Editors:
Francesco Calabrese
Esteban Moro
Vincent Blondel
Alex ‘Sandy’ Pentland

5-7 APRIL 2017
VODAFONE VILLAGEMILAN

BOOK OFABSTRACTS

POSTER

POSTER SESSION 1 5 APRIL 2017
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Churn Prediction in the Telecommunication Industry using Social Network Analytics
Maria Oskarsdottir
Joint Spatial and Temporal Classification of Mobile Traffic Demands
Marco Fiore
Understanding mall visiting patterns and mobility in Santiago de Chile with CDR data
Mariano Beiró
Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges
Vsevolod Salnikov
The Cost of Taxing Network Goods: Evidence from Mobile Phones in Rwanda
Daniel Bjorkegren
PRIVA’MOV: Analysing Human Mobility Through Multi-Sensor Datasets
Antoine Boutet
Developing a mobility monitoring application hand in hand with the end user
Jerome Urbain
Effects of Network Architecture on Model Performance when Predicting Churn in Telco
Maria Oskarsdottir
A Neural Network Framework for Next Place Prediction
Langford Chad
Explorative analyse of two Italian cities: Turin and Venice for diversity
Didem Gundogdu
PyMobility: an open source Python package for human mobility analysis and simulation
Gianni Barlacchi
An exploratory analysis of ethnic groups in the city of Milan through mobile phone data
Gianni Barlacchi
Determininig an optimal time window for roaming data for tourism statistics
Martijn Tennekesi
Measuring transnational population mobility with roaming data
Rein Ahas

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POSTER SESSION 2 6 APRIL 2017
1.
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Climate change induced migrations from a cell phone perspective
Sibren Isaacman
High speed analysis of volatile mobile data applied to road safety
Josè Gómez Castaño
Context-Aware Recognition of Physical Activities Using Mobile Devices
Gabriele Civitarese
Mining the Air -- for Research in Social Science and Networking Measurement
Gabriele Civitarese
User Authentication with Neural Networks Based on CDR Data
Bartosz Perkowski
Analysis of Tourist Activity from Cellular Network Data
Marco Mamei
A neural embedding approach to recommender systems in telecommunication
Nikolaos Lamprou
Drivers of spatial heterogeneity of HIV prevalence in Senegal: disentangling key features of human activity and mobility
Lorenzo Righetto
Automatic stress assessment using smartphone interaction data
Matteo Ciman
A New Point Process Model for the Spatial Distribution of Cell Towers
Carlos Sarraute
Evolving connectivity graphs in mobile phone data
Sanja Brdar
Using Mobile Phone Signalling Data For Estimating Urban Road Traffic States
Thierry Derrmann
Characterizing Significant Places using Temporal Features from Call Detail Records
Mori Kurokawa
Estimating the Indicators on Education and Household Characteristics
and Expenditure from Mobile Phone Data in Vanuatu
Jonggun Lee

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5-7 APRIL 2017 / VODAFONE VILLAGE / MILANINDEXPOSTER5 APRIL 2017

POSTERSESSION 1

NetMob 5-7 April 2017 – Vodafone Village – Milan

POSTER SESSION 1

Churn Prediction in the Telecommunication
Industry using Social Network Analytics
Marı́a Óskarsdóttir∗ , Cristián Bravo† , Wouter Verbeke‡ , 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‡ Faculty of Economic and Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Belgium,

Email: wouter.verbeke@vub.ac.be§ Grandata Labs, Argentina, Email: charles@grandata.com

Abstract—Identifying potential churners is important in competitive and saturated markets such as the telecommunicationindustry. Research has shown, that performance of models thatpredict customer churn is increased when social network methods
are applied. Studies also indicate that relational learners, applied
to customer call networks, are capable of predicting churn accurately. In this research, the difference in performance of a varietyof relational learners is tested by applying them to a number of
CDR datasets from around the world. In addition, performance ofrelational classifiers and collective inference methods is comparedand the performance of models which combine relational learners
with mainstream classifying methods, such as logistic regression,is studied. Our results show that collective inference methods do
not improve the performance of relational classifiers and thatthe scores of relational learners combined with other customer
features result in the best performing models.

I. I NTRODUCTION
In competitive and saturated markets, such as mobile
telecommunications, identifying potential churners quickly
and effectively is important. Telecommunication providers(telcos) are increasingly making use of network features, inaddition to regular customer features, to capture behavior
and interactions of customers when building churn prediction
models, as these have been shown to add valuable information[1], [2]. Call detail records (CDR) are aggregated to build
call networks, from which the network features are extracted.
Alternatively, churn propensity can be inferred from call networks directly by means of relational learners [3], [4]. Thesemethods simulate how churners affect the other customers, by
propagating ’churn energy’ through the network. The result isa score for each customer, that can either be used as indicatorof churn directly or to enrich the customer dataset before
building models using classical binary classifiers. In this study,
we evaluate the predictive performance of various relational
learners when used on their own and when combined withclassical classifiers. In particular we• test the difference in performance of a selection of
24 relational learners when predicting churn in mobile
networks,• study the effect of the two components of relational
learners, relational classifiers and collective inference
methods, andcombine relational learners scores with other customer
features when building churn prediction models using
binary classifiers, to determine which sets of features
provide the best performance.
To empirically evaluate the results, the methods were applied
to eight distinct CDR datasets from around the world. Thishigh number of datasets allows us to draw conclusions regarding the statistical significance of observed differences andprovide conclusive answers.
•

II. M ETHODOLOGY
A. Learning in Networks
Relational learning in network data can be used to classify interlinked nodes in partially labelled networks, as wasdemonstrated in the network learning framework and toolkit
NetKit [3]. Assuming that each node can belong to one of nclasses and that the nodes have been partially classified, that
information together with structure of the network, such as
links and weights, can be used to infer classes for the unknown
nodes.
Typically, relational learners are composed of two types ofmethods, namely relational classifiers and collective inference
methods. Relational classifiers assign a class or score to eachnode in the network, depending on which class the neighboring
nodes belong to and the weights of links between them. In thisstudy, we use the weighted vote relational neighbor classifier(wvrn), the class distribution relational neighbor classifier(cdrn), the network-only link-based classifier (nlb) and the
spreading activation relational classifier (spaRC), which isthe classifier part of the spreading activation method [4].
In contrast, collective inference methods iteratively apply arelational classifier to stabilize the inferencing process [5].
Here, we’ve used Gibbs sampling (gibbs), iterative classification (ic), relaxation labelling (rl), relaxation labelling withsimulated annealing (rlsa) and spreading activation collective
inference method (spaCI), which together with spaRC makes
up the spreading activation method [4]. We refer to [2] fora description of all these methods. To make up a relational
learner, each relational classifier can be applied on its own or
combined with a collective inference method, which results in
24 different methods.

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POSTER SESSION 1

Fig. 1. The figure shows an example of an application of a relational learner. The figure on the left, displays a graph with seven customers, of which two
have churned (black) and five have not churned (white). The figure on the right shows the same network after the RL has been applied. Each customer now
has a score or probability of churning.

Although NetKit can be used for any kind of network,
the specific application for customer churn in telco requires
some adjustments [2]. In particular, the relational learners willproduce a score between 0 and 1, since there are only twoclasses, churn (1) and non-churn (0). For churn, prediction are
typically made into the future, where all labels are unknown.
The learners were therefore applied to networks at time t,
assuming that the churn status of all customers was known, tomake prediction for the following time period t + 1.
III. DATASETS AND E XPERIMENTAL S ETUPTABLE ID ESCRIPTIONS OF DATASETS
ID
1
2
3
4
5
6
7
8

OriginBelgium
Belgium
North-America
North-America
EuropeEuropeBelgium
Iceland

Year
2010
2010
2015
2015
2009
2008
2012
2015

#Customers
1.41 · 106
1.21 · 106
1.57 · 106
1.32 · 106
4.33 · 106
4.52 · 106
1.70 · 105
9.36 · 104

Churn Rate
4.4%
0.84%
0.71%
2.5%
8.5%
3.5%
8.3%
2.3%

Sparsity
7.93·10−7
2.20·10−6
3.14·10−6
1.69·10−6
9.42·10−7
9.44·10−7
1.86·10−5
1.04·10−4

Table I shows a summary of the eight distinct CDR datasetsthat were used in the study. Originating from around the world
and ranging from 2008 to 2015, they vary in size, churnrates and sparsity. Each dataset spanned six months of calldetail records, some including text messages in addition tophone calls. To ensure comparability of results, all datasets
were preprocessed in the same way. Phone calls lasting lessthan a few seconds were disregarded and the activity ofcustomers used to determine their churn status. In that regard,
day of churn was defined as the first of 30 consecutive days
without any activity. Subsequently, the datasets were used tobuild both long and short term networks, covering three andone month, respectively. The weights between customers in
the networks were defined in two ways, with total durationof phonecalls between customers and the total number ofphonecalls, aggregated over the given time period. Finally,
churners in each network were labelled using their day ofchurn. In total there were four training networks for eachdataset, and each of the two dozen relational learners wasapplied to all of them.

