data model includes the topology information of the traffic light environment and its status. The protocol conforms to the standard
J2735 SPAT and MAP messages [6].
The goal of this study it is the application of protocols and data models to the Madrid traffic light network. The data of 52948 traffic lightsfrom the open data portal of Madrid City Council and the Singularity mobility platform have been used. The tools used have been open
source, Python and java languages and PostGIS database. The solution has been implemented into an AWS Amzaon infrastructure.

References[1] "IEEE 1609 - Family of Standards for Wireless Access in Vehicular Environments (WAVE)". U.S. Department of Transportation. April 13, 2013.
Retrieved 2014-11-14.
[2] EN 302 663 Intelligent Transport Systems (ITS); Access layer specification for Intelligent Transport Systems operating in the 5 GHz frequency band
[3] EN 302 663 Intelligent Transport Systems (ITS); Access layer specification for Intelligent Transport Systems operating in the 5 GHz frequency band
ISO/IEC 20922:2016(en) Information technology— Message Queuing Telemetry Transport (MQTT) v3.1.1[4] N. Tantitharanukul, K. Osathanunkul, K. Hantrakul, P. Pramokchon and P. Khoenkaw, "MQTT-Topic Naming Criteria of Open Data for Smart Cities,"
2016 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, 2016, pp. 1-6. doi: 10.1109/ICSEC.2016.7859892[5] I. S. Suwardi, D. Dharma, D. P. Satya and D. P. Lestari, "Geohash index based spatial data model for corporate," 2015 International Conference on
Electrical Engineering and Informatics (ICEEI), Denpasar, 2015, pp. 478-483. doi: 10.1109/ICEEI.2015.7352548[6] Amsterdam Group, 2015 Signal Phase and Time (SPAT) and Map Data (MAP)Mobility Analytics for Location Based
Advertisement
Jangjun Kim, Hongchan Roh, Jungmin Moon, Jungmin Yoo
SK Telecom, Repulic of Korea{jjkim81,hongchan.roh,jmmoon,jungminyoo00}@sk.com
Abstract—Big data analysis based on mobile communication
call-log is essential to manage the flow of large amount ofdata traffic efficiently, and well-analyzed subscriber-based data isuseful in location-based service (LBS). In this paper, we introduce
the application method of location-based advertisement (LBA)
service with call-logs of non-identified communication subscriber
and its advertisement use case. Data is more valuable once it issecured more based on the flow of population. However, in orderto generate the data of population flow, pattern analysis of big
data collected from the entire communication network shouldbe performed through the correlation analysis between a specificuser equipment(UE) and network equipment(NE). High-speed
analytic platform, in order to process a large amount of locationbased call-log whose data is spatio-temporal, is shown built in a
distributed processing platform combined with geometry-specificfunctions.
Index Terms—Call-log Analytics, Location Based Service/Advertisement

I. I NTRODUCTION
As the mobile internet connection becomes common, thedaily life of individual exists in the mobile environment. To
improve the operational efficiency of the telecommunicationsindustry, it is highly required to manage service quality bycustomer/service base. An analysis system for real-time tracking and managing the changes of the communication protocol
between UE and NE should be built for this purpose. Also, thecause of quality degradation should be analyzed with the system. Recently, LBS services considering the geographical location of mobile terminal devices are expanded. For example,
there are messages of push/pull advertisements for point-ofinterest (POI), vehicle management, emergency management
and asset tracking. For LBS, telco’s communication call-logs
are most useful in terms of data size and its accuracy.
In this paper, we estimate the location of UE with call-log
data, not using location data like GPS tracking, and show its
use case in advertising. In order to maximize the effect ofindoor/outdoor advertisement shown in the subscriber’s path,
it is necessary to estimate the movement of people and to characterize the advertising targeted subscribers. For this purpose,
we introduce an elaborated location recognition method using
individual non-identified UE data. This method works wellin a huge network-based environment of the nationwide unit
without additional functions for positioning UE, and further
advanced tracking will be available through learning patternsof continuously changing signal level between cell-tower andUE. It has been tested based on call-Log generated from SK
Telecom’s network OSS called TANGO [1].

Fig. 1. Location Prediction Example : real-time population density of Incheon

II. E ND -T O -E ND C ALL - LOG BASED L OCATIONA NALYTICS FOR LBA
The number of advertisements targeted by the effective
location requires LBA to be precisely estimated. This can beobtained through analysis of the movement characteristics ofthe call-log generated by the telco network and the data planecharacteristics of the service subscribers. The location canbe estimated precisely by performing spatio-temporal analysis
on the information transmitted during the periodic or event
situation through the interface between UE and the basestation. The data plane of the subscriber can finely confirm
the target of the advertisement by evaluating the user profileand the user-utilized application information. To do this, we
can correlate the access network analysis with core network
analysis. We describe the construction of big data analytic
platform specialized for high-speed spatial analysis of thecorrelation and location analysis of access-core network.
III. E XPECTATION AND R ESULT
We focused on the possibility of utilizing the nationwide
LBA service on a building or a road using access-core endto-end call-log analyzed by TANGO. Data such as locationof cell tower, time-delay between UE and NE, RF-power, andneighbour cells for hand-over are used for this positioning
method. We will also discuss how to configure the analytic
platform for real-time LBA services.
R EFERENCES[1] https://www.telecomasia.net/content/skt-expanding-use-tango-aiplatform, ”SKT expanding use of TANGO AI platform”

New Potential Consumers Identification throughTelecommunications Network Interaction
Analysis?
Victoria Zevallos1 , Miguel Nunez-del-Prado1[0000 0001 7997 1739] , and Denisse
Amanqui2
1

Universidad del Pacı́fico, Lima - Peru
v.zevallosmunguia@up.edu.pem.nunezdelpradoc@up.edu.pe
2
Entel - Lima, Peru.
denisse.amanqui@entel.pe

Abstract. Competition among telecom operator has radically increased
in recent years in Latin America. As a result, operators are making significant investments in developing new strategies allowing them to increase
their market share. These strategies have three di↵erent approaches. The
first is to attract new users to the industry; the second, to attract customers of the competitors; and the third, in retaining the clients. While
all fronts are important, the objective of the following study is to attract
new clients from other telecom operators. This task presents considerable
and interesting challenges due to the lack of information about the subscriber behavior from other telecom operators. Therefore, we rely on theinformation of the interaction these users maintain with the company’s
subscribers to determine their future behavior. The main objective is determining which clients belonging to another telecom operator are more
likely to become new subscribers based on the analysis performed on theinteractions in the telecommunications network.
To attain this objective, we use Call Detail Records (i.e., call traffic
and internet consumption) of Small Office Home Office and post-pay
Entels subscribers to determine whether a subscriber from another telecom operator will become an Entel subscriber. We model the structureof the mobile social network as a directed graphs G(V,E), where thevertices are weighted by the internet consumption and edges by the volume of incoming and outgoing calls. Then, communities are detected
to infer information about the subscribers competitors behavior basedon the attributes of the Entel subscribers sharing the same community.
Once information is completed, we compute some variables to qualify
the changes in subscribers attributes over time. Finally, a classification
algorithm is applied to identify the most likely subscribers to migrate to
Entel. Our approach achieve an accuracy values around 0.9. However,this classification is not trivial since the dataset is unbalanced. Therefore, we also performed a comparative analysis of resampling techniques
to balance the dataset before performing the classification task.
?

Corresponding author: Miguel Nunez-del-Prado. m.nunezdelpradoc@up.edu.pe

Evaluation the wake-up time using mobile phone
network data
Gergő Pintér⇤ , Imre Felde⇤⇤ Óbuda University/John von Neumann Faculty of Informatics, Budapest, Hungary

pinter.gergo@nik.uni-obuda.hu, felde@uni-obuda.hu

Abstract—During the last decades, mobile phones have become
an integral part of urban life. Mobile devices became an ’extension of human body’ and provide ubiquitous possibilities of nearlyanytime/anywhere one-to-one communication. Their footprints inthe mobile network could help to understand the socioeconomic
aspects of urban life. In this paper, we are applying metrics
by which mobility and life style characteristics of urban area
can be evaluated. Our proposed approach relies on time series
analysis of Call Data Records acquisited by a Hungarian Mobile
Telecommunication Operator, Vodafone Hungary.

I. I NTRODUCTION
The mobile phone data used in this study was provided
by Vodafone Hungary, that consists of anonymous Call Detail
Records of calls, text messages and data transfer. This data
source contains only the so call active records, when a device
phone calls, text messages and data transfer, but the type ofthe record is not specified. Information about cell switchingis also not available. As the former would help significantly
to identify the wake-up time, the latter would provide much
more accurate trajectory of the device. In this paper, we arefocusing on two indicators derived from Call Data Records:
the Radius of Gyration and the ”wake-up” time.
II. R ADIUS OF G YRATIONThe original radius of gyration [1] is defined as
s
1 X
rg =
ni (ri rcm )2N
i2L

where L is the list of locations where phone activity isrecorded, ri is the geographical coordinates of the locationi, ni is a weight that can be the visitation frequency or thetime spent in a given location. And finally, rcm is the centerof mass of the places where the individual visited according
to the CDR data.
From the CDR data the ”staying” information is calculated
describing how long an individual stays in a given location.
As a Call Detail Record represents a momentary information
about the location of a device, a 15 minute threshold is applied
and it is assumed that the device had stayed in a given location
for at least 15 minutes. It has to be noted that when a device
is in motion while actively using the mobile phone network, it
leaves several traces in different locations in a short time andin this case the threshold does not need to apply as there isnewer information about the location of the device. Then thestay information is used to determine the most place where adevice present in working hours and out of the working hours
to determine the work and the home location of the individual[2]. In this study, the staying time is used as a weight for
the ni parameter and the home location is used instead of thecenter of mass for the rc m parameter. The radius of gyration
is calculated separately for the weekdays and the holiday inboth version (using center of mass and the home cell as thereference point). Using the home location as the center results
larger Radius of Gyration because the center of mass is alreadya mean of the locations. The Pearson correlation coefficient
between them is 0.8995 for the weekdays and 0.7454 for the
weekends.
III. WAKE - UP TIME
As the type of the phone activity is unknown, the wakeup time of the individual is hard to estimate. As making acall requires to be awake (whereas message could be received
and data could be transferred autonomously), knowing which
activities represent phone calls would provide more accurate
information about when people are certainly awake.
The Call Detail Records are counted for every single celland 15 minute time intervals. This results a time series that issmoothed using the Savitzky-Golay filter with the window sizeof 31 and the order of polynomial of 5. Using the smoothedcurve the minimum and the maximum activity value is selected
for every day. The minimum is usually in the middle of thenight, and the maximum is in the afternoon. To determine the”wake-up” time, basically the positive edge of the curve needs
to be detected, when the number of the mobile phone activity
increases drastically in a short period of time. The time that is
considered as the ”wake-up” time is simply where the activity
value is the mean of the minimum and the maximum.
R EFERENCES[1] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding
individual human mobility patterns,” nature, vol. 453, no. 7196, p. 779,
2008.
[2] G. Pintér, L. Nadai, G. Bognar, and I. Felde, “Evaluation of mobile phonesignals in urban environment during a large social event,” in 2018 IEEE
12th International Symposium on Applied Computational Intelligence andInformatics (SACI), May 2018, pp. 000 247–000 250.

Observing ‘devotional togetherness’ in Sénégal
with mobile phone data
David A. Meyer
Mathematics Department, UC San Diegodmeyer@math.ucsd.edu
In 2014 the French and Sénégalese telecommunications companies Orange and Sonatel
launched a Data for Development challenge, releasing several anonymized sets of mobile
phone data from calls within Sénégal during 2013: (1) hourly antenna to antenna calls,(2) call times and antennae for random subsets of 150,000 users in 25 non-overlapping
fortnights, and (3) call times and arrondissements for a random subset of 150,000 users
for the whole year.1 Development has as a prerequisite a functional politics. Sénégal
is exceptional among West African nations in having a more stable and less repressive
political system than others,2 and since, as O’Brien puts it, “The state in Africa enters theimagination along a religious path.”,3 it is important to understand its relative religious
harmony.
Attending the 1963 opening ceremony for the Great Mosque of Touba, the first President
of independent Sénégal, Léopold Sédar Senghor, explained:4
Lundi dernier, le Chef de l’État assistait au Pèlerinage national catholique au Sanctuaire Marial de Popenguine. Aujourd’hui, il est présent à l’inauguration de la grande
Mosquée de Touba. Cette double présence n’étonnera que ceux qui persistent à ignorer
l’Afrique et ses réalités. Pour nous, Sénégalais, ce sont, là, les fondements de notre
politique nationale.
In this paper we examine this “devotional togetherness”3 using the mobile phone data fromthe Orange-Sonatel D4D challenge: Time series analysis of cell tower traffic from dataset(1) reveals variations due to religious rituals, from daily prayers to annual holidays, disaggregated spatially. Comparison of individual call data from datasets (2) and (3) with thesebaselines indicates participation in, and travel to, these rituals, including, for example, theGrand Magal in Touba.5 Furthermore, by a novel cross-referencing of individuals between
data sets (2) and (3) we can search for those who, like Président Senghor, participate inrituals across religious communities.

1

Y.-A. de Montjoye, Z. Smoreda, R. Trinquart, C. Ziemlicki and V. D. Blondel, “D4D-Senegal: The
second mobile phone Data for Development challenge”, arXiv:1407.4885 [cs.CY].

2

L. A. Villalón, Islamic Society and State Power in Senegal: Disciples and citizens in Fatick (Cambridge:
Cambridge University Press 1995).

3

D. B. C. O’Brien, Symbolic Confrontations: Muslims Imagining the State in Africa (New York: Palgrave2003).

4

L. S. Senghor, Liberté 1 : Négritude et Humanisme (Paris: Seuil 1964).

5

C. Coulon, “The Grand Magal in Touba: A religious festival of the Mouride Brotherhood of Senegal”,
African Affairs 98 (1999) 195–210.

Identifying Call Detail Records (CDRs) Affected by
Load Sharing
Yashothara Shanmugarasa, Viren Dias, Lasantha Fernando
LIRNEasia, 12 Balcombe Place, Colombo 08, Sri Lanka{yashothara, viren, lasantha}@lirneasia.net
I. I NTRODUCTION
Pseudonymized CDR datasets are increasingly used as aubiquitous data source to extract insights into human mobility,transportation, social networks and disaster response behaviors. The utility of these datasets is impaired by the load
sharing effect - during peak network traffic, some calls are
offloaded to a base transceiver station (BTS) alternative to theclosest. Mobile network operators can triangulate locations
more accurately using other data sources. However, in theabsence of such datasets, the potential implications of load
sharing on research must be addressed cautiously.
Existing literature briefly explores this issue; Isaacman etal.1 handled load sharing by spatially clustering BTSs for each
subscriber. In this paper, we discuss several criteria and outline
a methodology that can be used to identify records affectedby load sharing. We go on to apply this methodology to an
existing CDR dataset and also discuss its limitations.

III. R ESULTSTABLE IR ESULTS OF APPLYING DIFFERENT CRITERIA TO THE DATASETDistance
5
10
15
20
25
N/A
5
10
15
20
25
N/A

Only neighbor
cells

False

True

Pct. of distinct
subscribers
48.81
51.85
52.32
52.42
52.46
52.56
43.39
45.26
45.39
45.40
45.40
45.41

Pct. of candidate
records
0.72
0.89
0.92
0.93
0.94
1.00
0.53
0.60
0.61
0.61
0.61
0.61

II. M ETHODOLOGY
We used pseudonymized CDR data spanning a single monthfor approximately 2 million subscribers who had more than
50% of their activity in the Western Province of Sri Lanka,
which we selected due to its widespread urbanization.
We clustered BTSs by drawing a grid of 1 km2 cells acrossthe country based on the methodology described by Maldeniya
et al2 .
A trip constituted of consecutive records of a subscriber
recorded by different BTSs. We assumed that load sharing
happens predominantly between nearby BTSs and is moreprevalent than device cloning. Based on these assumptions,
we explored different criteria to narrow down possible records
affected by load sharing:• A mean velocity exceeding 110 km/h.
• Between neighboring BTSs.
• A distance less than 5km, 10km, 15km, 20km or 25km
Given the velocity criterion, we conducted analyses based on
all possible combinations of the remaining two criteria related
to neighbour BTSs and distance.
1 Sibren Isaacman et al. “Identifying important places in people’s lives from
cellular network data”. In: International Conference on Pervasive Computing.
Springer. 2011, pp. 133–151.
2 Danaja Maldeniya, Sriganesh Lokanathan, and Amal Kumarage. “OriginDestination Matrix Estimation for Sri Lanka Using 2 . the Four Step Model”.
In: Proceedings of the 13th International Conference on Social Implicationsof Computers in Developing Countries May (2015), pp. 785–794.

Fig. 1. Density of records affected by load sharing against distanceDensity graph in Fig. 1, and Table I suggests that beyond
a distance of 10 km, the records affected by load sharing
are minimal. This provides a threshold for which the load
sharing effect is significant. The marginal increments beyond
this distance can be attributed to the presence of cloned devices
or infrequent cases of load sharing.
IV. C ONCLUSION
Our method successfully captures a subset of records affected by load sharing. However, some of these records are
the result of device cloning. The neighbor BTSs and distancecriteria serve to isolate records affected by load sharing fromthose affected by device cloning, but not wholly. Further
experimentation is required to identify a larger portion ofrecords affected by load sharing and to segregate them fromthose affected by device cloning.

DO YOUNG PEOPLE USE THEIR PHONE DIFFERENTLY?
YOUNG ADULT DETECTION THROUGH MOBILE DATA ANALYSIS
Alberto Vázquez (alberto.vazquez@vodafone.com), Rodrigo Buendía(rodrigo.buendia@vodafone.com) and Alberto de Santos (Alberto.santos@vodafone.com).
Vodafone Research, Spain

INTRODUCTION
In a convergent market, it is common practice thatseveral telco products (such as Fiber, Mobile Lines, TV
and so forth) are associated to a single paying
customer. However, in practice, such products might
be used by several people with a diverse range ofcharacteristics (e.g. different members of the samefamily with varying ages, genders and needs).
Unfortunately, the information about the other usersof the products and services is missing as the only
available information is that of the paying customer.
This lack of information is a prevalent and important
challenge, which prevents a proper personalization of
the customer experience.
A second mobile line is a frequent telco service
affected by this ambiguity regarding who is the actualuser of the service. A typical use case scenario for thissecond mobile line are young users whose mobile
phone is paid by their parents. Given the different
interests and needs of young users versus older
adults, we focus on this paper in tackling the problem
of automatically differentiating between these twogroups through the analysis of their mobile phone
behavior. The main hypothesis underlying our work isthat young adults exhibit different patterns of mobile
usage from older adults and hence may bedifferentiated.
While several previous works have explored thischallenge [1][2][3][4][5], we contribute to the existing
literature in three ways: First, we carry out a detailed
assessment of the usage of certain social apps,
especially those with increasing popularity among
young adults, such as Instagram; secondly, we build
our models using a large scale dataset of over one
million and a half users, much larger than the datasets
used in previous works; finally, we study the optimal

age threshold to distinguish the behavior between
young and older adults.

DATA AND METHODOLOGY
We analyze several data sources that capture mobile
human behavioral data. All data is fully pseudoanonymized, provided with user consent andcompliant with existing data privacy regulation andour institution’s code of ethics.
In particular, we analyze the data sources below for arandom sample of more than a million customers fora period of one month in 2018:
•
•

•

•
•

Volume of data traffic per app and per month,differentiated by hour.
Use of Vodafone Pass special tariffs. These
tariffs, hired individually by each user, allows
the customer to enjoy unlimited data
consumption of certain apps, such as social
networks, chats, video, music apps, etc.
Description of the services purchased by eachcustomer, such as fiber network services,
cable TV, prepaid or postpaid mobile services.
Billing information, including the amount paidby each client per month.
Geolocation information extracted from Call
Detail Records (CDRs).

We apply state-of-the-art supervised machinelearning methods in a binary classification task.
In order to train the models, we generate a groundtruth dataset composed of the data of over one
million and a half customers for whom we have theirage and who only have one mobile phone line.
From the available data sources we compute 734
features, as a result of a feature engineering processwhich includes combining features, aggregating bydifferent time slots, transforming categorical features
to numerical features and dealing with null values andoutliers.
We split the data into 70% for training/validation and
30% for testing. We then train and test twoclassification algorithms: Random Forests (RFs) andGradient Boosted Trees (GBTs).

RESULTS AND DISCUSSION
One of the primary goals of this project is toempirically define the optimal age that would serve as
our threshold to define young vs older adults. Thus,
train/test our binary classifiers using a range of ages
between 19 and 35, such that users younger than thethreshold age would be labelled as young.
In all our tests, RFs outperform GBTs. Hence, we only
report the age analysis results for RFs. The optimal
results have been obtained setting the threshold todistinguish between users older/younger to the age of21, achieving AUCROC of 0.89, which is at par withprevious work [3][6].
Beyond the classification performance, it is interesting
to analyze the most predictive features to shed lighton the behavioral patterns that differ between
younger and older adults. In our models, the most
predictive features are:•••Intensity of usage of social apps, being
significantly higher in the younger adultgroup.
Volume of data consumed during certain hourslots (especially, at midnight), being also
higher in younger adults.
Usage of special tariffs: related with the useof special Vodafone tariffs (such as the socalled social pass).

CONCLUSION
In this work we have found empirical evidence of abehavioral difference regarding mobile phone usage
between younger and older adults which enables theautomatic segmentation of these two groups ofcustomers with high accuracies. We plan to use ourmodels to provide a more personalized andmeaningful experience to our customers.

BIBLIOGRAPHY[1] Nadeem, S. M. S. J. T., & Weigle, M. C. (2012).
Demographic prediction of mobile user from phone
usage. Age, 1, 16-21.
[2] Sarraute, C., Blanc, P., & Burroni, J. (2014, August). A
study of age and gender seen through mobile phone
usage patterns in Mexico. In 2014 IEEE/ACM
International Conference on Advances in Social
Networks Analysis and Mining (ASONAM 2014) (pp.
836-843). IEEE.
[3] Fixman, M., Berenstein, A., Brea, J., Minnoni, M., &Sarraute, C. (2016, November). Inference ofSocioeconomic Status in a Communication Graph.
In Simposio Argentino de GRANdes DAtos (AGRANDA
2016)-JAIIO 45 (Tres de Febrero, 2016).
[4] Wang, P., Sun, F., Wang, D., Tao, J., Guan, X., & Bifet,
A. (2017, April). Inferring Demographics and Social
Networks of Mobile Device Users on Campus From APTrajectories. In Proceedings of the 26th International
Conference on World Wide Web Companion (pp. 139147). International World Wide Web Conferences
Steering Committee.
[5] Herrera-Yagüe, C., & Zufiria, P. J. (2012, July).
Prediction of telephone user attributes based on
network neighborhood information. In International
Workshop on Machine Learning and Data Mining in
Pattern Recognition (pp. 645-659). Springer, Berlin,
Heidelberg.
[6] Jahani, E., Sundsøy, P., Bjelland, J., Bengtsson, L., & deMontjoye, Y. A. (2017). Improving official statistics inemerging markets using machine learning and mobile
phone data. EPJ Data Science, 6(1), 3.

Tourism analysis on a large scale using mobile location data
Francesco Tullii, Fabio Pinelli, Francesco Calabrese
Vodafone Research
Email: {Francesco.Tullii, Fabio.Pinelli, Francesco.Calabrese}@vodafone.com
Abstract—Mobile phones today represent an important source of
information for studying people behaviors, for environmental monitoring,transportation, social networks and tourism. The interest in the use of thedata generated by mobile phones is growing quite fast, also thanks to thedevelopment and the spread of phones with sophisticated capabilities. In
particular, this source of information provided a new and effective way
to measure and understand the mobility of the people at different spatial
granularity, and it can be used in different scenario by public authorities
to improve the quality of service they provide. In this paper, we leveragesuch kind of data to describe the tourism phenomenon by means ofanonymous and aggregated KPIs. In particular we analyzed data of the
Italian Sardinia region in August 2018.

I. R ELATED W ORK
Despite the presence of several works employing mobile location
datasets, they often bring advanced solutions, but working on accurate
and selected datasets. The few works that attempt to describe atourism phenomenon on a large scale in this domain lack of thespatial resolution of the others. For example, [1] describes the spatiotemporal and compositional dimensions of tourists flows in Estonia,
but the results are aggregated by counties. Other works present
analysis at city level considering a limited temporal window ofanalysis. In our work we deliver a large-scale analysis on a morerefined spatial scale.
II. DATA PROCESSING
How do tourists travel across the region? What are their profiles?
Where do they come from? Do they travel alone or with theirfamilies? Do tourists change habits while traveling? The answers tothese questions can be used by public tourism authorities to re-design
the touristic offers, to improve the quality of services and/or to handle
unexpected behaviors. The aim of this work is to build an analytics
framework able to leverage mobile network data to provide correct
answers to those questions. The proposed framework is designed as
an analytics pipeline that ingests mobile network events and extracts
mobility indicators (KPIs). These indicators are used, for instance,
to describe how and when people move during a tourist visits. In
particular, the process pipeline introduced in this work is composedby various steps. Firstly, a cleaning data procedure is performed inorder to remove possible erroneous data points on the raw network
events with the associated cell id, and to anonymize the data set. On
the cleaned data, on a daily basis, dwelling locations (stay locations)for each users are computed using spatial and temporal thresholds.
Stay locations represent areas where a user have spent at least aminimum time. Furthermore, a set of anonymous and aggregated
KPIs is daily computed on the output of dwelling time process: where
people access Sardinia, the number of people visiting an area, how
the people move across different area. Moreover, we computed otherindicators commonly used in human individual mobility, such as thetravel distance distribution and the radius of gyration, to carry out amobility study. The extracted KPIs are segmented by different profiles
based on mobility behaviours. Information related to the roamers arealso considered in our analytics framework.

Fig. 1. Tourists vs Local comparison

Clever combinations of the computed KPIs are finally extracted
in order to provide advanced analytics such as frequent movement
patters, detect families on trip, and so on.
III. R ESULTS
In this section, we describe a series of experiments that we
conducted to validate the steps of the framework. In particular, we
focus on the results of the dwelling time algorithm, the mobility
indicator distributions, and the detected tourist families. In order tovalidate the results gathered by means of our dwelling time algorithm
we carried out two experiments. Firstly, we compared the detectedstay locations with the points obtained from another GPS dataseton the same users and the same time window, reaching a level ofaccuracy of 87.4%. Secondly, we split our set of users, in local andtourists. As an example of a computede KPI, we present in picture
1 a comparison between the two sets in terms of most visited towns,
considering the stay locations. The tourists privilege the coasts, inparticular the northeastern touristic area. The locals naturally prefer
the largest cities. Moreover, we compared the travel distance and radiiof gyration distributions between the locals and tourists. We noticed
that tourists travel more but constrained in small regions and from asea town to another, whereas the Sardinians travel less, mainly fromand to largest cities.
Furthermore, we performed a comparison between the families
extracted among the tourists and the families obtained using mutual
calls and text messages. We noticed that we are able to detect agreater number of families which includes the ones obtained withthe second method. Moreover, we conducted several experiments tohighlight behavioral changes on the smartphone usage while people
are travelling. We empirically demonstrate that users on a touristic
trip change their usage behaviour w.r.t their typical one. This is truein particular for chat, social networks and maps applications.
R EFERENCES[1] Janika Raun, Rein Ahas, and Margus Tiru. Measuring tourism
destinations using mobile tracking data. Tourism Management,
57:202–212, 12 2016.

A General Approach to Detecting Migration Events in Trace Data
Guanghua Chi1 , Fengyang Lin2 , Guangqing Chi3 , Joshua Blumenstock1
1 University of California, Berkeley, 2 Shanghai University of Finance and Economics, 3 Pennsylvania State University{guanghua, jblumenstock}@berkeley.edu, linfengyang28@gmail.com, gchi@psu.edu

Introduction and Motivation

Summary of Results

Our approach showed a good performance in identifying migration
Migrants play a central role in the global economy. It is estimatedevents in both CDR and geo-tagged tweets. Figure 1 displays thethat there are 244 million international migrants annually, or roughly
location history, movement segmentation, and the migration date
3.3% of the world’s population [1]. Internal migrants are even moreof a random migrant in Rwanda. The detected segments represent
common, with recent estimates suggesting that as many as 750 milhome locations in each period of time. Prior modal location based
lion people in the developing world are permanent internal migrants
methods will define this person’s home as Kigali from 11/15/2008 to[2]. However, empirical research on migration has historically been
12/01/2008. But our approach can avoid this issue which is usuallyhindered by a lack of granular data. Traditional methods — whichcaused by temporary tourists. Our approach can be applied to otherrely on surveys — are expensive and time-consuming, and are plagued
digital trace data sets as long as they contain individual trajectories.
by issues of attrition (since migrants, by definition, do not remain in
Most importantly, the migration date can be accurately estimatedthe same place) [3]. Thus, a growing body of literature has relied
because our method is segmentation based rather than modal locaon large-scale ‘digital trace’ data, from mobile phones and online
tion based. Identifying migrants and their migration date would pave
sources, to study the migration of populations [cf. 4, 5].
the way for many research questions: chain migration, return migraA critical limitation of prior work is the ad hoc approach used tion, and dynamic networks, which are impractical to answer using
to infer migration events from digital trace data. Most existing ap- previous methods.
proaches aggregate all traces in a given time interval (e.g., a month,or a week), assign an individual a location based on the modal or Overview of Methods
mean location in that interval, and then classify as migrants individuals who are assigned to different locations in adjacent intervals. Our approach to detecting migrants contains three main steps: deThis approach has two main limitations. First, the modal location, tecting movement segments, inferring migrants, and inferring miwhich only focuses on the most common location in a given time gration date. We borrowed the idea from the DBSCAN algorithm
interval, does not necessarily represent the home location. For ex- to detect movement segments. In our algorithm, the radius is theample, we found evidence that the second most common location number of days to search whether a person also appears in the same
could have a similar number of appearances to the first one, espe- location. The minimum number of points per cluster is the minimum
cially for drivers and commuters who move back and forth between number of days to define a segment. People whose two consecutive
two districts every day. Besides, we also found that people who live segments are located in different locations are migrants. Migration
close to the border of two districts might receive signals from cell dates are inferred based on the number of error days between home
towers in both districts. Second, migration dates are crucial to un- segment and destination segment, which is the number of days when
derstand how social networks evolve before and after migration. But a migrant appears at destination before migration adding the number
migration dates are unknown in prior work because home locations of days when the migrant appears at home after migration.
are estimated based on each interval, which can be any day between[1] G. J. Abel, N. Sander, Science 343, 1520 (2014).
the two adjacent intervals when the home location changed.
This paper proposes a novel and general approach to detect- [2] R. E. B. Lucas, Handbook of the Economics of International
Migration (North-Holland, 2015), vol. 1, pp. 1445–1596.
ing migration events in large-scale digital trace data. We develop
a segmentation-based algorithm that can flexibly detect both short- [3] M. Bell, et al., Population, Space and Place 21, 1 (2015).
term displacement and long-term migration, and isolate the data of [4] J. E. Blumenstock, Information Technology for Development 18,
migration. We empirically deploy this method on two longitudinal
107 (2012).
mobile phone datasets (one covering roughly 7 million Afghans over [5] E. Zagheni, V. R. K. Garimella, I. Weber, B. State, WWW ’14
4 years; the other covering 1.5 million Rwandans over 4.5 years) as
Companion (ACM Press, Seoul, Korea, 2014), pp. 439–444.
well as a randomly selected set of 20,000 Twitter users in the U.S.

Figure 1: Diagram of a migrant’s location history. Note: A black bar represents that this person appears in that district on that day. Red
boxes are detected segments. The orange line is the detected migration date. This person migrated from Kigali to Nyamagabe on 7/27/2008.

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Study of power-law like distribution in urban scaletraffic simulations
Daigo Umemoto1,2 , Nobuyasu Ito2
1)

Research Institute for Economics and Business Administration, Kobe University
2)
Center for Computational Science, RIKEN
daigo.umemoto@riken.jp

Power-law like distributions are observed and reported in various complex
systems. It was also found in urban scale traffic flow simulations [1]. Because theheavy tail corresponds to congestions, understanding the origin of the behavior
is crucial to construct the ideas to design the efficient urban traffic. However, its
specific origin is still unknown. Here we show that bottlenecks in networks play
key role to produce the heavy tail. Distributions of traffic volumes on road segments (hereafter traffic distribution) robustly obeys power-law like distribution,using digital map of Kobe city in Japan, and randomly generated two dimensional road networks. The traffic demand does not a↵ect the results [1]. Also, As
shown in the right panel of Figure 1, traffic distribution obtained from the resultof shortest path search without simulation in the network of Kobe city obeys
power-law like distribution[3]. Power-law behavior also appears in percolation
clusters and Cayley trees. The latter result is shown purely mathematically.
These results imply that the origin of the behavior is in road network topology,
and specifically, bottlenecks and hieralchy in real networks cause congestions.

Figure 1: left : Digitized map of Kobe city. Red and blue lines represent
most crowded segments, quoted from [2]. right : distribution of road usage on
segments, obtained from shortest path searches on randomized OD pairs.

References[1] Umemoto, D., & Ito, N. (2018). Power-law distribution in an urban traffic
flow simulation. Journal of Computational Social Science, 1(2), 493-500.
[2] Uchitane, T., & Ito, N. (2016). Applying Factor Analysis to Describe Urban Scale Vehicle Traffic Simulation Results. Transactions of the Society of
Instrument and Control Engineers, 52, 545-554. (in Japanese)[3] Umemoto, D., & Ito, N. (2019). Power-law distribution found in city-scaletraffic flow simulation. CSP workshop proceedings (in prep).

TitleRoaming services in Europe: Estimating the impact of regulatory framework over european
consumers surplus
AuthorRafael Rubio-Campillo
Affiliation:
Comisión Nacional de los Mercados y la Competencia (CNMC)
E-mail:rafael.rubio@cnmc.es
Abstract
Since 2007, intra-EU/EEA roaming services have been addressed through successive EU
regulations. The aim of this article is to evaluate the impact this regulation has had on the pricelevel of roaming services and, most importantly, on European consumers’ surplus. To reach thisobjective, this paper uses data on intra-EU/EEA roaming calls from most Member States
between 2007 and 2016. We find that the impact of the regulatory framework has critically
improved the European consumer surplus over the last 9 years. In addition, the results show
that, in spite of the overall improvement in EU countries, there are significant differences amongindividual countries that can be explained by their own particular characteristics.

NetMob, Oxford, UK, July 8

10th 2019Loïc Bonnetain

Multi-modal Itinerary Reconstruction via Fusion of
Cellular Network Signaling, Floating Car and Smart Card DataLoïc Bonnetain (PhD Student), Angelo Furno (Advisor), Nour-Eddin El Faouzi (Advisor)
Univ Lyon, ENTPE, IFSTTAR, LICIT UMR_T9401, F-69675, Lyon, France
Email: loic.bonnetain@ifsttar.fr, angelo.furno@ifsttar.fr, nour-eddin.elfaouzi@ifsttar.fr
I. I NTRODUCTION
Mobile phone data collected by network operators provide fundamental insights on individual and aggregate humanmobility at unprecedented spatio-temporal scales. However,traditional solutions based on Call Detail Records (CDR) arelimited due to low accuracy of such data along both the spatial
and the temporal dimensions. This reduces their suitability forfine-grained studies on urban mobility.
In our contribution, we focus on mobile phone signaling
data and discuss a novel methodology leveraging multi-layer
networks and Hidden Markov Model (HMM) to accurately
reconstruct multi-modal urban itineraries from such kind ofdata. In order to accurately grab the dynamics of the transportation system, the parameters of the proposed HMM-based
map-matching solution are computed based on network characteristics estimated via fusion of diverse aggregated mobility
data sources: floating car data for the road network and smart
card data for the public transport network.
II. M ETHODOLOGY
We represent a multi-modal transportation network as amultiplex network composed of four graph layers corresponding to four urban transportation modes: road, bus, tramway
and subway. Concerning the cellular network, we propose amethod joining traditional Voronoï tessellation to the usageof additional information such as the antenna’s directionalazimuth, in order to more precisely identify the areas coveredby each antenna co-located at the same base station.
The core of the approach is a three-steps map-matching
technique. The first step consists in a cleaning phase aiming
to reduce noise and oscillation effect in the mobile phone data.
Then, by building on best practices from the state of the art [1],[2], we propose an HMM modified solution to compute thek most-likely sequences of nodes of the transportation networkfrom the cellular trajectory (k being an non-zero integer), by
using a k-Viterbi algorithm which is a generalization of theoriginal algorithm (corresponding to k = 1) [3]. The HMM
parameters (initial, transition and emission probabilities) arecomputed based on the topological properties of the networks
and by including dynamic information estimated via fusion ofmulti-source data (floating car and smart card data). The third
step returns the k most-likely itineraries on the multi-layer
network with associated probabilities. This step reconstructs
complete multi-modal paths (i.e., sequences of adjacent nodes
on the multi-layer network) by connecting the possibly disconnected sequences of nodes returned by the HMM step, via(dynamic) shortest path detection.

III. F IRST RESULTS
We evaluate our approach in a case-study leveraging realindividual anonymized network signalling cellular traces collected by a major french operator in the city of Lyon, France.
We propose a validation study at both microscopic and macroscopic levels. At the microscopic level, our approach has beentested using GPS-based traces collected for multiple users via amobile phone app. Such GPS data, available for a subset of theusers of our mobile phone signaling dataset, have been used as
ground truth in the evaluation of the reconstructed itineraries.
The results show that our approach can properly handle
sparse and noisy cell phone trajectories in urban complex
environments. Our framework allows associating a likelihood
to the different reconstructed alternative itineraries, using theinformation produced during the k-Viterbi step. Moreover, at
the macroscopic level, our approach is compared to the spatial
distributions of mobility flows obtained via simulation. The
results are promising towards detecting popular paths andperforming finer-grained reconstruction of Origin-Destination
matrices. Finally, the accuracy of the reconstructed itineraries
significantly improves by fusing mobile phone data withfloating car and smart card data.
IV. C ONCLUSION AND F UTURE W ORK
The key contributions of our work are the following:• An unsupervised dynamical HMM-based map-matching approach fusing sparse cellular network trajectories with multisource data to precisely infer individual itineraries on theurban multi-modal network.
• Instead of inferring a single path, our approach computes k
most-likely itineraries on the multi-layer network with associated probabilities to cope with the unavoidable incertitude
due to spatio-temporal sparsity of the cellular traces.
• Despite the scarcity of ground truth data, we evaluate
our approach in two case studies (i.e., macroscopic andmicroscopic). In both cases, the potential of mobile phonesignaling data for urban mobility analytics is shown.
Future directions should consider the application of the
proposed approach on regional scales and in real-time settings
towards real-time, large-scale human mobility analytics.
R EFERENCES[1] F. Asgari, A. Sultan, H. Xiong, V. Gauthier, and M. A. El-Yacoubi,“CT-Mapper: Mapping sparse multimodal cellular trajectories using a
multilayer transportation network,” Computer Communications, 2016.
[2] E. Algizawy, T. Ogawa, and A. El-Mahdy, “Real-Time Large-Scale Map
Matching Using Mobile Phone Data,” ACM Transactions on Knowledge
Discovery from Data, vol. 11, pp. 1–38, 7 2017.
[3] A. Viterbi, “Error bounds for convolutional codes and an asymptotically
optimum decoding algorithm,” IEEE Transactions on Information Theory,
vol. 13, pp. 260–269, 4 1967.

Activity-based Land-use Classification
with Cellular Data
Peter Widhalm

Norbert Brändle

Klaus Steinnocher

Austrian Institute of Technology
Giefinggasse 2, A-1210 Vienna
Email: peter.widhalm@ait.ac.at

Austrian Institute of Technology
Giefinggasse 2, A-1210 Vienna
Email: norbert.braendle@ait.ac.at

Austrian Institute of Technology
Giefinggasse 6, A-1210 Vienna
Email: klaus.steinnocher@ait.ac.at

The role of massive CDR data as proxy for human movement provides high potential to analyse mobility behaviour fora large number of applications. For example, mobility patterns
are a strong indicator for functional land use. Existing workon land use classification can be grouped into unsupervised
approaches [1], [2], [3], semi-supervised approaches [4], [5],and supervised techniques [6]. Since the rationale of existing
approaches is to observe and analyze aggregate statistics
related to local network behaviour (e.g. time series of hourly
average number of connected mobile phone subscribers orcall volumes), they neglect dependencies within sequences ofhuman activities. Here we propose an approach that equatesfunctional land use with the type of activities conducted ina particular region. To extract activities from CDR we first
identify stays using the technique described in [7]. Next, wecluster the stays into 18 activity types by training a Hidden
Markov Model (HMM) on the daily stay sequences of mobile
users, using the start time-of-day and stay duration to describe
each activity. We aggregate the extracted activities into spatialraster cells according to a regular 500-by-500 meters grid andcompute the total number of stays per day as well as the
distribution over the activity clusters to characterize land use.

(a) zoning labels(b) CDR

Fig.
2:
Residential
areas(blue)
fice/commercial/industrial land-use (red).

and

of-

use mix based on fractions of the area covered by each landuse type. In contrast, our approach describes land-use mix
as distribution over activity types. Using a Random Forest tolearn a regression function between the two representations weobtain results directly comparable to zoning labels (Fig. 2).
ACKNOWLEDGEMENTS
This work was funded by the Austrian Federal Ministry forTransport, Innovation and Technology in the Austrian Space
Applications Programme, grant No. 865977.
R EFERENCES

Fig. 1: Correlations between the 18 activity clusters extracted
from CDR and zoning labels of Vienna, Austria.
Comparing the aggregated activities to zoning data for Vienna(Austria), we found clear and intuitively comprehensible correlations between activity clusters and land-use types (Fig. 1).
However, there is a mix of several different land-use types
in most raster cells. Traditional land-use maps describe land-[1] K. Madhawa, S. Lokanathan, D. Maldeniya, and R. Samarajiva, “Using
mobile network big data for land use classification,” in Communication
Policy Research South Conference, 2015.
[2] J. Reades, F. Calabrese, and C. Ratti, “Eigenplaces: analysing cities using
the space–time structure of the mobile phone network,” Environment andPlanning B: Planning and Design, vol. 36, no. 5, pp. 824–836, 2009.
[3] G. Engelmann, J. Goulding, and D. Golightly, “Estimating activity-basedland-use through unsupervised learning from mobile phone event series
in emerging economies,” 2017.
[4] T. Pei, S. Sobolevsky, C. Ratti, S.-L. Shaw, T. Li, and C. Zhou, “A new
insight into land use classification based on aggregated mobile phonedata,” International Journal of Geographical Information Science, vol. 28,
no. 9, pp. 1988–2007, 2014.
[5] V. Soto and E. Frı́as-Martı́nez, “Automated land use identification using
cell-phone records,” in Proceedings of the 3rd ACM international workshop on MobiArch. ACM, 2011, pp. 17–22.
[6] J. L. Toole, M. Ulm, M. C. González, and D. Bauer, “Inferring land
use from mobile phone activity,” in Proceedings of the ACM SIGKDD
international workshop on urban computing. ACM, 2012, pp. 1–8.
[7] P. Widhalm, Y. Yang, M. Ulm, S. Athavale, and M. C. González,“Discovering urban activity patterns in cell phone data,” Transportation,
vol. 42, no. 4, pp. 597–623, 2015.

Inference of Mode of Transportation with Mobile Phone
Network Data
Eduardo Graells-Garrido
Universidad del Desarrollo
Santiago, Chile

Diego Caro

Universidad del Desarrollo
Santiago, Chile

Denis Parra

Ponti�cia Universidad Catolica
Santiago, Chile

ABSTRACT

Figure 1: Non-Negative Matrix Factorization Schema.

Figure 2: Tower labeling based on Urban Infrastructure.

Figure 3: Association between towers and mode of transportation.

Traditionally, commuting has been studied with well-known methods such as travel surveys. Even though surveys deliver valuable
information, they have drawbacks, including the lack of repeatedobservations over time and reporting biases and errors. Moreover,surveys (and other traditional methods) are not able to keep the paceof city growth and change, making relevant dynamic phenomena
to be invisible for transportation and urban planners.
The availability of large amounts of digital traces has allowed to
study urban phenomena at unseen spatio-temporal granularity. Oneof these data sources is the set of billing records from mobile phone
networks, known as Data Detail Records (XDR). In this abstract,
we overview a recently published work [2] where we sought toanswer the following research question: how to infer the distributionof mode(s) of transportation in commuting within a city using XDR?
An answer would provide insights to manage, plan, and design urban transportation systems, urban infrastructure, and public-policy,
among other applications. In this regard, we proposed the following
pipeline of analysis to infer mode of transportation usage in thecity, with a focus on commuting: 1) identify trips and stays using
computational geometry algorithms over XDR trajectories; 2) infertrip purpose, using known methods [1]; 3) focus on billing records
generated while commuting, which we represent in a waypoint
matrix, similar to document-term matrices in Information; 4) decompose the matrix using Topic-Supervised Non-Negative Matrix
Factorization [3] (TS-NMF), a semi-supervised matrix factorization
algorithm that guides the discovery of latent features toward adesired pattern; and 5) interpret the latent dimensions obtainedwith TS-NMF and their associations with mode of transportation.
By performing a case study in a big city, Santiago, Chile, we foundthat the proposed method delivers coherent results, as all modes oftransportation under study exhibit similar rank-correlations with atravel survey held in the city (from 0.59 to 0.68, c.f. Fig. ??). Given
that the current source of this kind of insight for transportation
experts are surveys, that may be outdated, we believe that our work
contributes to both disciplines, Data Science and Transportation. By
considering our proposed methods and its results, transportation
and urban planners will be able to augment their work in a coste�ective way by performing a �ner analysis of how people live
their cities.

REFERENCES
Figure 4: Comparison of O.D. matrices between our method
and a travel survey.

[1] Lauren Alexander, Shan Jiang, Mikel Murga, and Marta C González. 2015. Origin–destination trips by purpose and time of day inferred from mobile phone data.
Transportation research part c: emerging technologies 58 (2015), 240–250.
[2] Eduardo Graells-Garrido, Diego Caro, and Denis Parra. 2018. Inferring modes oftransportation using mobile phone data. EPJ Data Science 7, 1 (2018), 49.
[3] Kelsey MacMillan and James D Wilson. 2017. Topic supervised non-negative
matrix factorization. arXiv preprint arXiv:1706.05084 (2017).

CDR mining for emergency situations management
Imed eddine Semassela, Marouen Kachroudia, Gayo
Diallob, Sadok Ben Yahiaa
abFaculté des Sciences de Tunis University of Tunis, Tunisa

ERIAS INSERM U897, ISPED, University of Bordeaux, F-33000, France

imededdine.semassel@fst.utm.tn, marouen.kachroudi@fst.rnu.tn, gayo.diallo@u-bordeaux.fr,
sadok.benyahia@fst.rnu.tn

Abstract
Anonymous Call Detail Records (CDRs) provide metadata about when and where a person sends
or receives a call and/or an SMS message. Using anonymized CDR datasets, researchers has now
been enabled to augment spatio-temporal models of both transmitted and non-transmitted
diseases. These CDR data can provide information on people’s behaviors and relationships withdetailed resolution. researchers can track relative population levels in each region of the country,
individual movements, seasonal locations, population changes, and migration.
With these datasets, data mining applied to the frequency and timing of calls and/or texts canidentify population trends. With these newly, available CDR datasets, population and movement
models can now be produced to simulate diseases, which help policies to act with earlier
decisions. In this context, data from Orange Senegal Sonatel were used and analyzed to support
our proof of concept of our contribution.
Our goal is to analyze this data, to design and develop a recommendation system for the
management of critical health cases. The approach analyzes the data by the geolocation of theantennas. Indeed, each resulting model is stratified by a set of complementary data to arrive at
two cases.
The first case concerns cardio-vascular attacks and management constraints to save the patient.
The second case concerns contagious and communicable diseases.
Our approach is based on simple statistic technique that help to infer information about user’s
movements considering the differences between day and night, which can show and visualize thetime of distances in case of non-transmitted diseases, or the gathering places in case oftransmitted diseases.
Our analysis results can be used by the health policies, to defines a vision and plan for future
decisions.

Keywords: Call Data Record, CDR, Mobile data, Public Health, emergency situations

Characterizing Transport Perception using Social Media:
Differences in Mode and Gender
Paula Vasquez-Henriquez, Eduardo Graells-Garrido, Diego Caro
Data Science Institute, Universidad del Desarrollo, Santiago, Chile.
Transportation plays a key role in people's
development in society, greatly affecting thequality of life [1]. Since users base their mobility
decisions on factors such as cost, comfort,
accessibility, punctuality, quality of service andsecurity [2], transport policies face the growing
need to understand their users’ perception.
However, there is a gap between their perception
and the perception of the transportation
administrators, since information is collected withan “average'' user in mind, usually with little
consideration of the opinions of other importantuser groups, such as women [3].
In this work, we quantify the perception of 300K
tweets about transportation in Santiago, Chile. For
this, we study the language used by users in their
daily experiences about transportation. First, weclassify users into the mode(s) of transportation
they talk about using a semi-supervised method
named Topic-Supervised Non-Negative Matrix
Factorization; next, we analyze the lexical
associations of the psycho-linguistic lexicon
Linguistic Inquiry and Word Count (LIWC) to eachmode of transportation and gender. Our work
presents
thefollowing
contributions:amethodology to infer mode of transportation usage
from social media content, and a case study ofmeasured differences between modes oftransportation, with a gender perspective, using
Twitter data from a big city.
This research represents a method of capturing the
subjective travel experience in an inexpensive anddynamic way. Our results provide evidence on
which aspects of transportation are relevant in thedaily experience, working towards defining metrics
and indicators of the travel experience as seen fromsocial media. The creation of subjective experience
metrics could help to identify pains specific of asegment
of
users
that
is
otherwise
under-represented when transport is designed onlywith the “average user'' in mind.

Figure 1:​ Gross Perception per Mode of Transportation.

Figure 2: Mean Gender Gaps in Gross-Perception.

References[1] Alois Stutzer and Bruno S Frey. 2008. Stress that doesn’t
pay: The commuting paradox. Scandinavian Journal of
Economics. 110, 2 (2008), 339–366.
[2] Joe B Hanna and John T Drea. 1998. Understanding andpredicting passenger rail travel: an empirical study.
Transportation Journal 38, 1 (1998), 38–46.
[3] Daphne Spain. 2000. Run, don’t walk: how transportation
complicates women’s balancing act. In Women’s Travel Issues
Second National Conference, Drachman Institute of the
University of Arizona; Morgan State University; Federal
Highway Administration.

Portraits of Swiss Cantons based on Telecom Mobility Insights
Extended abstract
Camelia Elena Ciolac
ciolac c@yahoo.co.uk

�is paper presents a case study of spatial data science applied to open
telecom data in the cantons of Switzerland. Combining methods fromnetwork science and machine learning with 3D visual analytics, wemanage to extract new insights from these multi-faceted datasets.
�ere exist a multitude of approaches to derive insights into
human mobility based on raw telecom data records, many of whichwere disseminated during the past editions of NetMob Conference.
�e recent move towards open innovation motivated several telecom operators to open their data in the form of privacy-compliant
aggregates related to footfall, OD matrices and network activity at
the spatial granularity of o�cial censuses.
�is paper bene�ts from four such datasets [1] and, based onthem, we develop several spatial analytics that capture facets of the
Swiss cantons’ mobility portraits.
�e trips between the Swiss cantons provide the basis for building the OD matrix of inter-canton mobility on a daily basis. Using
the complex networks science theory, we further detect communities among the cantons based on the �ows of people they exchange
daily (Fig. 1a). �us, we build a weighted directed graph whose
nodes represent the cantons and the directed edges link the origin
to the destination of each mobility �ow. �e number of travelers
on each edge is converted to percentage of the origin’s total dailyout�ow, its median being used as weight in the Louvain method[6].

We train a machine learning model to predict the mobility out�ow
ratio of a source to other canton based on their geographic and socioeconomic a�ributes[2]. Features’ importance and the force plot forindividual predictions help interpret our XGBoost classi�er(Fig 1b).
Next, we proceed at performing unsupervised machine learningon the CDR derivates (GB of data downloaded, SMS sent and voice
calls), which are good proxies for footfall at the canton level.
We compute a spatial clustering among the cantons (Fig. 2), which isa geographically constrained clustering technique aimed at �nding
similar and geo-spatially contiguous regions in the multivariate
timeseries data.

Figure 2: Spatial clustering based on multivariate timeseriesof network activity in a regular business day, along with
CDR aggregates at canton level during morning time
�irdly, we investigate the anomalies in the hourly CDR summaries of each canton. For the multivariate outlier detection we
use the Isolation Forest, an ensemble model that outperforms thedensity-based and distance-based methods. �e detected anomalies,
timestamped (hourly) and geolocated (canton geometry centroid),
are then input to Knox space-time interaction tests. De�ning theevent as the occurrence of such an anomaly, we test whether we canreject the null hypothesis that these events are distributed randomly
in space and time and hence conclude that there exist pa�erns of
their spatio-temporal clustering.

References

Figure 1: a) OD �ows and the corresponding communities
of cantons detected for a regular business day; b) XGBoostclassi�er of inter-canton mobility quanta explained

1. Swisscom Open data, h�ps://opendata.swisscom.com/explore/
2. Federal Statistical O�ce, Up-to-date regional key �gures on the 26 cantons,h�ps://www.bfs.admin.ch/bfsstatic/dam/assets/4662881/master3. Scikit-learn: Machine Learning in Python, Pedregosa, F. et al,JMLR 12,2825-2830,2011
4. XGBoost: A Scalable Tree Boosting System, Chen, T. and Guestrin, C., 22nd ACM
SIGKDD ACM. 785-794, 2016
5. Consistent individualized feature a�ribution for tree ensembles, Lundberg, S. et al.,arXiv:1802.03888
6. Fast unfolding of communities in large networks, Blondel, VD et al., arXiv:0803.0476
7. Exploring network structure, dynamics, and function using NetworkX, Hagberg AA
et al., SciPy2008, 2008
8. PySAL: A Python Library of Spatial Analytical Methods, Rey, S.J. and L. Anselin,
Review of Regional Studies 37, 5-27, 2007

Special Session
Future Cities Challenge

Disentangling activity-aware human flows reveals the hidden functional organizationof urban systems
1

Riccardo Gallotti1 , Giulia Bertagnolli1,2 , and Manlio De Domenico1

arXiv:1908.02538v1 [physics.soc-ph] 7 Aug 2019

CoMuNe Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Povo (TN), Italy2
Department of Mathematics, University of Trento, Via Sommarive 14, 38123 Povo (TN), Italy
Increasing evidence supports the fact that cities are complex systems, with a broad spectrum ofstructural and dynamical features which lead to unexpected and emerging phenomena. Understanding urban dynamics at individual level – but also as the outcome of collective human behaviour –will open the doors to uncountable applications ranging from enhancing the sustainability and theresilience of the city to improving health and well-being of its inhabitants.
Here, we use a unique data set of longitudinal human flows provided by Foursquare, a leader
platform for location intelligence, to characterize the functional organization of a city. First, we build
multidimensional network models of human flows corresponding to di↵erent types of activities acrosstime. We quantify the efficiency of flow exchange between areas of a city in terms of integration andsegregation, respectively. Results reveal unexpected complex spatio-temporal patterns that allow
us to gain new insight on the function of 10 megacities worldwide. We discover that large cities
tend to be more segregated and less integrated, and that human flows at di↵erent hours of the day
or between di↵erent types of activities enable the identification of di↵erent “cities within the city”
which indeed show clear dissimilarities in terms of both functional integration and segregation.
Our analysis provides new insights on how human behaviour influences, and is influenced by, theurban environment and, as an interesting byproduct, to characterize functional (dis)similarities ofdi↵erent metropolitan areas, countries, and cultures.
I.

INTRODUCTION

Cities are complex systems which process information,
evolve and adapt to their environment [1]. To understandhow complex systems – and cities more specifically – operate, it is thus important to quantify how information isprocessed in terms of integration and segregation. To thisaim, on the one hand many relevant network descriptors
have been introduced, based either on topological features or on dynamical ones, or both. On the other hand,
integration has been reflected either in how information
flow is accounted for by more complex topological models
where multiple relationships co-exist simultaneously [2–5], namely multilayer systems [6, 7], or in causal e↵ects
observed in the time course of systems’ units [8–17].
Concerning the topological analysis of classical singlelayer networks, to date a clear definition of integrated
and segregated information flow is still debated andmany proxies are used across a broad spectrum of disciplines, ranging from neuroscience to social and urban
sciences [18–33], often indicating with the same namevery di↵erent concepts.
The recent availability of a large amount of humangenerated data enables the analysis of urban systems
from di↵erent perspectives which could not be even considered until a few years ago [34]. In recent times, however, models and analytical tools inspired by complexity science are proliferating. More and more examples
are providing convincing evidences of their fruitful application to real cities [35–40]. Applications range fromhuman mobility [41–44] and traffic congestion [45–49],
to energy consumption [50], air quality [51, 52] and climate [53], health and wellbeing [54–57], and the associated topic of accessibility to important facilities like hos-pitals [58]. Indeed, the city can be seen as a growing complex system [59, 60] whose spatial organization [61, 62]
dynamically experiences a transition from monocentric
to polycentric [63, 64].
A particularly relevant perspective is provided by
activity-aware information [65], such as the one provided
by users of Foursquare – a leading location intelligenceplatform – which allows people to investigate humanflows at di↵erent scales with unprecedented detail [66].
This type of data is of special interest because one caninvestigate the interplay between the structure of a cityand the dynamics of its inhabitants to gain novel insights
about the functional organization of the underlying urban ecosystem.
In this work, we stratify human activities in Foursquare
to build network models describing the human movements across the urban space – from hours to months– within the di↵erent areas (see Methods) of 10 di↵erent
metropolitan systems worldwide – namely Chicago, Istanbul, Jakarta, London, Los Angeles, New York, Paris,
Seoul, Singapore and Tokyo – representing 3 continents.
By classifying existing activities into a few representative macro-categories (see Methods for details) we build
a multilayer network [4, 7], where the flows encode howusers move between venues of the same macro-category(e.g., from a pub to another one) and between venues ofdi↵erent macro-categories (e.g., from a pub to a cinema).
In the following, we will refer to intra-layer flow to indicate movements of the first type, and to inter-layer flow
to indicate movements of the second type.
Our main goal is to better characterize the functionalorganization of a city through the lens of network science.
To this aim we measure to which extent di↵erent areasof the city facilitate human flows – i.e., functional inte-

2

FIG. 1. Modeling Structure and Function of Urban Systems. Left: Urban structural backbone of the 10 megacities
considered here, as described from their street networks (data obtained from Open Street Map [67]). Middle: Urban functional
networks described by the Foursquare data. The nodes are obtained by dividing the area analysed into cells of 500m ⇥ 500m.
The edges are subsequent check-ins that might be between activities of the same type (intra-links: e.g. Food-Food, TourismTourism) or di↵erent types (inter-links: e.g. Food-Tourism, Food-Sport). The collection of layers and inter-layer flows defines
a multilayer network [4, 6, 7], i.e., a multidimensional functional representation of the urban areas. Right: The mobility flows
between areas are captured as the edges’ weights. In the example, describing New York City, we can observe the di↵erent
spatial distribution of flows between and across di↵erent activity layers (see also Fig. 2).

gration – and to which extent there are separate clustersof areas characterized by within-cluster flows larger thanbetween-cluster flows – i.e., functional segregation – (seeMethods for details) [68]. By considering those measuressimultaneously, it is possible to characterize how well human flows mix through the city according to the existing
distribution of venues and the way residents use them.
In fact, the dichotomy between integration and segregation – often improperly used as antonyms – is relevant
for improving our understanding of the interplay between
the urban structure, social relationships and human behavior. To avoid confusion in the reader, it is worth remarking that our measure of integration and segregation
is not related to population or cultural mixing [69], but
only to how cities are lived by their users.

II.

RESULTS

Overview of the data set.— The Foursquare data
rendered available for the Future Cities Challenge [70]describe 24 months of check-ins between April 2017 andMarch 2019 (included). The 10 world mega-cities included in the challenge are Chicago, Istanbul, Jakarta,
London, Los Angeles, Tokyo, Paris, Seoul, Singapore and
New York City (see Fig. 2 left). The extensive characteristics of the datasets are shown in Tab. I. The flows
between di↵erent areas are derived by subsequent check-ins to the Foursquare’s location-based services and coarse
grained with a 500m ⇥ 500m granularity (see Fig. 2 middle, and Methods). In the data provided, check-ins are
already aggregated by couple of venues (origin and destination), month and hour of the day (morning, midday,
afternoon, night, and overnight). The metadata of thevenues include a category field which describes the typeof venue in great detail (e.g.: Knitting Stores, Mini Golf
Courses, Rock Clubs, . . . ). We defined a set of macrocategories we used to define a limited number of layers(see Methods and Fig. 2 middle). By disentangling themobility flows into a multilayer network structure, we
are able to quantify the di↵erences in the functional organization of the di↵erent “cities within a city” that are
outlined by movement between di↵erent types of activities in a limited number of layers (see Methods and Fig. 2
right).
In Figure 2 we can visually inspect some examples ofactivity-aware layers. Remarkably, for all the cities considered in this study, the intra-layer connectivity characterizing the transport layer provides a natural link between our functional analysis and the underlying structure of the city. In the data, however, it can be clearlyseen in cities where public transport is well developed
and largely used, such as Tokyo or Seoul, way more thancities where private transportation is dominant, such as
Los Angeles and Istanbul.
Quantifying Functional Integration and Segre-

3

FIG. 2. Disentangling human flows. We illustrate here, for four of the ten cities studied in this paper, the strikingly
distinct views on the functional organization of a city extracted by isolating intra- or inter-layer flows. These maps outline thedi↵erent “cities within the city” which we disentangle by decoupling the urban flows into activity-aware multilayer networks.
City#Venues #check-ins L (km)
Chicago13904 10629110
18.9
Istanbul
113752 13083383
39.3
Jakarta
21813
9281181
27.6
London22689 10146880
24.0
Los Angeles
15868 10362146
23.6
New York City
32971 11048584
19.3
Paris
13588
9521723
16.6
Seoul
15545
9347489
18.1
Singapore
23324
969151723.5
Tokyo57810 11545155
30.4
TABLE I. Foursquare data set extensive characteristics. The figures here are aggregated for all layers, hours, andcomprise all 24 months. The linear size L is here estimatedas the square root of the total area covered by the data after
the aggregation into squares of 500m ⇥ 500m.

gation.— As previously mentioned, we focus on urban
integration and segregation. Integration quantifies, interms of information exchange efficiency, the ability of acity to favor the flow of people across its areas. Segregation, on the other hand, evaluates the strength of segregated communities, areas of the city with strong flows
inside the area and weak inter-areas flows (see Methods
for further details).
Synthetic Models.—
Lastly, for the RGGs we also measured the importanceof the spatial extension of the network. Fixing the radiusbelow which nodes are connected, we find (see Supple-

mentary Fig.1) that the largest the area (A = L2 ) covered by a square RGG the more the network is segregated
and the less it is, at the same time, integrated. Indeed,
here again integration and segregation seem to be verystrongly correlated and increasing the radius have a similar e↵ect as reducing the spatial extension.
Functional organization of empirical Urban
Systems.— We use the results above as a reference in
our analysis of real cities, shown in Fig. 4. More specifically, we stratify human flows by month of the year andby activities, to analyze the corresponding mobility networks. Results are intriguing: the functional organization of some urban systems can dramatically change indi↵erent periods of the year (left panel), ranging between
small-world and random geometric organization. Comparing with the bottom panel, we notice that the valuesof segregation for the single months are approximately
the same as the aggregated 2 years, and is the value of
integration that shows a clear drop. This shows the danger of having under-sampling which could yield to incorrect view on a city functioning by under-estimating
its integration. Even if the underlying urban structurechanges slowly, or not at all, the functional use of thecity, as observed through Foursquare data, may instead
display significant variations. When flows are instead
aggregated over a long period of time (bottom panel)
our view on cities tends to be more compatible with random geometric models, suggesting that the functional organization of large mega-cities is strongly influenced byintermediate-distance movements, captured by random

4

Watts-Strogatz "Small Worlds"

Random Geometric Networks

FIG. 3.
Functional organization of synthetic urban models. Left: small-world networks according to the WattsStrogatz model (see Methods) with di↵erent rewiring probabilities (multiple points) and dimensions (from 1D to 3D, encodedby color). The dashed line represents the linear regression relating integration and segregation for this class of models, whereas
the solid line is y = 1 x and it is shown as a reference. Right: random geometric networks (see Methods) with di↵erent
characteristic spatial scale (encoded by color) and di↵erent rewiring probabilities (encoded by size). Qualitatively, this class ofmodels clusters along the y = 1 x line.

geometric models but not by the lattice underlying thesmall-world model. More in detail, these intermediate
distances would here fall beyond the 500m–1000m scalecharacteristic of lattice first neighbors in the square tilingthat we used for describing the urban space (see Methods), but do not represent the long range – across-city– movements, that in our model would be captured byrewiring the existing connectivity.
The fact that some of the cities seem to be even more“integrated and segregated at the same time” than arewired RGG suggests that the network might be characterized by sparsely connected spatial patches. This result suggests, indeed, that also other complex networkmodels characterized by a mesoscale organization – suchas the broad family of stochastic block models – might
be suitable candidates for modeling the complexity of
megacities.
It is worth checking if this pattern is an intrinsic featureof urban systems or if it is proper of some specific activity layers. To this aim, we perform targeted attacks oneach layer of the corresponding multilayer network andmeasure the response of the systems in terms of changes
in segregation and integration. In the bottom panel ofFig. 4 we observe how removing those flows coming froma specific activity type significantly changes urban functional segregation. This is especially true if the activity
is Transport, whose removal yields the rightmost outliersin the figure. An even stronger variation would be ob-served in the integration and segregation restricted to
movements between similar layers.
Finally, in Supplementary Figs. 2 and 3 we can observedi↵erent “cities within a city” by comparing snapshots of
the urban flows at di↵erent times of the day or betweendi↵erent types of activities. Indeed, the variability observed for these disaggregated functional networks canbe compared with the variability among di↵erent cities.
Comparing di↵erent hours, we find that urban functional
integration is systematically higher at daytime and lower
in the night, while it is difficult to identify common patterns across cities in its evolution along the day.
When looking at di↵erent activities, is even harder toobserve common patterns between cities. Segregationand integration of human flows between venues belonging to the same activity layer exhibit high variance withthe “food” and “lodging” layers which to be systematically among the more integrated and least segregated. To
better understand these di↵erences, in Fig. 5 we link theaverage values of segregation measured for flows between
the same categories across all cities with the corresponding weighted average of geographical distances between
nodes. We observe a bulk of correlated points and four
clear outliers: two of them represent the typical “second
place” of a commute (work or education), one the natural
long-range linking layer of transportation, and lastly thelocations not associated to a macro category and left as“unknown” (see Methods). Excluding the four outliers,5Single−layer Segregation●

EDUCATION

0.7

HOUSING

PUBLICHEALTH●●● SPORT●SERVICES●●● TOURISM
ENTERTAINMENT● ●RELIGIONLEISUREFOOD● ●0.5 ●LODGING
0.6

COMMERCIAL●UNKNOWN

●

WORK

●

0.4

TRANSPORT

4

5

6

Average Distance (km)FIG. 5. Average functional segregation for di↵erent
activity categories. The regression, done excluding the 4clear outliers, highlights a proportionality between the average distance covered in movement inside one layer and thevalue of segregation. Among the four outlying categories,
three are the two “second places” after the home location in
individual mobility (education and work), the transport layer,
which represents the structural backbone of the city with longrange movements and low segregation/high integration (seeSupplementary Fig. 4).