The model building process was twofold. On one hand,the scores produced by the relational learners were viewed
as probabilities of churn. On the other hand, in a featurization
process, common network features were extracted from eachof the four networks together with RMF variables from the
CDR datasets. We refer to these variables as Network Only
features. Subsequently, churn prediction models were built
using three sets of features: Network Only features, scoresof the relational learners and both of these together. The
binary classifiers which were selected to build the models were
Logistic Regression, Neural Networks and Random Forest
because of their high predictive performance and popularity
in the industry [6].
Predictions were made for the month following the train periods, and was the same for each dataset. Model performancewas evaluated using lift at 10% and AUC.
IV. R ESULTS
For the empirical evaluation of our results we follow theguidelines for statistical comparisons of classifiers over multiple datasets presented by [7]. To test for significant differences
between the performance of the 24 relational learners, Friedman tests were applied to the rankings of both AUC and lift.
Both tests were rejected, and so was the null hypothesis ofequal average ranks, which means that there is a differencein performance between some of the relational learners. We
refer to [8] for more details on these differences. Friedman
tests were again applied to the performances of the relationalclassifiers and collective inference methods separately. These
tests were also rejected, meaning that there are significant
differences in performance, which were further explored using
a post-hoc Nemenyi test. Figure 2 shows a comparison of thedifference in performance measured in AUC of the relationalclassifiers and the collective inference methods. Blue boxes
indicate differences that are significant. Performance measured
in lift at 10% gave the same results. Regarding the relationalclassifiers, the only one that is always significantly different
is the network-only link-based classifier, which consistently
outperforms the other classifiers. This classifier builds a logistic regression model using information from the nodes’
neighborhoods that is then used to classify the unknown nodes
in the network [9]. For the collective inference methods, the

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POSTER SESSION 1
Relational Classifiers
0.1
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spaCI − icrl − ic

no − ic

spaCI − gibbs

rl − gibbs

rlsa − gibbs

ic − gibbs

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Fig. 2. Differences of Relational Classifiers and Collective Inferences. Blue
boxes indicate differences that are significant.

lower figure in figure 2 indicates that the iterative classification
method always performs significantly worse than the othermethods and that using no collective inference method always
results in higher performance. This result was confirmed using
Kruskal-Wallis test, which was rejected with a p-value of lessthan 0.01.
The performance of classical classifiers with different sets offeatures was also compared. First, Friedman tests were applied
to the results ranked by model performance in AUC and lift.
The tests were rejected, confirming a difference in averageranks in the performance of models using the three sets offeatures. A further exploration of these differences showed thatmodels built with both sets of features performed significantly
better than models using only one set of features, and that there
was not a significant difference between the models whichused only one set of features. The performance of models built
using logistic regression for each of the eight datasets can beseen in figure 3, where the black line indicates models with
network variables only, the blue line models with relational
learner scores only and the red line models with both sets offeatures combined.
V. C ONCLUSION
In the study we compared different ways of applying social
network analytics methods when predicting churn in telcoand evaluated our results empirically by applying them to anumber of CDR datasets. According to our results, combining
collective inference methods with relational classifiers does
not improve performance, and the best performing relationalclassifier is the network-only link-based classifier [9]. In
addition we showed that combining relational learner scores

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DatasetFig. 3. Performance of logistic regression models using three sets of featuresfrom the eight datasets, measured in AUC and lift at 10%.

with other customer features in a binary classifier produces
models with higher performance than using either of thesefeatures on their own.
R EFERENCES[1] A. Backiel, Y. Verbinnen, B. Baesens, and G. Claeskens, “Combining
local and social network classifiers to improve churn prediction,” in Proceedings of the 2015 IEEE/ACM International Conference on Advancesin Social Networks Analysis and Mining 2015. ACM, 2015, pp. 651–658.
[2] W. Verbeke, D. Martens, and B. Baesens, “Social network analysis forcustomer churn prediction,” Applied Soft Computing, vol. 14, pp. 431–
446, 2014.
[3] S. A. Macskassy and F. Provost, “Classification in networked data: A
toolkit and a univariate case study,” The Journal of Machine Learning
Research, vol. 8, pp. 935–983, 2007.
[4] K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea,
A. A. Nanavati, and A. Joshi, “Social ties and their relevance to churnin mobile telecom networks,” in Proceedings of the 11th international
conference on Extending database technology: Advances in databasetechnology. ACM, 2008, pp. 668–677.
[5] D. Jensen, J. Neville, and B. Gallagher, “Why collective inference
improves relational classification,” in Proceedings of the tenth ACM
SIGKDD international conference on Knowledge discovery and data
mining. ACM, 2004, pp. 593–598.
[6] T. Verbraken, C. Bravo, R. Weber, and B. Baesens, “Development
and application of consumer credit scoring models using profit-basedclassification measures,” European Journal of Operational Research, vol.
238, no. 2, pp. 505–513, 2014.
[7] J. Demsar, “Statistical comparisons of classifiers over multiple data sets,”
The Journal of Machine Learning Research, vol. 7, pp. 1–30, 2006.
[8] M. Óskarsdóttir, C. Bravo, W. Verbeke, C. Sarraute, B. Baesens, andJ. Vanthienen, “A comparative study of social network classifiers forpredicting churn in the telecommunication industry,” in Advances in
Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM
International Conference on. IEEE, 2016, pp. 1151–1158.
[9] Q. Lu and L. Getoor, “Link-based classification,” in ICML, vol. 3, 2003,
pp. 496–503.

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POSTER SESSION 1

Joint Spatial and Temporal Classificationof Mobile Traffic Demands
Angelo Furno∗† , Marco Fiore‡ , Razvan Stanica∗∗ Université de Lyon, INRIA, INSA-Lyon, CITI-INRIA, F-69621, Villeurbanne, France – name.surname@inria.fr† IFSTTAR-ENTPE, Université de Lyon, F-69675 Bron, France – angelo.furno@ifsttar.fr‡ CNR-IEIIT, Corso Duca degli Abruzzi 24, 10129 Torino, Italy – marco.fiore@ieiit.cnr.it

Abstract—We present an original approach to infer both
spatial and temporal structures hidden in mobile traffic demands,
by tailoring Exploratory Factor Analysis (EFA) techniques to thecontext of mobile phone datasets. Casting our approach to thetime or space dimensions of such datasets allows solving different
problems in mobile traffic analysis, i.e., network activity profilingand land use detection.

I. C ONTEXT
The surge in mobile data traffic –estimated globally at
3.7 exabytes in 2015, with a 74% increase over 2014 andan overall 4,000-fold growth over the past ten years [1]–has fostered the interest of the computer network community
towards better understanding the dynamics of the mobile
demand. Indeed, a proper characterization of how mobile
services are consumed by subscribers can enable an informed,
more efficient tailoring of network resource planning andmanagement to the end users’ needs. Knowledge mining ofreal-world datasets has revealed important features of mobile
traffic. Examples include a strong temporal periodicity andgeographic locality that enable effective prediction of thedemand; the appearance of significant fluctuations induced bysocial events with the consequent need for dedicated resource
management policies; or, a neat heterogeneity of the capacity
consumed by subscribers that is however captured by a limited
number of typical profiles, which enables the informed tuningof traffic plans. A survey of these results is in [2].
Our work focuses on the problem of classification, i.e., finding hidden regular structures in the network-wide aggregate
traffic generated by mobile users. The problem can be cast in
the temporal or spatial dimensions, both of which are relevant
to network operation management.
• In the temporal dimension, the problem is that of network
activity profiling, i.e., classifying together time periods
that show a similar, stable spatial distribution of themobile traffic demand. Network activity profiles findapplications in cognitive networking [3], as they can drive
the establishment and relocation of resources in concert
with the temporal variations in the mobile demand [4].
• In the spatial dimension, the problem is that of land use
detection, i.e., the decomposition of a geographical area
into zones where the mobile traffic dynamics are homogeneous over time. These zones typically correspond toland uses, i.e., the combination of urban infrastructuresand predominant undertakings of people at a location.

Fig. 1. Mobile demand classification with EFA in a toy scenario. (A) The oneweek demand in the target region is aggregated daily with respect to a spatial
tessellation of n cells. The resulting demand in the i-th cell during the j-th
day is EFA observation oij . (B) Network activity profiling: days are the EFA
variables, each characterized by a set of observations over the cell samples.
(C) Land use detection: cells are the EFA variables, each characterized by aset of observations over the daily samples. Figure best viewed in colors.

This knowledge can support the dynamic allocation of
capacity at individual base stations, and help mitigatinghigh fluctuations of resource needs in small-sized network
areas [4]. In addition, land use detection has applications
in geoinformatics, as an effective way to automaticallylabel the urban tissue, at lower cost and with higher
accuracy than traditional survey methods.
We present an original methodology for the joint spatiotemporal classification of the aggregate demand that a mobile network has to serve. Our solution stems from Exploratory Factor
Analysis (EFA), a well established instrument in psychology
research. EFA aims at identifying, in a fully automated way,latent factors that cause the dynamics observed in the data.
When tailored to the specific use case of mobile trafficclassification, EFA offers the possibility of exploring the space
and time dimensions of the data at once. Moreover, EFA
allows immediate extrapolation of the structures hidden in
the secondary dimension of each problems. In other words,
it provides, at no additional cost, knowledge of the spatial
patterns that characterize each network activity profile, and of
the precise temporal dynamics that distinguish each land use.
II. EFA FOR MOBILE DEMAND CLASSIFICATION
Given a set of observed variables of interest, EFA isformally defined as “a model of hypothetical component
variables (named factors) that explain the linear relationships
existing between observed variables” [5]. EFA can be tailoredto the classification problems of network activity profiling and

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CDF
0.4 0.8

POSTER SESSION 1

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Land use detection
Network activity profiling

−0.2

0.0

0.2
0.4
0.6
Pearson Correlation Coefficient

0.8

1.0

Fig. 2. Distributions of the Pearson correlation coefficient computed between
all pairs of EFA variables in the two mobile demand classification problems.

land use detection, as exemplified in Fig. 1. In both cases,
the input is an aggregate representation of the communication
activity of mobile subscribers in the geographical region ofinterest. This definition of input is general and can accommodate any level of spatial and temporal aggregation, as well as
any notion of mobile user activity (e.g., voice, text, data).
Network activity profiling. We model time intervals as the
EFA variables. Each variable is thus described by the mobile
traffic demand (i.e., the EFA observations) recorded over all
spatial cells during a given time interval, as shown in Fig. 1.
In this EFA configuration, the common factors seeked by EFA
are temporal structures that explain at what time instants thespatial distribution of the mobile demand is comparable: thesestructures are precisely network activity profiles.
An important remark is that, here, spatial cells map to
EFA samples: hence, EFA scores relate cells to temporalprofiles, revealing which geographical areas are important fora given temporal profile. This allows the joint inspection of
the classification results in the space and time dimensions.
Land use detection. EFA variables correspond to geographical locations. Each variable consists in the mobile traffic
demand (i.e., the EFA observations) recorded at a specificcell through the complete monitoring period, as in Fig. 1. In
this EFA configuration, the EFA common factors represent
structures in the geographical space that explain in what areas
the mobile demand follows similar temporal dynamics: bydefinition, such areas correspond to land use classes.
Interestingly, time intervals become now the EFA samples.
Therefore, EFA scores point out the time periods when themobile demand is especially distinctive within each land use.
This offers an unprecedented spatiotemporal perspective onland uses, and showcases again the potential of EFA for the
concurrent spatiotemporal analysis of mobile traffic data.
Finally, we recall that EFA builds on the hypotheses oflinearity of the functional relationships among the observed
variables and the unknown hidden factors. In order to verify
the validity of such an assimption in the case of mobile
traffic data, we run the Kaiser-Meyer-Olkin (KMO) Measureof Sampling Adequacy on the two datasets used for our
performance evaluation in Sec. III. In both our classification
problem formulations, and for all datasets, KMO returns values
around 0.99, indicating a high suitability of the data to EFA.
As additional checks, we verify: (i) the linearity of all pairwise
relationships between EFA variables in the two mobile demandclassification problems, finding strong correlation in 70–80%
of cases, as shown in Fig. 2; (ii) the sample-to-variable ratio,finding that it is always much larger than one, i.e., a good ruleof thumb for a meaningful factor analysis.

Fig. 3. Network activity profiling. EFA of the total communication activity(sum of incoming/outgoing calls and SMS) over the median week in the TIM2013 dataset. Loadings of the 24×7 hours of the week (i.e., EFA variables)on the six profiles (i.e., EFA factors). Figure best viewed in colors.

III. S ELECTED RESULTS
We demonstrate the quality of the spatiotemporal classification provided by EFA with real-world mobile traffic data
recorded by national operators in two major European cities.
TIM-2013 dataset. The data was released by TIM as partof their Big Data Challenge. The dataset describes the mobile
traffic generated by subscribers in the Milan conurbation
over a two-month period spanning November and December
2013. The data includes voice, text, and Internet traffic ofapproximately 400,000 users, aggregated during time intervalsof 10 minutes. Traffic volumes are georeferenced with respect
to a regular-grid space tessellation of 235 × 235-m2 cells.
Orange-2014 dataset. This dataset consists of Call Detail
Records (CDR) collected for billing purposes by the operator.
CDR describe hourly volumes of voice and text activity in the
Paris metropolitan region, on a per-antenna basis. The data
was collected from a sample of 100,000 users in September,
October and November 2014. We employ a standard Voronoi
tessellation to represent the spatial coverage of cells and thegeographical distribution of traffic.
A. Network activity profilingWe investigate the performance of EFA for the profilingof network-wide mobile traffic activity over time. Let us first
focus on a one-week period that is representative of the typicalcommunication activity recorded in the TIM-2013 dataset. To
that end, we condense the two months of data into one single
median week, so as to mitigate potential classification biases
due to outlying behaviors. For each cell in the Milan area: (i)we aggregate the demand of incoming/outgoing calls and texts
on a hourly basis; (ii) we associate to every hour of the week
the median value of all corresponding hourly demands (e.g.,
all Mondays at 8 am).
The network activity profiles (i.e., EFA factors) identified
by EFA in the TIM-2013 median week are portrayed in Fig. 3.
Each plot shows the loadings of every hour of the week (i.e.,every EFA variable) on a specific profile, according to the color
range on the right of the figure. For example, hours from 8
am to 6 pm on Monday to Friday have very high loadings on
factor 1, and low loadings on all other factors: hence, the first

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POSTER SESSION 1
Centro Direzionale

Politecnico

Garibaldi

Lambrate

La Defense
downtown
Paris

De Angeli

Mercatodowntown
Milan

Università
BocconiCattolica(a) Factor (network activity profile) 1

Bolivar
Navigli

Risorgimento(b) Factor (network activity profile) 2

Fig. 4. Network activity profiling. EFA of the total communication activity(sum of incoming/outgoing calls and SMS) over the median week in the TIM2013 dataset. Thurstone’s scores of the 434 cells (i.e., EFA samples) on thefirst two profiles (i.e., EFA factors). Figure best viewed in colors.

network activity profile characterizes the work hours. Overall,
EFA allows identifying the following profiles in Milan: (1)working hours; (2) relax hours in the evenings and weekend
mornings; (3) overnight hours; (4) weekend afternoon hours;(5,6) morning and afternoon hours corresponding to the startand end of the work activity.
EFA also lets us understand which geographical cells (i.e.,
EFA samples) are the most relevant to a specific profile (i.e.,
EFA factor). In other words, scores tell us where the mobile
communication activity that characterizes a profile takes place.
Fig. 4 shows the scores, estimated via Thurstone’s regression,of all geographical cells in Milan on the six network activity
profiles. As an example, we can remark how the cells loadedby the first profile clearly highlight downtown Milan, where
the business district and most offices are located. Additional
geographical areas that have high scores on this profile areuniversity campuses (Politecnico di Milano, Università diMilano, Bocconi, Cattolica) and commercial zones (public
entrance to Mercato Ortofrutticolo). Clearly, these are thelocations where mobile communication activities surge during
the work hours, i.e., those hours that have high loadings on
the first profile.
Equivalent analyses are possible for all of the other profiles.
For instance, during evening and weekend mornings (profile2), the network activity is much reduced in the city center, as
shown in Fig. 4b. Prevalent areas are within the inner city beltway and characterized by a dense presence of bars, restaurants,
and clubs (Navigli, Lambrate, Porta Garibaldi, Piazza Bolivar)
or by a strictly residential nature (Risorgimento, De Angelis).
B. Land use detection
When considering classifications in the spatial dimension,
Fig. 5 shows two out of fourteen land use classes detectedby EFA in the Orange-2014 dataset. For the sake of clarity,
in each plot we only show cells (i.e., EFA variables) thathave a high loading on the class (i.e., EFA factor) the map
refers to. The factor in Fig. 5a corresponds to business areas(e.g., downtown Paris, La Defense or Issy-les-Moulineaux).
The factor in Fig. 5b is less trivial, and characterizes very well
(93% precision with 96% recall) the deployment of subway
stations in the city (black dots in Fig. 5b). This shows how
EFA can reveal many microscopic land uses where the mobile
traffic demand follows distinctive dynamics.

Issy-les-Moulineaux(a) Factor (land use class) 2(b) Factor (land use class) 3

Fig. 5. Land use detection. EFA of the total communication activity (sumof incoming/outgoing calls and SMS) in the Orange-2014 dataset. Loadingsof the 1596 Voronoi cells (i.e., EFA variables) on two (out of fourteen)
representative classes (i.e., EFA factors). Figure best viewed in colors.

(a) Factor 2(b) Factor 3

Fig. 6. Land use detection. EFA of the total communication activity (sum of
incoming/outgoing calls and SMS) in the Orange-2014 dataset in November.
Thurstone’s scores of the 91×24 hours (i.e., EFA samples) on two (out offourteen) classes (i.e., EFA factors). Figure best viewed in colors.

EFA also returns scores that indicate the relevance of time(i.e., EFA samples) to each class (i.e., EFA factor). Thus,
scores tell us when each land use class shows an especiallyremarkable mobile communication activity. Fig. 6 shows thehourly scores during one month of Orange-2014 data on theland use classes presented above. As expected, the working
hours are most relevant to business areas in factor 2, shownin Fig. 6a: interestingly, morning hours seem more concerned
than afternoon ones. One can also easily detect that the typicalworking day spans from 8 am to 6-7 pm: these are the morningand afternoon commuting times when the subway stations offactor 3 experience exceptionally high loads, as displayed inFig. 6b. In all plots it is easy to spot an irregularity, due to a
public holiday (Armistice day, November 11).
IV. C ONCLUSIONS AND PERSPECTIVES
We propose an original approach to the spatiotemporalclassification of mobile traffic data, based on Exploratory
Factor Analysis (EFA). Tests with heterogeneous real-world
datasets demonstrate the versatility of EFA, which provides
supplementary knowledge (i.e., the geographical perspectiveof profiles and the temporal view of land uses) that provesparamount to the interpretation of the results.
R EFERENCES[1] Cisco VNI Forecast, “Visual Networking Index: Global Mobile Data Traffic Forecast
Update, 2015–2020,” 2016.
[2] D. Naboulsi, M. Fiore, S. Ribot, R. Stanica, “Large-scale Mobile Traffic Analysis:
A Survey,” IEEE Communications Surveys & Tutorials, 18(1), 2016.
[3] R.W. Thomas, L.A. Da Silva, A.B. MacKenzie, “Cognitive networks,” IEEE
DySPAN, 2005.
[4] H. Assem, T. Sandra Buda, L. Xu, “Initial use cases, scenarios and requirements,”
H2020 5G-PPP CogNet, Deliverable D2.1, 2015.
[5] S.A. Mulaik, Foundations of Factor Analysis, CRC Press, 2009.

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POSTER SESSION 1

Understanding mall visiting patterns and mobility in Santiago deChile with CDR data
M. G. Beiró, C. Cattuto,1 L. Ferres, E. Graells-Garrido, L. Bravo and D. Caro2
1. ISI Foundation, Turin, Italy
2. IDS, Faculty of Engineering, U. Del Desarrollo & Telefónica R&D, Santiago, Chile

1.

Introduction

Shopping malls became popular in Chile in the early 80’s, and are one of the first signs of the globalizationand liberalization of consumption in Latin America. They are currently one of the main elements of Chilean
popular culture, influencing people’s mobility, daily activity, social mixing and segregation processes [1, 2].
In this work we analyze the mobility patterns of people going to malls in the Chilean capital city, Santiago,
in order to determine which factors influence mall election and what types of social mixing are found in malls.
We used Call Detail Records (CDRs) provided by Telefónica R&D. Our study comprises 16 malls and 481
cellphone towers inside those malls. The dataset consists of 1,023,118 unique mobile devices (i.e., containinga Movistar/Telefónica SIM card), of which we identified 942,091 as visitors who went to malls 1,471,637
times in the course of a single month, August 2016.

2.