FIG. 4.
Functional (re-)organization of empiricalurban systems. We consider the multilayer networks ofhuman flows for each city (encoded by color). Solid anddashed curves are as in Fig. 3. Top: Flows are stratified
according to di↵erent months (multiple points). Remarkably,some cities exhibit a large variability across time, ranging
from a functional organization resembling random geometric networks to one resembling small-world functional organization. Paris, London, Tokyo and Istanbul seem to varysimilarly to small worlds, whereas Chicago, Jakarta, Seoul,
Singapore, New York and Los Angeles exhibit a broad variability in monthly segregation, associated to higher averagevalues of integration. Bottom: Flows are stratified according
to di↵erent macro-categories used in this work (see Method)
and targeted attacks are performed on the layers of each multilayer urban system. Each point corresponds to integrationand segregation measured after removing a specific layer ofactivities, while the first letter of a city name falls in correspondence of the values without any removal. Remarkably,
all cities cluster around the solid curve, well describing the
behavior observed in random geometric models.

we observe a rather counterintuitive e↵ect: the larger the
distance covered by a typical link in that layer, the larger
the functional segregation.
This counterintuitive trend – the longer the mobility range the higher the segregation – is also confirmed
by comparing the aggregated values for di↵erent cities.
In Fig. 6 we show how segregation grows and integra-

tion drops in cities having larger extension L. We argue that this trend might be a direct e↵ect of geometricconstraints, as observed in random geometric networks,
leading to an increased amount of segregation when, ceteris paribus, we consider networks of larger extension(see Supplementary Fig.1)
A bridge with the urban spatial organization:
hotspots’ analysis.— Our network analysis of the urban functional organization of megacities leads to a result that, remarkably, create a bridge between network
science and quantitative geography. In fact, the organization of cities can be also understood in terms of mobility hotspots [38], i.e., areas characterized by exceptional
human flows. The top panel of Fig. 7 shows that thelarger is the city the lower is the fraction of nodes which
are identified as hotspots using the method introducedin [38]. At the same time, a larger fraction of hotspots
is associated to high integration and low segregation (seethe bottom panel of the same figure and Supplementary
Fig. 5). From a network science perspective, hotspots
encode high-degree hubs in the urban functional organization and our results suggest that high-integration andlow-segregation features are favored by the abundance of
this class of hubs rather than by the presence of a few
central nodes.
CONCLUSIONS

Understanding how cities process information, here encoded by human flows, is of paramount importance fordesigning more efficient and smart urban systems and6●Seoul

0.04

Paris

0.60

●

Segregation

Istanbul

0.55●

Los Angeles

Tokyo

●

0.50

New York●

LondonSeoul●●
Chicago

0.45

0.40

●●

●
Jakarta

Singapore

Fraction of Hotspots●

New York●

0.03

Chicago●Singapore●

Jakarta

●

0.02

●

Istanbul

Los Angeles●●
London

0.01

Tokyo

●

0.00

●

Paris

20

25

30

35

40

20

25

L (km)
Chicago

● ●●

Paris

Tokyo

●

● Los Angeles●

New York

Integration40

Istanbul

0.55● Singapore
0.6

35

●

Seoul

●

●

LondonJakarta●Tokyo●

Istanbul

0.5

Segregation

0.7

30

L (km)●

Los Angeles

0.50

●

New York●
London0.45

Singapore●

Jakarta

●

Seoul

●

●
Chicago

0.40

●

Paris

20

25

30

35

40

L (km)
FIG. 6. Functional segregation and integration versus
city size. For each city, we measure A = N a, where N is
the number of areal units in the city and a = 500m ⇥ 500m
theirsurface. As suggested in [71], the linear extension L =pA, represented here, is therefore proportional to the typicalcommuting distance in a city. Top: Functional segregation
grows with a city extension. Bottom: Functional integration
drops with a city extension (Pearson correlation coefficient of-0.93).

communities. By considering together multiple activityaware mobility networks, a functional proxy for the underlying backbone of a city, we have characterized 10large-scale urban systems in terms of their integrated andsegregated flows, revealing interesting spatio-temporal
patterns that would otherwise remain hidden without
multilayer modeling and analysis.
More specifically, we have shown how network-based
analysis can support, and further expand, ongoing discussions about and novel understanding provided by the
ICT-data driven quantitative urbanism [38]. For growing
cities, it is expected a transition from a monocentric toa polycentric organization, characterized by a sub-linear
growth of the number of hotspots with population [63].
Similarly, in the urban functional networks we have extracted from the Foursquare data we observe that in thelarger cities hotspots cover, in proportion, a smaller frac-

0.01

0.02

0.03

0.04

Fraction of Hotspots
FIG. 7. City size, fraction of hotspots, and functionalsegregation. Top: As the size L of the city grow, the fraction of area that is represented by hotspots obtained with the
LouBar method [38] decreases (Pearson correlation coefficientof -0.65). Bottom: Having in proportion a larger fraction of
the urban area covered by hotspots makes the cities less segregated (Pearson correlation coefficient of -0.61), and at thesame time the more integrated (see Fig.5).

tion of the total area. However, our analysis of urban
segregation and integration suggests a rather di↵erent
description than that one suggested in [38]: we provide
evidence that large polycentric cities ara characterized bya smaller fraction of hotspots (or hubs) and consequently
they appear to be more segregated and less integrated
than smaller, and monocentric, cities.
From a more methodological perspective, our analysis
highlights the importance of data sources for the analysisof the interplay between the city and its main users, i.e.,the citizens. Thanks to the unique data set provided by
Foursquare we have been able, on the one hand, to quantify the e↵ects of incomplete information: missing humanflows related to one activity type dramatically alters theestimated urban segregation. On the other hand, theanalysis of longitudinal data at di↵erent temporal resolutions, from hours to months, reveals that some cities
dynamically change their functional organization even if

7their backbone does not, whereas other cities do not show
the same variability. This allows us to gain novel insights
on urban and human behavior in terms of dynamical reorganization of the system. Our analysis of attacks targeted towards specific layers unraveled the importance ofdi↵erent types of human flows for integrating (or segregating) a urban system, revealing the emergence of thephenomenon of cities within a city.
Lastly, from a modeling perspective, we discover thatmany features of complex megacities can be understoodfrom simple mechanisms related to geometric constraints
and city’s characteristic size, with larger cities tending
to be more segregated and less integrated. Random geometric models with long-range connections seems to bea good candidate to reproduce the most salient features
measured from empirical data and further research is required in this direction to confirm this finding for a wider
spectrum of urban systems. Interestingly, the interplay
between heterogeneities in the underlying network connectivity and spatial constraints might be responsible forthe emergence of integrated/segregated structures thatmight be reflected in the functional organization of thecity, and future research should point in this direction to
gain new insights.
III.

METHODS

Geographic coarse-graining.— We reconstruct theflows network by aggregating data over areal units of 500m
⇥ 500m, in all 10 cities considered. Flows are reconstructedfrom subsequent check-ins into Foursquare venues, ignoring
the order (undirected network). Flows inside the same area
have been integrated into a self-loop link only if the check-ins
were between two di↵erent locations. Subsequent check-ins inthe same location have been excluded from the analysis.
Temporal stratification.— We decouple the functionaluse of a city i) at di↵erent hours of the day (morning, midday,
afternoon, night, overnight), and ii) in di↵erent months of theyear.
Activity stratification.— We use Foursquare’s rich system of categories and manually associate them to a reduced
number of macro-categories (food, lodging, tourism, work, religion, services, education, health, sport, transport, entertainment, leisure, public, housing and commercial). We do notuse [72], except for venue icons in Fig. 1. The few categories
that did not fit any macro-category have been labelled as ‘unknown’. These categories allow us to build “activity-aware
multilayer networks”, where activities of di↵erent types areassociated to di↵erent layers of our model. Flows between activities of the same macro-category are encoded by intra-layer
links, while flows between di↵erent categories are encoded byinter-layer links.
Measuring functional integration.— We measure towhich extent a network is integrated in terms of communication, i.e. how efficient nodes are in exchanging information.
Given two areal units i and j we can reasonably assume thatthe communication efficiency between them is inversely proportional to their topological distance dij ; in terms of connections, if information has to travel a long path, the probability

that the message is corrupted along the way is high and thecommunication is inefficient. Globally, the communication
efficiency [18] of a city is given by the sum of the pairwise
efficiency, adequately normalized:P
1
X j2V,i6=j dij
1
E=
.
(1)
N
N 1
i2V

Please note that here we did not use distances weighted by
the observed flows, to allow for a fair comparison against thevalues of E obtained from the analysis of synthetic networks,
which are not weighted.
Measuring functional segregation.— A usual measureof network segregation, or how strongly the units are organized in into M non-overlapping blocks, is the modularity [73]
"
!2 #
X
X
Q=
euu
euv
(2)
u2M

v2M

where euu is the proportion of links inside module u, while
euv accounts for the connectivity between two distinct modules u and v. More in detail, our measure of segregationis the maximum value of the modularity that we find using
the Louvain algorithm [74]. We also verify that the observed
modularity is significant, by comparison with the values of Q
computed over an ensemble of configuration models obtained
reshu✏ing the network. Finally, note that here, instead, weused the weights defined by flows. Values of Q for weighted
and unweighted networks are indeed comparable, as opposite
to what discussed above for E, and using weights here allowed
us to better discern the characteristics of di↵erent layers.
Synthetic network models.— We use two standard
spatial network models for our analysis.
We first consider a class of networks characterized by small
average geodesic distance: the Watts-Strogatz (WS) model.
Starting from a regular graph, e.g. a two-dimensional lattice,each link has a probability p of being rewired, that is removed
and re-placed randomly in the network. If p is large the resulting WS network will look more like an ER random graphthan the original lattice. WS networks are also highly clustered, where nodes tend to form closed triangles. WS model
are usually referred to as small-world networks.
Alternatively to WS, we study also the simplest networkmodel actively involving the spatial dimension model is therandom geometric graph (RGG), where nodes randomly distributed in space are connected if they are closer than a fixed
threshold distance. The RGGs share many important properties with regular lattices, in particular they are not “small
world”. For this reason, similarly to the WS case, here also
for the RGG we perform a rewiring with probability ↵.

ACKNOWLEDGMENTS

The authors thank Foursquare for granting accessto the data set used in this study and acknowledge
Matthew Kamen, Renaud Lambiotte, Jesse Lane, Anastasios Noulas, Cecilia Mascolo, Vsevolod Salnikov, Sarah
Spagnolo and Adam Walksman for organizing the Future
Cities Challenge. Tha authors asknowledge Giuseppe
Lupo for fruitful discussions.

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10

SUPPL. FIG. 1. Segregation and Integration of Random Geometric Graphs of di↵erent sizes. In this paper,
we generate RGGs by i) throwing N nodes in random locations in a square of edge L; ii) connecting all nodes i,j with
distance d(i, j) < r; iii) rewiring a fraction ↵ of edges. Here,to study the e↵ect of size, we generate networks with identical node density N/L2 and with no rewiring ↵ = 0. For
each value of L and r we averaged the values of segregation(modularity Q and integration (Global efficiency E). The result show that, in this scenario, segregation and integration
are strongly anti-correlated. High integration is attained forsmall networks (L = 10) with large r, while the opposite
yields high segregation.

11

SUPPL. FIG. 2. Segregation and Integration at di↵erent hours of the day.

12

SUPPL. FIG. 3. Single Layer Segregation and Integration.

Single−layer Integration

0.60

●

COMMERCIAL

0.55

TRANSPORT● ●LODGING●●LEISURE

FOOD

TOURISM

0.50●●PUBLIC● ●ENTERTAINMENT

0.45

HEALTH

HOUSING●●● SPORT●●WORKSERVICESEDUCATION●UNKNOWN●

0.40

RELIGION

4

5

6

Average Distance (km)
SUPPL. FIG. 4. Average Single Layer Integration versus edges characteristic distance. Similarly to what observedin Fig. 5, if we exclude four outlying layers, the characteristic length of edges on a layer and the layer’s Functional integration
are correlated (here, more precisely, anti-correlated).

13

0.8
ChicagoIntegration

●

●

Paris

0.7
Los Angeles

Seoul

●

●

●

Singapore

New York

●

0.6

●

LondonJakarta

●

●

Tokyo

●

0.5

Istanbul

0.01

0.02

0.03

0.04

Fraction of Hotspots
SUPPL. FIG. 5. Functional integration versus fraction of hotspots. Conversely to what observed in Fig. 7, the Functional
integration of a city is larger for cities with a larger number of hotspots per unit area.

hood2vec: Identifying Similar Urban Areas Using Mobility
Networks
Xin Liu

University of Pittsburgh
Pittsburgh, Pennsylvania
xil178@pitt.edu

Konstantinos Pelechrinis
University of Pittsburgh
Pittsburgh, Pennsylvania
kpele@pitt.edu

arXiv:1907.11951v1 [cs.SI] 17 Jul 2019

ABSTRACT
Which area in NYC is the most similar to Lower East Side? Whatabout the NoHo Arts District in Los Angeles? Traditionally thistask utilizes information about the type of places located within
the areas and some popularity/quality metric. We take a di�erent
approach. In particular, urban dwellers’ time-variant mobility is a re�ection of how they interact with their city over time. Hence, in thispaper, we introduce an approach, namely hood2vec, to identify thesimilarity between urban areas through learning a node embeddingof the mobility network captured through Foursquare check-ins.
We compare the pairwise similarities obtained from hood2vec withthe ones obtained from comparing the types of venues in the di�erent areas. The low correlation between the two indicates that themobility dynamics and the venue types potentially capture di�erent
aspects of similarity between urban areas.

KEYWORDS
Similarity Metric, Urban Area Representation, Venue Category

1

INTRODUCTION

Identifying similar areas in a city can facilitate dwellers and visitors
exploring the city better. An intuitive approach for comparing urban
areas and providing recommendations is to utilize information
about the type of venues within an area. The types of venues within
an area (zip code, neighborhood, block etc.) can be thought of as
the “signature” of this area. However, there are several assumptions
behind this consideration. For example, this implicitly assumes
that all venues within the area are active through the whole day.
For instance, while two areas can appear to have similar venues,
they can be signi�cantly di�erent when introducing the temporaldimension (e.g., an area with “lunch restaurants” compared to one
with “dinner restaurants”). Furthermore, this static view does notinclude information on how urban dwellers interact with theseareas, as captured through their movements between them.
In this paper, we explore the mobility patterns of Foursquareusers in the three US cities included in the Future Cities Challenge (FCC) dataset, namely, New York, Los Angeles and Chicago,borrowing analytical tools from the network science literature. In
particular, we build hood2vec that �rst designs a network between
urban areas in a city using the FCC dataset and then obtains a vector
representation of each area using a network embedding. We thenuse these representations to identify similar areas and make comparisons with other similarity metrics based purely on venue types
and checkins. Similar urban areas will be represented by points(i.e., vector) closer together in the latent space identi�ed by the
network embedding. We then calculate the similarity between two

Alexandros Labrinidis
University of Pittsburgh
Pittsburgh, Pennsylvania
labrinid@cs.pitt.edu

urban areas using the (Euclidean) distance between the latent spacepoints for the two areas.
Existing literature has attempted to identify the functionality ofurban areas, and consequently, cluster areas based on their functionality. Topic modeling is the dominant techniques in this line ofresearch (e.g., [2, 6]). Other studies have attempted to identify similar areas across cities mainly using the type of activities recordedin the di�erent areas of the di�erent cities (e.g., [3, 5]). In the restof the paper we formally describe our approach and present theresults obtained from the three cities aforementioned.

2

HOOD2VEC: LEARNING AN URBAN AREA
VECTOR REPRESENTATIONThe FCC dataset provides information about the mobility patternsof Foursquare users. Each data point has the following tuple format: <start venue, end venue, trip year and month, trip
period in a day, number of checkins>. The number of checkins captures the number of times that the speci�c transitions wereobserved in the dataset. The dataset also provides information about
the name, geographic coordinates and category for each venue.
The majority of the transitions recorded in the dataset are observed only one time. In particular, 95% of the transitions are observed less than 3 times. In order to avoid �tting the noise, weaggregate the transitions (movements) over a wider geographicalscale. We also separate the movements according to the time period of movement occurrence according to the data - i.e., overnight(00:00 to 05:59), morning (06:00 to 09:59), midday (10:00 to 14:59),
afternoon (15:00 to 18:59), night (19:00 to 23:59). Using MapQuest’s
Geocoding API1 we obtain the zip code for each venue and weaggregate the movements at the zip code level (the wider scale).
More speci�cally, we transform the original data to the following format per period: <start zip code, end zip code, trip year andmonth, number of checkins>. At zip code level, only 10% of the
movements have less than 2 observations. However, 20% of the zip
codes contain fewer than 10 venues and hence, we �lter them out
from our analysis. While this might sound a large number to ignore,
the checkins within these zip codes cover only 0.5% of the total
checkins in the dataset.
Finally, for each city c 2 C = {New York, Los Angeles, Chicago}
we de�ne its directed urban �ow network Gc,p per period p 2 P= {overnight, morning, midday, afternoon, night} at the zip-codelevel as follows: Gc,p = (U, E), where the set of nodes U is the setof zip code areas in city c. A directed edge ei j 2 E exists between
two zip codes ui , u j 2 U if there has been observed at least one
movement from a venue in ui to a venue in u j during period p. We
1 https://developer.mapquest.com/documentation/geocoding-api/

also annotate every edge ei j with a weight w(ei j ), which captures
the number of checkins of such movements observed.
We would like to note here that while we have chosen the zip
codes as our unit, one can de�ne an urban area di�erently. For
instance, one can use the notion of neighborhoods that can include
several zip codes, or the census tracts, or any other de�nition ofneighborhood [1].

2.1

O

MO

MI

A

N

New York
Los Angeles
Chicago0.116***

0.152***

0.147***

0.152***

0.284***

0.316***

0.327***

0.336***

0.144***
0.142***
0.323***

0.184***

0.290***

0.229***

0.219******p < 0.01, **p < 0.05, *p < 0.1
Table 1: Correlation between movement and category representations.

3

Vector Representation by node2vec

URBAN AREA SIMILARITY

One of the questions is which urban area representation should weuse? Do they even provide us with a di�erent view of the similarity between two areas? In order to explore this we will calculate
the pairwise similarities using the network embedding learnt fromhood2vec and compare them with the corresponding pairwise similarities obtained from a simple venue-based representation of urban
areas (see Section 2.2). Formally, the similarity of two areas i and j,with vector representations xi and xj respectively, is de�ned as:

In order to obtain a vector representation for the nodes of Gc,p , i.e.,
the zip codes at c in period p, we will rely on learning a network
embedding. There are several ways to learn a node embedding fora network but in this work we make use of node2vec [4]. Brie�y,node2vec utilizes second order random walks to learn a vector representation for the network nodes that optimizes a neighborhoodpreserving objective function. The framework is �exible enoughto accommodate various de�nitions of network neighborhood andfacilitate the projections of the network nodes in the latent spaceaccording to di�erent similarity de�nitions. Given that we are interested in the structural equivalence of the urban areas, we pick
the parameters of node2vec accordingly (p = 1 and q = 2 [4]). We
also utilize 1,000 random walks for the sampling process, while we
set the dimensionality of the latent space to d = 10. This is consistent with the dimensionality of another vector representation
to be introduced in Section 2.2. node2vec �nally provides us witha vector vi 2 Rd , 8ui 2 U, that we can then use to identify thesimilarity between two urban areas.

2.2

Periodi j = exp(

dists (xi , xj ))(1)

where dist(xi , xj ) is the (Euclidean) distance between the representations of i and j.
We can now examine whether di�erent representations for theurban areas provide di�erent views for their similarity. In particular,0
if i j and i j are the similarities between areas i and j using di�erent
vector representations, their Pearson correlation coe�cient , 0
will be high if the two representations provide similar information,and low otherwise. We can further compare in the same way thesimilarity of two areas for the same vector representation overdi�erent time periods.

Vector Representation utilizing Venue
Categories

4

EXPERIMENTS AND RESULTS

In this section, we will present the results of our analysis andcompare the pairwise similarities obtained from hood2vec and asimple venue category-based representation.

As alluded to above, a straightforward way to de�ne the similarity
between two areas is to compare the distribution of the type ofvenues they host. More speci�cally we can de�ne a vector zi forneach urban area node, such that its k t h element zik = ik , whereNi
nik is the number of venues of type k within area i and Ni is thetotal number of venues within i. For de�ning vectors zi we use the
10 top-level venue categories in Foursquare (thus, zi 2 R10 ) : Arts
& Entertainment, College & University, Event, Food, Nightlife Spot,
Outdoors & Recreation, Professional & Other Places, Residence,
Shop & Service, Travel & Transport. Similar to hood2vec, we can
now de�ne the similarity between two urban areas i and j using
the distance between vectors zi and zj .
Similar to number of venues, the number of checkins in venuesof di�erent types can also be used as the vector representation of anurban area. In particular, we de�ne a vector zicheck for each urban
check = nik , where c is thearea node, such that its k t h element zik
ikCi
number of checkins of venues of type k within area i and Ci is thetotal number of checkins of venues within i. We follow the same
10 top-level venue categories for zicheck (i.e., zicheck 2 R10 ). Then
we can also de�ne the similarity between two areas i and j by the
distance between vectors zicheck and zcheck
.
j

4.1

Movement and Venue Categories

We calculate the correlation between two representations, v andz, (by the method in Section 3) in three cities: New York City, Los
Angeles and Chicago. There is a total of 141 zip codes ui (9870
pairs) in New York city, 111 zip codes (6105 pairs) in Los Angeles,
and, 59 zip codes (1711 pairs) in Chicago. We further extend ourcomparisons to each time period provided in the data. The results
are presented in Table 1. Note that we use the following notation
for the �ve time periods - O: overnight; MO: morning; MI: midday;
A: afternoon; N: night. As we can see all the correlations are positive, albeit, small, pointing to the two representations capturingdi�erent types of information. We also calculate the correlation
between z and zcheck in three cities. The correlations for these threecities are 0.839, 0.930, 0.936, respectively. I.e., the representationsof venue category based on number of venues and checkins arehighly correlated. This indicates low correlation of representations
between hood2vec and checkin-based venue category.
We further inspect the relationship between the two approaches
from the perspective of the top-k neighbors for each zip codes. In
2Period

O

MO

MI

A

N

New York City
Los Angeles
Chicago

0.036
0.098
0.139

0.065
0.254
0.136

0.062
0.150
0.170

0.029
0.104
0.164

0.016
0.015
0.129

Table 2: Jaccard index (k = 5) for the three cities for the di�erent time periods averaged over the corresponding zip codes.
particular, for each zip code ui we �nd the k = 5 closest zip codes to i
based on their hood2vec representation (v), N5,i,hood2vec . Similarly,
we calculate the top-5 neighbors of zip code ui based on their venue
category representation (z), N5,i,cat . We then calculate the Jaccard
index of the two sets:(N5,i,hood2vec , N5,i,cat ) =|N5,i,hood2vec \ N5,i,cat ||N5,i,hood2vec [ N5,i,cat |Figure 1: Average Jaccard index as function of number ofclosest neighbors k.

(2)

Table 2 presents the average Jaccard index for every city and time period. Furthermore, Figure 1 presents the Jaccard index as a functionof the number of neighbors k considered for every city, averaged
over di�erent time periods and zip codes. As one might have expected from the earlier results presented, in general, under di�erentk, there are few shared neighbors when using the two di�erent representations for the zip codes. This strengthens our hypothesis thatthese two types of representations capture di�erent information
for the areas.
Moreover, Figures 2-4 illustrate the Jaccard index for every zip
code per city, averaged over the di�erent time periods. As we cansee most of the zip codes in all cities have a fairy low Jaccard
index. New York City’s zip codes exhibit overall lower Jaccard index
compared to Chicago and Los Angeles (in accordance to the resultsin Table 1, 2). Zip codes with high Jaccard index are essentiallyurban areas for which the two di�erent representations examined
identify a high overlap on areas similar to them. This happens to alarger extend in Los Angeles and Chicago compared to New York
City. This can potentially be due to (a) the compact nature of NYC
that allows people to explore several di�erent areas and hence,
geographically remote zip codes are close in the hood2vec latent
space, and/or, (b) the di�erent geographic distribution of venues inthe three di�erent cities. More speci�cally, the compact nature may
cause venues in New York city more evenly distributed, since they
are easily accessible by dwellers. In contrast, scattered nature of
Los Angeles may lead to biased venue distribution due to various
accessibility of di�erent regions; this could be the reason for slightly
high Jaccard indices in some areas. Chicago has fewer zip code
areas such that an area can has higher probability of sharing thesame closest area(s) in two representations; this can cause slightly
high Jaccard indices in some areas. Nevertheless, regardless of thereasons for the di�erences across the cities examined, in all cases
the Jaccard index does not go beyond 0.4. Simply put, there is no
zip-code in these three cities, for which the overlap between the
top-5 neighbors identi�ed by hood2vec and a simple venue-based
vector representation is more than 40%, supporting our hypothesisthat these two di�erent approaches capture di�erent information
with respect to the similarity of the areas.

Figure 2: Average Jaccard index over the di�erent timeperiods for the zip codes New York City.

4.2

hood2vec representation across time

We further explore how the representation obtained for a zip code
through hood2vec changes over time (i.e., over the di�erent timeperiods in the dataset). Let us assume the two periods p1 and p2 ,and the corresponding hood2vec representation vectors vp1 andvp2 respectively. Then following similar steps as the ones describedin Section 3, we can obtain the pairwise correlation of the between
periods p1 and p2 for the same city. The correlations of each city
are shown in Fig. 5, 6, 7.
One can observe that for these three cities, the correlations between any pair of periods are very high, all over 0.9. This means that,the patterns of movements are similar regardless of the time of a day(based on the hood2vec representation). Since New York City andChicago are more geographically compact, it is easier for dwellers tomove within the city for any purpose at any time. This could be thereason that the overall movement patterns within a day are similar.
Los Angeles is geographically scattered, which limits the convenience of movements; dwellers tend to move within nearby areas at3

Figure 6: Correlation among representations of di�erent periods in Los Angeles.

Figure 3: Average Jaccard index over the di�erent timeperiods for the zip codes in Los Angeles.

Figure 7: Correlation among representations of di�erent periods in Chicago.

5

CONCLUSIONSIn this paper, we propose hood2vec to identify the similarity between urban areas through learning a node embedding of the mobility network captured through Foursquare check-ins. We compare
the pairwise similarities obtained from hood2vec with the ones
obtained from comparing the types of venues in the di�erent areas.
The low correlation between the two indicates that the mobility
dynamics and the venue types potentially capture di�erent aspectsof similarity between urban areas.
Acknowledgments: This work is part of the PittSmartLiving
project (https://pittsmartliving.org/) which is supported in part by
National Science Foundation Award CNS-1739413.

Figure 4: Average Jaccard index over the di�erent timeperiods for the zip codes in Chicago.

REFERENCES[1] Justin Cranshaw, Raz Schwartz, Jason Hong, and Norman Sadeh. 2012. The
livehoods project: Utilizing social media to understand the dynamics of a city. In
AAAI ICWSM.
[2] Justin Cranshaw and Tae Yano. 2010. Seeing a home away from the home: Distilling
proto-neighborhoods from incidental data with latent topic modeling. In NIPS
Workshop of Computational Social Science and the Wisdom of the Crowds.
[3] 4SQ Eng. [n. d.]. What neighborhood is the “East Village” of San Francisco?
Retrieved June 9, 2019 from https://medium . com/foursquare-direct/a-hackdayproject-what-neighborhood-is-the-east-village-of-san-francisco-229f317f597a[4] Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for
networks. In Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. ACM, 855–864.
[5] Géraud Le Falher, Aristides Gionis, and Michael Mathioudakis. 2015. Where is
the Soho of Rome? Measures and algorithms for �nding similar neighborhoods incities. In Ninth International AAAI Conference on Web and Social Media.
[6] Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of di�erent functions
in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD
international conference on Knowledge discovery and data mining. ACM, 186–194.

Figure 5: Correlation among representations of di�erent periods in New York City.
any time of the day. This may cause similar movement patterns ofall day. The interested reader can explore the di�erent urban area
representations at: http://www.pitt.edu/~xil178/hood2vec.html
4Mining behavioural constraints in urban mobility
sequences of Tokyo
Galina Deeva⇤ and Marı́a Óskarsdóttir⇤⇤ Faculty of Economics and Business, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium,

Email: {galina.deeva, maria.oskarsdottir }@kuleuven.be

Abstract—We analyse the longitudinal mobility data from asequence mining perspective using a technique that discovers
behavioural constraints in sequences of movements between
venues. Our goal is to discover distinctive behavioural patterns inthe sequences relative to when in the day they were formed. We
analyse sequences of venues as well as sequences of subcategories
and categories to discover how people move through Tokyo.

TABLE IA N EXAMPLE OF VENUES , THEIR SUBCATEGORY AND CATEGORY
Venue
Leuven train station
Bart’s bar
Eskimo attire
Moskow Meals

Subcategory
Train Stations
Bars
Clothing Stores
Russian Restaurants

Category
Travel & Transport
Nightlife Spots
Shops & Services
Food

I. I NTRODUCTION
In this project, we look at the longitudinal mobility data
set from a sequence mining perspective. The movements ofindividuals across time can be viewed as sequential data
where each location is an item in the alphabet of venues
and a sequence is comprised of an ordered list of locations
that describe people’s behaviour when moving through acity. In particular, we apply a novel and powerful sequencemining technique called the interesting Behavioural Constraint
Miner or iBCM [1]. The technique is capable of discovering
expressive and concise patterns using behavioural constraint
templates, such as simple occurrence, looping and position
in a sequence. In addition, the technique can discover theabsence of a particular behaviour, such as the co-existenceof two items, which is interesting for the understanding ofurban mobility patterns. The output of iBCM is a set of features that represent behavioural constraints in the sequences.
Subsequently, supervised analytics techniques can be applied
to the feature set to discover meaningful patterns, interesting
signals and to classify the sequences.
The goal of this project is to discover distinctive anddiscriminating behavioural constraints in mobility sequences
during different periods of the day. In the context of urban
mobility we take a fine-grained look to the venues to see how
individuals travel through a city. We apply iBCM to commute
sequences and use its window-based approach to discoverparticular behaviour in certain time periods. We apply thetechnique in a supervised setting with the goal of classifying
the sequences with respect to the part of day. For urban growthand dynamics, we consider, on the one hand, the subcategory
and, on the other hand, the category of the venues. Thereby,
we analyse sequences of categories, i.e., whether they arefor example Residence, Food, or Travel & Transport, or ofsubcategories. Interesting behavioural constraint templates inthis case could be whether after visiting a museum do people
go to a restaurant and whether the restaurant is close to
the museum or is public transport needed to get there. For
this application, our goal is again to classify the sequenceswith respect to the part of day and to study the feature sets
generated by iBCM to discover behavioural constraints that areprominent at each time. This look at urban dynamics allows
us to study in which context event interactions happen andcould also be applied for location intelligence. Our analyses
transition from a fine grained view of movements between
venues to an aggregated approach that looks at mobility inthe broader sense using categories. This gives alternative
perspectives of urban dynamics and behavioural patterns in
the context of venue interaction.
Our approach consists of three steps, namely building
networks of venues and the movements between them, thegeneration of pseudo sequences that represent the collective
behaviour of the population as it moves through the city, andthe subsequent sequence classification and constraint mining.
As the data is too aggregated to analyse the mobility sequencesof individuals, we will extract pseudo sequences from the
networks built using information about individual movements
between venues. The networks thus represent the collective
movements of the population.
For our analyses we focus on Tokyo and the months April,
May and June 2017.
II. N ETWORK BUILDING
We make networks at three levels of granularity: venue(V ),subcategory(S) and category(C). Table I shows an example
of a few venues together with their corresponding subcategory and category. The venue networks are created using
the aggregated movements between venues. To create thesubcategory and category networks, we replace the venues
by their corresponding subcategory or category, respectively,
and aggregate the edges. To distinguish mobility behaviour
at different times of the day, we build separate networks
for each time period. The time periods are morning(M O),
midday(M I), afternoon(AF ), night(N I) and overnight(OV ).
The definition of the time periods can be seen in Table II.