Methodology and results

Device filtering. In an initial pre-processing step we designed an algorithm filtering non-visitor devices (inparticular, SIM cards associated to card readers, and those associated to people who are not visitors, suchas employees or providers). This algorithm is based on the frequency of appearances in the mall network. If
a device is there every single day or at late night hours, it’s not a “visitor”.
Home location determination. For those visitors identified by the method above, we determined thegeneral area where they live by finding the first (before 8AM) and last (after 10PM) tower to which they
connected each day of the month. We then computed, for each user: (i) The number of times in which we
detected a first/last tower connection and (ii) the relative frequency of the most observed tower (i.e., thenumber of times in which the user’s first or last tower was that tower, divided by the previous quantity).
Results show that more than 70 % of people were connected for at least 80 % of days. About half the users
repeated their most common first-last tower at least 60 % of the days in which they are seen. In order toensure a good confidence for the home location determination, we only kept these last consumers.
Antenna Filtering. We started with a database of 13,733 antennas in all the province of Santiago. Malls
have their own cellphone towers and antennas within their walls. Since they are usually low power, connections to those antennas are mostly established from inside the mall. We identified these antennas by(i) finding antennas with the word “mall” in a human-targeted description field in the dataset, and (ii) bydrawing each mall’s polygons and using the towers’ coordinates to know which ones were contained by them.
After this preprocessing we built a mall interaction network (Figure 1) identifying people who visitedseveral malls during the month. The network was built as a weighted graph whose 16 nodes are malls andthe weights in the edges represent the number of people visiting both malls at least once during the month.
As some malls have more inherent activity than others, we normalize these weights by the total number ofvisits of each mall. Then, the network becomes directed and asymmetric, and the weight of the interaction
A → B will represents the probability that a visitor goes to mall B, given that he visits mall A.
1

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POSTER SESSION 1

Figura 1: Mall co-visitation network.
From this network we extracted clusters of malls with similar characteristics in terms of client profile andlocation. The Human Development Index (HDI) at the comuna level was obtained from [3], and we used the
HDI of the home comuna as a proxy for socio-economic status. Figure 2 plots the distribution of the HDI,
computed through a kernel density estimation, for the clients of each mall. We also computed the degree ofsocial mixing in each mall, in terms of the entropy of the distribution of their clients’ socio-economic profile.
We detected three clusters with different client profiles:
A first cluster comprises the malls Plaza Tobalaba, Lı́der Puente Alto, Jumbo Departamental andMall Plaza Vespucio in the south-eastern area of the city. These malls mix people from different socioeconomic levels: the middle-class comunas of La Florida and Macul and the low-class comunas of La
Pintana, La Granja and San Joaquı́n. Interestingly, these people are the ones that travel less from theirhome to the mall, with average distances ranging from 3.64 km to 4.93 km.
In the western cluster, involving Mall Arauco Maipú, Plaza Oeste, Mall Paseo Estacin and Plaza
Alameda, we find low-income consumers from the comunas of Maipú, Lo Prado, Pudahuel and EstaciónCentral. However, they also capture middle-class people from the Santiago and San Miguel comunas.
In this cluster people travel more (from 4.90 km to 6.83 km) from their homes to the mall.
Though the rest of the malls constitute a third large cluster, there are some interesting differencesin their visitors’ profiles. In particular, the north-eastern malls of Lder La Dehesa, Apumanque, Alto
Las Condes and Parque Arauco are exclusive for high-income people from Las Condes, Vitacura,
Providencia and Lo Barnechea. In order to arrive there, people travel between 5.76 km to 7.00 km.
Interestingly, people do not mix here, as people from low income comunas do not arrive. Instead, theCostanera Center, Plaza Alameda and Panorámico are the most heterogeneous ones, receiving people
from all the city, and from average distances between 6.83 km and 7.36 km. Together with Plaza
Egaña, they are the 4 malls with largest heterogeneity among their visitors, in terms of the entropy of
their distribution of socio-economic levels. Finally, Plaza Norte is a very particular mall, because it is
the only one which covers the northwestern area of the city. Because of this, people travel an average
distance of 7.86 km to arrive here (the largest among all malls), and the mall mixes people from low(Conchalı́ and Renca) and middle socio-economic levels (Quilicura).
2

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POSTER SESSION 1

3.

Conclusion

We observed that mall choice is mainly determined by two factors: distance and socio-economic level.
People from poor peripheral areas have their own local malls which they visit and they do not travel much,while people closer to the city center, from low and middle classes, are willing to travel more in order toarrive to central, heterogeneous malls in which people mix socially. People from rich classes in the northeastof the city are not so willing to move, and they usually visit high-target malls which are more exclusive andhomogeneous in their visitor profile. Population “mixing” patterns is an important topic and we we plan toextend this work by analyzing how they are induced by other retail “hubs” and other socio-economic factors.

Figura 2: Probability distribution of the HDI (Human Development Index) of the origin comunas for thecustomers of each mall.
[1] Salcedo R., De Simone, L., Los Malls En Chile: 30 años, Cámara Chilena de Centros Comerciales, Santiago
de Chile, 2013.
[2] Davila A., The Spatial and Class Politics of Shopping Malls in Latin America, Univ. of California Press,
2016.
[3] Aedo A., Delgado I., Dı́az R., Godoy S., Márquez R., Melis F., Palma A., Salfate V., Sierra M.L., Las
trayectorias del Desarrollo Humano en las comunas de Chile (1994–2003), Temas de Desarrollo Humano
Sustentable, 11, 2004.
3

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POSTER SESSION 1

Developing and Deploying a Taxi Price Comparison
Mobile App in the Wild: Insights and Challenges
Anastasios Noulas∗ , Vsevolod Salnikov† , Desislava Hristova‡ , Cecilia Mascolo‡ and Renaud Lambiotte†∗ Data Science Institute, Lancaster University, a.noulas@lancaster.ac.uk† Department of Mathematics, University of Namur, name.surname@unamur.be‡ Computer Laboratory, University of Cambridge, name.surname@cl.cam.ac.uk

I. S UMMARY

that define a price range for the costs of its Uber X service. Next,
the mean estimate is calculated from these values, rounding
to the closest integer value. We chose to provide the meanas opposed to ranges, as in a list of a few providers it would
be easier to compare on a single value as opposed to a range.
Traditional taxi providers do not typically provide APIs on
pricing. Instead, different taxi companies use different tariff
schemes. We therefore combine information on tariffs for
Yellow and Blacks Cabs in New York and London respectively,
with routing information offered by HERE Maps 1 . HERE
Maps return a shortest, in terms of time duration, routing paththat is sensitive to traffic information the company gathers froma variety of sources. We then simulate the taxi’s meter along
II. A PPLICATION AND S YSTEM D ESIGN
the route and estimate the price of a journey according to theWe have first launched the app in New York City in March tariff information in each city. Black Cabs in London feature a
2015, and subsequently in London in the very beginning of more complex tariff logic 2 than the Yellow Taxi company inJanuary 2016. As mentioned above, users that install the app New York 3 . In principle, tariff schemes apply a flat cost known
need first to select their city of interest (London or New York). as flag in the beginning of the journey when passenger boards
Subsequently in the journey query submission screen they can and then the price meter increases as a function of time andspecify their trip’s origin and destination. We provide two distance. For example, a rule may suggest that fare increases
functionalities to enable user localisation: first, a button next to by a fixed ammount (e.g. X U.S. Dollars) after Y meters or
the origin input tab that automatically sets the origin address, Z seconds (whatever comes first). HERE Maps returns thegiven the user’s geographic location (through GPS / WiFi routes as a set of segments, technically referred in the systemsensing), and, second, a text-input geocoding that parses user as manoeuvres. For each route segment there is information on
the length in kilometers and the typical driving time taken to
input and matches it to the most similar address name.
After setting the origin and destination addresses for a drive on the segment. We exploit this information to increment
journey the user can press a button, ‘Uber or Yellow?’ in the fare of the journey according to the tariff rules. Tariff rules
New York or ‘Uber or Black’ for London for comparison depend also on time (e.g. morning versus night) and dates
between Uber X and Yellow Cabs or Black Cabs respectively. (e.g. holidays versus regular days) and we have integrated thisThis will push the input query to our server where Uber prices aspect of pricing into OpenStreetCab as well. What is more,are compared to the competing local provider. Next, the user special destination or origin points such as airports or train
is presented with a screen where price estimates are provided, stations can imply additional costs as well as costs that areincluding an indication on the price difference (‘Savings’), specific to the route of the journey taken such as tolls. As
with an additional projection of a colored header at the top of currently there is no system that provides such information on
the screen clearly indicating the taxi provider for which the routes, we have relied on keeping our system to date throughestimate is lower (e.g., Yellow for yellow taxis in New York). manual labour and very critically on user feedback.
In addition to the user input, data is collected on the time ofIII. U SER G ROWTH AND A PPLICATION S TATISTICS
the user query: a GPS sample of the user’s current location
Overall,since the launch of the app in March 2015, more
and the application installation unique identification number.
than
13,
000
users have installed it in the two cities withThe latter has been useful to associate users with submitted
75%of
all installs taking place on an iOS platform
aroundqueries over time, as we required no registration informationfor our users.
1 https://here.com/
2 https://tfl.gov.uk/modes/taxis-and-minicabs/taxi-fares
Uber prices for the journey are collected through the Uber
3 http://www.nyc.gov/html/tlc/html/passenger/taxicab_rate.shtml
developer API [1]. The API returns two values, min and max,
In response to the growing complexity of taxi transport
dynamics, which affects a growing number of cities aroundthe world [4], we describe the process of development of
OpenStreetCab, a mobile application that aims to assist users
in choosing a taxi provider in a city in real time, offering
estimates on taxi prices. We reflect on our design decisions anddiscuss the application’s usage and pricing statistics between
two cities and two taxi providers. We provide a validation of
the app’s price estimates and a comprehensive study of priceand journey time measurement through a real world experiment
which compared taxi providers in the city of London.