MI(a) Midday network NC

MO(c) Morning network NC

AF(b) Afternoon network NC

NI(d) Night network NC

Fig. 1. Tokyo category networks.

We denote the networks using the granularity as a subscript
and the part of day as a superscript. For example, NVM O is
the morning venue network and NSN I is the night subcategory
network. The venue networks have 58 thousand nodes, thesubcategory networks have 456 nodes and the category networks have 10 nodes. All the networks are directed, indicatinga movement from one location to another, and weighted by
the number of movements in the given time period. Figure 1
shows category networks for Tokyo in June 2017 for different
parts of the day. The network on the left in the top row shows
peoples movements during midday and the network on theright in the top row shows the movements in the afternoon.

NI(e) Overnight network NC

The networks in the bottom row show, from left to right,
movements in the morning, night and overnight. Although
the networks appear very similar, there are some noticeable
differences. For example, at midday Residences have no selfloops and there are no movements from Events to Nightlife
Spots. In addition, in the afternoon the weights on the edges
from Food to Travel & Transport and from Travel & Transport
to Food are almost the same, whereas at midday the weight on
the edge from Travel & Transport to Food is 36% higher thanon the edge in the opposite direction. The overnight networkis also less densely connected than the other networks.
III. P SEUDO S EQUENCE GENERATIONTABLE II
T HE TIME PERIODSPart of day
Morning
MiddayAfternoon
Night
Overnight

Start hour
6:00
10:00
15:00
19:00
24:00

End hour
10:00
15:00
19:00
24:00
6:00

As we do not have individual mobility sequences, we
generate pseudo sequences that portray the collective mobilityof the population. The pseudo sequences are generated byinitialising a number of random walks in the networks. These
random walks give a representation of the movements between
venues and are used in lieu of sequences. In the networks,
we know for certain that transitions from A to B and from
B to C happen, but we do not know if the order of thesetransitions is from A to B to C. However, with an assumptionthat some people have moved more than once around the city,and given that there is a high chance for the location B to befollowed by C, we can conclude that some people who moved
from A to B continued their way with a movement from B
to C. Furthermore, as we generate a considerable number ofsuch random walks, we capture the variability in movementswith more common behaviour appearing more frequently inthe sequences. Thus, with such pseudo sequences we get anapproximation of the average movements. We consider random
walks of length 10, 20 and 30.
From the venue networks NV we get venue sequences
SV in which we replace the venues with their corresponding
subcategory and category to obtain the sequences SV !S and
SV !C . From the subcategory networks NS we obtain subcategory sequences SS and we replace the subcategories with
their corresponding category to obtain the category sequences
SS!C . Finally, from the category networks NS we obtain the
category sequences SC .
A pseudo sequence for a venue network with the venues inTable I could for example be
Leuven train station!Eskimo attire!Moskow Meal!Leuven train station 2 SV

with the corresponding subcategory and category sequences

Train Stations!Clothing Stores!Russian Restaurants! Train Stations 2 SV !STravel & Transport!Shops & Services!Food!Travel & Transport 2 SV !C .

The first sequence is very detailed since it shows movements
between exact locations. As there are 58 thousand venues there
is a lot of variability in the sequences. The venue networks
furthermore have multiple small components of only one or
two nodes, which means that these venues are never together ina sequence with the majority of venues. The second sequenceon the other hand shows a more eminent mobility pattern andthe same holds for the third sequence, where the pattern iseven more high-level.
The sequences are labelled according to which part of day
network they come from.
IV. S EQUENCE CLASSIFICATION AND CONSTRAINTDISCOVERYiBCM is a state-of-the-art sequence classification techniquethat discovers discriminative patterns from sequential data. The
behavioural constraints mined by iBCM are based on Declarelanguage. iBCM is a direct sequence classification technique,which means that it generates sequential patterns separately foreach class of sequences. Next, each sequence is transformed
to a feature vector with binary features indicating whethera certain pattern is present in the sequence. Subsequently, a
predictive model is built based on these features.
We look into 5 classes of sequences that represent 5
time periods described above (morning(M O), midday(M I),
afternoon(AF ), night(N I) and overnight(OV )). iBCM derives
binary features, which are subsequently used in a classification
algorithm. During the feature generation procedure, we vary
the support level (the percentage of sequences that contain acertain sequential pattern) from 0.4 to 0.8 with 0.1 intervals,
thus obtaining 5 different feature sets for each dataset. Next,
we apply a Random Forest algorithm, chosen because of its
fast and efficient performance.
V. R ESULTS
Table III presents an overview of the accuracy (acc) andlift results produced by iBCM and Random Forest, as well as
the number of behavioural constraints generated for different
pseudo sequences. The results are calculated using 10-foldcross validation. For each dataset, we only present the results
for the support level (sup) that gave the best accuracy.
TABLE IIIC LASSIFICATION RESULTS FOR PSEUDO SEQUENCES GENERATED FROMNETWORKS WITH DIFFERENT PARAMETERS

Granularity
category
category
category
category
category
subcategory
subcategory
subcategory
subcategory
category
category
category
category
category
category
subcategory
subcategory
subcategory
subcategory
category
category
category
category
category
category
subcategory
subcategory
subcategory
subcategory

L
10
10
20
20
20
10
10
20
20
10
10
10
20
20
20
10
10
20
20
10
10
10
20
20
20
10
10
20
20

Type
C
S
C
S
V
S
V
S
V
C
S
V
C
S
V
S
V
S
V
C
S
V
C
S
V
S
V
S
V

Month
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6

Sup
0.5
0.7
0.7
0.8
0.4
0.6
0.5
0.5
0.4
0.7
0.7
0.4
0.8
0.8
0.4
0.6
0.5
0.5
0.4
0.5
0.6
0.4
0.8
0.8
0.4
0.6
0.5
0.5
0.4

Con
24
23
20
22
36
14
10
31
15
24
24
28
23
23
41
14
10
41
20
24
19
28
22
23
36
19
12
26
18

Acc
1.0
0.99
0.99
0.87
0.96
0.67
0.74
1.0
0.85
0.86
0.87
0.97
0.99
0.87
0.99
0.66
0.71
0.99
0.85
0.85
0.99
0.97
0.84
0.87
0.99
0.83
0.74
0.99
0.87

Lift
4.9
4.51
4.9
4.54
3.44
2.74
3.09
4.54
2.30
3.35
3.24
3.4
4.99
4.59
3.39
2.67
1.85
4.59
2.3
4.99
4.53
3.44
4.99
4.54
3.44
4.54
3.09
4.54
2.48

It can be observed that the accuracy results obtained by
iBCM and Random Forest are relatively high, ranging from0.66 to 1.0. Such high accuracy obtained in multi-class classification confirms that iBCM was able to find patterns thatwere discriminating between the classes.
Some examples of the observed patterns for different sequences are listed in Tables IV and V, which illustrate behaviour in April and May 2017. For example, the pattern
Existence3(Travel & Transport) in Table IV indicates that
Travel & Transport item occurred at least 3 times during acertain window in the morning, which confirms the intuition
that people tend to use several types of transportation in asequence to commute to work in the morning. Similarly, we
can conclude that at lunchbreak people tend to use transport
to get to a restaurant, that they are less likely to visit Colleges& universities in the afternoon, they go from shops to the train

station in the evening, and, finally, that they will to a lesser
extent visit restaurants during the night.
Similar patterns occur in Table V. We can observe thatpeople tend to visit some food places in the morning, followedby commuting to work. Then, during lunchbreak, some people
might use transportation to visit Shops & Services. Finally,
it can be concluded, that the restaurants don’t get visits inthe afternoon, Colleges & Universities are not visited in theevening, and Shops & Sevices are not visited at night.
TABLE IVE XAMPLES OF CONSTRAINTS DERIVED BY I BCM (T YPE = VENUE ,GRANULARITY = CATEGORY, L = 20, MONTH = M AY )
Label
MO
MI
AN
NI
OV

Constraint
Existence3(Travel & Transport)
CoExistence(Travel & Transport, Food)
Absence(Colleges & universities)
CoExistence(Shops & Services, Travel & Transport)
Absence(Food) 1, Absence(Food)

TABLE VE XAMPLES OF CONSTRAINTS DERIVED BY I BCM (T YPE = SUBCATEGORY,GRANULARITY = CATEGORY, L = 20, MONTH = A PRIL )
Label
MO
MI
AN
NI
OV

Constraint
CoExistence(Food, Travel & Transport)
CoExistence(Travel & Transport, Shops & Services)Absence(Food)
Absence(Colleges & universities )Absence(Shops & Services)

In terms of the influence of various parameters on classification results, we make the following observations:• We generate sequences of 3 possible lengths (L): 10, 20
and 30 items. For sequences with L = 30 no features
were obtained, which is in line with intuition that no
meaningful sequences of so many movements could bepossible within one time period. The results for L=10
and L=20 are similar, however, there is a slight tendency
for the accuracy to be higher for longer sequences withthe average accuracy of 0.85 and 0.93, respectively. Thisconfirms the expectation that longer sequences could
potentially contain more information; however, in thecontext of this study, there is a clear limitation to a lengthof pseudo sequences that can still be realistic.
• The sequences with 3 different levels of granularity are
analysed: category, subcategory and venues. The latter
didn’t yield any results, because the alphabet of possible
venues was simply too large (58 thousands), making it
challenging to search for patterns in short sequences oflength 10-20. The average accuracy results for subcategory and category are 0.83 and 0.93, respectively. Thus, it
is possible that more interesting patterns can be extracted
when looking at the movements between venues from amore general perspective. However, more experiments areneeded to confirm this observation.
• Finally, there is no clear tendency for a certain type of
network generation to produce better results.

VI. C ONCLUSION
In this project we used a sequence classification technique
to discover behavioural patterns that are distinctive in mobility
patterns during different parts of the day. The high accuracy
levels the we obtained show that the pseudo sequences that we
generated from the venue, subcategory and category networks
contain constraints that are distinct and discriminative for thevarious parts of the day. The technique is fairly good at
detecting these constraints, which in addition are intuitive.
They provide an understanding of urban mobility and thespecific constraints in sequences from each class could be used
to improve transport and accessibility in the city.
For future work, we would like to to carry out the same
analysis for the other cities that were provided to see if themobility patterns are universal. If they are not, we could
apply the same technique to classify the cities and discover
patterns that are unique for each city. We would have liked
to work with real sequences for our analyses, but the data
was aggregated for privacy reasons. However, the pseudo
sequences provided a representation of movements between
venues that were both logical and plausible in the context ofurban mobility throughout the day. It would be very interesting
to apply iBCM to individual mobility sequences to see how
well they are approximated by the pseudo sequences.
R EFERENCES[1] J. De Smedt, G. Deeva, and J. De Weerdt, “Mining behavioral sequence
constraints for classification,” IEEE Transactions on Knowledge and Data
Engineering, 2019.

Using Foursquare data to reveal spatial and temporal patterns in London.
Maarten Vanhoof1⇤ , Antonia Godoy-Lorite1 , Roberto Murcio1 , Iacopo Iacopini2,1 , Natalia
Zdanowska1 , Juste Raimbault3,1 , Richard Milton1 , Elsa Arcaute1 , Mike Batty1
1
Centre for Advanced Spatial Analysis, University College London, London, UK
2
School of Mathematical Sciences, Queen Mary University of London, London, UK
3
Institut des Systèmes Complexes, National Centre for Scientific Research (CNRS), Paris, France
1

Introduction

Understanding the functional structure of contemporary cities
can find many applications ranging from urban planning to local governance regarding inequality issues. This exploration
is often constrained by the availability of large-scale data at
fine spatio-temporal resolution. Longitudinal data, such as
the Foursquare data, allow researchers to explore different
aspects of the functional structure of cities, such as the roleof places and the human mobility that occurs between them.
However, never mind their size, this type of data is often incomplete, capturing only parts of the functional city due tothe biases in the data collection that might represent a part ofthe population only.
In this paper we explore how Foursquare data can help unveil
aspects of the functional structure of London. We explore thespatial pattern of venue locations and visit patterns captured
in the Foursquare data. We also touch upon the temporal patterns of these data, as well as the interactions between categories of places (as derived by the semantic information thatcomes with the venues). Our findings reveal some of the characteristics and limitations of the Foursquare data with regard
to investigating the functional structure of London and other
cities.

2

Brief description of data and data handlingIn this report we mostly focus our attention on London. We
deployed both movement and venue data. Based on the category tree retrieved from the Foursquare api, we were able
to classify the venues to higher level categories. It should
be noted that, within this category tree, not all categories
have the same depth of branches or number of leaves; andthat venues in the data can be classified at different depths ofthe tree. As such, one can use this category tree only onedirectional and it seems wise to evaluate at the highest level
only. Those highest categories, and their number of available
venues in the London data are in table 1.
To perform our analysis we used open source software, more
specifically Python and GIS. If wished for, our codes can bemade available on Github to wider audience.

⇤

m.vanhoof@ucl.ac.uk

Highest level of category
Food
Nightlife Spot
Shop and Service
NaN
Travel and Transport
Professional and Other Places
Outdoors and Recreation
Arts and Entertainment
College and University
Event
Residence

Number of venues
7340
3169
3012
2929
2643
1352
1114
828
256
37
9

Table 1: Number of venues in London per highest category in the
Foursquare category tree.

3

Venue locations

By investigating the location of London venues, one candistinguish a spatial pattern in which more venues are observed in Central London compared to it’s surrounding neighbourhoods (figure 1 top). One exception to this pattern
is Heathrow Airport in the west, where a high number ofFoursquare venues is observed despite its remote locationfrom Central London. The map of the diversity of venues, as
given by the entropy of observed categories per LSOA level(a low level administrative boundary in the UK), shows a pattern in line with the number of venues (figure 1 middle). Indeed, the higher diversity of venues in observed in Central
London gradually fades out when moving away from it. It
is known, however, that entropy values are related to volume
and so when normalising entropy by the number of venues,
the spatial pattern changes in an interesting way. In fact, after normalising the entropy by the number of venues (figure
1 bottom) one can observe that both London city centre andsome remote areas around the City depict very low entropy
scores. In Central London, this is due to the high amount ofvenues observed which brings down the normalised entropy
to a large degree. Regarding the remote areas, this is due toa very low number of observations in the first place (as canalso be observed in figure 1 top). At the same time, several
areas away from Central London display a high normalised
entropy, or thus a high diversity of venue categories, despite
having a limited number of Foursquare venues.

In a way, this finding stresses the importance of diversified
functional activity in areas that are away from Central London. One outstanding question is whether limited number ofvenues in these areas correspond to a diminishing presence ofsuch venues or simply by a diminishing popularity of theseareas on the Foursquare platform. A quick comparison between the number of Foursquare venues and the number ofregistered companies in London -as can be found in United
Kingdom’s government registrar of companies, Companies
House - does not offer a closing answer to this question. The
spatial pattern of registered companies appears to be similar
to the one of Foursquare venues (figure 1 top vs. bottom), andthe distribution of the number of Foursquare venues per registered company for all LSOAs in London is narrow around 2
percent expressing differences between areas when it comesto Foursquare coverage (figure 2). The latter of course does
not mean that the Foursquare venues cover companies of all
types and sorts, but further investigation is needed in order
to better understand this ”popularity bias”. Concluding, one
could roughly say that there are three types of areas when it
comes to Foursquare venues location in London. Areas thathave a lot of venues and diversity (e.g. Central London), areasthat have a limited but diversified number of venues (someof the out-of-centre areas) and areas that have little to novenues in the data (e.g. the South-East region of London).
Although it is unclear what drives the presence of venues in
the Foursquare data, there probably exists a ”popularity bias”
which calls for a better understanding.

Figure 2: PDF for the number of Foursquare venues over number ofregistered companies per LSOA.

4

Figure 1: Number of Foursquare venues (top), entropy of
Foursquare venue categories (middle top), normalised entropy of
Foursquare venue categories (middle bottom), and number of registered companies in the Companies House dataset (bottom) for all
LSOA administrative regions in London.

Spatial patterns of categories

Having investigated the diversity of venue categories in areas, the next logical question is whether spatial patterns per
category differ much from one another, and if these spatial
patterns might form a reflection of the popularity bias. Figure 3 shows the location of different venues according to theirhighest level category. Clearly, spatial patterns differ by category. For example, the outdoor and recreation pattern depicts
a remarkable uniform distribution over the city as the category includes places such as parks or sporting grounds which
are typically provided by local authorities to their inhabitants.

Venues in the food, and shops and services categories, aremostly located in Central London and around main roads or
local centres. They reflect elements of accessibility and access to a market in order for these venues to function. Another patterns is that of colleges and universities, which areentirely grouped in Central London, expressing their higherlevel functional role, their historical character, and their independence from local planning or market demand.

Examining hot-spots for different periods of the day reveals some different patterns too. The morning check-ins, forexample, are done mostly at train stations, whereas some areas like Oxford Street (a shopping area) are fundamentally
midday locations and others, like Kensington are mostly afternoon/night areas. Some of these patterns can be investigated for an area of central London depicted in figure 5).

Figure 4: Hot-spots of pairs of venues in London based in the
Foursquare movement data. Top 10 Enablers and Receivers are displayed as wellFigure 3: Spatial patterns of venues with different categories in London

5

Hotspots of movements

The movement data provided by Foursquare allows us to
add a behavioural component to the location information ofvenues. To mine this potential, we perform a hot-spot analysis of the venue pairs that were most observed in the provided movement data. For London, we had access to a total of
7,650,994 pairs (Venue i to Venue j) that were subsequently
visited by Foursquare users. We define our hot-spots on thenumber of check-ins that happened for each pair during thewhole period the data was provided for. Focusing on pairs
with more than 1000 check-ins renders a set of 101 pairs ofvenues (figure 4). As can be observed, most of them reflectmicro-movements located in Central London and Heathrow
Airport. Nevertheless, some of the pairs depict movements
towards train stations or towards outside venues such as Croydon (an extensive night-time and shopping district) or Wimbledon (famous for its tennis competition).
Finally, we defined two types of venues from these hotspots, Enablers and Receivers. An Enabler venue is drawn
from the Venue i subset and ranks high in frequency (theamount of months-different times combinations this venue
was observed in the data) and check-ins. On the other hand,a Receiver venue also ranks high in these characteristics but
is drawn form venue j. The top ten Enables and receivers areshown in (figure 4). They are Heathrow airport, train stations,Harrods, Hyde Park, Marble Arch, and Piccadilly. From theseten, nine are at the same time Enablers/Receivers; St. Pancras
is only enabler and Euston is only receiver.

Figure 5: Hot-spots of pairs of venues in London based in the
Foursquare movement data. Top 10 Enablers and Receivers are displayed as well

6

Transition matrix between categories

The analysis of movements between pairs of venues and theirrelation to the time of the days makes one wonder whether,at the very high level, one could detect temporal patterns between categories of venues. Are movements between transport venues and universities more likely to occur during daytime? Is there a seasonal pattern that can be distinguished forthe movement between outdoor locations and venues in the
category food?

To investigate this, we constructed transition matrices between all high-level categories based on the movement data.
In the case of London, most movements occurred between
venues in the categories of food, travel & transport, and shop
& service. Interestingly, these high-level transition matrices
are not influenced by the different seasons in a year (figure6). And they were only influenced to a limited degree by thetime of the day as can be observed in figure 7. In London,for example, interactions with the food category are limitedin the morning, but pop up throughout the day and peak in
the night period. We found the limited sensitivity to seasonsand time of the day to be the case for London, but also for thedifferent other cities available in the Foursquare data such as
Istanbul, Tokyo, or Jakarta.
What did reveal though, is that the transition matrices between different cities differ as can be observed in figure 8. In
most cities the food, travel & transport, and shop & service
remain the major categories, but the degree of their interaction differs. In Istanbul for example, food is by far the maincategory while travel & transport are almost non existent.
The inverse is observed in Tokyo where travel & transport
is by far the main category and food is almost non existent.
Interestingly, the importance of other categories is different
between cities. In Istanbul outdoor& recreation places arerather prominent, while in Jakarta professional places pop up
more than in any other city. Without further investigation itis difficult to detect the reasons for these differences, but they
could be: a different usage of Foursquare in cities, a different amount of venues from different categories in the cities,
and a different functional composition of the cities. Each city
thus has its own specific pattern of interactions between venue
categories, enabling and limiting the study of their functional
structure in different ways.

Figure 6: Transition matrices between the different high-level categories of venues for the different seasons in the Foursquare data for
LondonFigure 7: Transition matrices between the different high-level categories of venues for different times of the day in the Foursquare data
for London

7

Small conclusion

In this report, we have briefly discussed the foursquare data
and their potential to offer a view on the functional structures on contemporary cities like London. Exploring thefoursquare venue locations we touched upon the idea of
understanding functions and the diversification of function
within different city areas to better understand their role inthe city as a whole. The spatial patterns of individual categories serve as a good for this example. Nevertheless wereckon that, given the way the foursquare data is collected,
some sort of popularity bias exists within the data. Validation
with external databases is necessary to better understand thisbias, and so we touched upon such validation by comparing
the number of foursquare venues in London with the number of firms in London based on the xxxx database on firms
in London. Combining the location, functional aspect of thevenues with the movement data provided by users interaction
with venues we added a behavioural component to our analysis. The analysis of hot-spot of movement pairs allowed us
to better understand popular movement patterns in London.
In general, we found hot-spots of movement often to be related to tourism movements (airport-center shuttle, Harrods)
and places of transport (the many train stations in London).
A closer look at places that serve as enablers and receivers,
we note that throughout different periods of the day, different
places become more important. Functional places, in otherwords, are depending on the rhythm of the day. This might
not be surprising, but when studied in more depth, it will reveal some interesting information on the functioning of placesin the city.

Finally, we also investigated the sensitivity of larger patterns of functional interactions to time. More specifically, we
wondered whether interactions, as described by the pairs ofvenues, between high-level categories would differ significantly at city scale over the time periods of season or evendifferent times of the days. For London, we found these highlevel interactions, as expressed by the transition matrices, tobe little to not influenced by time periods. We found a similarinsensitivity for other cities in the data, such as New Yorkor Jakarta. We did, however, find differences in the general structure of the high level transition matrices between
cities. The reasons for these differences can only be hypothesised, but could relate to different city structures or usageof Foursquare, meaning that the interpretation of functional
structures in cities based on foursquare data might be city dependent.

8

Future work

Some ideas for further work we came up with are:
• Elaborate an investigation of the popularity bias by validation foursquare venues with a register of existing companies• Follow check-ins through day. After aggregating bytime of the day the hotspot locations, record Venuei Venuej time (let’s say Morning). Then look at all
Venuej -Venuek pairs and keep those with time=Midday;
then took V enuek -V enuem pairs and keep pairs withtime=Afternoon and so on. This only works if we assume that check-ins happen the same day and only forwalkable location, like from Harrods to Hyde Park to
Marble Arch or follow locations from train stations.
• Use the patterns of the high-level transition matrices
to identify similarities and differences between cities
worldwide (unsupervised learning). Investigate whether
similarities relate to i) types of Foursquare usage, ii)
functional structure of the city and 3) the morphological patterns of the cities.

Figure 8: Transition matrices between the different high-level categories of venues for different cities based on the foursquare data

A Geographic Data Science Framework for the Functional and Contextual Analysis
of Human Dynamics within Global Cities
Alex Singleton⇤ , Daniel Arribas-Bel , Sam Comber , Alessia Calafiore and Gregory Palmer
Geographic Data Science Lab, Department of Geography and Planning
University of Liverpool
alex.singleton@liverpool.ac.uk
Abstract
This study implements a Geographic Data Science
framework that transforms the Foursquare check-in
locations and user behaviour data into a series ofglobal urban networks. Through an innovative spatially weighted community detection algorithm we
uncover functional regions for 9 global cities; andthrough linkage to locally derived catchments foraround 330k check-in locations; attribute regions witha range of measures that describe their diversity, morphology and mobility. We then applied k-means clustering to derive a global set of 4 functional region
types, enabling the comparison of Human Dynamics
and their contexts across the sample of global cities.

1

Introduction

Networks are an increasingly important conceptual and methodological tool in contemporary urban theory to both represent
and model various types of interaction, flow or relation (e.g.
movement, finance, communication, friendships etc), or to elucidate hidden structure manifest through the agglomeration ofhuman interactions [3, 4]. Many applications of networks focus
on human dynamics across a range of temporal scales: frommapping patterns of global migration to daily commuting patterns [2]. However, developing an understanding of contemporary human mobility, behaviour, context and outcome poses
great challenge to many existing instruments that urban scholars
have traditionally relied upon for the empirical study of cities(e.g. Census, Survey etc) [5].
However, new opportunities to explore the form and function of cities have been enabled by the volunteering of spatiotemporally referenced digital traces left by human use of urban
environments [2]. In their raw form, data such as Foursquarecheck-ins provide insight into both where and when activities
are taking place within cities; but as we demonstrate in thisshort study, through their linkage, augmentation and analysis:
also provide great opportunity to model the functional form ofcities.
The universality of our implemented Geographic Data Science framework enables transfer across multiple countries to: 1)
Define functional regions within cities; 2) Define check-in POI(POI) catchments; 3) Create POI catchment contextual mea⇤

Contact Authorsures related to urban and functional structure; 4) The mappingof diversity of functional and contextual geography.
Through these measures we provide insight into human dynamics within and between sets of defined functional regions
across: Chicago, Istanbul, Jakarta, London, Los Angeles, New
York, Paris, Seoul, Singapore and Tokyo.

2

Urban Networks & Functional Regions

The objective of this analysis was to provide insight into theemergent functional structure of different cities created throughthe ordering of human activities into cohesive zones of interconnected activity. In this analysis we create global urban networks
of user interactions and implement a geographically weighted
community detection technique to identify functional agglomerations of POI.

2.1

Creating and Cleaning the Network

For each city, a network was created using Python’s NetworkX
library, with nodes associated with each POI and connections
between them determined by the agglomeration of all individual’s sequences of Foursquare check-in activities. Edges were
therefore ascribed the total count of user check-in links. Cities
were isolated into separate graphs through the removal of edges
that spanned cities; and further edges removed that existed between non-travel related POI categories that exceed what one
would consider to be ’a walking distance’. Hypothesizing thatthese check-ins are a result of the app not registering the use oftransportation, we focus only on localized movements, excluding all edges that exceed the median distance value between
POIs for each city. Although in this instance we were interested
in aggregate functional regions within cities (all activities), thistechnique could be extended to different temporal bins to ascertain how connectivity between different areas of the city varies
by time of day, day of the week or even month of the year.

2.2

Geographically Weighted Community Detection

Although recent work within Urban Geography has demonstrated the utility of community detection techniques in the definition of functional regions [3], these do not explicitly accountfor geography, which is a primary driver or constraint to interaction.
Broadly speaking, interactions are more likely between POI
in “close” proximity; although interactions will vary between
urban contexts. As such, we first needed to examine the distances that Foursquare users travelled between check-in loca-

tions. For simplicity, we measured the euclidean distance between each POI for every user; these were then aggregated into
distance bins. Figure 1 shows how there is a distance decay intravel between check-ins, but, for some cities (e.g. London andParis) there is complexity to these general patterns, with lower
initial propensity to travel to POI in close proximity.
Community detection was implemented using Python’s community package’s best partition method that searches for network partitions by maximizing the modularity using the Louvain heuristics. However, to better account for geography, we
first weighted the network edges by the inverse of geographic
distances between their associated nodes; thus prioritizing theimportance of more spatially proximate flows. The outcome
was a set of functional regions for each of the cities (See Table
1 and Figure 2). Polygon boundaries were created from POI
locations by applying Alpha Shapes to the associated POI locations within each identified community.
City

Frequency of Regionsµ POIµ Distance

Distance

Istanbul

811344.6

1.497

1.182

Paris

20

643.6

0.627

0.446

Seoul

7

1901.2

0.771

0.670

Singapore102102.6

0.794

0.740

Tokyo

26

2133.5

0.548

0.508

London20

1069.1

0.659

0.494

Los Angeles

81708.8

1.508

1.152

Jakarta

181101.6

1.055

0.853

New York

16

1918.5

0.579

0.391

Chicago71656.0

0.850

0.638

Table 1: The frequency of regions identified for each city, mean (µ)
POI per community, alongside the mean (µ) and standard deviation( ) of the edge distance in KM.

check-in trajectory profile varied from the associated city average. To illustrate this we compare two clusters within Tokyo,
Japan in Figure 3. The area labelled partition 4 represents thecore of central Tokyo and has high POI density, and more compact structure; whereas partition 14 is more peripheral, and has
lower POI density. Sub-figure 3b illustrates how partition 14
has a more distinctive set of trip behaviour relative to partition 4
which is more similar to the city average.

(a) Two functional regions within
Tokyo, Japan; highlighting a(b) The proportion of check-ins
central area, and one towards the between two functional regions,urban periphery.
and the Tokyo Average.
Figure 3: Functional regions within Tokyo.

3

POI linkage through user interaction and their geographic location are key drivers of those spatially located functional regions
demonstrated in the previous section; however, at a local arealevel patterns of use are both driven by POI type (e.g. travel,
food etc); and other measures of the wider built environment.
There is much literature within urban planning and architecturethat explores how the morphology of the built environment (e.g.
street geometry) may influence activity within places and limit
or enhance attraction between locations[4]. The objective here
was therefore to capture a range of contextual measures for each
POI, that would be later used to compare the functional regions.

3.1

Figure 1: The proportion of check-ins by distance.

(a) New York, USA.

Contextualizing POI Locations

Defining a Catchment and Input Variables

The first stage was to define a “catchment” for each of the 330k
POI. A polygon delineating the bounding box of all POIs withineach city was used to create a NetworkX graph from OpenStreetMap (OSM) data using the OSMnx library [1]. From
this city-wide graph of the street network, a sub graph was constructed that was centered at the latitude and longitude of each
POI. The radius of each sub graph was extended according toa ten minute walking distance from the POI. A convex hull of
the sub graph was then extracted for each POI which are thecatchments we use in the subsequent analysis (Figure 4).

(b) Istanbul, Turkey.

Figure 2: Functional regions within Istanbul and New York with POI
shown as white dots (Note; POI data for Brooklyn was not provided).

Each functional region represents a distinctive cluster of aggregate Foursquare user activity. Prior to the contextualization of these areas, we first examined how the aggregate user

Figure 4: A Catchment showing a ten minute walking catchment
along the street network in Paris from a selected POI (red), with other
POI within the area highlighted in yellow.

Within each POI catchment area, we defined a series of measures using either OSMnx [1] or spatial analysis of the catchment area characteristics; these are summarized in Table 2.
MeasureDescription

Label

Average Circuity

Total edge length divided by sum of great circle distance between nodes incident to each edge.

circuity avg

k Average

Average degree of catchment.

k avg

n

Number of nodes in catchmentnNode Density Km

n divided by area in square kilometers.

node density km
self loop proportion

Self-loop Proportion

Proportion of edges in catchment that have a single incident node.

Street Length Average

Mean edge length in undirected representation of catchment (meters).

street length avg

Street Length Total

Sum of edge lengths in undirected representation of catchment.

street length total

Street Per Node Average

Average number of streets per node.

street per node avg

Eigenvector Centrality Ratio

Ratio of eigenvector centrality between POI and other POIs within catchment.

ratio eig

Percentage of POIs of same category inside catchment.

percent same type

Percent Same TypePOI Count

Number of other POIs inside catchment.

n poisAverage Afternoon

Average Foursquare check-ins of all POIs in catchment between 15:00 and 18:59 (%).

avg afternoon

Average Midday

Average Foursquare check-ins of all POIs in catchment between 10:00 and 14:59 (%).

avg midday

Average Morning

Average Foursquare check-ins of all POIs in catchment between 06:00 and 09:59 (%).

avg morning

Average Night

Average Foursquare check-ins of all POIs in catchment between 19:00 and 23:59 (%).

avg night

Average Overnight

Average Foursquare check-ins of all POIs in catchment between 00:00 and 05:59 (%).

avg overnight

Table 2: Initial measures used to compare POI contexts.

MeasureDescription

Diversity

Entropy score (Shannon Index) measuring diversity in the categories of POIs

Morphology and Behaviour

The aggregation (mean) of POIs information resulted from the catchment area (CA) morphological profiles and average check-ins by time of day

Mobility

Functional region distance decay function

city’s empirical observations y, optimizing parameters c and k
by minimizing the L2 loss between y and f (x). This gave a setof exponential decay parameters that estimated likely relative
impedance between POI locations for given regions.
f (x) = maxi yi ⇥ c ⇥ ek(

4.4

maxi xi

x/max x )i i(2)Comparing Multidimensional Characteristics

A challenge when implementing k-means clustering is to selectan appropriate number of clusters. Here we utilize a clustergram
plot which along the x axis plots a range of potential k values;
and along the Y, a weighted mean principal component score.
Each line relates to a functional region and relative score foreach iteration of k; and dots represent the cluster average PCA
scores. An indication of the best fit for k relates to a model
where these centroids are well separated (See Figure 6). From
this figure, we selected 4 clusters as an appropriate k value.

Table 3: Functional region contextual differentiation.

4

Mapping the Diversity of Functional Regions

To gain an understanding of the functional regions similarities
and differences we developed a multidimensional comparison
implementing k-means clustering. We posited three drivers offunctional region differentiation, which are shown in Table 3.

4.1

Diversity

Entropy was calculated as follows:H=sXpi lnpi(1)i=1(a) PC1 & PC2.

where s is the number of categories, pi is the proportion of
POIs of each category and ln is the natural log.

4.2

Morphology and Behaviour

The variables calculated for each POI catchment were aggregated into the functional regions. The variables describing the
proportion of check-ins by time of day (morning, midday,...)
were averaged over those POI within each region. Additionally,
the means of the variables(n pois, percent same type, ratio eig,
streets per node avg, street length total, street length avg,
self loop proportion, circuity avg, k avg, n, node density km)
characterizing catchment morphology were reduced throughapplication of principal component analysis. Four principal
components that explained 77% of the variance derived (Fig 5).

4.3

Mobility

We defined the aggregate regional mobility mathematicallythrough the parameter exponential decay function f (x) in the
Equation below, where x is a vector of distances between POI.
We then used TensorFlow to obtain the best fit for each of the(b) PC3 & PC4.
Figure 5: Vars contributing to PC1 & PC2 (a) and PC3 & PC4 (b).

As such, for k=4, we ran k-means 10,000 times (as it isstochastic), extracting the result that had the lowest total withincharacteristics in terms of POI diversity, urban morphology anduser check-in behaviour.

Figure 6: A Clustergram showing how different iterations of K
impacted cluster separation.

(a) London.

(b) Paris.

(c) Chicago.

(d) New York.

Figure 8: Archetypal functional regions (Cluster 2).
Figure 7: The % of POI within functional region clusters by city.

cluster sum of squares; ie, most compact clusters. After clustering, we appended the original data back onto the clusters andexamined these distributions relative to the global averages.
• C1: has low diversity (-0.82), the highest value of night
check-ins (0.95), and low PC3 (which seems to be mostly
driven by the proportions of self loops - see Figure 6)• C2: has moderately high diversity (0.3), check-ins followed
typical peak hours (morning and afternoon), adopts an almost linear decay with regards to the travel distance between
venues (high c and low k). This is the most numerous cluster.
• C3: has the lowest diversity and check-ins at night andovernight, while the highest avg of check-ins at midday. PC1
which seems to be driven by node density is very low (theseclusters are mostly peripheral areas of the city)• C4: has the highest diversity (0.65) and overnight check-ins.

4.5

Human Dynamics within Global Cities

The frequency of POI within each of the functional region clusters is shown in Figure 7. No city other than Istanbul (which
has a very high POI frequency) are ascribed all clusters, however these distributions do offer insight into the compatibility ofthese locations in terms of human dynamics and contexts. For
example, New York, Chicago, London and Paris share similarly
distributed clusters; whereas other cities have more unique distributions such as Singapore and Tokyo.
The utility of these distributions can be illustrated by plotting
archetypal functional regions across comparable cities. We do
this by examining the input scores for each region, and selecting
those that are are closest to the average for a particular cluster(see Figure 8). We would expect these clusters to have similar

5

Conclusions

This report has outlined how social media data such as
Foursquare can be utilized to provide insight into the structureand function of cities. Through our innovative Geographic Data
Science methodology we create a framework for identificationand description of functional regions across global contexts,
linking a range of measures ascribed down to the local level.
There are numerous avenues for future research: for example,
the exploration of potential modes of transit linking POI sequences, although, this may require more granular time stamps,
or the use of modeled distance decay as integral models thatmay predict future system states.

References[1] Geoff Boeing. Osmnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65:
126–139, 2017.
[2] Andrew T Campbell, Shane B Eisenman, Nicholas D Lane,
Emiliano Miluzzo, Ronald A Peterson, Hong Lu, Xiao
Zheng, Mirco Musolesi, Gahng-Seop Ahn, et al. The riseof people-centric sensing. IEEE Internet Computing, 12(4):
12–21, 2008.
[3] Garrett Dash Nelson and Alasdair Rae. An economic geography of the united states: From commutes to megaregions.
PloS one, 11(11):1–23, 2016.
[4] Carlo Ratti. Space syntax: some inconsistencies. Environment and Planning B: Planning and Design, 31(4):487–
499, 2004.
[5] Alex Singleton and Daniel Arribas-Bel. Geographic data
science. Geographical Analysis, 2019.

An Intelligent Tree Planning Approach Using
Location-based Social Networks Data
J. H. van Staalduinen1

J. Tetteroo1

D. Gawehns

M. Baratchi

LIACS – Leiden University
Leiden, The Netherlands

LIACS – Leiden University
Leiden, The Netherlands

LIACS – Leiden University
Leiden, The Netherlands

LIACS – Leiden University
Leiden, The Netherlands

j.h.van.staalduinen@
umail.leidenuniv.nl

j.tetteroo@
umail.leidenuniv.nl

d.gawehns@
liacs.leidenuniv.nl

m.baratchi@
liacs.leidenuniv.nl

Tree locations

Abstract—How do we make sure that all citizens in a city canenjoy the necessary amount of green space? While an increasing
part of the world’s population lives in urban areas, contact withnature remains important for the human well-being. As optional
tree planting sites and resources are limited, the best site toplant must be determined. Can we locate these sites based on
the popularity of nearby venues? How can we detect groups ofpeople who tend to spend time in tree deprived areas?
Currently, tree location sites are chosen based on criteria fromspatial-visual, physical and biological, and functional categories.
As these criteria do not give any insights into the amount ofpeople benefiting from the tree placement, we propose a newcriterion taking socio-cultural aspects into account. We combine
the Foursquare mobility data set with a tree location data set,
both of New York as a case study. Using the Foursquare data
set we create a venue interaction network from which we extract
venue communities. These communities are then scored basedon the amount of trees in the vicinity of their venues. By
combining the popularity of venues with the tree density of venue
communities we can identify locations where planting a tree canbenefit the most number of people and make the largest impact.
Index Terms—Urban computing, tree planning, social networks

I. I NTRODUCTION
As of 2018, 55% of the world’s population lives in urban
areas, a number which is projected to grow to 68% by 2050
[4]. The North-American continent stands out in particular,
where this number is already at 82%. While it is easy to pointout the economical reasons for moving to the city – at leastat the first sight [7] – there are certainly downsides attachedto urban life. One of them is the inescapable fact that cities,
by definition [6], have a higher population density, leading tomore built-up areas and thus a scarcer supply of nature than inrural areas. However, as Rohde and Kendle put it, “it is obvious
from any casual observation that many human beings do not
like to be dissociated from the natural world; as a nation we
spend millions of pounds every year on garden and household
plants” [15]. Indeed, contact with nature does seem to belinked to human well-being and positive emotional effects
and is even said to strengthen urban communities [10, 13].
Apart from socio-cultural benefits, urban greenery can help tomitigate two characteristically urban problems: air pollution
1 Both authors contributed equally to this work.

Venue communities

Venue locations

Fig. 1: We combine three types of data (tree locations, venue
locations, venue communities) to determine a new criterion
which can be used in selecting potential tree planting sites.
due to traffic [11] and (extreme) warmth due to the urban heatisland effect [12]. The inclusion of parks and street trees incity landscapes is, therefore, an important aspect of the urban
planning process.
To date, socio-cultural arguments play a marginal if not
non-existent role in formal frameworks describing criteria for
selecting potential tree planting sites. The criteria in theseframeworks do not account for the amount of people that areaccommodated by the newly planted trees. When following
the established criteria, trees may end up in places where they
are beneficial to some people, but its effects may not serve themajority of people, or may never reach the people yearning
for them most.
To tackle this problem, we propose an additional tree planning criterion. Prioritization should be given to sites visitedby many people and specifically people who tend to move
between areas lacking trees.
We identify such locations by combining two ways ofanalyzing the structure of a venue interaction network. By
combining the knowledge about venue popularity and venue
communities with a low tree density, we can detect popularvenues within tree deprived communities and thus provide
a prioritization that can be used for site selection in thetree planning process, as schematically shown in Fig. 1.
This prioritization can be embedded within the criteria of
established tree planning frameworks that currently lack thissocio-cultural value and insight.
Our paper has the following contributions:• We describe a novel criterion for potential tree planting

site selection based on network communities within avenue interaction network;• We combine this criterion with venue popularity, basedon network analysis of venue interaction data from thesocial media platform Foursquare;• We apply this method to prioritize venues as potential
tree planting sites in New York City.
II. R ELATED WORK
Most of the work in the field of tree planning revolves
around selecting appropriate tree species for predeterminedplanting sites [17, 18]. This reflects the observations by
Spellerberg [18] and Pauleit [14] that tree planning is often– or at least has been for some time – an afterthought inthe urban design process and characterised by pragmatism.
According to an Australian survey, while visual aesthetic oftrees and socio-cultural function of green spaces in the city
seem to be important motives for planting trees, the first
motive only plays a small role in the tree planning process[16] and the second motive is not reflected in the sparsebody of site selection criteria that we could find. The workby Amir and Misgav [2], in which they aim to describe acomplete tree planning decision framework, does incorporate
criteria on site selection. They define three useful criterion
categories, which are spatial-visual, physical and biologicaland functional. Criteria relating to the socio-cultural functionof green spaces however, are missing. We observed several
works describing site selection criteria [8, 14], but those fallwithin the category of physical and biological criteria thatare essential for the survival of the tree. Moriani [11] did
use population density in their planting priority index, but asthey focused on the air pollution-reducing quality of trees, thisstill falls within the category of functional criteria. We believethen, that the body of site selection criteria is still incomplete
and that we can contribute to this framework by introducing
a new socio-cultural criterion which takes people movement
into account.
III. M ETHODS
A. Venue popularity
A naive approach to maximize the impact of planting a tree,is planting it near a place where many people go. To find thisplace we compute the degree of all nodes in the undirected
network graph G = (V, E, W ), where nodes v 2 V are venues
and edges e = (v1 , v2 ), e 2 E movements of people between
two venues v1 and v2 , with weight we 2 W as the number ofmovements between the pair of venues. The degree of a nodev is then defined as the sum of the weights of the edges thatare connected to it:deg(v) =

X

we

(1)

e2{(u,v)|u2adj(v)}B. Venue community tree density
Although trees near popular venues may reach many people,
they may not reach groups of people who tend to visit othervenues. It may be the case that some people never comeacross arboreal areas. To deal with this shortcoming of thenaive approach, we introduce a measure we call the tree
density coefficient, which is based on communities in the
network. A community is a group of nodes which is densely
connected with each other, but much less with the rest of
the network [5]. By looking at these communities, we use
the fact that it is not necessarily bad for a venue not to becovered in trees, if people often move from that venue to avenue that is covered. To detect the communities, we use theLouvain community detection algorithm [3]: a fast algorithm
able to find communities with high quality. It is based on
the optimization of modularity, a measure that compares thedensity of connections within a community with the density
between communities.
As it is computationally heavy to compute the modularityof a community, the Louvain algorithm uses heuristics toapproximate it. Therefore, it does not necessarily return thebest community layout. In order to gain confidence in therobustness of our communities we choose to run the algorithm
many times to create a large number of community layouts.
To compute the tree density coefficient for a venue, we first
count trees in the vicinity of the venues. We approximate thisvicinity by creating a grid of the city, where each grid cell is 50
by 50 meters, calculated using Universal Transverse Mercator
coordinate system [9]. Each venue vi is mapped to a cell in
the grid and is assigned the number of trees in the cell as its
venue tree density vtdi .
We compute the community tree density ctdi for a venue vi
by averaging the vtdi with the venue tree densities of all theother venues in its community Ci , over multiple iterations kof the community detection algorithm:
ctdki =

1 X
vtdj , 0 < k  kmax .
|Ci |

(2)

vj 2CiIn the end, the tree density coefficient ci for a venue vi is
its average community tree density value over all iterations of
the community detection algorithm:
ci =C. Combined method

kmax
1 X

kmax t=1

ctdki .

(3)

A venue with a low tree density coefficient could have only
one visitor, whereas other venues in the same community thathave a similarly low coefficient could have many visitors. In
this case, the latter venue(s) would be more appropriate as atree planting site.
We extend the community based density coefficients withvenue degrees by combining the two measures and detecting
the set of venues that are Pareto efficient, i.e. the venues
that are found by minimizing the tree density coefficient andmaximizing the influence of the venue: the optimal trade-offs
between the two measures. Also called the Pareto frontier,the venues in this set meet our criterion of helping mostpeople needing trees. Tree planners could choose any of thevenues along the Pareto frontier, depending on their preference
towards either of the two measures.
IV. C ASE STUDY
A. City of choice: New YorkWe conducted a case study to investigate the implementation
and workings of our criterion using real data. For this we chose
to focus on New York City as data on both venue interactions
and tree locations was richly available.
We used two data sets to construct our criterion. We usedvenue interaction data of New York, provided by Foursquare aspart of the Future Cities Challenge 2019, to create the venue
interaction network. To assign tree density scores, we useda Street Tree Census data set [1]. This section describes theproperties of both data sets and how we processed them to
implement our methods.

(a)

(b)

Fig. 2: The power law distribution of venue degrees (a) anddistribution of tree density coefficients (b).

B. Venue interaction data
The Foursquare venue interaction data set comprises of
two parts: venues and movements between them. The data
set contains information on ten different cities around theworld. As we focused on New York in this case study, weused the New York data, but it should be noted this study isapplicable to any of the other cities, provided we have accessto a corresponding tree location data set.
As not all venues found in the movement data occur inthe venue information data, we considered only the venues
with known locations for the construction of the network.
Additionally, we omitted all 86 venues not connected with the
big component as the small components that are not connected
never exceed a size of 3 nodes. In the end, we were able touse 15,803 venues in our analysis.
C. Street Tree Census

Fig. 3: The distribution of venues according to degree and tree
density coefficient. The Pareto frontier shows the venues withthe optimal tree planting location according to our criterion.
(Venue labels correspond with Fig. 4b and Table Appendix-I)

The Tree Census data set contains information on street
trees in New York City and surrounding cities. It contains
information on among others the species, health, as well as
longitude and latitude. Only street trees were counted, whichmeans that trees in parks were not taken into account and are
not present in the data set.
V. R ESULTS
A. Venue popularity
We computed the venue popularity as the degree of eachnode and observed that the distribution follows a power law(see Fig. 2a), as is generally the case in scale-free networks
modeling natural phenomena. To decide which venues would
be interesting as a tree planting site according to this method,
one should prioritize venues with higher degrees.

(a) One of the 1,000 community
partitions.

(b) Optimal tree planting locations (see Appendix–Table I).

Fig. 4: Map of New York City showing the optimal tree
planting locations based on community structures.

B. Venue community tree density
We used the Louvain community detection algorithm as
implemented in the Python NetworkX package. We set theresolution to 0.5 to find decently small communities. One such
community lay-out is shown in Fig. 4a.
As the communities are detected using the heuristic Louvain
algorithm, we averaged the community tree density of thevenues over 1,000 runs of the algorithm, each time possibly
detecting slightly different communities in the network, toobtain their tree density coefficients.
To find tree-deprived communities, we combined the locations of the venues within the communities with the treelocations in the street tree data set. First, we calculated thetree density for each venue. Then, the average tree density ofthe venues in the community was computed and returned toeach of those venues as its community tree density.
We show the distribution of the tree density coefficientvalues in Fig. 2b. The distribution is slightly skewed to
the right, which means most communities are filled withtrees. Some, however, would still benefit from planting more.
Prioritization for tree planting sites using this method should
be given to the venues with the lowest coefficients.
C. Combined method
In order to select the most impactful planting locations,
we combined both methods. This results in the distributionof venues and associated Pareto frontier as shown in Fig. 3.
Here we minimize the tree density coefficient of the venues
while maximizing their degree. These venues are highlighted
by the Pareto frontier and should be prioritized according toour new criterion. To indicate the locations of the venues on
the Pareto frontier, we show the venues on a map in Fig. 4b
and provide additional insights in the data in Table I in the
Appendix.
It is interesting to see that one of the selected venues (venueH) is a rose garden, amidst a park lush with trees. This is
explained by the fact that the tree data set contains only street
trees, and not trees in parks. Additionally, we found uponinspection using Google Street View that some of them (mostnotably venues A, B, D, G and H) do seem to be near anumber of trees. When inspecting these locations in the tree
data base,2 we see that there are either only a few (venues B
and G) or no trees (venues A, D, E and H) recorded in theimmediate vicinity of the venues. We see that along with parktrees, trees on private grounds are also not recorded.
VI. C ONCLUSION
In this paper, we propose a novel criterion that can beused when selecting potential tree planting sites. The natureof the criterion is socio-cultural, capturing people movement
along venues and tree-lacking (social) communities into one
measure. Having implemented the measure for a case study on
New York City, we show that the measure is applicable in the
2 The tree database can be explored on a map at https://tree-map.
nycgovparks.org/, last visited 21 May 2019.

field and can be used to support decision makers by providingthem with optional planting sites along a Pareto frontier.
We do see however, that some venues indicated by ourcriterion as tree lacking seem to actually be in a green area.
We believe that the application of our method can be improved
with a more detailed tree location data set. Then, the criterion
proposed in this paper can be a meaningful addition to theestablished site selection criteria.
ACKNOWLEDGMENTS
We thank Foursquare and the organisation of Netmob for organising the
Future Cities Challenge and providing us with access to Foursquare’s venue
interaction data set.

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A PPENDIXTABLE I: The venues that are, according to the Pareto analysis, the most efficient to place trees next to.
DegreeTree density
coefficient

Venue ID

Venue Name

LatitudeLongitude

Venue Category

A

1864

2.09323399

4b637f59f964a5207b7e2ae3

MTA Subway - West Farms
Square/E Tremont Av (2/5)

40.8402-73.8800

Metro Stations

B

1561

1.94936904

4f940fe7e4b059d7da88be53

Junction Blvd

40.7491-73.8694

Miscellaneous Shops

C

1212

1.86431926

4e7647cffa76059701632021

MTA Subway - 179th St (F)40.7125-73.7846

Metro Stations

D853

1.70625431

4bace08af964a520cf143be3

Sammy’s Fish Box Restaurant

40.8390-73.7836

Seafood Restaurants

E532

1.55978734

4cc86db294e1a0933e6c978b

Rockaway Beach - 116th Street

40.5779-73.8359

Beaches

F
G

305
112

1.18353191
1.18192331

4abcfe4bf964a520fa8720e3
4c516433d2a7c9b6c4c61911

Hulu Theater
Bean & Bean Organic Coffee

40.750940.7509-73.9941-73.9941

Music Venues
Coffee Shops

H

98

0.87556379

4debdb6b52b11677f060802e

Peggy Rockefeller Rose Garden

40.8592-73.8735

GardensI

40

0.41121454

4d93a4489ef2721e6bffc3d2

I-495 / Grand Central Parkway
Interchange

40.7400-73.8455

Intersections

J

12

0.39198899

4e26fd0f1f6eb1ae139ad929

TSA Security Screening

40.6457-73.7762

General Travel

Love of food: Do eateries drive
London property prices?
Matthias Qian, Charles Rahal and Jan Callies

Abstract

We show that Foursquare venue interaction data can be used
to predict out-of-sample changes in property prices. The
Foursquare venue interaction data is first used to identify
London cafés, restaurants and bars with a surge in popularity.
We then show that the neighbourhood around eateries ofrising popularity exhibited a 4% higher property price growthrate in 2018 as compared with the rest of London properties.
The difference in property growth rates is statisticallysignificant. We conclude that the change in popularity ofeateries can forecast property prices.

Introduction

As the Irish playwriter George Bernard Shaw noted, “Thereis no love sincerer than the love of food.” In a similar vein,
the World Happiness Report by Helliwell et al. (2012) argues
for the essential role of food for the happiness levels of
populations. Yet, are epicure homebuyers willing to pay a
premium to live close to their favourite eateries? In thispaper, we study whether residential property prices areaffected by the availability of popular cafés, restaurants orbars, which we collectively call eateries.
The literature on gentrification reveals that popular eateries
are, of course, only one of many sources of abnormal
property price returns. Zhou et al. (2017) identify an effectof cultural investment on urban deprivation, while
Henneberry (1998) measures the effect of improved
infrastructure on property markets. Glaeser et al. (2018)highlight the role of local groceries as an indicator for
gentrification.
We introduce the concept of eateries gentrification which
we define as the price premium of residential real estate
induced by the availability of popular cafés, restaurants andbars in its neighbourhood. Eateries gentrification does notrefer to the location decision of venue managers, who havean incentive to locate their services next to epicure citizens.
Instead, we refer with our term only to the causal
relationship from popular eateries towards higher property
prices and property rents.
Reasons for eateries gentrification are threefold. First, theprice premium on residential real estate is justified throughthe productivity gains of having a popular food source inclose proximity to the home. This holds particularly true forthe ‘can’t cook, won’t cook’ households who rely on eateries
as their primary food source. Venues in close proximity totheir home can reduce the time they spend to procure foodand ‘shoe-leather’ costs. Second, popular eateries in theneighbourhood can improve the relative ranking of a
1 The Telegraph, for example, mentions the restaurants in Brixton Village,
as a driver for higher house prices in Brixton:
https://www.telegraph.co.uk/property/house-prices/how-the-uks-bestrestaurants-are-driving-up-property-prices/

property in the preference relation of prospective buyers or
renters. More affluent households can better realise theirpreferences by outbidding competitors and thereby driving
up property prices. Third, eateries gentrification can be
explained via positive spill-over effects. These popular
services can encourage existing property owners to improve
the quality of their capital stock which can in turn attract
more affluent households. Quality food services can kickstart a virtuous cycle of neighbourhood improvements.
Pundits have long suggested neighbourhoods to blossom
around well-received eateries, but their analysis was all too
often limited to anecdotical evidence.1 The contribution of
our paper is to put the hypothesis of eateries gentrification
to a rigorous statistical test. Specifically, we make threecontributions. First, we showcase a novel methodology to
generate network based predictive features for property
prices. Second, we establish the usefulness of a large-scale
dataset by Foursquare with venue interactions to predict thereturns on housing. Third, our results suggest the use offinancial and regulatory incentives to establish new eateries
as a powerful tool at the disposal of urban planners.
Figure 1: Comparison of OPC property returns for all ofLondon and the selected OPCs only.
5%
3.75%
Δ 3.7%, p-value: 0.03
2.5%

4.8%

1.25%

1.1%
0%

London OPCs

Selected OPCs

Note: The p-value refers to the Welch test of difference in means withunequal subsample variances. The error bars refer to confidence intervals
on the individual mean return estimates. OPC stands for outward
postcode.

Data

Our analysis relies on two distinct large-scale datasets that
we bring together to put the hypothesis of eateries
gentrification to a test. Our first dataset comes from
Foursquare, an infamous location technology platform. Theirlongitudinal mobility dataset describes venue interactions in
London from April 2017 to March 2019. It lists the ties
between pairs of venues (over 7 million of them) which areequally sampled between the months.
To protect the privacy of individuals, the data does not
specify the exact day of a venue interaction. Instead, thetemporal dimension of the data is aggregated to a monthly
frequency.
We match Foursquare’s venue interactions dataset withinformation on the geographical location of venues tocompute distances individuals are willing to travel to visit avenue. We use these distances to compute the degree
centrality of venues in a directed and weighted network.
The second large scale dataset is the price paid dataset from
Her Majesty's Land Registry. It lists from 1995 onwards thetransaction price of residential property sales. In total, theLand Registry details over 24 million property transactions.
We match Foursquare venues with properties in the Land
Registry via outwards postcode (OPC) districts. In Britain,
every building is assigned a postcode of the form: LLNA NLL
where L is a letter, N a number and A alphanumeric. The
second L and first A are optional. The first two (optionallyone) characters refer to the postcode area, the second two(before the space) the outward postcode district, then the
postcode sector, and the final two letters the individual
unit\low level clusters of addresses. The outward postcode
clusters hundreds or thousands of buildings intoneighbourhoods.
Figure 2: The 2018 property returns for London OPCs

the ties. To define our measure of node centrality, we first
introduce a measure for the degree of a focal node as(!"# = % &"'# ,')*where , is the focal node, - represents all other nodes, t is
the timeframe, . is the total number of nodes, and & is theadjacency matrix, in which the cell &"' is 1 if node , isconnected to node -, and 0 otherwise. The node strength,
which is the sum of weights, is(/"# = % 0"'# .
')*As we consider a directed network, we distinguish between
ties that originate, ! 23# , and that are directed towards a
node, ! "4 . The two node centrality measures are defined as:
9/"#23#=>?
8956723# (,, ;) = !"# × A 23# B
!"#
D
/"#"4
8D
EF
567"4 (,, ;) = !"# × A "4 B!"#
where α is a positive tuning parameter. It balances the node
strength and the number of contacts.
As our measure of popularity, we choose to use the latter
8D(,, ;). In ourof the two node centrality measures, 567"4
analysis, we choose to use α = 0.5. With this choice of thetuning parameter, given a fixed total node strength, ahigher number of contacts over which the strength isdistributed increases the value of our measure.

Note: OPC stands for outward postcode.

Quantitative Measures

In this section we explain how we compute the key
quantitative measures for our analysis which are first,
popularity measures for venues, and second, changes inproperty prices.
Popularity
Our measurement for popularity exploits the venue
interactions in the Foursquare dataset. We use the Opsahl etal. (2010) node centrality measure for weighted and directed
networks, where the weight is based on the geographicaldistance between venues. Their central idea is that
popularity can be defined as the diversity of connectedvenues. A higher number of connected nodes should resultin higher centrality measure for our focal node.
On the other hand, popularity can be defined as costs that
customers are willing to take on to arrive at a venue. The
costs grow proportionally with the distance between venues
and only popular venues are able to attract a substantial
customer base beyond the local neighbourhood. Our node
centrality measure allows us to trade-off between thenumber of relationships between nodes and the strength of

Change in popularity
We identify a change in popularity of venues using anunequal variances t-test, also known as Welch’s t-test from
Welch (1947). Specifically, we compare the mean of
8D(,, ;) before and after January 2018. We then select567"4those venues, for which there was the largest increase incentrality, as measured by the p-value of Welch’s test. We
make the discretionary choice of limiting our attention to ten
postcodes that contain the twelve eateries with the largest
increase in popularity.
Property prices
To compute OPC level real estate indices, we employ therepeat-sales methodology introduced in Bailey et al. (1963).
The idea is to convert the index construction problem to aregression problem, by taking the logarithm of
L#K
J"##K =
× M"##K
L#
where J"##K is the ratio of the final sales price in period t’ toinitial sales price in period t for the i-th pair of transactions
with initial and final sale in these two periods. L# and L#K are
the true but unknown indexes for period ; and ;′, where ; =0, 1, … , Q − 1 and ; K = 1, … , Q. The term M"##K is thetransaction specific error term, where we will assume thatits log form S"##K has zero mean and the same variance T U ,and that they are all uncorrelated to each other. The
estimation of the unknown B’s is treated as a regression
problem. Let &# take the value -1 if period t is the period of
initial sale, +1 if the period of final sale, and 0 otherwise foreach pair of transactions. Let LV = 1 and WV = 0. Based ontaking the logarithm, the regression problem is thenZ

X"## Y = % W' &' + S"## Y')*

To make the computation of the real estate price index morerobust, we limit ourselves to consider only those outward
postcode districts with more than 100 property transactions
per year since 1995.
Residential property returns vary widely across outwards
postcode (OPC) districts. As Table 1 shows, the best andworst performing OPCs in London in 2018 have over 30%
annual gains and losses, respectively. The standard deviationof OPC property returns in 2018 was 7.6%. The average
return was 1.1%. In total there were 118,247 propertytransactions in London; on average 425 property transaction
per OPC. Figure 2 shows a map for the London OPCs withcolour-coded 2018 property returns. It indicates thatproperty returns are spatially correlated. Further, we seethat the threshold of 100 property transactions per year isparticularly binding in the centre of London, which isrepresented by greyed out OPCs.
Table 1: Best & worst OPC property returns in 2018
Best performers
OPC
Count Return
SW17
877
31.7%
E14
1815
16.5%
TW2
422
16.4%
SE3
564
14.0%
SE28
246
12.4%
KT9
261
12.2%
E12
215
11.8%
RM12
485
11.0%
CM23
793
10.9%
TW12
339
10.9%

Worst performers
OPC
Count Return
UB9
242-39.5%
W10
229-39.5%
KT17
383-20.9%
KT8
333-19.5%
SE12305-18.3%
W1H
111-13.5%
SM3
336-11.9%
SE22
463-11.4%
N18
235-11.1%
SW12
527-10.6%

Note: OPC stands for outward postcode.

Analytical strategy

We follow three steps to identify the eateries gentrification
effect. First, we filter the Foursquare data for cafés,
restaurants bars. We screen the filtered list of eateries forvenues with large increases in popularity after January 2018.
Second, we compute OPC level price changes in residential
property prices for the full year of 2018. Third, we perform ahypothesis test about the significance of the restaurant
gentrification effect.
The filtering of eateries follows the factor modelling
literature for explaining the cross-section of equity returns,
as pioneered by Fama and French (1993). We sort postcodesby the popularity of eateries, while they sort equities via sizeand value factors.
We consider changes in the popularity of venues, instead ofthe level of popularity, to attempt to identify a causal
relationship. The locality of popular venues may not be
random, as the manager of the venue may choose to locate
his eatery in an affluent neighbourhood. Our identifying
assumption is that the change in the popularity of venues isdriven by exogenous factors unrelated to property prices.

The interpretation of the eateries gentrification effectchanges with an analysis of changes in popularity of venues.
While we conceptualised eateries gentrification as a pricepremium on residential property due to popular eateries, ananalysis in changes in popularity then corresponds todifferences in returns on property. We thus find evidence ofeateries gentrification if we reject the following null
hypothesis:\V : µ_2_3`ab = µc24d24
where µ_2_3`ab is the expected 2018 property return inpostcodes with venues of rising popularities and whereµc24d24 is the corresponding expectation for the rest of
London.

Results

Table 2 displays the 12 eateries in the ten OPCs that had thelargest increase popularity post-2018. On average,
customers visit these venues from 20 distinct connectedvenues per month. They travel on average 3.9 kilometres tovisit these eateries. The surge in popularity, as measured byour degree centrality indicator, pre- and post-2018 was 5.89
on average.
Table 2: Characteristics of selected eateries
Name
What the
Pitta
Leon
Starbucks
The
SpreadEagle Bar
Costa
CoffeeStarbucks
GoodfareRestaurant
Panzer's
Costa
CoffeePret A
MangerStarbucks
Pret A
MangerCategory
Vegan
Restaurant
Café
Coffee
Shop
Bar

OPC
E1

Nodes
19

Dist
4.0

Surge
4.5

E14
E16

36
11

3.7
4.0

2.3
5.9

E2

9

1.6

2.3

CoffeeShop
CoffeeShopItalianRestaurant
Deli
CoffeeShop
Sandwich
Place
CoffeeShop
Sandwich
Place

E3

21

4.6

2.2

NW1

21

3.0

31.0

NW1

7

3.1

5.0

NW8
SE18

22
20

3.2
6.4

4.0
3.0

SW5

21

4.1

3.0

W6

32

4.7

2.8

W6

21

4.0

3.0

Note: ‘Nodes’ refer to the number of interactions that the venue has with distinct
other venues on average per month. ‘Dist’ refers to the average distance to theconnected venues measured in kilometres. ‘Surge’ refers to the multiple ofFoursquare check-ins before and after January 2018. OPC stands for outward
postcode.

Figure 1 showcases an example for the surge venue
popularity. We fitted the time-varying mean to the timeseries to highlight the structural break in its popularity.
We find high predictive power of the Foursquare venue
interaction data for out-of-sample property pricemovements. On average, OPC districts with one of theselected London venues, exhibit a 4% higher property pricegrowth than postcode districts without these venues. Figure
1 illustrates the differences in 2018 property returns. A

Welch test at a 5% significance level rejects the null
hypothesis that the expected return for OPCs of eateries
with rising popularity is equal to the return of all London
OPCs. Thus we have established eateries gentrification in
London, which we estimate to be 4% in 2018.

recessions? How does it compare to supermarket
gentrification or the London Crossrail gentrification? While
open questions remain, one fact is undeniable. The
Foursquare dataset provides valuable information to predict
residential property markets.

Figure 3: Popularity of “The Spread Eagle Bar”

References

Bailey, Martin J., Richard F. Muth, and Hugh O. Nourse. "Aregression method for real estate price index construction."
Journal of the American Statistical Association 58, no. 304(1963): 933-942.

Normalised Popularity(April 2017 = 1)

Fama, Eugene F., and Kenneth R. French. "Common risk
factors in the returns on stocks and bonds." Journal of
financial economics 33, no. 1 (1993): 3-56.