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POSTER SESSION 1
Number of Active Users

9000
8000
7000
6000
5000
4000
3000
2000
1000
0

5
5
6
5
6
6
5
6
5
6
201 201 201 201 201 201 201 201 201 201
Apr Jun Aug Oct Dec Feb Apr Jun Aug Oct

Date

Fig. 2: User growth over time considering users with at least
one query since install.
Application Statistics
City

Fig. 1: Query distribution in New York considering journeyorigin.

iOS installs

Android installs

Queries

9340
1030

3095
345

25804
3371

New York
LondonTABLE I: Summary of application statistics across platforms
and cities.
and the rest on Android. In Figure 2 we present the number of
OpenStreetCabs users that have submitted at least one journeyquery. Approximately 8000 users have submitted a query, more in the city of London: Uber – focusing on their basic Uber
than 70% of those ever installed the application. Usage trends X service, and the city’s traditional Black Cab service. Therevary seasonally, but the number of total users with at least are some well known differences between the two services
one query every three months is in the range of 1500 to which we take into consideration in analyzing the output of our2000. The average number of queries per user is equal to experiments. To acquire a license in London, Black Cab drivers
3.12 with almost 350 user having submitted 10 queries or need to attend a school that takes about three years to complete
more. In Table I we provide a summary of the statistics by city and pass The Knowledge [3] test that thoroughly examines thetogether with the total number of journey queries submitted. ability of drivers to know by heart the whereabouts of a large
Regarding the number of queries, in New York there were a number of streets and points of interest in central London.
total of 25, 804 queries submitted to our server. The geographic Notably, medical tests on these drivers have suggested that
dispersion of user queries is shown on the map of New York their training and profession results to a larger number of cells
in Figure 1, where the heatmap shows the spatial variations in the hippocampus region of the brain which is the region
in query frequency. As expected most activity is concentrated that hosts the spatial navigation mechanism for mammals [2].
in Manhattan with occasional hotspots in peripheral areas that Another advantage of the Black Cab service is that they arelicensed with Transportation for London, which means they
include New York’s La Guardia airport.
We have measured an average saving of 8 U.S. Dollars can use bus lanes across the city. On the other hand, Uberper journey considering the mean difference between provider drivers do not receive any special training and rely exclusively
prices in each query. This corresponds to total potential savings on their navigation system. These differences are noticeableof almost 206, 000 U.S. Dollars for the app’s users assuming to users of the two services in the city but no quantifiable
that they always choose the cheapest provider. The number of data-driven insights exist on these differences until now.
queries in London are 3, 371 with potential savings of 12, 405
British Pounds on an average price difference of 3.68 GBP A. Experimental Setup and Conduct(Great British Pounds). While this number may not be reflective
The experiment took place in London over three consecutiveof the real amount of money saved, since users may not pick days in February 2016. Two researchers performed 29 side-byalways the cheapest provider (e.g. due to personal criteria side journeys comparing the prices, times and routes between
regarding service quality), its scale is indicative of the potential Uber X and Black Cab in London. Using an in-built route
financial impact that similar apps can have on the taxi market. tracking functionality (in the latest version of the app online
the feature is enabled for all users), the GPS coordinates of
IV. TAXI E XPERIMENTS IN THE W ILD
trajectories followed by each provider were recorded along
In order to validate the app’s price estimates we ran a three with their respective timestamps, start and stop journey times
day experiment on the ground in the city of London. Beyond and price estimates from the app. Black Cab and Uber receipts
validating prices, we took this opportunity to measure journey were collected in the end of each trip so estimates could betimes and routing behavior for the two competing taxi providers compared to actual prices.

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POSTER SESSION 1
17.5

Time Difference [mins]

provider� Black

Uber

15.0

Actual Price (GBP)

�
�

12.5
�

10.0

�

0
−5
−10
−4 −2 0

�

�

5

�
�

7.5

5.0
5.0

7.5

10.0

12.5

15.0

Price Estimate (GBP)

17.5

Fig. 3: Application price estimates versus average actual
amounts paid in London. The errors bars correspond to standard
errors.

(a) Trafalgar Square - Old
Street

2

4

6

8

10 12

Price Difference [GBP](b) London Bridge - Trafalgar
SquareFig. 4: Taxi provider trajectories in two areas of London. BlackCab in black color and Uber X in pink. Origins are marked
with a Green circle and Destinations with a Red triangle.
B. Experimental Results
Price Estimates: For every journey with an Uber X
or a Black Cab in the experiment, we have compared ourapplication’s estimate measured as described in Section User
Growth and Application Statistics against the actual pricecharged by the provider. In Figure 3 we plot the mean actual
price charged for a given price estimate and the corresponding
standard error. We consider the overall estimates for bothproviders satisfactory, yet deviations exist. For Black Cabs
deviations were higher for journeys that cost between 7 and
9 GBP. In the case of Uber, estimates tend to be more stable,
however, deviations still remain.
Provider Comparison: We have empirically observed
significant variations in terms of how the two providers compare
in terms of actual and estimated prices, with routing choices
being the most probable reason for these deviations. In Figure 4

Fig. 5: Price versus time differences where price difference isdefined as Black Cab price minus Uber price in GBP and timedifference as Black Cab journey time minus Uber X journey
time in minutes. Black colored circles correspond to fasterjourney times for Black Cabs, pink for Uber X and yellow forties.

we show two characteristic journeys where routes had very
little geographic or no overlap at all between the two providers.
Black cab drivers tend to take more complex routes in termsof picking side streets as opposed to larger main streets thatare recommended more often by GPS navigation systems as
part of shortest path routing. As already implied in previous
sections by tariffs applied Black Cabs were in general more
expensive. Uber X would cost on average 74% of a Black
Cab’s journey price.
Nevertheless, Black Cabs were faster and took on average
88% of an Uber’s trip duration, where average journey time
has been 14.06 minutes Black Cabs and 16.34 minutes for
Uber (Uber or Black Cab waiting times excluded). Out of the
29 journeys, Black Cabs were faster in 18 cases, there were 4
ties and Uber X was faster in 7 instances. Figure 5 presents a
scatter plot reflecting the relationship between price and timedifferences. The faster Black Cabs have been, as one would
expect by definition of the pricing schemes that depend on timein addition to route length, the smaller the price difference.
Further, when Black Cabs have been faster, in almost half ofthe occasions (10 times) they have been faster by 5 minutes
or more.
R EFERENCES[1] Uber API, 2016. https://developer.uber.com/.
[2] E A Maguire, D G Gadian, I S Johnsrude, C D Good, J Ashburner, R SJ
Frackowiak, and C D Frith. Navigation-related structural change in the
hippocampi of taxi drivers. PNAS, 2000.
[3] The Public Carriage Office. The knowledge, 2016. www.the-london-taxi.
com/Public_Carriage_Office.
[4] Uber. Uber list of cities, 2016. https://www.uber.com/cities/.

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POSTER SESSION 1
how to best exploit this opportunity. There is a widespread concern that countries may continue to tax
telecom heavily in the short term at the expense oflong term growth; the former Director of ICT at the
DANIEL BJÖRKEGREN∗World Bank, Mohsen Khalil, cautions: “the indirect
benefits to the economy of having affordable access to
BROWN UNIVERSITY
telecommunications services far outweigh any shortDeveloping countries heavily tax mobile phones. term benefit to the budget.”However, these taxes can stunt growing networks. I
Developing countries thus face a tension between
use a method to estimate and simulate the adoption generating revenue and extending service, particuof mobile phones, using transaction data from nearly larly to rural and low income areas (‘a paramountthe entire network of Rwandan mobile phone sub- concern’ in the words of former World Bank ICT Discribers over 4.5 years. Standard metrics that ig- rector Mohsen Khalil). Governments typically mannore network effects would suggest handset taxes are age this tension with a set of telecom-specific taxes,
desirable for governments, with a welfare cost un- and regulations and programs that encourage access
der $1.22 per dollar of government revenue. I find to the rural poor. However, there is little evidence
that these grossly underestimate the cost of taxing to guide the design of these policies, and standard
a growing network—accounting for network effects, approaches that do not account for network effects
the true cost exceeds $2.93: much more costly than can give misleading estimates.
other taxes. Further, baseline taxes heavily burden
While theoretical work provides intuition about
the poor: the lowest half of users receive only 2% of network effects, there is little empirical work to guideconsumer surplus, but account for 19-20% of govern- policy choices.2 Empirical work has been limited forment revenue. Had the government shifted taxes en- three reasons. It is costly to measure an entire nettirely from handsets to usage it could have increased work using traditional data sources. It is also difficult
the consumer surplus of the lowest half of users by to identify network effects: one individual may adopt
38%, without substantially lowering the surplus of after a contact adopts because the contact provides
the upper half of users and while raising more gov- network benefits, or because connected individuals
ernment revenue.
share similar traits or are exposed to similar environFull version of paper: http://dan.bjorkegren.com
ments. And even if these two issues are overcome,it is difficult to evaluate policies, which can cause
effects to ripple through the entire network. As aresult, there remain open questions about how to de1. Introduction
sign policies that better capture the spillover beneGenerating public revenue is a perennial challenge fits associated with network effects, as well as polifor developing countries, where collection costs are cies that overcome suboptimal provision arising fromfirst order. Many governments are confined to a small high concentrations in industries providing network
set of feasible instruments which can be distortionary goods.
(Gordon and Li, 2009). However, even in countries
In this project, I overcome previous limitations uswith very little other capacity to collect revenue, tele- ing an empirical approach further detailed in Bjorkegren
com represents a thriving sector operated by a few (2015) and 5.3 billion transaction records from Rwanda’s
formal firms that can easily be taxed.
dominant mobile phone operator as the network exDeveloping country governments recognize this con- panded from 300,000 to 1.5 million subscribers. I
venient source of revenue: the mobile industry con- estimate a structural model of demand for mobile
tributed an average of 7% of government revenue in phones, and then use this model to simulate the efsub-Saharan Africa as early as 2007 (GSMA, 2012).1 fects of alternate tax policies.
In addition to standard taxes, governments charge
spectrum license fees and specific taxes on telecom
2. Modelequipment, mobile handsets, and airtime. While it
My method has three steps:
is clear that this emerging sector provides a publicFirst, acknowledging that the utility of owning afinance opportunity for poor countries, it is unclear
mobile phone is derived from its usage, I model theutility of using a phone. I observe every connection∗E-mail:danbjork@brown.edu,
Web:
between subscribers, as well as the calls placed across
http://dan.bjorkegren.com
THE COST OF TAXING NETWORK GOODS:EVIDENCE FROM MOBILE PHONES IN
RWANDA

Revision December 28, 2016. Preliminary and incomplete.
I am grateful to Michael Kremer, Greg Lewis, and Ariel Pakes
for guidance and encouragement. Thank you to Nathan Eagle for providing access to the data, computing facilities, andhelpful conversations. In Rwanda, I thank the staff of mytelecom partner and government agencies. This work was
supported by the Stanford Institute for Economic Policy Research through the Shultz Fellowship in Economic Policy.
1For a sample of 19 countries from which data is available.

2Early theoretical work includes Rohlfs (1974), Katz andShapiro (1985), and Farrell and Saloner (1985). Most empirical work on network goods measures the extent of network
effects; see for example Saloner and Shepard (1995), Goolsbee
and Klenow (2002), and Tucker (2008). The paper closest in
spirit to this one is Ryan and Tucker (2012), which estimates
the adoption of a videoconferencing system over a small corporate network, and evaluates policies of seeding adoption.