2.3x multiple

Glaeser, Edward L., Hyunjin Kim, and Michael Luca.
"Nowcasting gentrification: using yelp data to quantify
neighborhood change." In AEA Papers and Proceedings, vol.
108, pp. 77-82. 2018.
These results hold in a difficult market environment for
London residential property, which has been dominated byconcerns over the outcome of the Brexit referendum. This
explains why the property price returns in 2018 for theselected OPCs is still modest at an annual growth rate of 5%.
Table 3: The 2018 property returns on selected OPCs
Name

OPC

Return

Leon

E14

16.5%

The Spread Eagle Bar

E2

10.5%

Starbucks

E16

6.2%

Pret A Manger

SW5

6.1%

Costa Coffee

SE18

5.2%

What The Pitta

E1

4.5%

Starbucks & Goodfare

NW1

3.8%

Costa Coffee

E3

1.1%

Starbucks & Pret A Manger

W6

-0.2%

Panzer's

NW8

-0.7%

Average Price Increase in selected OPCs

4.8%

Average prince increase in all London OPCs

1.1%

T-statistic for Welch’s test

2.5

P-value of Welch's test

3.2%

Note: OPC stands for outward postcode.

Conclusion

The goal of this paper is to both test the hypothesis of theexistence of eateries gentrification and to estimate themagnitude of this effect. We construct a measure for the
popularity of restaurants and show that postcodes withcafés, restaurants and bars of rising popularity exhibit higher
investment returns on residential property. The effect isstatistically significant. In London of 2018, the effect size has
been 4 percent.
Our results raise further questions. Does the eateries
gentrification effect change over time? Does it persist in

Helliwell, John F., Richard Layard, and Jeffrey Sachs. "World
happiness report." (2012).
Henneberry, John. "Transport investment and house
prices." Journal of Property Valuation and Investment 16,
no. 2 (1998): 144-158.
Opsahl, Tore, Filip Agneessens, and John Skvoretz. "Nodecentrality in weighted networks: Generalizing degree andshortest paths." Social networks 32, no. 3 (2010): 245-251.
Welch, Bernard L. "The generalization ofstudent's' problem
when several different population variances are involved."
Biometrika 34, no. 1/2 (1947): 28-35.
Zhou, Xiao, Desislava Hristova, Anastasios Noulas, Cecilia
Mascolo, and Max Sklar. "Cultural investment and urbansocio-economic development: a geosocial network
approach." Royal Society open science 4, no. 9 (2017):
170413.

Metaheuristic macro scale tra�ic flow optimisation from urban
movement data
Laurens Arp∗
Dyon van Vreumingen∗

Daniela Gawehns
Mitra Baratchi{l.r.arp,d.van.vreumingen}@umail.leidenuniv.nl
Leiden Institute for Advanced Computer Science
Leiden, The Netherlands

ABSTRACTHow can urban movement data be exploited in order to improvethe �ow of tra�c within a city? Movement data provides valuable
information about routes and speci�c roads that people are likely
to drive on. This allows us to pinpoint roads that occur in manyroutes and are thus sensitive to congestion. Redistributing someof the tra�c to avoid unnecessary use of these roads could be a
key factor in improving tra�c �ow.Many proposed approaches
to combat congestion are either static or do not incorporate any
movement data. In this work, we present a method to redistributetra�c through the introduction of externally imposed variable costs
to each road segment, assuming that all drivers seek to drive thecheapest route. We use a metaheuristic optimisation approach tominimise total travel times by optimising a set of road-speci�cvariable cost parameters, which are used as input for an objectivefunction based on tra�c �ow theory. The optimisation scenario forthe city centre of Tokyo considered in this paper was de�ned using
public spatial road network data, and movement data acquired from
Foursquare. Experimental results shows that our proposed scenario
has the potential to achieve a 62.6% improvement of total travel
time in Tokyo compared to that of a currently operational road
network con�guration, with no imposed variable costs.

KEYWORDS
tra�c �ow, urban movements, metaheuristics

1

INTRODUCTION

Even though extensive road networks have been developed to satisfy the high demand for vehicular transportation, overoccupancy
of roads still occurs on a daily basis, causing tra�c jams which hurt
the environment, the economy and the drivers’ moods. Finding
a solution to tra�c congestion is a challenging problem that has
occupied many in the past century. After all, tra�c dynamics aredi�cult to predict, due to complex �uctuations in tra�c demand,
both spatial and temporal. This makes it hard to devise a protocol
for tra�c �ow redistribution that works well in varying conditions.
To date, various approaches have been proposed to alleviate congestion in some way [8]. However, these methods tend to be either
static, data-independent protocols, micro-scale solutions (on thelevel of individual roads) or primarily driven by theoretical models.
Our objective instead is to construct a dynamic, data-driven, macroscale (road network level) approach to address tra�c congestion.
In this sense, dynamic means that a solution can be adapted to new
tra�c data with relative ease.
∗ Both authors contributed equally to this research.

{d.gawehns,m.baratchi}@liacs.leidenuniv.nl
Leiden Institute for Advanced Computer Science
Leiden, The Netherlands
In this work, we propose a method for tra�c redistribution fueled by metaheuristic optimisation, which we test on the case of thecity centre of Tokyo. We seek to shift the tra�c situation away froma state where each driver chooses the fastest, or shortest, route (thuscausing congestion on roads that occur in many shortest routes),
towards a system optimal equilibrium, as coined by Wardrop [14],where the total travel time for all drivers is minimised. By introducing externally imposed variable costs (e.g. tolls, or any other�nancial or non-�nancial method a supervisory institution might
deploy) on each road, we aim to discourage drivers from all taking
the same congested roads. This approach asserts that, on average,
each driver is willing to take the cheapest route from their point of
departure to the destination, where the total costs to drive a routedepend both on the distance travelled, through a spatial cost, andthe imposed variable costs encountered along the route.
In order to make predictions of tra�c �ow and occurrence ofcongestion, we infer tra�c demand from a data set of urban movements. A number of public tra�c data sets, such as the Dutch NDW[11], report the tra�c �ow or density at certain points in time;
however, while this gives a detailed picture of a local situation, it
provides no information as to what routes drivers are following.
Hence, such data is of little use when we wish to redistribute tra�cby encouraging sensible alternative routes. For this reason, we use
a data set of urban movements, provided by Foursquare as part of
the Future cities challenge, which allows for the inference of tra�clevel information needed for this research such as the origin anddestination of movements [2].

2

RELATED WORKThe objective of combatting tra�c congestion by altering road network setups has been addressed in a large body of work. The use ofroad pricing as a means to achieve this goal is a prevalent approach[6, 13, 15, 16]. In this context, the marginal cost of congestion is afrequently employed measure to assess the optimal road pricing.
A key di�erence between these papers and our work is that thepreviously proposed road pricing policies are �xed (e.g. charge a fee
within a certain radius of the city centre) instead of dynamic, and do
not follow from an optimisation procedure based on actual movement data. Approaches for optimising road networks and tra�c
�ow from a di�erent viewpoint, unrelated to road pricing policies,
include metaheuristic optimisation of road improvements [3], development of intelligent tra�c light systems [10], optimisation of roadgraph architectures with evolutionary algorithms [12], and prediction of optimal tra�c �ow through maximum-entropy methods [9].
A more exhaustive list of methods is provided by Kumar Shukla
and Agrawal [8]. In this work, we explore the use of optimisation

L. Arp, D. van Vreumingen, D. Gawehns and M. Baratchi
algorithms in proposing a dynamic pricing mechanism using actual
movement data.

3

PROBLEM STATEMENT

The problem of optimising tra�c �ow through adaptive road pricing
is twofold. First, we must estimate tra�c �ow and congestion ina road network, which is the underlying cause of high total travel
time when all drivers follow the cheapest routes from their origins
to their destinations. By combining movement data describing thetra�c demand in the network and the spatial road network data,tra�c �ow theory yields these estimations. The demand data is a
set D M consisting of movements between venue locations within
the road network. Each element of this set (indexed as k) is a tuple(Ak , Bk , Nk ) where Ak and Bk are elements of a venue data set
DV containing spatial information about the venues, and Nk is therecorded frequency of the speci�c movement from Ak to Bk . Second,
having found a method to express the total travel time as a functionof the variable cost parameters, we aim to optimise the parameters
for minimal total travel time. Our methods for addressing thisoptimisation problem are set out in detail in the next section.

4 METHODS
4.1 Tra�c �ow estimation
4.1.1 Road network and routing model. The �rst step towards prediction and minimisation of congestion is to represent the physicalroad network as a planar graph G that has road segments for edges,
which may be traversed in order to travel from an origin to a destination. Speci�cally, the graph is a tuple G = (V , E, S) with V thenode set, E the edge set and S the set of Haversine lengths of alledges. The node set V contains intersections in the road network,
as well as nodes for the origin and destination locations from DV .
We then introduce, for each road segment (i, j) 2 E in the graph,a cost that a driver needs to pay to traverse this segment. The mainpart of this cost is a variable cost pi j . All variable cost parameterscollectively form the variable cost vector P which we seek to optimise for minimum congestion. Next, we assign to each segmenta spatial cost, which is an immutable base cost for travelling fromnode i to j that is linearly dependent on the length si j of the segment by a tunable factor s . Since the movement data is aggregatedinto frequency numbers, and is not provided on an individual level
for anonymity reasons, we take s to be equal for all drivers. Put
together, the total cost for a driver to travel via a connected routeof segments R = {(i, j)} from some origin to a destination, is givenby the sum of the individual segment costs occurring on the route:’cost(R) =(1)
s s i j + pi j .
(i, j)2R

For the development of tra�c �ow, we assert that all drivers aresel�sh and seek to drive the route which incurs the lowest total cost.
These routes can be found using a weighted shortest path algorithm.
Note that if pi j = 0 on all edges, each driver will drive the route oflowest spatial cost, which is exactly the shortest route. From thecheapest routes, which are jointed collections of segments, and thefrequency numbers Nk , we can predict the vehicle count ni j oneach segment, from which the degree of congestion is computed as
set out in the following subsection.

4.1.2 Congestion model. In our congestion model, we assume thatthe �ow of tra�c on a road segment is fully described by a Greenshields fundamental tra�c �ow curve [4], which is a widely usedtheoretical model for predicting tra�c dynamics on a road segment.
The used variables are �ow f (vehicles passing by per unit time)and density (vehicles present per unit length). In this model, there
exists a maximum density m that the road can support, beyond
which the total �ow is zero. Furthermore, there is a critical density c at which the �ow reaches its maximum value: f ( c ) = fm .
Naturally, f (0) = 0, as no �ow exists when no cars are present.
A basic curve that �ts this description is a concave quadratic
function, with zero �ow at > m , which we de�ne asfm
f ( ) = 2 max 0, [2 c
],
(2)
c

where we note that, since f is quadratic in , m = 2 c . The
maximum �ow is directly related to the critical density; assumingthat the tra�c is able to drive at the maximum allowed speed mwhen the density is at its critical point, we set fm = c m .
From this density-�ow dependence, we can extract the spacemean speed ( ), the average speed of all vehicles on the roadsegment, as [4]✓◆f( )( ) = min m ,,(3)where again the maximum speed enters the relation, this time as abound on the space mean speed. Finally, the space mean travel timet( ) on the segment, taken to have length s, is computed asst( ) =.
(4)( )From the expected number of vehicles ni j on each road segment,
we obtain the segment density as i j = ni j /si j . For multi-laneroads, we divide this number by the number of lanes. By inserting
the segment density into the density-time relation described above(eq. 4), we �nd the space mean time ti j spent on the segment. The
collection of segment travel times then leads to the de�nition of theobjective function for optimisation by a metaheuristic algorithm.

4.2

Parameter optimisation

4.2.1 Objective function. The objective function computes the measure obj(P), which re�ects the extent to which the system optimalequilibrium is reached by a variable cost con�guration P. This equilibrium occurs when the total travel times for each driver on theirroutes are minimal. As such, we de�ne the objective function as
the total space mean travel time over all routes driven on the road
network. This total travel time can be conveniently expressed using
the segment vehicle counts ni j , which are directly dependent on P:’
obj(P) =ni j (P) ti j (P).
(5)(i, j)2E

Note that each segment mean travel time ti j is also a function of P
since it is dependent on the vehicle count ni j . Algorithm 1 shows,in pseudocode, the routine to compute the objective function.
4.2.2 Optimisation algorithms. The purpose of the optimisation
algorithms is to �nd an optimal variable cost con�guration suchthat the value of the objective function, the total travel time, isminimised. In principle, any black-box metaheuristic optimisation

Metaheuristic macro scale tra�ic flow optimisation from urban movement data
Data: road network graph G = (V , E, S ) with node set V inferredfrom venue data DV , E and S inferred from road network data;
movement data D M ;spatial cost factor s
Input: parameter vector P
Result: objective function value obj(P)initialise n i j

0, t i j0,

ij

0 for all (i, j) 2 E// Compute predicted vehicle counts n i j on each segment
forall origin-destination-freq. tuples (Ak , B k , N k ) in D M do
Find cheapest route R k from Ak to B k according to s and P
using weighted shortest-path algorithm
forall (i, j) 2 R k do
ni j
ni j + Nk
end
end// Find mean travel times t i j on each segment
forall (i, j) 2 E do
n i j /s i jij
ti jt( ij )
(eqs. 2–4)
end
Õobj(P)(eq. 5)(i, j)2E n i j t i j
return obj(P)Algorithm 1. Routine for computing the objective function
for a given parameter vector P.
algorithm could be used to search for local optima of variable costcon�gurations which might approximate a system optimal equilibrium. That said, for these purposes, algorithms which are robust
for high-dimensional problems are preferred, as the number of parameters increases proportionally to the number of edges in thegraph.
For our proof-of-concept implementation we use simulated annealing (SA) [7], which is a variation of hill climbing where worse
solutions can get accepted depending on the algorithm’s decreasing
temperature value, and a genetic algorithm (GA) adapted for continuous optimisation. For both algorithms each iteration contains
40 objective function evaluations; after one iteration, the GA updates its population, whereas SA resets its temperature value. Bothalgorithms use mutations generated using a normal distribution
with zero mean and unit variance, at a mutation rate of 0.2 perparameter.

5

CASE STUDY: TOKYO CITY CENTRE

In order to test our tra�c �ow optimisation method, we applied
it to movements inside the city centre of Tokyo (i.e. excluding theGreater Tokyo area). We brie�y discuss the movements and road
network used for the case study, and present experimental results.

5.1

Movement data

The movement data was provided by Foursquare as part of the
Future cities challenge [2]; we selected only those parts related to theTokyo city centre. The data contains a list of venues together with
their GPS locations, forming the data set DV introduced in section
3, and a list of movements between the venues. The movement datacontains movements of the same form as the tuples in D M , but withadditional indications of the month during which the movement
occurred, and the time of the day (periods of 4 to 6 hours). We

Figure 1. Road network graph example. Green nodes arevenues, red nodes are intersections. Map source: Google
Maps [5].
considered only movements made in the afternoon, and combined
the frequencies Nk for the same movement in di�erent months
into a single �gure. Since the Foursquare dataset does not cover
the entirety of vehicular movements inside Tokyo (and, in fact,
also includes other types of movements such as subway, biking
and walking trips), we viewed the frequencies as ratios rather thanabsolute numbers, asserting the law of large numbers for su�cientaccuracy. We then normalised the frequencies to numbers that theroad graph we constructed (see next subsection) could support.
As a last modi�cation, we selected the venues occurring in the
100 most popular (i.e. frequent) routes, and clustered all othervenues together with their nearest neighbour (in terms of Haversine distance) from the set of most popular venues. The routes
were clustered accordingly, going between venue clusters insteadof individual venues. This was done in order to substantially reduce
the number of routes and therewith the computational complexityof the problem.

5.2

Road network data

The road network data used is based on the Asia shape�le provided
by the Earthdata Global roads open access data set [1]. It contains
information on the road networks of the entirety of Asia with avariable resolution; for the city Tokyo, its resolution is well suitedfor the algorithm. The road data is translated into a graph representation by �nding intersections between lines, and turning these into
intersection nodes. The lines themselves are used to create edges
between intersections. Venue nodes are created by identifying thelocation of the venues from the venue data set, which are connectedto the nearest intersection node.

5.3

Experimental results

The improvement progress for both optimisation algorithms isshown in �gures 3 and 4. For comparison, we ran the objectivefunction once with all variable costs set to 0 to obtain the default
�ow of the road network. This con�guration had an objective valueof around 416 million hours spent on the road network. It should be
noted that the objective function values are higher than what would
be a realistic amount of time spent on roads. As the theoreticaltra�c �ow models do not take relief methods into account for fullycongested roads, the speed on those roads in considered to be zero.
In these cases, we use an arbitrary low value to represent the speed
on the road, resulting in somewhat unrealistic amounts of hours.

L. Arp, D. van Vreumingen, D. Gawehns and M. Baratchi
fast as possible. Future work could include more thorough exploration of optimisation algorithms and their parameter settings.

6

Figure 2. Objective function value plotted to the number of
GA iterations. Values are the total time spent on the roads
over 6 hours in the afternoon.

CONCLUSION

We have shown that we can successfully address tra�c congestion
by redistributing tra�c through imposing of variable road segment
costs, and optimising this cost con�guration using metaheuristic
algorithms. The best variable costs con�guration was found by asimulated annealing routine, improving upon the total travel timecorresponding to a con�guration with zero variable costs by 62.6%.
Both simulated annealing and a genetic algorithm were e�ective at
optimising solutions.
Though the practical implementation of the variable costs may
be another non-trivial problem to address �rst, the positive results
show that, at least conceptually, this method could result in improved tra�c �ow when applied in practice. Consequently, cities
may enjoy shorter travel times, better accessibility, cleaner air and,not unimportantly, improved drivers’ moods.

ACKNOWLEDGMENTS
We would like to extend special thanks to Foursquare and Netmob
for the movement data and to Giorgos Kyrziridis for his support.

REFERENCES
Figure 3. Objective function value plotted with the numberof SA iterations. Values are the total time spent on the roads
over 6 hours in the afternoon.
Both the GA and SA were able to �nd solutions which substantially improved tra�c �ow over the network. After 30 iterations,the lowest �tness value of the GA was around 175 million hours(an improvement of 57.9%), and the lowest �tness value of SA wasaround 155 million hours (an improvement of 62.6%). E�ectively,
this means the solution found by SA was able to reduce the total
amount of hours spent over a period of 6 hours in the afternoon
by 261 million, due to having found a good distribution of variable
costs for each road segment. Although the results of the GA were
not as good as those of SA, the GA was still successful in improving
the unoptimised con�guration, which supports the view that any
optimisation algorithm could be used for these purposes.
Interestingly, even the �rst iterations of the algorithms showedvalues which were an improvement compared to the default tra�c
�ow. This is likely due to the nature of the shortest path algorithm
when taking only distance into account, which sends many cars
over the same roads unnecessarily. Though this type of behaviour
was the basic premise allowing us to optimise, given the large variety of available, very similar roads in the network, any deviationfrom the over-use of single roads caused by the random initialisation of variable costs would result in better tra�c �ow. From sucha randomly initialised state, both algorithms then improved thesolutions such that the more well-suited alternatives were used.
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phpro.2012.02.231

Love of food: Do eateries drive
London property prices?
Matthias Qian, Charles Rahal and Jan Callies

Abstract

We show that Foursquare venue interaction data can be used
to predict out-of-sample changes in property prices. The
Foursquare venue interaction data is first used to identify
London cafés, restaurants and bars with a surge in popularity.
We then show that the neighbourhood around eateries ofrising popularity exhibited a 4% higher property price growthrate in 2018 as compared with the rest of London properties.
The difference in property growth rates is statisticallysignificant. We conclude that the change in popularity ofeateries can forecast property prices.

Introduction

As the Irish playwriter George Bernard Shaw noted, “Thereis no love sincerer than the love of food.” In a similar vein,
the World Happiness Report by Helliwell et al. (2012) argues
for the essential role of food for the happiness levels of
populations. Yet, are epicure homebuyers willing to pay a
premium to live close to their favourite eateries? In thispaper, we study whether residential property prices areaffected by the availability of popular cafés, restaurants orbars, which we collectively call eateries.
The literature on gentrification reveals that popular eateries
are, of course, only one of many sources of abnormal
property price returns. Zhou et al. (2017) identify an effectof cultural investment on urban deprivation, while
Henneberry (1998) measures the effect of improved
infrastructure on property markets. Glaeser et al. (2018)highlight the role of local groceries as an indicator for
gentrification.
We introduce the concept of eateries gentrification which
we define as the price premium of residential real estate
induced by the availability of popular cafés, restaurants andbars in its neighbourhood. Eateries gentrification does notrefer to the location decision of venue managers, who havean incentive to locate their services next to epicure citizens.
Instead, we refer with our term only to the causal
relationship from popular eateries towards higher property
prices and property rents.
Reasons for eateries gentrification are threefold. First, theprice premium on residential real estate is justified throughthe productivity gains of having a popular food source inclose proximity to the home. This holds particularly true forthe ‘can’t cook, won’t cook’ households who rely on eateries
as their primary food source. Venues in close proximity totheir home can reduce the time they spend to procure foodand ‘shoe-leather’ costs. Second, popular eateries in theneighbourhood can improve the relative ranking of a
1 The Telegraph, for example, mentions the restaurants in Brixton Village,
as a driver for higher house prices in Brixton:
https://www.telegraph.co.uk/property/house-prices/how-the-uks-bestrestaurants-are-driving-up-property-prices/

property in the preference relation of prospective buyers or
renters. More affluent households can better realise theirpreferences by outbidding competitors and thereby driving
up property prices. Third, eateries gentrification can be
explained via positive spill-over effects. These popular
services can encourage existing property owners to improve
the quality of their capital stock which can in turn attract
more affluent households. Quality food services can kickstart a virtuous cycle of neighbourhood improvements.
Pundits have long suggested neighbourhoods to blossom
around well-received eateries, but their analysis was all too
often limited to anecdotical evidence.1 The contribution of
our paper is to put the hypothesis of eateries gentrification
to a rigorous statistical test. Specifically, we make threecontributions. First, we showcase a novel methodology to
generate network based predictive features for property
prices. Second, we establish the usefulness of a large-scale
dataset by Foursquare with venue interactions to predict thereturns on housing. Third, our results suggest the use offinancial and regulatory incentives to establish new eateries
as a powerful tool at the disposal of urban planners.
Figure 1: Comparison of OPC property returns for all ofLondon and the selected OPCs only.
5%
3.75%
Δ 3.7%, p-value: 0.03
2.5%

4.8%

1.25%

1.1%
0%

London OPCs

Selected OPCs

Note: The p-value refers to the Welch test of difference in means withunequal subsample variances. The error bars refer to confidence intervals
on the individual mean return estimates. OPC stands for outward
postcode.

Data

Our analysis relies on two distinct large-scale datasets that
we bring together to put the hypothesis of eateries
gentrification to a test. Our first dataset comes from
Foursquare, an infamous location technology platform. Theirlongitudinal mobility dataset describes venue interactions in
London from April 2017 to March 2019. It lists the ties
between pairs of venues (over 7 million of them) which areequally sampled between the months.
To protect the privacy of individuals, the data does not
specify the exact day of a venue interaction. Instead, thetemporal dimension of the data is aggregated to a monthly
frequency.
We match Foursquare’s venue interactions dataset withinformation on the geographical location of venues tocompute distances individuals are willing to travel to visit avenue. We use these distances to compute the degree
centrality of venues in a directed and weighted network.
The second large scale dataset is the price paid dataset from
Her Majesty's Land Registry. It lists from 1995 onwards thetransaction price of residential property sales. In total, theLand Registry details over 24 million property transactions.
We match Foursquare venues with properties in the Land
Registry via outwards postcode (OPC) districts. In Britain,
every building is assigned a postcode of the form: LLNA NLL
where L is a letter, N a number and A alphanumeric. The
second L and first A are optional. The first two (optionallyone) characters refer to the postcode area, the second two(before the space) the outward postcode district, then the
postcode sector, and the final two letters the individual
unit\low level clusters of addresses. The outward postcode
clusters hundreds or thousands of buildings intoneighbourhoods.
Figure 2: The 2018 property returns for London OPCs

the ties. To define our measure of node centrality, we first
introduce a measure for the degree of a focal node as(!"# = % &"'# ,')*where , is the focal node, - represents all other nodes, t is
the timeframe, . is the total number of nodes, and & is theadjacency matrix, in which the cell &"' is 1 if node , isconnected to node -, and 0 otherwise. The node strength,
which is the sum of weights, is(/"# = % 0"'# .
')*As we consider a directed network, we distinguish between
ties that originate, ! 23# , and that are directed towards a
node, ! "4 . The two node centrality measures are defined as:
9/"#23#=>?
8956723# (,, ;) = !"# × A 23# B
!"#
D
/"#"4
8D
EF
567"4 (,, ;) = !"# × A "4 B!"#
where α is a positive tuning parameter. It balances the node
strength and the number of contacts.
As our measure of popularity, we choose to use the latter
8D(,, ;). In ourof the two node centrality measures, 567"4
analysis, we choose to use α = 0.5. With this choice of thetuning parameter, given a fixed total node strength, ahigher number of contacts over which the strength isdistributed increases the value of our measure.

Note: OPC stands for outward postcode.

Quantitative Measures

In this section we explain how we compute the key
quantitative measures for our analysis which are first,
popularity measures for venues, and second, changes inproperty prices.
Popularity
Our measurement for popularity exploits the venue
interactions in the Foursquare dataset. We use the Opsahl etal. (2010) node centrality measure for weighted and directed
networks, where the weight is based on the geographicaldistance between venues. Their central idea is that
popularity can be defined as the diversity of connectedvenues. A higher number of connected nodes should resultin higher centrality measure for our focal node.
On the other hand, popularity can be defined as costs that
customers are willing to take on to arrive at a venue. The
costs grow proportionally with the distance between venues
and only popular venues are able to attract a substantial
customer base beyond the local neighbourhood. Our node
centrality measure allows us to trade-off between thenumber of relationships between nodes and the strength of

Change in popularity
We identify a change in popularity of venues using anunequal variances t-test, also known as Welch’s t-test from
Welch (1947). Specifically, we compare the mean of
8D(,, ;) before and after January 2018. We then select567"4those venues, for which there was the largest increase incentrality, as measured by the p-value of Welch’s test. We
make the discretionary choice of limiting our attention to ten
postcodes that contain the twelve eateries with the largest
increase in popularity.
Property prices
To compute OPC level real estate indices, we employ therepeat-sales methodology introduced in Bailey et al. (1963).
The idea is to convert the index construction problem to aregression problem, by taking the logarithm of
L#K
J"##K =
× M"##K
L#
where J"##K is the ratio of the final sales price in period t’ toinitial sales price in period t for the i-th pair of transactions
with initial and final sale in these two periods. L# and L#K are
the true but unknown indexes for period ; and ;′, where ; =0, 1, … , Q − 1 and ; K = 1, … , Q. The term M"##K is thetransaction specific error term, where we will assume thatits log form S"##K has zero mean and the same variance T U ,and that they are all uncorrelated to each other. The
estimation of the unknown B’s is treated as a regression
problem. Let &# take the value -1 if period t is the period of
initial sale, +1 if the period of final sale, and 0 otherwise foreach pair of transactions. Let LV = 1 and WV = 0. Based ontaking the logarithm, the regression problem is thenZ

X"## Y = % W' &' + S"## Y')*

To make the computation of the real estate price index morerobust, we limit ourselves to consider only those outward
postcode districts with more than 100 property transactions
per year since 1995.
Residential property returns vary widely across outwards
postcode (OPC) districts. As Table 1 shows, the best andworst performing OPCs in London in 2018 have over 30%
annual gains and losses, respectively. The standard deviationof OPC property returns in 2018 was 7.6%. The average
return was 1.1%. In total there were 118,247 propertytransactions in London; on average 425 property transaction
per OPC. Figure 2 shows a map for the London OPCs withcolour-coded 2018 property returns. It indicates thatproperty returns are spatially correlated. Further, we seethat the threshold of 100 property transactions per year isparticularly binding in the centre of London, which isrepresented by greyed out OPCs.
Table 1: Best & worst OPC property returns in 2018
Best performers
OPC
Count Return
SW17
877
31.7%
E14
1815
16.5%
TW2
422
16.4%
SE3
564
14.0%
SE28
246
12.4%
KT9
261
12.2%
E12
215
11.8%
RM12
485
11.0%
CM23
793
10.9%
TW12
339
10.9%

Worst performers
OPC
Count Return
UB9
242-39.5%
W10
229-39.5%
KT17
383-20.9%
KT8
333-19.5%
SE12305-18.3%
W1H
111-13.5%
SM3
336-11.9%
SE22
463-11.4%
N18
235-11.1%
SW12
527-10.6%

Note: OPC stands for outward postcode.

Analytical strategy

We follow three steps to identify the eateries gentrification
effect. First, we filter the Foursquare data for cafés,
restaurants bars. We screen the filtered list of eateries forvenues with large increases in popularity after January 2018.
Second, we compute OPC level price changes in residential
property prices for the full year of 2018. Third, we perform ahypothesis test about the significance of the restaurant
gentrification effect.
The filtering of eateries follows the factor modelling
literature for explaining the cross-section of equity returns,
as pioneered by Fama and French (1993). We sort postcodesby the popularity of eateries, while they sort equities via sizeand value factors.
We consider changes in the popularity of venues, instead ofthe level of popularity, to attempt to identify a causal
relationship. The locality of popular venues may not be
random, as the manager of the venue may choose to locate
his eatery in an affluent neighbourhood. Our identifying
assumption is that the change in the popularity of venues isdriven by exogenous factors unrelated to property prices.

The interpretation of the eateries gentrification effectchanges with an analysis of changes in popularity of venues.
While we conceptualised eateries gentrification as a pricepremium on residential property due to popular eateries, ananalysis in changes in popularity then corresponds todifferences in returns on property. We thus find evidence ofeateries gentrification if we reject the following null
hypothesis:\V : µ_2_3`ab = µc24d24
where µ_2_3`ab is the expected 2018 property return inpostcodes with venues of rising popularities and whereµc24d24 is the corresponding expectation for the rest of
London.

Results

Table 2 displays the 12 eateries in the ten OPCs that had thelargest increase popularity post-2018. On average,
customers visit these venues from 20 distinct connectedvenues per month. They travel on average 3.9 kilometres tovisit these eateries. The surge in popularity, as measured byour degree centrality indicator, pre- and post-2018 was 5.89
on average.
Table 2: Characteristics of selected eateries
Name
What the
Pitta
Leon
Starbucks
The
SpreadEagle Bar
Costa
CoffeeStarbucks
GoodfareRestaurant
Panzer's
Costa
CoffeePret A
MangerStarbucks
Pret A
MangerCategory
Vegan
Restaurant
Café
Coffee
Shop
Bar

OPC
E1

Nodes
19

Dist
4.0

Surge
4.5

E14
E16

36
11

3.7
4.0

2.3
5.9

E2

9

1.6

2.3

CoffeeShop
CoffeeShopItalianRestaurant
Deli
CoffeeShop
Sandwich
Place
CoffeeShop
Sandwich
Place

E3

21

4.6

2.2

NW1

21

3.0

31.0

NW1

7

3.1

5.0

NW8
SE18

22
20

3.2
6.4

4.0
3.0

SW5

21

4.1

3.0

W6

32

4.7

2.8

W6

21

4.0

3.0

Note: ‘Nodes’ refer to the number of interactions that the venue has with distinct
other venues on average per month. ‘Dist’ refers to the average distance to theconnected venues measured in kilometres. ‘Surge’ refers to the multiple ofFoursquare check-ins before and after January 2018. OPC stands for outward
postcode.

Figure 1 showcases an example for the surge venue
popularity. We fitted the time-varying mean to the timeseries to highlight the structural break in its popularity.
We find high predictive power of the Foursquare venue
interaction data for out-of-sample property pricemovements. On average, OPC districts with one of theselected London venues, exhibit a 4% higher property pricegrowth than postcode districts without these venues. Figure
1 illustrates the differences in 2018 property returns. A

Welch test at a 5% significance level rejects the null
hypothesis that the expected return for OPCs of eateries
with rising popularity is equal to the return of all London
OPCs. Thus we have established eateries gentrification in
London, which we estimate to be 4% in 2018.

recessions? How does it compare to supermarket
gentrification or the London Crossrail gentrification? While
open questions remain, one fact is undeniable. The
Foursquare dataset provides valuable information to predict
residential property markets.

Figure 3: Popularity of “The Spread Eagle Bar”

References

Bailey, Martin J., Richard F. Muth, and Hugh O. Nourse. "Aregression method for real estate price index construction."
Journal of the American Statistical Association 58, no. 304(1963): 933-942.

Normalised Popularity(April 2017 = 1)

Fama, Eugene F., and Kenneth R. French. "Common risk
factors in the returns on stocks and bonds." Journal of
financial economics 33, no. 1 (1993): 3-56.

2.3x multiple

Glaeser, Edward L., Hyunjin Kim, and Michael Luca.
"Nowcasting gentrification: using yelp data to quantify
neighborhood change." In AEA Papers and Proceedings, vol.
108, pp. 77-82. 2018.
These results hold in a difficult market environment for
London residential property, which has been dominated byconcerns over the outcome of the Brexit referendum. This
explains why the property price returns in 2018 for theselected OPCs is still modest at an annual growth rate of 5%.
Table 3: The 2018 property returns on selected OPCs
Name

OPC

Return

Leon

E14

16.5%

The Spread Eagle Bar

E2

10.5%

Starbucks

E16

6.2%

Pret A Manger

SW5

6.1%

Costa Coffee

SE18

5.2%

What The Pitta

E1

4.5%

Starbucks & Goodfare

NW1

3.8%

Costa Coffee

E3

1.1%

Starbucks & Pret A Manger

W6

-0.2%

Panzer's

NW8

-0.7%

Average Price Increase in selected OPCs

4.8%

Average prince increase in all London OPCs

1.1%

T-statistic for Welch’s test

2.5

P-value of Welch's test

3.2%

Note: OPC stands for outward postcode.

Conclusion

The goal of this paper is to both test the hypothesis of theexistence of eateries gentrification and to estimate themagnitude of this effect. We construct a measure for the
popularity of restaurants and show that postcodes withcafés, restaurants and bars of rising popularity exhibit higher
investment returns on residential property. The effect isstatistically significant. In London of 2018, the effect size has
been 4 percent.
Our results raise further questions. Does the eateries
gentrification effect change over time? Does it persist in

Helliwell, John F., Richard Layard, and Jeffrey Sachs. "World
happiness report." (2012).
Henneberry, John. "Transport investment and house
prices." Journal of Property Valuation and Investment 16,
no. 2 (1998): 144-158.
Opsahl, Tore, Filip Agneessens, and John Skvoretz. "Nodecentrality in weighted networks: Generalizing degree andshortest paths." Social networks 32, no. 3 (2010): 245-251.
Welch, Bernard L. "The generalization ofstudent's' problem
when several different population variances are involved."
Biometrika 34, no. 1/2 (1947): 28-35.
Zhou, Xiao, Desislava Hristova, Anastasios Noulas, Cecilia
Mascolo, and Max Sklar. "Cultural investment and urbansocio-economic development: a geosocial network
approach." Royal Society open science 4, no. 9 (2017):
170413.

Metaheuristic macro scale tra�ic flow optimisation from urban
movement data
Laurens Arp∗
Dyon van Vreumingen∗

Daniela Gawehns
Mitra Baratchi{l.r.arp,d.van.vreumingen}@umail.leidenuniv.nl
Leiden Institute for Advanced Computer Science
Leiden, The Netherlands

ABSTRACTHow can urban movement data be exploited in order to improvethe �ow of tra�c within a city? Movement data provides valuable
information about routes and speci�c roads that people are likely
to drive on. This allows us to pinpoint roads that occur in manyroutes and are thus sensitive to congestion. Redistributing someof the tra�c to avoid unnecessary use of these roads could be a
key factor in improving tra�c �ow.Many proposed approaches
to combat congestion are either static or do not incorporate any
movement data. In this work, we present a method to redistributetra�c through the introduction of externally imposed variable costs
to each road segment, assuming that all drivers seek to drive thecheapest route. We use a metaheuristic optimisation approach tominimise total travel times by optimising a set of road-speci�cvariable cost parameters, which are used as input for an objectivefunction based on tra�c �ow theory. The optimisation scenario forthe city centre of Tokyo considered in this paper was de�ned using
public spatial road network data, and movement data acquired from
Foursquare. Experimental results shows that our proposed scenario
has the potential to achieve a 62.6% improvement of total travel
time in Tokyo compared to that of a currently operational road
network con�guration, with no imposed variable costs.

KEYWORDS
tra�c �ow, urban movements, metaheuristics

1

INTRODUCTION

Even though extensive road networks have been developed to satisfy the high demand for vehicular transportation, overoccupancy
of roads still occurs on a daily basis, causing tra�c jams which hurt
the environment, the economy and the drivers’ moods. Finding
a solution to tra�c congestion is a challenging problem that has
occupied many in the past century. After all, tra�c dynamics aredi�cult to predict, due to complex �uctuations in tra�c demand,
both spatial and temporal. This makes it hard to devise a protocol
for tra�c �ow redistribution that works well in varying conditions.
To date, various approaches have been proposed to alleviate congestion in some way [8]. However, these methods tend to be either
static, data-independent protocols, micro-scale solutions (on thelevel of individual roads) or primarily driven by theoretical models.
Our objective instead is to construct a dynamic, data-driven, macroscale (road network level) approach to address tra�c congestion.
In this sense, dynamic means that a solution can be adapted to new
tra�c data with relative ease.
∗ Both authors contributed equally to this research.

{d.gawehns,m.baratchi}@liacs.leidenuniv.nl
Leiden Institute for Advanced Computer Science
Leiden, The Netherlands
In this work, we propose a method for tra�c redistribution fueled by metaheuristic optimisation, which we test on the case of thecity centre of Tokyo. We seek to shift the tra�c situation away froma state where each driver chooses the fastest, or shortest, route (thuscausing congestion on roads that occur in many shortest routes),
towards a system optimal equilibrium, as coined by Wardrop [14],where the total travel time for all drivers is minimised. By introducing externally imposed variable costs (e.g. tolls, or any other�nancial or non-�nancial method a supervisory institution might
deploy) on each road, we aim to discourage drivers from all taking
the same congested roads. This approach asserts that, on average,
each driver is willing to take the cheapest route from their point of
departure to the destination, where the total costs to drive a routedepend both on the distance travelled, through a spatial cost, andthe imposed variable costs encountered along the route.
In order to make predictions of tra�c �ow and occurrence ofcongestion, we infer tra�c demand from a data set of urban movements. A number of public tra�c data sets, such as the Dutch NDW[11], report the tra�c �ow or density at certain points in time;
however, while this gives a detailed picture of a local situation, it
provides no information as to what routes drivers are following.
Hence, such data is of little use when we wish to redistribute tra�cby encouraging sensible alternative routes. For this reason, we use
a data set of urban movements, provided by Foursquare as part of
the Future cities challenge, which allows for the inference of tra�clevel information needed for this research such as the origin anddestination of movements [2].

2

RELATED WORKThe objective of combatting tra�c congestion by altering road network setups has been addressed in a large body of work. The use ofroad pricing as a means to achieve this goal is a prevalent approach[6, 13, 15, 16]. In this context, the marginal cost of congestion is afrequently employed measure to assess the optimal road pricing.
A key di�erence between these papers and our work is that thepreviously proposed road pricing policies are �xed (e.g. charge a fee
within a certain radius of the city centre) instead of dynamic, and do
not follow from an optimisation procedure based on actual movement data. Approaches for optimising road networks and tra�c
�ow from a di�erent viewpoint, unrelated to road pricing policies,
include metaheuristic optimisation of road improvements [3], development of intelligent tra�c light systems [10], optimisation of roadgraph architectures with evolutionary algorithms [12], and prediction of optimal tra�c �ow through maximum-entropy methods [9].
A more exhaustive list of methods is provided by Kumar Shukla
and Agrawal [8]. In this work, we explore the use of optimisation

L. Arp, D. van Vreumingen, D. Gawehns and M. Baratchi
algorithms in proposing a dynamic pricing mechanism using actual
movement data.

3

PROBLEM STATEMENT

The problem of optimising tra�c �ow through adaptive road pricing
is twofold. First, we must estimate tra�c �ow and congestion ina road network, which is the underlying cause of high total travel
time when all drivers follow the cheapest routes from their origins
to their destinations. By combining movement data describing thetra�c demand in the network and the spatial road network data,tra�c �ow theory yields these estimations. The demand data is a
set D M consisting of movements between venue locations within
the road network. Each element of this set (indexed as k) is a tuple(Ak , Bk , Nk ) where Ak and Bk are elements of a venue data set
DV containing spatial information about the venues, and Nk is therecorded frequency of the speci�c movement from Ak to Bk . Second,
having found a method to express the total travel time as a functionof the variable cost parameters, we aim to optimise the parameters
for minimal total travel time. Our methods for addressing thisoptimisation problem are set out in detail in the next section.

4 METHODS
4.1 Tra�c �ow estimation
4.1.1 Road network and routing model. The �rst step towards prediction and minimisation of congestion is to represent the physicalroad network as a planar graph G that has road segments for edges,
which may be traversed in order to travel from an origin to a destination. Speci�cally, the graph is a tuple G = (V , E, S) with V thenode set, E the edge set and S the set of Haversine lengths of alledges. The node set V contains intersections in the road network,
as well as nodes for the origin and destination locations from DV .
We then introduce, for each road segment (i, j) 2 E in the graph,a cost that a driver needs to pay to traverse this segment. The mainpart of this cost is a variable cost pi j . All variable cost parameterscollectively form the variable cost vector P which we seek to optimise for minimum congestion. Next, we assign to each segmenta spatial cost, which is an immutable base cost for travelling fromnode i to j that is linearly dependent on the length si j of the segment by a tunable factor s . Since the movement data is aggregatedinto frequency numbers, and is not provided on an individual level
for anonymity reasons, we take s to be equal for all drivers. Put
together, the total cost for a driver to travel via a connected routeof segments R = {(i, j)} from some origin to a destination, is givenby the sum of the individual segment costs occurring on the route:’cost(R) =(1)
s s i j + pi j .
(i, j)2R

For the development of tra�c �ow, we assert that all drivers aresel�sh and seek to drive the route which incurs the lowest total cost.
These routes can be found using a weighted shortest path algorithm.
Note that if pi j = 0 on all edges, each driver will drive the route oflowest spatial cost, which is exactly the shortest route. From thecheapest routes, which are jointed collections of segments, and thefrequency numbers Nk , we can predict the vehicle count ni j oneach segment, from which the degree of congestion is computed as
set out in the following subsection.

4.1.2 Congestion model. In our congestion model, we assume thatthe �ow of tra�c on a road segment is fully described by a Greenshields fundamental tra�c �ow curve [4], which is a widely usedtheoretical model for predicting tra�c dynamics on a road segment.
The used variables are �ow f (vehicles passing by per unit time)and density (vehicles present per unit length). In this model, there
exists a maximum density m that the road can support, beyond
which the total �ow is zero. Furthermore, there is a critical density c at which the �ow reaches its maximum value: f ( c ) = fm .
Naturally, f (0) = 0, as no �ow exists when no cars are present.
A basic curve that �ts this description is a concave quadratic
function, with zero �ow at > m , which we de�ne asfm
f ( ) = 2 max 0, [2 c
],
(2)
c

where we note that, since f is quadratic in , m = 2 c . The
maximum �ow is directly related to the critical density; assumingthat the tra�c is able to drive at the maximum allowed speed mwhen the density is at its critical point, we set fm = c m .
From this density-�ow dependence, we can extract the spacemean speed ( ), the average speed of all vehicles on the roadsegment, as [4]✓◆f( )( ) = min m ,,(3)where again the maximum speed enters the relation, this time as abound on the space mean speed. Finally, the space mean travel timet( ) on the segment, taken to have length s, is computed asst( ) =.
(4)( )From the expected number of vehicles ni j on each road segment,
we obtain the segment density as i j = ni j /si j . For multi-laneroads, we divide this number by the number of lanes. By inserting
the segment density into the density-time relation described above(eq. 4), we �nd the space mean time ti j spent on the segment. The
collection of segment travel times then leads to the de�nition of theobjective function for optimisation by a metaheuristic algorithm.

4.2

Parameter optimisation

4.2.1 Objective function. The objective function computes the measure obj(P), which re�ects the extent to which the system optimalequilibrium is reached by a variable cost con�guration P. This equilibrium occurs when the total travel times for each driver on theirroutes are minimal. As such, we de�ne the objective function as
the total space mean travel time over all routes driven on the road
network. This total travel time can be conveniently expressed using
the segment vehicle counts ni j , which are directly dependent on P:’
obj(P) =ni j (P) ti j (P).
(5)(i, j)2E

Note that each segment mean travel time ti j is also a function of P
since it is dependent on the vehicle count ni j . Algorithm 1 shows,in pseudocode, the routine to compute the objective function.
4.2.2 Optimisation algorithms. The purpose of the optimisation
algorithms is to �nd an optimal variable cost con�guration suchthat the value of the objective function, the total travel time, isminimised. In principle, any black-box metaheuristic optimisation

Metaheuristic macro scale tra�ic flow optimisation from urban movement data
Data: road network graph G = (V , E, S ) with node set V inferredfrom venue data DV , E and S inferred from road network data;
movement data D M ;spatial cost factor s
Input: parameter vector P
Result: objective function value obj(P)initialise n i j

0, t i j0,

ij

0 for all (i, j) 2 E// Compute predicted vehicle counts n i j on each segment
forall origin-destination-freq. tuples (Ak , B k , N k ) in D M do
Find cheapest route R k from Ak to B k according to s and P
using weighted shortest-path algorithm
forall (i, j) 2 R k do
ni j
ni j + Nk
end
end// Find mean travel times t i j on each segment
forall (i, j) 2 E do
n i j /s i jij
ti jt( ij )
(eqs. 2–4)
end
Õobj(P)(eq. 5)(i, j)2E n i j t i j
return obj(P)Algorithm 1. Routine for computing the objective function
for a given parameter vector P.
algorithm could be used to search for local optima of variable costcon�gurations which might approximate a system optimal equilibrium. That said, for these purposes, algorithms which are robust
for high-dimensional problems are preferred, as the number of parameters increases proportionally to the number of edges in thegraph.
For our proof-of-concept implementation we use simulated annealing (SA) [7], which is a variation of hill climbing where worse
solutions can get accepted depending on the algorithm’s decreasing
temperature value, and a genetic algorithm (GA) adapted for continuous optimisation. For both algorithms each iteration contains
40 objective function evaluations; after one iteration, the GA updates its population, whereas SA resets its temperature value. Bothalgorithms use mutations generated using a normal distribution
with zero mean and unit variance, at a mutation rate of 0.2 perparameter.

5

CASE STUDY: TOKYO CITY CENTRE

In order to test our tra�c �ow optimisation method, we applied
it to movements inside the city centre of Tokyo (i.e. excluding theGreater Tokyo area). We brie�y discuss the movements and road
network used for the case study, and present experimental results.

5.1

Movement data

The movement data was provided by Foursquare as part of the
Future cities challenge [2]; we selected only those parts related to theTokyo city centre. The data contains a list of venues together with
their GPS locations, forming the data set DV introduced in section
3, and a list of movements between the venues. The movement datacontains movements of the same form as the tuples in D M , but withadditional indications of the month during which the movement
occurred, and the time of the day (periods of 4 to 6 hours). We

Figure 1. Road network graph example. Green nodes arevenues, red nodes are intersections. Map source: Google
Maps [5].
considered only movements made in the afternoon, and combined
the frequencies Nk for the same movement in di�erent months
into a single �gure. Since the Foursquare dataset does not cover
the entirety of vehicular movements inside Tokyo (and, in fact,
also includes other types of movements such as subway, biking
and walking trips), we viewed the frequencies as ratios rather thanabsolute numbers, asserting the law of large numbers for su�cientaccuracy. We then normalised the frequencies to numbers that theroad graph we constructed (see next subsection) could support.
As a last modi�cation, we selected the venues occurring in the
100 most popular (i.e. frequent) routes, and clustered all othervenues together with their nearest neighbour (in terms of Haversine distance) from the set of most popular venues. The routes
were clustered accordingly, going between venue clusters insteadof individual venues. This was done in order to substantially reduce
the number of routes and therewith the computational complexityof the problem.

5.2

Road network data

The road network data used is based on the Asia shape�le provided
by the Earthdata Global roads open access data set [1]. It contains
information on the road networks of the entirety of Asia with avariable resolution; for the city Tokyo, its resolution is well suitedfor the algorithm. The road data is translated into a graph representation by �nding intersections between lines, and turning these into
intersection nodes. The lines themselves are used to create edges
between intersections. Venue nodes are created by identifying thelocation of the venues from the venue data set, which are connectedto the nearest intersection node.

5.3

Experimental results

The improvement progress for both optimisation algorithms isshown in �gures 3 and 4. For comparison, we ran the objectivefunction once with all variable costs set to 0 to obtain the default
�ow of the road network. This con�guration had an objective valueof around 416 million hours spent on the road network. It should be
noted that the objective function values are higher than what would
be a realistic amount of time spent on roads. As the theoreticaltra�c �ow models do not take relief methods into account for fullycongested roads, the speed on those roads in considered to be zero.
In these cases, we use an arbitrary low value to represent the speed
on the road, resulting in somewhat unrealistic amounts of hours.

L. Arp, D. van Vreumingen, D. Gawehns and M. Baratchi
fast as possible. Future work could include more thorough exploration of optimisation algorithms and their parameter settings.

6

Figure 2. Objective function value plotted to the number of
GA iterations. Values are the total time spent on the roads
over 6 hours in the afternoon.

CONCLUSION

We have shown that we can successfully address tra�c congestion
by redistributing tra�c through imposing of variable road segment
costs, and optimising this cost con�guration using metaheuristic
algorithms. The best variable costs con�guration was found by asimulated annealing routine, improving upon the total travel timecorresponding to a con�guration with zero variable costs by 62.6%.
Both simulated annealing and a genetic algorithm were e�ective at
optimising solutions.
Though the practical implementation of the variable costs may
be another non-trivial problem to address �rst, the positive results
show that, at least conceptually, this method could result in improved tra�c �ow when applied in practice. Consequently, cities
may enjoy shorter travel times, better accessibility, cleaner air and,not unimportantly, improved drivers’ moods.

ACKNOWLEDGMENTS
We would like to extend special thanks to Foursquare and Netmob
for the movement data and to Giorgos Kyrziridis for his support.

REFERENCES
Figure 3. Objective function value plotted with the numberof SA iterations. Values are the total time spent on the roads
over 6 hours in the afternoon.
Both the GA and SA were able to �nd solutions which substantially improved tra�c �ow over the network. After 30 iterations,the lowest �tness value of the GA was around 175 million hours(an improvement of 57.9%), and the lowest �tness value of SA wasaround 155 million hours (an improvement of 62.6%). E�ectively,
this means the solution found by SA was able to reduce the total
amount of hours spent over a period of 6 hours in the afternoon
by 261 million, due to having found a good distribution of variable
costs for each road segment. Although the results of the GA were
not as good as those of SA, the GA was still successful in improving
the unoptimised con�guration, which supports the view that any
optimisation algorithm could be used for these purposes.
Interestingly, even the �rst iterations of the algorithms showedvalues which were an improvement compared to the default tra�c
�ow. This is likely due to the nature of the shortest path algorithm
when taking only distance into account, which sends many cars
over the same roads unnecessarily. Though this type of behaviour
was the basic premise allowing us to optimise, given the large variety of available, very similar roads in the network, any deviationfrom the over-use of single roads caused by the random initialisation of variable costs would result in better tra�c �ow. From sucha randomly initialised state, both algorithms then improved thesolutions such that the more well-suited alternatives were used.
The results show that the algorithms were e�ective in �nding solutions improving tra�c �ow over the default setting of no variable
costs. That said, there is no guarantee the optima the algorithms
converged to were global optima, nor that the convergence was as[1] NASA Earthdata. [n. d.]. Global Roads Open Access Data Set. https://earthdata.
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approach for solving the urban network design problem. European Journal ofOperational Research (2010). https://doi.org/10.1016/j.ejor.2009.02.026[4] Bruce D. Greenshields, J. T. Thompson, H. C. Dickinson, and R. S. Swinton. 1934.
The photographic method of studying tra�c behavior.
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[6] Theodore E. Keeler and Kenneth A. Small. 2002. Optimal peak-load pricing,investment, and service levels on urban expressways. Journal of Political Economy(2002). https://doi.org/10.1086/260543[7] Scott Kirkpatrick, Charles Daniel Gelatt, and Mario P. Vecchi. 1983. Optimization
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phpro.2012.02.231

PolisNet: Designing Future Cities with Deep Neural Networks
Gianni Barlacchi∗

gianni.barlacchi@gmail.com
Fondazione Bruno Kessler
Trento, Italy

Marco De Nadai∗denadai@fbk.eu
Fondazione Bruno Kessler
Trento, Italy

Bruno Lepri∗

Cities are undoubting the protagonists of world migration of the last
decades. People have increasingly moved to urban centers attracted
by a greater pool of opportunities, jobs and education at eased reachthanks to shorter distances and public transportation. Recently, theadvent of new data sources and methods has acted as a real gamechanger in urban studies. The digitalization of society has allowed
to observe, understand end even predict many aspects of humanmobility and social behavior. However, despite the increasing ability
to describe human behavior in cities, how to exploit big data to
improve the design of urban areas is still unclear. Starting from thedata provided by the Future Cities Challenge, we propose PolisNet,
an AI-aided approach to design and modify urban areas to makethem successful. This framework is based on Deep Neural Networks(DNNs) that exploits urban imagery and Point Of Interests (POIs)
to propose what can be improved in an urban zone, how, and where.

1

INTRODUCTION

Urban planners, sociologists and policymakers have always been
concerned about improving distressed districts and create the conditions to spur urban life. Neighbourhoods, in particular, are the mostfundamental unit of city life that shape the crime [16], diversity [8]and even the individual’s economic success in life [18]. However,there is still little guidance on designing them and what improveon existing neighborhoods, how, and where.
Here, we propose PolisNet, an AI-aided approach to design andmodify neighborhoods to make them successful. This framework
is based on a Deep Neural Network (DNN) that exploits urban
imagery and Point Of Interests (POIs) data to propose what canbe improved in a neighborhood, how, and where. In particular, we
empirically describe each neighborhood in a city through an OpenStreetMap 1 aerial image, which describes the built environment,
its POIs and the metrics of urban success, which is based on thewell-established vitality of a neighborhood [3, 8, 21]. This metric
was previously used to prescribe planning principles and sustainable human activity and urban development [21]. By leveraging
aerial image, POIs and success data, PolisNet perturbs the aerial
image of each urban zone to propose where it might be modi�ed
and what POI to add or eliminate to make the urban zone more
successful. For example, given an aerial image of south-central Los
Angeles, PolisNet suggests the type and the geographical position∗ Authors equally contributed to the manuscript.

1 https://www.openstreetmap.org

yliu@fbk.eu
Fondazione Bruno Kessler
Trento, Italy

Luca Pappalardo∗

lepri@fbk.eu
Fondazione Bruno Kessler
Trento, Italy

ABSTRACT

Yahui Liu∗

luca.pappalardo@isti.cnr.it
ISTI-CNR
Pisa, Italy
of POIs that, added or deleted, increase the success of south-central
Los Angeles.
To the best of our knowledge, this is the �rst research e�ort
towards the creation of AI-aided design systems that could be avalid support for policymakers and city architects, and help reducethe considerable costs related to urban design and simulation. We
anticipate that our network will be a groundbreaking system toimprove urban life and design neighborhoods with lower crimerate, cultural and economic distress.
The rest of the paper is organized as follows. Section 2 provides
an overview on urban related works, whereas Section 3 presents
an in-depth description of system. In Section 4, we characterize theemployed datasets and explain our experimental setup, together
with the obtained results. Finally, conclusions and future research
are drawn in Section 5.

2

RELATED WORK

Our article primary speaks to two streams of literature. The �rst
one is the community of urban computing, which studies human behaviour in cities through massive data about human displacements,socio-economic and geographical data. The second one is the streamof literature that tries to understand, recommend and design cities
that work by collaborating with local authorities, communities andpolicymakers.
In Urban planning, many approaches tried to study neighborhoods and their revitalization. Notably, Jane Jacobs with her The
Death and Life of Great American Cities [8] in�uenced many urban
plans in the world. In her book she praised diversity as an ingredientto have convenient social interactions, face-to-face encounters, anda spontaneous sense of community that spurs economy, security
and urban life. More recently, some guidelines and policies havebeen shared to revitalize neighborhoods [10, 19] based on high-techentrepreneurship, walkability, and a sense of community similar tothe old-fashioned Jacobs’ ideas.
In Urban computing, many approaches tried to model and predict
the activities and characteristics of urban areas. Notably, Noulas etal. [13] proposed a model for predicting an urban zone’s prominent
activity using Foursquare and mobile phone data. Zhang et al. [22]proposed an online, semi-supervised, and multimodal embedding
method for geo-located information with space, time and text.
Several works have also targeted land use classi�cation andfunctional area detection. For example, Yuan et al. [20] propose aframework to classify functionalities of an area for the city of BeijingWoodstock ’18, June 03–05, 2018, Woodstock, NY

using POIs and trajectories of taxis. Barlacchi et al. [2] propose anovel machine learning representation based on the encoding of
Foursquare POIs to classify the most prominent land use of anurban area.
Another strand of research focus on de�ning measures of successfor a city or urban zone. Yue et al. [21] de�ne urban vitality as thecapacity of an urban environment to boost social activities. They
discover that urban vitality showed a positive correlation withphone usage density, and that the urban structure of Shanghai
plays a crucial role in its urban vitality. De Nadai et al. [3] use
the average number of mobile Internet connections throughout atypical business day, divided by the an urban zone’s area, as a proxyfor urban vitality.

3

POLISNET SYSTEM

Our framework, PolisNet, is an AI-based framework that uses digital
data and Deep Neural Networks (DNNs) to design a neighborhoodand propose what improve, how, and where. The architecture ofPolisNet is illustrated in Figure 3.

3.1

Urban Success Metric (USM)

De�ning urban success is not an easy task and many scholars have
tried to de�ne it through extensive studies [14]. Although urban
success can manifest itself in di�erent ways, we straightforwardlyde�ne what is not: an area without visitors. Thus, we build upon
the sociological theory of Jane Jacobs, which de�nes vitality anddiversity as an essential factors for urban success. Recent workin urban computing empirically de�ned vitality by measuring theactivity of people in a neighborhood from mobile phone data [3, 4].
Similarly, we here de�ne as vital a place where people geo-localize
through Foursquare data. Thus, vitality in a neighborhood i isformalized as:’vitality =
C(p)
(1)
p 2Pi

where Pi is the set of POIs in the neighborhood and C(p) is thenumber of check-ins for a particular POI. Similarly, we de�ne theeconomic vitality counting only the vitality of the Shops and Food
POIs. Note that our framework might be used with di�erent metrics
such as the number of crimes and the number of cultural events ina neighborhood.

3.2

Urban Success Evaluator (USE)As shown in Figure 1, we design an Urban Success Evaluator (USE)
to distinguish the zone to be successful or not, which can be formulated as a binary classi�cation problem, where “0" and “1" refer tounsuccessful and successful, respectively. Recently, deep convolutional neural networks (CNNs) have led to a series of breakthroughs for image classi�cation [6, 11]. Thus, we build our model
based on two state-of-the-art methods, including Inception-v3 [17]and ResNet-v2 [7], which are widely used as basic networks invarious computer vision tasks.
Specially, we de�ne the training set as S = {(In , Mn , Cn ), n =1, 2, . . . , N }, where the In denotes the original map image, Mndenotes the mask of POIs, and Cn 2 {0, 1} denotes the binary

Barlacchi, et al.

classes. To train our model, we calculate the cross-entropy loss:
1 ’
Cn log(p(Cn )) + (1N n=1N

Lcl s =Cn ) log(1p(Cn )),(2)

where p(Cn ) refers to the probability of zone (In , Mn ) to be correctlyclassi�ed. For simplicity, we set equal loss weights for both classes.

3.3

Urban Success Designer (USD)

We then design an Urban Success Designer (USD) to model the
distribution of POIs in the spatial space in di�erent cities, whichshows the potential ability that can be applied to propose ideal new
POIs to transform an unsuccessful zone in a successful one.
We re-formulate such kind of problem into a location regression
problem. Inspired by fast object detection methods [12, 15], we
assume that each POI can be enlarged with a bu�er area (formed
as bounding box in object detection). After that, our approach
discretizes the output space of POI locations into a set of default
boxes over di�erent aspect ratios per feature map location. At
prediction time, the network generates scores for the presence ofeach POI category in each default box and produces adjustments
to the box to better match the shape of the POI bu�er.
Figure 2 shows the pipeline of our USD, in which the training
objective is derived from the MultiBox objective [12] but is speci�ed
to handle the regression of multiple POI categories. In contrast withprevious object detection methods, we try to predict location andcategory of the removed POI. Thus, we de�ne the training set as
Y = {(In , Mn† , p† , C † ), n = 1, 2, . . . , N }, where the In denotes theoriginal map image, Mn† denotes the mask of POIs with a randomly
removed POI (located in p† with a category C † 2 {1, 2, . . . , K },K refers to number of POI categories). The overall objective loss
function is a weighted sum of the localization loss (loc) and thecon�dence loss (conf):1
Lr e = (Lconf (x, C † ) + Lloc (x, l, ))(3)Nwhere x iCj 2 {0, 1} be an indicator for matching the i-th default box
to the j-th ground truth box of category C. The localization loss isa Smooth L1 loss [5] between the predicted box (l) and the groundtruth box ( ) parameters. The con�dence loss is the softmax loss
over multiple classes con�dences (C) [12].

3.4PolisNet Framework

First, we de�ne a neighborhood as a uniform and non-overlapping
regular cell of approximately 600 ⇥ 600 meters based on the Slippy
Map standard 2 at zoom 15. Then, we associate each cell with thecorresponding OpenStreetMap aerial image, the metrics of urban
success, and the map mask built over the characteristics of the
POIs contained in the cell. For each unsuccessful urban zone, i.e.,
an urban zone with the success metric lower than 0.5, PolisNet
proposes several changes in order to make the zone successful.
The design process of PolisNet is divided into two phases: inthe �rst phase, the USE evaluates whether an urban zone needs tobe redesigned or not. In the case the evaluation reveals that thezone is successful, the system outputs the new generated successful
area indicating the required changes: (i) where it requires changes,
2 https://wiki.openstreetmap.org/wiki/Slippy_Map

PolisNet: Designing Future Cities with Deep Neural Networks

Woodstock ’18, June 03–05, 2018, Woodstock, NY

and (ii) what POIs should be added in such locations. Di�erently,
if the zone needs re�nements, the urban zone representation is
forwarded to the second step. In this step the USD, by relying on
the location regression model, proposes a new list of possible POIs,
with their corresponding geographical locations, that can be addedin order to turn the zone into a successful one. The two steps arerepeated in a loop for a maximum number of iterations or until
when the urban zone does not turn into a successful zone.
Successful
CNN

Unsuccessful

Map + POIs Mask

Convolutional
Neural Network

OutputFigure 1: Schema of the USE classi�er.

<x1,y1>, label1<x2,y2>, label2
CNN<xn,yn>, labeln

Map + POIs Mask

Feature learning with
Convolutional Neural Network

Feature map

Top-n possible POIs

Figure 2: Schema of the USD model: given an input area, it
decides where and what to add to make the input area successful.

Map

POIs Mask

Urban Success
Evaluator

4.2

Urban Designer
New generated
successful area

Figure 3: Schema of PolisNet: it proposes where an urbanzone should be modi�ed, and what POI to add or eliminate
to make the zone successful.

4

4.1

EXPERIMENTS

Our experiments aim at demonstrating the e�ectiveness of ourmodels on two phases: (i) the urban success evaluation and (ii) theurban success design. We use a database of cities composed by 8
kinds of POIs that belong to 9 di�erent cities around the world.

Experimental Setup

As aforementioned, we represent each neighborhood through nonoverlapping square cells based on Slippy Maps of zoom 15. For eachcell we download the aerial map of OpenStreetmap removing any
visual information connected to the POIs. This was done througha custom map style of Mapbox 3 and it ensures higher independence between the POI mask and the Map. Then, we use the Future
Cities Challenge [1] dataset to obtain the Foursquare venues and the
movements information. From the venues we extract the category,latitude and longitude, while from the movements we aggregated
the number of check-ins in each venue. We aggregate the categoriesof the venues to the highest hierarchy of the Foursquare category
tree 4 and assign a color to each category. We discard the event andresidents categories. For each cell we build a POIs Mask where
each venue is represented with a color based on its category, andlocated in a (x, ) pixel depending on its geographical location in
the map. Finally, we represent each neighborhood through a triplet(aerial image, POIs Mask, label), where the label is binarized into
successful and unsuccessful. The label is successful when thevitality (Equation (1)) and the economic vitality are above the 20th
percentile of the distribution of vitality for that city, unsuccessfulotherwise. We build our data for nine cities namely Chicago, Istanbul, London, Los Angeles, New York, Paris, Seoul, Singapore,
Tokyo. This resulted on 20,060 images of neighborhoods.
We train our model using Adam [9] with 1 = 0.5 and 2 = 0.999
and batch size 4 for evaluator model and batch size 1 for designer
model. For data augmentation we �ip the images horizontally witha probability of 0.5. The initial learning rate is set to 0.0002. We
train for 30 epochs and linearly decay the rate to zero over the last
20 epochs. It takes about several hours for each model with a single
GeForce GTX 1080 Ti GPU. All these models are implemented using
the deep learning framework PyTorch. In particular, the weightterm is set to 1.0 in the designer model. To measure the impactof our models as well as the baselines, we used well known metrics
for assessing the accuracy of DNNs.
We use the accuracy score to assess the performances on thebinary classi�er in the USE. To evaluate the USD we rely on two
metrics: (i) the top-k distance error, which is the minimal distanceamong the top-k predicted POIs and the target POI in the image
coordinate, and (ii) the closest accuracy, which measures whether
or not the label of closest predicted POI is equal to the target label.

Preliminary Results

In this section we present some preliminary results for the two steps
that compose our PolisNet system. Table 1 shows the accuracy of
the USE classi�er. The Table shows that conditioning the modelwith the city name does not provide signi�cant improvements. The
Inception-v3 and Resnet networks with the city information havea 0.4% and -4.8% of absolute points improvement, respectively.
In Table 2 we report preliminary results for the very challenging
task of proposing changes to apply to unsuccessful urban zones.
The accuracy and distance of the top-k predicted results show that
it is very di�cult to guess the correct POI category and image
3 https://www.mapbox.com/gallery/

4 https://developer.foursquare.com/docs/resources/categories

Woodstock ’18, June 03–05, 2018, Woodstock, NY

Model

Barlacchi, et al.