1

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POSTER SESSION 1
2

each connection. Because 99% of accounts are prepaid and the person placing a call pays for it by thesecond, a subscriber must value a connection at least
as much as the cost of calls placed across it. Because calling prices changed over this period, I canestimate the underlying demand curve for communication across each link, and thus the value of eachconnection.
Let G be the communication graph. Each individual i has a set of contacts Gi ⊂ G, where a directed
link ij ∈ G indicates that i has a potential desire
to call j over the mobile phone network. Let St be
the subset of nodes subscribing in month t. At eachperiod t, individual i can call any contact j that currently subscribes, j ∈ Gi ∩ St , to receive utility uijt .
Each month, i draws a communication shock ijt representing a desire to call contact j. Given the shock,i chooses a total duration d ≥ 0 for that month, solving:uijt = max vij (d, ijt ) − cijt dd≥0

where v(d, ) represents the benefit of making callsof a total duration of d and cijt represents the persecond cost.
Second, I model the decision to adopt a mobile
phone. The utility of having a phone in a given period
is given by the utility of communicating with contacts
that have phones: each month i is on the network,
he receives expected utility:
uit =



j∈Gi ∩St

Euijt (pt , φt ) + w · Eujit (pt , φt ) + ηi

where uijt represents calls from i to j (which i paysfor), ujit represents calls from j to i (which j paysfor), and w ∈ {0, 1} specifies whether recipients value
incoming calls. Individual i chooses when to adopt
by weighing the discounted stream of these benefits
against the declining price of a handset, which is represented by the price index phandset. Then, i considt
ers the utility of adopting at time τ to be:
Uiτ =

∞

δ t Euit (pt , φt ) − δ τ βprice phandset
τt=τ

I estimate the parameters of this model using maximum likelihood and moment inequalities.
Finally, to evaluate the impact of policies, I use
a simulation method that allows each individual toreact directly to a policy change, and to each other’s
responses, capturing effects that ripple through the
network and across physical space.
3. Results
I use this approach to evaluate alternate tax policies. At baseline, Rwanda taxed handsets at 48% andairtime at 23%. I find:

Taxing a growing network imposes a substantial welfare cost. I find an average welfare costof $2.93 ($3.14) for each dollar of government revenue
raised. This is a higher cost than estimates of marginal cost of public funds from the literature, of 1.21
for sub-Saharan Africa and 1.37 for Rwanda (Auriol
and Warlters, 2012), suggesting it would be preferable to use alternative instruments to raise these revenues. In this model telecoms earn no revenue fromhandset sales, so the entire effect on telecom revenue
is driven indirectly, by reduced usage.
Standard, nonnetwork estimates grossly underestimate the welfare costs of handset taxes
in a growing network. Network ripple effects account for up to 63% of the effect of handset taxationon telecom revenues. Additionally, ripple effects generate additional government revenue and consumer
surplus that would be neglected by a model that
only considered individual responses. A naïve estimate that treated phones like standard goods would
suggest the average cost of raising a dollar of government revenue from handset taxes would be much
lower–$1.22 in the lower equilibrium and $1.04 in theupper. Under these estimates handset taxes would
have looked more attractive than other tax instruments, as reported by Auriol and Warlters (2012).
The welfare costs of taxation may be heterogeneous over time as the network expands.
I consider the effect of taxes during different time
periods. This comparison combines two differences—the pace of potential adoption differs at different time
periods, but also I observe the dynamic effects ofearly taxes over a longer time horizon. These effects
combine to suggest that welfare costs during the early
period of adoption are very high. The welfare cost
per dollar raised for taxing handsets from 2007-2009
is $2.43 per dollar raised ($1.85). If taxes duringthat period were lifted, the welfare cost for taxing
handsets from 2005-2007 would be $5.21 ($13.27).
Similarly, the welfare cost for taxing usage, assuming complete passthrough, is $2.56 ($2.35) from 20072009, and if the tax during that period were lifted,$4.25 ($5.16) from 2005-2007.
Usage taxes may impose a similar welfare
cost. While handsets are offered by a competitivemarket so that taxes will be passed through, the telecom may choose whether to pass changes in usage
taxes through to consumers. If there is no passthrough,a change in usage tax directly takes revenue fromthe telecom without distortions. If there is complete
passthrough, usage taxes cause distortions similar inmagnitude to handset taxes.
Handset taxes impose a high cost on poor
users. Although I do not observe household income
or consumption, representative survey data suggestthat consumers with lower airtime usage have lower

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17

POSTER SESSION 1
3

consumption per capita.3 I show revenues and consumer surplus for the entire sample, and then for the
top half of users (above median average daily duration) and bottom half (below). Under the baseline
tax regime, the bottom 50% of users account for only
7% of firm revenue and receive only 2% of consumer
surplus, but account for 19-20% of government revenue. Since all users must pay the fixed cost of ahandset to join the network regardless of usage, poor
users end up paying a substantial portion of the tax
burden. Eliminating handset taxes would raise thesurplus obtained by these consumers by 56%.
Shifting taxes from handsets to usage would
have improved welfare of poor phone owners
by at least 38%. Earlier results suggest that it
would improve welfare to eliminate these taxes andrecoup government revenues through other instruments. However, if the government needed to earn afixed amount of revenue from telecom, it could shift
from handset to usage taxes. If the government eliminated handset taxes but raised usage taxes to 30%, itwould earn more revenue ($1.79m or $3.61m if usage
taxes are passed through; $3.3m or $4.88m if not) andincrease the consumer surplus accruing to the bottom half of users by at least 38%, without substantial
harm to the top half of consumers (if usage taxes are
passed through, their welfare changes by -$0.55m inthe low equilibrium or +$0.12m in the high; if not,their welfare increases by at least $18.45m in both
equilibria). Doing so would decrease firm revenues,
by $4.81m ($3.41m) if usage taxes are passed throughand $1.29m ($0.45m) if not. Since potential adopters
who are not in my data are likely to be light users,
these results likely understate the full distributional
impact of a change in policy. The Rwandan government ultimately did lower handset taxes and raise
usage taxes in 2010; these estimates suggest there
would have been substantial distributional gains toshifting these taxes as early as 2005.

References
Auriol, E. and M. Warlters (2012): “The marginal cost of public funds and tax reform in Africa,”
Journal of Development Economics, 97, 58–72.
Bjorkegren, D. (2015): “The Adoption of Network Goods: Evidence from the Spread of Mobile
Phones in Rwanda,” .
Farrell, J. and G. Saloner (1985): “Standardization, Compatibility, and Innovation,” The RAND
Journal of Economics, 16, 70–83.
Goolsbee, A. and P. J. Klenow (2002): “Evidence on Learning and Network Externalities inthe Diffusion of Home Computers,” Journal of Law
and Economics, 45, 317–343.
Gordon, R. and W. Li (2009): “Tax structures indeveloping countries: Many puzzles and a possible explanation,” Journal of Public Economics, 93,
855–866.
GSMA (2012): “Taxation and the Growth of Mobile
Services in Sub-Saharan Africa,” .
Katz, M. L. and C. Shapiro (1985): “Network Externalities, Competition, and Compatibility,” The
American Economic Review, 75, 424–440.
Rohlfs, J. (1974): “A Theory of Interdependent
Demand for a Communications Service,” The Bell
Journal of Economics and Management Science, 5,
16–37.
Ryan, S. P. and C. Tucker (2012): “Heterogeneity
and the dynamics of technology adoption,” Quantitative Marketing and Economics, 10, 63–109.
Saloner, G. and A. Shepard (1995): “Adoption ofTechnologies with Network Effects: An Empirical
Examination of the Adoption of Automated Teller
Machines,” The RAND Journal of Economics, 26,
479–501.
Tucker, C. (2008): “Identifying Formal and Informal Influence in Technology Adoption with
Network Externalities,” Management Science, 54,
2024–2038.

4. Conclusion
This project uses a model to estimate and simulate the adoption of a network good to analyze mobile
phone taxation. Mobile phones have been successful
among the poor in part because usage charges areprimarily marginal; these simulations suggest thatgovernments can encourage adoption among the poorby taxing on the margin of usage rather than adoption. The result could be reversed for technologies
like smartphones if there is a wider range of handset qualities available and users upgrade at different
frequencies. These results all describe a growing network; optimal taxes are likely to differ once a network
is mature.
For more details, find the full paper at http://dan.bjorkegren.com
3The 2010 Rwandan EICV suggests that, among households with mobile phones, the households with the top half ofairtime expenditure have 3.7 times more overall consumption
than those in the bottom half.

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POSTER SESSION 1

PRIVA’ MOV : Analysing Human Mobility ThroughMulti-Sensor Datasets

Sonia Ben Mokhtar, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie,
Mathieu Cunche, Stephane D’Alu, Vincent Primault, Patrice Raveneau, Herve Rivano, Razvan Stanica
University of Lyon, LIRIS, CNRS, INSA-Lyon, UMR5205, EVS, UMR5600, INRIA, CITI, ENSEEIHT, ENTPE, France{sonia.benmokhtar, antoine.boutet, olivier.brette, lionel.brunie, stephane.dalu, mathieu.cunche, razvan.stanica, vincent.primault}@insa-lyon.fr ; patrice.raveneau@univ-lr.fr ; herve.rivano@inria.fr ;{louafi.bouzouina, patrick.bonnel}@entpe.fr
Abstract—The wide adoption of mobile devices has createdunprecedented opportunities to collect mobility traces and makethem available for the research community to conduct interdisciplinary research. However, mobility traces available in the publicdomain are usually restricted to traces resulting from a single
sensor (e.g., either GPS, GSM or WiFi). In this paper, we present
the PRIVA’ MOV dataset, a novel dataset collected in the city ofLyon, France on which user mobility has been collected using
multiple sensors. More precisely, this dataset contains mobility
traces of about 100 persons including university students, staff
and their family members over 15 months collected throughthe GPS, WiFi, GSM, and accelerometer sensors. We provide
in this paper both a quantitative and a preliminary qualitative
analysis of this dataset. Specifically, we report the number ofvisited points of interests, GSM antennas and WiFi hotspots andtheir distribution across the various users. We finally analyse theuniqueness of human mobility by considering the various sensors.

I.

I NTRODUCTION

The large adoption of mobile devices combined to theirembedded localisation capabilities opens novel opportunities
to provide mobility traces to the research community at large.
Classical usages of mobility datasets include the analysis ofuser mobility patterns and their regularity [1], discovering hot
places in a city [2] or studying privacy threats due to thedisclosure of mobility data [3].
However most of the available datasets (e.g., the Cabspotting [4], the Geolife [5], or the T-Drive [6] datasets to nameda few) contain data collections coming from only one sensor(i.e., the GPS, GSM or WiFi sensor). While single sensor
mobility traces have been widely used in the literature toanswer classical research questions, the availability of mobility
data coming from multiples sensors opens the door for richer
studies. This include comparative studies (where the data
provided by a single sensor is compared to the data provided
by another sensor) and compositional studies (where the data
provided by a given sensor complements the data provided byanother sensor). For instance, one may perform a comparative
study of personal data leakage due to the sharing of the data
provided by a given type of sensor versus the one due to thesharing of another type of sensor. On the other hand, one may
combine the data provided by multiple sensors to increase theprecision of user mobility data. For instance, one may use the
WiFi and GSM mobility data to remove erroneous localisations
provided by the GPS sensor.
We present in this paper the PRIVA’ MOV dataset that con-tains the mobility traces of 100 users around the city of Lyon
collected using various sensors, namely the GPS, WiFi, GSM,
and accelerometer sensors. This dataset has been collectedfrom October 2014 to January 2016 by equipping volunteer
students from three universities, staff members and sometimes
their relatives with smartphones on which a crowdsensing
application was periodically collecting records from the above
mentioned sensors.
In the remaining of this paper we present the data collection
process (Section II), a quantitative analysis of the resulting
dataset (Section III) as well as a preliminary qualitative analysis of its records (Section IV). More precisely, we analyse therelation between meaningful locations of the city and users
and the uniqueness of mobility traces of users considering thevarious sensors. We finally draw our conclusions and future
research directions (Section V).

II.

T HE PRIVA’ MOV C ROWD S ENSING C AMPAIGN

The PRIVA’ MOV dataset has been collected during a crowdsensing campaign that took place in the city of Lyon from
October 2014 to January 2016 in the context of PRIVA’ MOV
project funded by the LABEX IMU1 funding agency. In thecontext of this project 100 smartphones (52 Wiko Rainbow
and 48 Wiko Cink 5) have been equipped with a crowdsensing
application and distributed to student, staff members and theirrelatives from three universities: INSA Lyon, ENS Lyon andUniversité Claude Bernard, Lyon 1. Volunteers were asked touse the PRIVA’ MOV phone as their primary phone and to carryit during their daily activities. The crowdsensing application
was a modified version of the application developed in the Funf
project 2 . A complementary fleet management web application
and a trace visualisation tool depicted in Figure 1a andFigure 1b, respectively, have also been developed.
The crowdsensing application has been configured to collect the data every time the system used them (e.g., change oflocation, new WiFi scan). In order to save battery, the collected
data were uploaded to the server only when the smartphone
was connected to a WiFi network. The resulting dataset isdescribed in the following section.
1 LABEX IMU: http://imu.universite-lyon.fr
2 Funf: http://funf.org

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POSTER SESSION 1(a) Fleet management portal
Fig. 1.

(b) Visualisation tool

PRIVA’ MOV Fleet management portal (Figure 1a) ; PRIVA’ MOV trace visualisation tool (Figure 1b).

III.

PRIVA’ MOV Q UANTITATIVE A NALYSIS

We describe in this section a quantitative analysis of thedataset. Table I shows the number of records
collected by the various sensors in the overall dataset.
PRIVA’ MOV

TABLE I.

T HE PRIVA’ MOV DATASET CONTAINS MOBILITY DATACOLLECTIONS CAPTURED THROUGH DIFFERENT SENSORS .

From these data collections, it is then possible to extract
mobility traces. We call a mobility trace a list of spatiotemporal points belonging to a given user. This can be donein the WiFi and GSM data collections by associating a GPS
location to each WiFi and GSM antenna by relying on public
datasets such as WiGLE 3 or Google 4 .
In the PRIVA’ MOV dataset we did not perform the mappingof WiFi and GSM antennas to GPS locations. Instead, we use
the unique identifier of the cellular antenna to which the user
is connected (respectively the MAC address of the WiFi accesspoint to which the user is attached, or discovered through aperiodic WiFi scan) as a spacial indicator of the user location.
In addition, mobility traces built from the GPS data collections could be enriched with user Points of Interest (POIs).
A POI is a meaningful location where the user has marked asignificant stop. To compute POIs, we used a methodology
similar to [7]. The idea behind this method is to identify
restricted areas where users stay more than a specific duration.
More precisely, POIs can be extracted using a simple spatiotemporal clustering algorithm parametrised with a maximum
POI diameter d and a minimum stay time t. This POIs
extraction is done in two clustering steps, the first one identifies
POIs for each user and the second one assigns identifiers tounique POIs, thus allowing to identify POIs shared by several
users. For instance, Figure 2 illustrates the POIs of users in
the Lyon sub-area for a diameter of 250 meters (d = 250) anda stay time of 30 minutes (t = 30).
3 WiGLE: Wireless Network Mapping, http://wigle.net

4 Google Maps Geolocation API: https://developers.google.com/maps

Fig. 2.

Location of Points Of Interests (POIs) across the Lyon area.

In the PRIVA’ MOV dataset, we extracted from the GPS datacollections the set of all POIs and assigned an identifier toeach unique POI inside the whole dataset. Then, similarly to
the GSM and WiFi datasets we used unique POI identifiers as
a spatial component.
Let us consider the extracted mobility traces as explained
above (i.e., using cellular antenna IDs, WiFi mac addresses,
and POI IDs from the data collections). To analyse the resulting
mobility traces, Figure 3 shows the Complementary Cumulative Distribution Function (CCDF, defined as P (X > x)).
Figures 3a shows the number of unique GSM antennas, WiFi
access points and POIs per user. Figures 3b depicts the numberof unique users identified per GSM antenna, WiFi access point
and POI. These tail distributions show that most of GSM
antennas, WiFi access points and POIs have been visited only
by one user. For instance, 75% of the WiFi access points havebeen seen by only one user. Conversely, mobility traces ofmost of the users are composed of several GSM antennas, WiFi
access points and POIs. Finally, these figures show that users
have discovered more WiFi access points than GSM antennas,
and the number of POIs is lower than the two others.

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

0.25

Wifi
Gsm
Poi

Wifi
Gsm
Poi

0.2
CCDF

Number of Records
25,655,480
8,076,512
156,041,576
90,066,831
7,008,504

CCDF

Sensor
WiFi
CellularGPS
Accelerometer
Battery

0.15
0.1
0.05
0

1

10

100

1000 10000 100000

Number of different ID(a) # of Identifiers

1

10

100

Number of different Users(b) # of Users

Fig. 3. The tail distributions of the number of different GSM antennas, WiFi
access points, and POI per user (Figure 3a) ; and the number of unique users
per GSM antenna, WiFi access point, and POI (Figure 3b).

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POSTER SESSION 1
PRIVA’ MOV Q UALITATIVE A NALYSIS
0.8
0.6
0.4
SpatialTemporalSpatio-Temporal

0.2
0

A. POI relationship inference
In this experiment, we analysed the relationship between
POIs appearing in the dataset. Specifically, we consider that
two POIs are related if there exist at least one user that visitedthe two POIs. Figure 4 shows the resulting graph obtainedby extracting the relationship between all the POIs of the
PRIVA’ MOV dataset. From this figure, we observe that whilesome POIs have been widely visited by many users (at thebottom left), some places have been only visited by sub groups
of users (clusters of point at the top right).

1

1

2

3

We report the uniqueness of mobility traces built from the
GPS and the WiFi traces. Figure 5 depicts the probability to
be unique according to the number of points in the considered
sub-trace. The evaluation reports results for spatial, temporal,
and spatio-temporal traces. As shown in these figures, the

0.6
0.4
SpatialTemporalSpatio-Temporal

0.2

8

9

1

(a) GSM

2

3

4
5
6
7
Number of points

89(b) WiFi

Fig. 5. Four spatio-temporal points are enough to uniquely identify 97% of
the individuals.

results for spatio-temporal mobility traces from GSM and WiFi
show a strong uniqueness. More precisely, four spatio-temporal
points are enough to identify on average 97% of the users. Thishigh uniqueness is the result of combining the temporal and thespatial mobility information of users, which are discriminative
enough to uniquely identify them. Although this analysis usesa smaller set of data, this result comforts and generalizes theprevious study performed in [8] on call logs.
C ONCLUSIONSTo address the lack of multi-sensor mobility datasets, theproject PRIVA’ MOV developed and deployed a crowdsensing
platform to collect mobility traces from a sample of realusers equipped with mobile phones. We presented the resulting
dataset and reported quantitative and qualitative experiments
over this dataset, which show the potential of using thelatter for answering novel research questions. The PRIVA’ MOV
datasets is available for research community upon request andunder a set of usage conditions. Our future work targets thedata collection of multi-sensor information over a larger population of users with a real time data analysis and a validation
tool to ask the users to validate or not the inferred information(i.e., leaked personal information, mobility behavior).

B. Uniqueness of human mobility
To quantify the uniqueness of mobility traces, we use themethodology proposed by De Montjoye and all in [8]. Moreprecisely, for each mobility trace T , we evaluate the uniquenessof a given sub-trace Ip of p randomly chosen spatio-temporal
points. A sub-trace Ip is said to be unique if only one user
has Ip ∈ T . To measure this uniqueness, we performed a brute
force search of users who have the p points composing Ip in
their mobility trace T . The size of this set of users sharing
the same Ip , noted k, characterizes the uniqueness of the subtrace Ip . If k = 1, the sub-trace is unique. The uniquenessof traces is estimated as the percentage of 2500 random subtraces that are unique given the p points composing them. We
use the same methodology to evaluate the uniqueness of spatialor temporal only mobility traces. In this case, the sub-trace Ip
contains spatial or temporal points, respectively.

1
0.8

0

4
5
6
7
Number of points

V.

Fig. 4. Relationship between points of interest inferred from the mobilityof users (two points of interest are connected if at least one user has visitedthese places).

Probability of UniquenessIn this section, we report two qualitative experiments
conducted on the PRIVA’ MOV dataset. Specifically, we first
analyse the spatial relationship between POIs inferred fromthe activity of users. Then we study the uniqueness of user
mobility traces over the three data types.

Probability of UniquenessIV.