City-conditioned

Accuracy

⇥
X
⇥
X

0.778
0.798
0.794
0.750

Inception-v3
Inception-v3
Resnet-v2-50
Resnet-v2-50

Table 1: Accuracy of the USE in the classi�cation of successful areas.
coordinates of the removed POI. However, when more POIs candidates are considered, the model shows promising results bothin terms of accuracy and distance error. This suggests that potential improvements can be applied to further develop this idea. The
low performances with k < 50 might be due to the very basic input representation we are providing to our network. Indeed, some
improvements can consider (i) to design a more expressive dense
representation of POIs instead of using the simple one-hot encoding
representation and the POI macro-category; (ii) to better balance
the dataset both in terms of city map and categories of POIs; (iii) toprovide a more informative visual input than a simple map maskwith colored pixels.
k

Accuracy

Distance

1020
50

0.074 ± 0.078
0.112 ± 0.074
0.149 ± 0.014

155.37 ± 37.51
140.51 ± 35.48
125.77 ± 37.50

Table 2: Accuracy and distance error of the USD in proposingchanges for a given unsuccessful urban zone.

5

CONCLUSION AND FUTURE IMPACT

In this paper we presented PolisNet, an AI-based system that recommends how to improve the success of urban zones in a city. Since
the underlying idea is rather �exible, PolisNet can be improved andextended in several directions.
First, while we use in this paper a �xed threshold for de�ning
an urban zone as successful or unsuccessful (i.e., the median of
the urban vitality), we may let the user choose the threshold touse for the discrimination. In this way, a policy maker may decide,according to the available economic resources, how much to change
a zone to achieve the desired level of urban success.
Second, with slight modi�cations, our system can be adapted togenerate an optimized street network given the current mobility
�uxes observed in an urban zone. In this case, PolisNet would
perturb the underlying street network or the position of existing
POIs, given certain constraints, in order to reduce indicators ofmobility success, such as travel times or distances, the emergenceof tra�c jams, or the number of car accidents.

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A Multi-Objective Approach for Optimal Store Placement
Jeroen Rook

Brent Verpaalen

Mitra Baratchi

LIACS, Leiden University

LIACS, Leiden University

LIACS, Leiden University

j.g.rook@umail.leidenuniv.nl

b.a.a.verpaalen@umail.leidenuniv.nl d.gawehns@liacs.leidenuniv.nl

m.baratchi@liacs.leidenuniv.nl

Abstract - When a store owner wants to open a newstore he or she desires a location that attracts a large
number of customers. Previous work has shown how
location based social networks can contribute to thisdecision process. However, opening a store also has
an impact on the surrounding neighbourhood. Withthe use of urban planning theories we define a scoreshowing the impact of these store placements. We
propose a framework, that selects the best venue categories for a given location in a city according to theirscores for both perspectives. These scores are computed from metrics extracted using a location basedsocial network from Foursquare. Our experiments,
based on the city of New York, show that the number of suitable store categories, for a single location,
are often not singular. This indicates that this multiobjective approach is necessary in solving the optimal
store placement problem.

1.

Daniela Gawehns

LIACS, Leiden University

INTRODUCTION

Imagine you want to open a new restaurant. Where in thecity would you allocate your restaurant? As a store owner you
desire a location where the characteristics of the surroundingarea give you a large number of customers, resulting in an
increased revenue. However, store owners need permissions
from local governments, who also consider urban planning objectives of the city. These objectives are set out to maintaina liveable and economically thriving city. As a store owner
it is beneficial to take these objectives into account. These
objectives provide a sustainable future for the store, preventing bad cityplanning, which could be disastrous for the cities
economy [1]. Earlier research used Foursquare data sets inorder to predict the expected number of visitors [2]. With
the addition of the governmental view on store placement we
extended the optimal store placement problem. Approaching the placement problem, to our knowledge, has not been
covered in previous research.
In this paper, we rewrite the optimal store placement problem
by trying to find the location that is best for both the store
owner and the city itself. This is done by mining featuresfrom a location based social networks data set, provided by
Foursquare. We use known features, and also introduce two
new features derived from a relative neighbourhood graph.
With these features we predict the popularity of a locationand define an objective function representing the impact on
the city. These two functions allow us to obtain a Pareto optimal set of the most suitable categories for a given locations,
helping potential store owners to obtain popular store locations and local governments in understanding which venue
categories should be targeted for a specific location.

In this paper we focus on the following contributions:• Creating a multi-objective approach on location based
store category prediction;• Constructing new features from a relative neighbourhood graph in order to capture the function of the neighbourhood within the city;• Applying our proposed method to the problem of identifying optimal store locations in New York City using
a Foursquare movements network.

2.

RELATED WORK

Traditionally, the optimal store placement problem revolved
around the central place theory, the spatial interaction theory
and the theory of minimal di↵erentiation [3]. With the riseof location based social networks (LBSNs), which combines
data from social networks with real-world objects, other approaches in solving the placement problem arised. Karamshuk
et. al. [2] defined this problem as a ranking problem for aset of locations using one particular type of store. Anotherproposed approach is to make a prediction where a category
would be ranked by its popularity at a given location using
matrix factorisation [4]. Other socially generated data such as
written visitor reviews have shown to be able to contribute insolving this problem [5, 3]. The focus of these works are basedon the optimality from a store owners perspective, which isdefined as a maximum number of visitors.
Considering this problem from the point of view of urban
planners is much wider than only the number of visitors, butcannot be defined on one single property. Proposals of definitions have been made [6] and commonly fall back on traditional theories. One of these theories is mixed-land use [7],which sets out to have residential, commercial, and working
locations within the same neighbourhoods. This encourages
non-auto commuting, which accounts for a decrease of traffic
congestion, and results into less CO2 emissions. Both having
a positive e↵ect on the city.

3.

METHODS

Before the most suitable venue category for a given location
can be found, several steps need to be taken. Our proposed
approach is performed in two phases, training and ranking. In
the training phase, features for each location in the data set
are constructed, and the number of check-ins per venue arecalculated. These check-ins are used as our ground truth for aregression model predicting the number of check-ins from thegiven features. In the ranking phase, for any given location we
artificially simulate a venue category placement, computing
resulting features. This is done for all possible categories. For
these features the predicted popularity is retrieved using theregression model created in the training phase. Also the city
impact score for each of these categories is computed. In theTable 1: Overview of the constructed features extracted fromthe venue movements network

Figure 1: The full framework, for getting multiple objective
rankings comparing di↵erent categories at a given location.
These rankings are used to compute an optimal set of categories.

end we can obtain the Pareto optimal set of categories for thatparticular location, based on the two objectives. Categories
in the Pareto optimal set have the best pairs of scores. All
these steps and their underlying dependencies are visualisedin Figure 1.

3.1

Feature engineering

In order to make rankings on popularity and city impact we
hand-craft features, for each location per category, from theLSBN. We make heavy use of the features used by Karamshuk
et al. [2], which are based on the work of Jensen [8]. Karamshuk
et. al. extracted a total of eight di↵erent features from thelocation based social network, which are the first eight described features in Table 2. These features are divided in
two di↵erent groups. The first group are features constructedfrom the geographical situation and the other group are features constructed using the movements between venues, which
are called mobility. We extend this feature set by looking at
the geographical behaviour of the movements in the LSBN.
These features are described in 3.1.1.

Feature Description
GeographicalDensity Number of neighbours
Neighbours Entropy Spatial heterogeneity
Competitiveness Amount of same category
venues in the neighbourhood
Quality by Jensen Amount of categories which
tend to occur together
Mobility
Area Popularity The number of check-ins in
the neighbourhood
Transition Density The number of check-ins ofvenues within the neighbourhood
Flow The number of check-in frommovements outside the neighbourhood
Transition Quality The expected number ofmovements from venues in
the neighbourhood based on
their categories.
Geographical Flow
Bypass Indegree of bypassing users
Betweenness centrality Amount of times the locationis on a shortest pathwhole graph. As a byproduct it allows for a visualisation of
the interaction structure in the city, as can be seen in Figure
3. Features constructed from this graph are grouped under
Geographical Flow.

All of the Karamshuk et al. features are based on the interactions occurring in the direct neighbourhood of the venue
under investigation. A neighbourhood is defined as the area
within a circular radius r, which is set to 200m for all features.
The Haversine distance [9] is used as a distance metric. Dueto lack of space, we refer to their paper for the full details on
the construction of the features.
We will now outline the two new features Bypass and Betweenness centrality:

3.1.1

FlowGraph

The existing features do not capture the the function of theneighbourhood within the city as a whole. For example, itdoes not show what paths people potentially took to get fromone venue to another. To get a better understanding of thiswe create a relative neighbourhood graph (RNG) [10] from allvenues. This graph only has edges between neighbours from ageographical perspective. An edge between the venues (a, b)
only appears if the following holds:
8c 2 V : dist(a, c) > dist(a, b) ^ dist(c, b) > dist(a, b)(1)

Where V is the set of venues in the RNG and dist is the Haversine distance between two venues. When all eligible edges,
according to equation 1, are defined in the graph, the movements from the LSBN are mapped to corresponding edgesof the shortest path between the venues in the RNG. The
obtained weighted graph is referred to as FlowGraph, whichshows dense areas and areas which acting as hubs for the

Figure 2: Sequential construction of the FlowGraph, which isbuild by creating a relative neighbourhood graph based on thevenue locations and mapping the weights of the edges fromthe LSBN to all edges on the shortest paths in the RNG.

The bypass feature is the indegree of a location in the FlowGraph. A high value suggests that many people are passing
through the selected location. This is especially useful in lessoccupied neighbourhoods, since people passing by are potential customers.
Another feature we propose is betweenness centrality, which
was not considered in [2]. This centrality is defined by thefrequency a node appears in the shortest paths between all
nodes in the graph. This is related to the hub function foreach location. For example the area which connects Long Island with Manhattan has the highest betweenness centrality.

4.1

Data set

The data set consists of two di↵erent data structures. The
first structure holds information of all the known venues in
the city, such as venue name, longitude, latitude, and thevenue category. The other structure consists of aggregatedmovements between two venues. The provided movements are
between two venues within the city, and also includes movements going to or leaving a single venue in the city. We defined
the number of check-ins. i.e. popularity, as the total numberof movements going towards a venue.

Figure 3: The FlowGraph for the venues of New York City.
Busy areas and corridors can quickly be identified.

3.2

Objectives

In order to capture the venue categories that are most suitableconsidering both the store owners and the city perspectives wedefine an objective for each of them. Thus, for each category
at a given location, two scores are computed. A category iscovered if and only if there exists another category for which
all scores are higher than it’s own. From the set of categories
we define the optimal set as the ones that are not covered.

3.2.1

Popularity

The benefit for the store owner is defined in terms of popularity, i.e. check-ins the venue would potentially receive. For
each location we only know the popularity for the category,
which is already there. For all other categories at a location
this is unknown. A regression model is trained on the existing venues with their category in the data set. The model
predicts for a given category and corresponding extracted features, as described in the previous section, what the popularity probably will be. With this model we can get a better
understanding of how popular the venue would become for allother categories at a location. The popularity is the objective
for the store owners and should be maximised.

3.2.2

4.2

EXPERIMENTS

In this section we perform a study on the Foursquare dataacquired from the city of New York. We apply our proposed
methods for the city of New York in order to see if the extracted features from the FlowGraph significantly contributetowards better popularity predictions. Furthermore, we show
the results for the optimal set of categories. Before these experiments can be conducted the data set is pre-processed toa suitable format.

Popularity Prediction

We created venue popularity predictions for locations usingdi↵erent venue types and their corresponding features. For
this predictions we choose to use a regressor because of thecontinuous property of our popularity variable. A Catboost
regression model [12] is used for getting popularity predictions. Catboost uses gradient boosting on decision trees. We
evaluated the performance of the algorithm using the root
mean square error metric. The data set was split into a train
and test set with a distribution of respectively 80 and 20 percent. The model was trained on the train set and the test
set was used for evaluation, preventing data leakage. Whentraining our algorithm we got the first row of results shownin Table 2
Table 2: Error scores (RMSE) of the popularity regressions
models provided with di↵erent feature sets

All features
Original features

City impact score

We investigated what objectives could be important from thecity perspective. We found one important feature that canbe extracted from data, the Neighbours Entropy [11, 2]. Thisfeature represents the diversity of categories within a neighbourhood, which is in essence mixed-land use, the criteria
which is regularly considered by urban planners. When different categories are given for a location the entropy changes
accordingly. A high entropy means a high diversity of thesurrounding, so this score should be maximised.

4.

In total there are 17, 382 venues for the city of New York
where at least one check-in occurred. The number of distinct
categories is 503, which is quite specific. To be sure this would
not result in an insufficient number of training examples per
category, we clustered multiple categories to prevent overfitting on a handful of venues. To do this we reduced the categories to their top-level category from Foursquare, resulting ina total of 10 categories: Arts & Entertainment, Shops & Services, Professional & Other Places, Food, Residences, Travel& Transport, College & Universities, Outdoors & Recreation,
Nightlife Spots, and Events.

Train
129.18129.28Test
128.54128.65In order to see if our constructed features improved the popularity prediction we performed the test twice using di↵erent
feature sets. The first set included all described features, thesecond set contained only the original features excluding ours.
The results show that the added features in our situation have
low to no impact on the popularity prediction error. A paired
t-test between predictions from both sets yielded a p-value of
0.7. This means that our proposed features do not create asignificant di↵erence in performance.

4.3

Optimal sets

From all venues in the data set we took a random sample of1000 venues. We created features for each category available
at all locations. Each location we analysed the resulting category sets. Recall that each category is assigned with twoscores. As an example the categories are projected on a 2D
plot where each score is on one of the axis, as can be seenin Figure 4. In the original dataset this is a Taco Shop in
the Upper East Side. In total there are four categories in the
optimal set, highlighted in red. These categories are thus tonot limited to: fastfood, fine dining, co↵ee shop, etc.

Figure 4: Projection of all categories for a location, currently
used as a Taco store in the Upper East Side, with the optimalvenues shown in red.

Figure 5: Histogram of the number of categories in the optimal set from a sample of 1000 locations

be considered as most suitable for that particular location.
In this set residential and nightlife spots would give a higher
diversity in the area and a food venue is suggested be mostpopular.
The histogram at Figure 5 shows the distribution of categories
in the optimal set for all locations in the sample. A majorityof locations have more than one category in their optimal set.
Meaning that there often is a conflict between the selection of
the best category between each score. This conflict shows usthat the venue prediction problem is a multi-objective problem. Because a large majority has multiple optimal venue
categories we know that it is hard to find the best category
when only looking at one objective.

5.

CONCLUSIONSFor future work we want to take an in depth look into othercity planning objectives. Currently, we are focused on the
shop diversity as a city planning objective. City planning ismore than just this feature, but is also often a city specifictask. If we want to research this, we should create a closer
co-operation with the cities we are analysing, increasing thee↵ectiveness of our city score. Another addition is taking
more di↵erent venue categories into account. We focused ona generalised set of categories. If we could gather more datafrom di↵erent venues we could look at a lower type in the
Foursquare venue type hierarchy. One of the proposed goals
could be to analyse di↵erent food type venues, such as but

In conclusion, using a Foursquare movements network we created a framework where the optimal store placement problem
can be scored using the perspective of the store owner, andthe perspective of the city as a system. We introduced aprojection of movements on a relative neighbourhood graph,
which we called FlowGraph, and used this graph to create two
new features. The impact of these features were minimal andnot significant for the performance of popularity prediction.
Overall we showed that both objectives rank the categories
in a di↵erent way, resulting in multiple categories considered
optimal. This indicates that the multi-objective approachis a necessity in order to work towards a city improvement
based on city planning guidelines while having satisfactory
store owners.

6.

REFERENCES[1] M. Townsend, J. Surane, E. Orr, and C. Cannon.
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services for optimal retail store placement. In
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computational techniques for optimal store placement:
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Computational Science and Its Applications, pages
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Shop-type recommendation leveraging the data fromsocial media and location-based services. ACM Trans.
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[5] Y. Fu, G. Liu, S. Papadimitriou, H. Xiong, Y. Ge,
H. Zhu, and C. Zhu. Real estate ranking via mixed
land-use latent models. In Proceedings of the 21th ACM
SIGKDD International Conference on Knowledge
Discovery and Data Mining, pages 299–308. ACM, 2015.
[6] A. Gil Solá and B. Vilhelmson. Negotiating proximity
in sustainable urban planning: A swedish case.
Sustainability, 11(1):31, 2019.
[7] R. Cervero. Mixed land-uses and commuting: Evidence
from the american housing survey. Transportation
Research Part A: Policy and Practice, 30(5):361–377,
1996.
[8] P. Jensen. Network-based predictions of retail store
commercial categories and optimal locations. PhysicalReview E, 74(3):035101, 2006.
[9] C.C. Robusto. The cosine-haversine formula. The
American Mathematical Monthly, 64(1):38–40, 1957.
[10] J.W. Jaromczyk and G.T. Toussaint. Relative
neighborhood graphs and their relatives. Proceedings ofthe IEEE, 80(9):1502–1517, 1992.
[11] Jianhua Lin. Divergence measures based on the
shannon entropy. IEEE Transactions on Information
theory, 37(1):145–151, 1991.
[12] L. Prokhorenkova, G. Gusev, A. Vorobev, A.V.
Dorogush, and A. Gulin. Catboost: unbiased boostingwith categorical features. In Advances in Neural
Information Processing Systems, pages 6638–6648, 2018.

Crime Rate Prediction with Region Risk andMovement Patterns
Shakila Khan Rumi

Phillip Luong

Flora D. Salim

RMIT University
Melbourne, VIC
shakilakhan.rumi@rmit.edu.au

RMIT University
Melbourne, VIC
phillip.luong@rmit.edu.au

RMIT University
Melbourne, VIC
flora.salim@rmit.edu.au

Abstract—Location Based Social Network, Foursquare helps
us to understand the human movement of a city. It provides
data that characterises the volume of movements across regions
and Places of Interests (POIs) to explore the crime dynamics ofthe city. To fully exploit human movement into crime analysis, we
propose region risk factor which combines monthly aggregatedcrime and human movement of a region across different timeintervals. Number of features are derived from this risk factor.
Extensive experiments with real world data in multiple cities
verify the effectiveness of the features.

I. I NTRODUCTION
Safe and secure living place is one of the basic demands
to every person in society. Therefore, it is important to findways to control the crime rate. High crime rate hinders the
economic development of a city. Understanding the root causesof what increase the likelihood of any particular crime event
occurring at any time has great benefits for law enforcement
to prevent crime events. According to the criminology theory,the surrounding environment including neighbourhood regions
and movement of people play crucial role in crime event prediction. The widespread use of location based social networks
such as FourSquare open the door of opportunities to analyse
crime event occurrences in a timely manner. In this paper, westudy the crime rate prediction with the help of urban mobility
data.
Recently, there has been research exploring the link between
crime events and urban dynamics using FourSquare data [1].
However, this study focused on a region’s check-in information
to predict the crime events. There was no focus on hyperlinking human movement between two regions. In [2] the authors
considered the movement between regions using taxi flow datafor crime inference, however, did not consider the variation of
movement in different time periods of day. For example, thepeople who move to work place from home will move to the
opposite direction in the afternoon. In this paper, we analyse
the crime inference with the movement of people at different
period of day. In Figure 1 we can observe the difference in theamount of people moving in morning and afternoon for New
York City. The changes of human movements are highlighted
with different colour. To fully exploit the human dynamics
in crime inference problem, we further introduce region risk
which associates crime and people movement in that region ina certain time interval, which is denoted as Region Risk. The

hypothesis is that if the people are coming to a place froma high crime risk area, such movement implies a high crime
risk in the arrival area too. We derive numerous features withassistance of this region risk. The significance of this features
alongside other features is verified for three different cities
including Chicago, Los Angeles and New York City acrossdifferent time interval of a day. The contributions of this paper
are summarized as follows:• This is the first work that predicts crime rate based on the
dynamic features that associate region risk and movement
patterns between regions.
• Different features associated with region risk and thehuman mobility in different periods of time are crafted.
• The work verifies the effectiveness of different featuresin crime inference problem using correlation and regression analysis. Real-world crime data and FourSquare
movement data are used for evaluation. The experimental
results show that the region risk features are highly
correlated with crime count of a region.
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Fig. 1: Few check-in movements in New York in different timeintervals for ”2018-03”

II. R ELATED W ORK
Many data mining research have been developed recently toverify the impact of human mobility into crime study. In [3],the authors extracted human behavior from mobile network
activities and demographic features of people connected to the
network over different regions and times. The study showedthat the combination of mobile activity data and demographic
data can predict crime event in a region with better accuracy.

Ambient population is measured through FourSquare check-in
data and is used to understand the long-term crime event occurrences [4], [5]. In [1] the authors proposed several dynamic
features using FourSquare data to measure the social diversityof a region and predict short-term crime event occurrences.
To understand crime event occurrences, it is important to
explore the correlation between places. The mobility data
represented by taxi flows and Points of Interest (POI) canlift the performance of crime rate inference [2]. Here, theauthors’ hypothesis is that the social interaction between twoplaces can be inferred through taxi trips and the crime rate
propagate based on the connection between places. In [6], theauthors proposed crime-specific dynamic features by analyzing
individual risk factor of the users and extracted multiple
features based on the risk analysis.
However, none of this work correlate the large scale human
movement in different time period of day with crime in aregion. Our work attempts to fill this gap.
III. DATASET D ESCRIPTION
The datasets are collected for three different cities in USA
including Chicago, Los Angeles and New York City. We
collect different types of data including check-ins and crime
events for each city. We segment each city into 400⇥400 grid.
A. POI and Check-in Data
The POI and check-in information is collected fromFourSquare check-in. The dataset is provided as part of the
Future Cities Challenge at Netmob 1 . The check-in information describes an aggregated count of all movements from one
venue to another separated by month and five time intervals
including Morning, Midday, Afternoon, Night and Overnight.
In the collected data, we focus on the three cities mentioned
above for the year of 2018. The aggregated number of venues
and different venue movements are summarised in Table I.
TABLE I: Number of Venues and Venue Movements for each city
City
ChicagoNew York
Los Angeles

Venues13,90432,971
15,868Unique Movements
5,396,723
5,296,809
5,683,763

B. Crime Data
We collect crime event records that lie in 2018 for Chicago,
Los Angeles and New York from Open Data Portal of therespective city councils 2,3,4 . Each dataset consists of thelongitude, latitude, and the time and date of crime event
occurrences. The total number of crime event occurrences are
263,515, 226,498 and 452,958 for Chicago, Los Angeles and
New York respectively.
1 https://www.futurecitieschallenge.com/

2 https://data.cityofchicago.org/Public-Safety/Crimes-2001-topresent/ijzp-q8t2
3 https://data.lacity.org/A-Safe-City/Crime-Data-from-2010-toPresent/y8tr-7khq
4 https://data.cityofnewyork.us/Public-Safety/NYC-crime/qb7u-rbmr

IV. F EATURE D ESCRIPTIONThis section presents the detailed description of the features.
We consider each city as a directed graph, G = (V, E). Eachgrid, v 2 V , represents a node in the graph. E represents
the set of edges between two nodes, which are weighted anddirected. If there is a directed edge between two nodes, i andj (denoted as (i, j)), in a time interval, t, there is at least one
check-in from i to j over t. The cost (or weight) of each nodeis the aggregated number of check-ins in a month. For eachnode v 2 V in a time interval, t, the following nodal and edge
features are calculated.
A. Nodal Features
Nodal features describe the characteristics of the focal gridonly.
1) Historical Features: To retain the historical knowledge
about crime event occurrence, we calculate the following
feature:Crime Event History: We measure the number of crime
events in node, v, during interval, t, in the past month. Thisis represented as:X
N H(v, t) =Crj (v, t).
(1)j2mHere, Crj (v, t) denotes the number of crime events that
happened on j-th day in node, v, during time interval t. The
variable, m, represents the day in the past month.
2) POI Features: The regional information of a node is
described using the following features.
POI Density: For each node, v 2 V the POI density is
calculated as follows:
N P (v) =N (v).
N (V )(2)

The total POI of the city is represented as N (V ). N (v) denotes
the number of POI in focal node, v.
Venue Category Distribution: Each type of venue has
different impact on crime. Hence, it is important to extract
the distribution of venue types in node, v. It is calculated asfollows:
Ni (v)N Di (v) =
(3)
N (v)
Here, Ni (v) represents the number of i-th category venue innode, v.
Venue Diversity: Shannon’s entropy [7] measurement isapplied to determine the diversity of venue types, P , in node,v. Thus, Venue Diversity is modelled as:✓◆
X Ni (v)Ni (v)N E(v) =⇤ log2
.
(4)
N (v)
N (v)
i2P

3) Movement Features: The human dynamics of region intime interval is represented using the following features:

TABLE II: Feature Correlation Analysis for the New York and Chicago
Feature Name
Check-in Entropy
Risk Count
Risk Ratio
Risk
Number of Crimes
Historical Density
Food
Total POI
Venue Diversity

ChicagoMorning
0.365
0.210
0.113
0.154
0.001
0.637
0.175
0.517
0.318

Midday0.277
0.088-0.010
0.095
0.039
0.867
0.116
0.651
0.287

A'noon
0.273
0.077
0.002
0.070
-0.026
0.872
0.119
0.631
0.255

Night
0.360
0.095
0.042
0.116
0.035
0.671
0.228
0.638
0.366

O’Night
0.326
0.611
0.326
0.422
0.322
0.794
0.262
0.451
0.428

Incoming Movement: The density of incoming movement
into node, v, in time interval, t, is measured here. If a checkin is performed from other nodes to node, v, then it isconsidered as incoming movement. This we obtain the densityof incoming movement as,
N I(v, t) =Ci (v, t),C(v, t)(5)where C(v, t) and Ci (v, t) denote total check-in and incoming
check-ins respectively, performed at node, v, during timeinterval, t, in each month.
Outgoing Movement: If a check-in is performed from node,v, to other nodes then it is considered as outgoing movement.
The density of outgoing movement is represented as,
N C(v, t) =|Co (v, t)|,|C(v, t)|(6)

where Co (v, t) represents outgoing check-ins performed innode, v, during time interval, t, in each month.
Stationary Movement: When the origin and destinationof a check-in is the same node, it is denoted as stationary
movement. The density of stationary movement for Cs (v, t)
stationary check-ins in node, v, in time interval, t, is asfollows:|Cs (v, t)|
N S(v, t) =(7)
|C(v, t)|Diversity of Movement: The heterogeneity of movement
type is measured here:✓◆
X |Ci (v, t)||Ci (v, t)|
N M (v, t) =⇤ log2. (8)|C(v, t)||C(v, t)|i2M

New York
Morning
0.387
0.402-0.018
0.113
0.357
0.936
0.034
0.509
0.410

Midday0.264
0.424-0.036
0.050
0.074
0.983
0.016
0.655
0.367

A'noon
0.304
0.512
0.000
0.076
0.053
0.723
0.018
0.673
0.356

Night
0.300
0.282
0.037
0.225
0.309
0.961
0.031
0.586
0.375

O’Night
0.250
0.421
0.249
0.355
0.040
0.921
0.233
0.524
0.361

2) Region Risk: To compute region risk, we analyse the risk
associated with each node to support the following intuition.
If the incoming check-ins of a node in a time interval are fromhigh risk area, it imposes high risk of crime event occurrencein that node in that time interval. The region risk of node, v,
for time interval, t, is as follows:
RR(v, t) =|Cr(v, t)|,|C(v, t)|(9)where Cr(v, t) denotes the crime events that happened innode, v ,in time interval, t. Several edge features are crafted
which consider this region risk, RR(v, t).
Risk Distribution: The risk distribution consists of the mean
and median of the region risk associates with the regions r 2
R(v, t) from where people are moving to focal node, v, intime interval t.
Risk Count: Risk count in node, v in time interval, t,
determines the number of regions with high risk than average
from where incoming movement occur in node, v, in that timeinterval. The risk count is denoted by,
1 X
RC(v, t) = |{r : r 2 R(v, t) and RR(r) >RR(n, t)}|.
|R|
n2V
(10)
Here, R(v, t) denotes the regions which are origin of checkins to node v, and |R| is the total number of regions.
Risk Ratio: Risk count determines the absolute number ofregions with high risk. We normalise this feature based on total
regions with incoming movement. The risk ratio is modelledusing,
RC(v, t)RT (v, t) =.
(11)
|R(v, t)|The set, M , consists of three movement types: incoming,
outgoing and stationary.

Region Risk: This feature represents absolute value ofregion risk that is associated to the focal node, v in timeinterval,t, RR(v, t)

B. Edge Features

C. Feature Correlation Analysis

Edge features determine how crime rate of a region is
influenced by its connected region. Two types of feature
have been crafted. One is based on the crime rate in theneighbourhood region and another one is based on the risk
analysis of a region based on movement data.
1) Neighbourhood Crime: The number of crime events foreach adjacent node of focal node, v, is computed in each timeinterval. It reflects the situation of the surroundings.

We conduct Pearson correlation analysis to see how the
proposed features are individually correlated with the monthly
crime count of a region in a certain time interval of day.
The correlation value between the features and crime count is
illustrated in Table II for Chicago and New York City acrossdifferent periods of a day. Here, we note only the features
which have high correlation with crime count due to space
limit. We observe that crime count is highly correlated with

Crime Event History in both cities in many intervals. According to the Near Repeat theory [8], crime tends to happen in thevicinity of past crime. The features derived from region risk
analysis also have good correlation with crime count for bothcities especially overnight. Such positive correlation proves
the importance of such features in crime count. Surprisingly,
many POI densities are negatively correlated with crime count.
Although venue diversity has good correlation with crime
which verifies that mixed land use has good impact on crime.
V. M ODEL D ESCRIPTION
The main purpose of this paper is to show the effectivenessof the features derived from human movement in crime
inference. To serve this purpose, we apply Linear Regression(LR) to count the features of a node in a time interval. We
adopt LR as inference model because it is a simple and moststraightforward regression technique.
In this study, we only use the region and time interval wherecheck-in movement exists to train the model. For example, ina month, m, for a region, v, during time interval, t, if there is
any type of movement, it generates the training and test data
based on the features described in previous section. Finally,
LR model is trained with different feature settings to know theeffectiveness of the feature. The performance metric compares
which feature sets are significant for the crime inference.

C. Feature Importance
We measure the importance of each group of feature using
regression method. If the MAE and RMSE value is higher
without a set of features, it verifies the importance of thatfeature set. The importance of each feature set is illustrated
in Figure III for New York City. We observe that historicalfeatures are the dominating set of features among all of thefeatures. The edge features based on Region Risk analysis also
show the effectiveness across different time interval. The same
analysis has been done for the other two cities.
TABLE III: Mean Absolute Error (MAE) and Root Mean Square
Error (RMSE) for different times of the day for New York
Experiment Settings.a
Error
1
2
3
4
5
6
MAE
7.76
12.25
7.69
7.78
7.86
7.65
Morning
RMSE
11.1
17.01
11.14
10.53
11.32
10.74
MAE
12.38
23.08
12.3
12.24
12.35
12.4
Midday
RMSE
16.36
32.28
16.28
15.22
16.36
15.76
MAE
21.29
23.45
21.3
21.88
21.29
22.02
A’noon
RMSE
42.55
32.11
42.61
44.11
42.54
44.46
MAE
12.62
23.71
12.44
13.47
12.7
13.986
Night
RMSE
16.56
31.29
16.51
17.82
16.66
17.52
MAE
13.39
17.34
14.06
14.78
13.35
18.10
O’night
RMSE
17.5
21.3
18.09
18.55
17.46
21.93
a List of experiments:
1. All Features Present, 2. Historical Features Omitted
3. Nodal Check-In Omitted, 4. Geographic (POI) Features Omitted,
5. Neighbourhood Features Omitted, 6. Region Risk Features OmittedTime

VI. E XPERIMENT
A. Settings
The dataset used in this experiment is introduced in Section
3. Each day is segmented in five intervals and for each interval
the are aggregated in monthly level. To prevent extreme sparsity situations, only check-in movement data with 10 or moreunique movements in a month. The aim of the experimentis to examine the effectiveness of the proposed features incrime inference model for a month in a certain period ofday. We partition the data about 75% as training set and therest 25% as test set for all three cities. Particularly, the datalies between January 2018 and September 2018 (inclusive) isused as training data and the data between October 2018 to
December 2018 (inclusive) as test data. If new regions arefound with check-in movement greater than, or equal to 10 intest data for a time interval, the risk value for that region isset 0.
Two performance metrics, Mean Absolute Error (MAE) andRoot Mean Squared Error (RMSE) have been applied to verify
the effectiveness of the crime inference model.
B. Performance Study
We evaluate the performance of the proposed features formonthly crime count. We build linear regression based modeland apply ‘leave-one-out’ to measure the performance of a setof features. First, we train a model with all the features. Next,
we train another model without a set of features to identify theeffectiveness of that set of features. If the MAE and RMSE
increase for second model compare to the first one, that set offeatures is considered important in monthly crime count.

VII. C ONCLUSION
This work provides new perspective to understand crime
dynamics with the help of human mobility. It captures therelationship between the monthly aggregated crime data andthe movement of people in a region across different time
period of a day. The experiments verify that a group of people
from high crime risk area increase the crime risk of theirdestination.
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Dynamic Features,” EPJ Data Science, 2018, in Press.
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