R EFERENCES[1]

M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding
individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779–
782, June 2008.
[2] J. Yuan, Y. Zheng, and X. Xie, “Discovering regions of different functions
in a city using human mobility and pois,” in KDD, 2012, pp. 186–194.
[3] S. Gambs, M.-O. Killijian, and M. N. n. del Prado Cortez, “Show me
how you move and i will tell you who you are,” in SPRINGL, New York,NY, USA, 2010, pp. 34–41.
[4] M. Piorkowski, N. Sarafijanovic-Djukic, and M. Grossglauser, “CRAWDAD dataset epfl/mobility (v. 2009-02-24),” Downloaded fromhttp://crawdad.org/epfl/mobility/20090224, Feb. 2009.
[5] Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, “Mining Interesting Locations
and Travel Sequences from GPS Trajectories,” in WWW. ACM, 2009,
pp. 791–800.
[6] J. Yuan, Y. Zheng, X. Xie, and G. Sun, “Driving with Knowledge fromthe Physical World,” in KDD, 2011, pp. 316–324.
[7] Y. Zheng, L. Liu, L. Wang, and X. Xie, “Learning transportation mode
from raw gps data for geographic applications on the web,” in WWW,
2008, pp. 247–256.
[8] Y.-A. de Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel,“Unique in the crowd: The privacy bounds of human mobility,” Scientific
Reports, vol. 3, 2013.

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POSTER SESSION 1

Developing a mobility monitoring application hand in hand with the end user
Jérôme Urbain, Nicolas Snel
Dalberg Data Insights; {jerome.urbain, nicolas.snel}@dalberg.com

1. Introduction
Nowadays over 50% of the world’s population is living inurban areas. This proportion is even expected to increase
to 66% over the coming 35 years [1]. Consequently, it isurgent to act on mobility issues all around the globe.
It is however difficult, particularly in developing regions,to gather representative data of population’s
movements in order to plan road infrastructure, set up
public transports or react to abnormal events [2].
Generally, the only data available come from surveys
conducted every 5 or 10 years. Alternatives are hence
explored. Among them, Call Detail Records (CDRs) anddata connection logs collected by telecom operators
offer opportunities to systematically track the
movements of a significant proportion of the population.
Mobility studies from Telecom Operators’ data havelargely increased in the past years. Insightful results havebeen published (e.g., [3]–[6]). Nevertheless, theseanalyses are frequently conducted a) over a limited timeframe (the period of the data shared by the Mobile
Operator) and b) independently of their potential endusers – the focus being on improving technicalmethodology rather than ensuring wide usage of thealgorithms. Here, we take another approach. We havebeen mandated to develop mobility applications that areused by the end user on a day-to-day basis. Namely, ourend user is the Kampala Capital City Authority (KCCA),
which is responsible for the development andmanagement of Uganda’s capital. Hence it is critical for
us to understand their work, their pains and how we can
improve their situation. We will present here the processthat was followed, focusing on the collaborative design
methodology.
KCCA was involved from scratch in the development of
our applications. We first discussed an initial idea, thendrafted a mock-up of each application to validate it withthem. Once the look and feel had been validated, we
started developing the application (both the back-end
computations and the data visualization) in an
incremental way, continuously asking for KCCA’s
feedback through the process. That agile methodology
enabled us to stay close to KCCA’s needs and to build
together the next features.

Two key elements have been identified to drive daily
application usage. First, the application must be
convenient to use: it must be straightforward tounderstand the data displayed. This puts a tremendous
importance on data visualization. Second, theapplication must display the latest data available: it isimportant the user accesses every day data that reflects
the current situation, so (s)he can take actions
accordingly and with confidence that those will bring the
expected impact. It obviously affects the way data iscomputed: for instance, all daily computations must run
in maximum 24 hours to ensure we stay up-to-date.
Following our discussions with KCCA, we have started thedevelopment of 2 applications: 1) detailed OriginDestination maps; 2) visualization of traffic evolution
throughout a typical day. For both applications, our input
data consists in the location (cell tower) of subscribers.
These are extracted from CDRs and data logs provided
daily by Airtel Uganda, the second largest operator withover 8 million customers (~43% market share).

2. Detailed Origin-Destination
2.a Back-end computations
The detailed Origin-Destination (O-D) application
enables to identify regular commutes, which is essential
to design long-term mobility master plans. In agreementwith KCCA, we have distinguished 4 periods of the day(instead of the usual home and work hours): night (9PM
to 5AM), morning (5AM to 11AM), midday (11AM to4PM) and evening (4PM to 9PM). The most used site ofeach subscriber is computed at the beginning and end ofeach of these periods over the last 30 days. The O-D
matrices during each period are inferred from thecorresponding most used sites. These numbers will bevalidated in the coming weeks by comparing them tosurvey results that will be available soon. Besides O-D
matrices, we also estimate the typical travel time
between each O-D pair and compute the evolution of theflow of people between one O-D pair over the course ofa day. The methodology here is to look at trips
performed between stable locations (the person
remained at least 60 minutes at the origin anddestination sites) and to count one person in movement
between the last time seen at departure and the firsttime seen at arrival.

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POSTER SESSION 1
2.b Visualization
As shown on Figure 1, flows of people are displayed on
an interactive colored map that allows users to quickly
identify areas of interest. Clicking on one area (or acombination of areas) filters the whole application withrespect to that area. To further ease the identification ofimportant trips, flows are also displayed via lists andmatrices. Users can control the geographical level ofaggregation they would like to see (here sub-county orparish), whether they want to see the suburbs or only thecity center, the period of the day and the type of day(business day, Saturday or Sunday). The distribution overtime of the flow of people between the selected O-D pairis displayed below (see Figure 2). This enables to identify
the peak times in the morning and in the evening.

3. Traffic evolution
3.a Back-end computations
It is particularly important for KCCA to visualize theevolution of traffic in the city throughout a typical day.
This allows for short-term corrective measures (e.g.,
regulating the traffic at one junction or diverting traffic
from one road). To compute traffic evolution, thefollowing process has been implemented. Every 15
minutes, the last known position of all subscribers is
updated. When there is no new location, people areassumed to have stayed in the last location we have seen
them. Then, traffic is estimated: a subscriber S isconsidered incoming to an area X if X differs from her/his
previous location (Y). Symmetrically, S will be consideredoutgoing from Y in that case. A subscriber is consideredin traffic internally to an area X if (s)he was incoming to
X during the last Q quarters of hour (Q can be customized
and is set to 2 by default) and has had no other position
since then. The 15-minutes daily traffic values can be
aggregated over N days to obtain the average traffic.
3.b Visualization
Traffic is displayed on a colored map (see Figure 3). Users
can decide to focus on incoming, outgoing or externaltraffic and select the type of day. It is possible to select a
specific time for analysis or to play the evolution of traffic
15 minutes by 15 minutes. Following a demand from
KCCA, traffic is displayed on roads rather than cell tower
Voronois. Currently we uniformly distribute the trafficvalue among the main roads of the Voronoi.

Figure 1: Detailed Origin-Destination flows

Figure 3: City traffic evolution

Figure 2: Flow distribution over time

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POSTER SESSION 1

4. Future work
Our approach differs from usual scientific research. We
are developing our application in close collaboration
with its end user. This is bringing initial focus on datavisualization. Yet, after successfully iterating on what
KCCA would like our applications to display andpopulating those applications with initial data, we canidentify
shortcomingsoftheimplemented
methodologies and improve them. We are listing future
work below, and will extend that list after validating the
O-D matrices thanks to survey data we are about toreceive.
First, we want to address instabilities in travel time
estimations, likely due to lack of user activities at certain
moments (e.g., night). Peak detection could be
implemented, and unreliable estimates could becorrected considering the number of active people atthat time.
Second, KCCA and other potential end users request
other geographical groups than what we naturally obtainfrom Telecom Operators. Areas covered by cell towers
do not mean anything to many end users. Sometimes,
even the administrative levels we use for aggregation do
not correspond to operating areas of the end users. As
already stated, KCAA has asked us to display information
on road maps. We aim at improving the current uniform
road repartition with a more advanced method to better
estimate the actual roads taken by subscribers (e.g., [7]).
Finally, considering KCCA’s latest feedback, we willcomplement the current applications with an impact
assessment application. The objective is to allow for
traffic comparisons between two periods, understandthe influence of events (elections, concert, …) and hence
better predict the impact of future events. As a first step
we will flag abnormal events, following the methodology
described in [6]; in a second stage we will automaticallysegment long periods of traffic (e.g., 60 days), inspired
for instance from audio segmentation algorithms ([8]–[10]). That will allow the end users to visualize significant
changes in traffic and relate them to events they areaware of.

Bibliography
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]

United Nations, “World’s population increasingly urban with more than half living in urban areas,” 2014. [Online].
Available:
http://www.un.org/en/development/desa/news/population/world-urbanization-prospects-2014.html.
[Accessed: 27-Jan-2017].
S. Lokanathan, G. E. Kreindler, N. H. N. De Silva, Y. Miyauchi, D. Dhananjaya, and R. Samarajiva, “The Potential of Mobile
Network Big Data as a Tool in Colombo ’ s Transportation and Urban Planning,” Inf. Technol. Int. Dev. [Special Issue],vol. 12, no. 2, pp. 63–73, 2016.
A. J. Tatem, Z. Huang, C. Narib, U. Kumar, D. Kandula, D. K. Pindolia, D. L. Smith, J. M. Cohen, B. Graupe, P. Uusiku, and
C. Lourenço, “Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination
planning.,” Malar. J., vol. 13, no. 1, p. 52, 2014.
A. Wesolowski, N. Eagle, A. J. Tatem, D. L. Smith, A. M. Noor, R. W. Snow, and C. O. Buckee, “Quantifying the impact ofhuman mobility on malaria,” vol. 338, no. 6104, pp. 267–270, 2013.
A. Wesolowski, T. Qureshi, M. F. Boni, P. R. Sundsøy, M. A. Johansson, S. B. Rasheed, K. Engø-Monsen, and C. O. Buckee,“Impact of human mobility on the emergence of dengue epidemics in Pakistan,” Proc. Natl. Acad. Sci., vol. 112, no. 38,
pp. 11887–11892, 2015.
Z. Zhou and C. Volinsky, “Quantifying urban traffic anomalies,” in Bloomberg Data for Good Exchange Conference, 2016.
M. Chen, “A study of transportation network design problems,” University of Manitoba,Winnipeg, Canada, 1991.
S. S. Cheng, H. M. Wang, and H. C. Fu, “BIC-based audio segmentation by divide-and-conquer,” in Acoustics, Speech andSignal Processing, 2008. ICASSP 2008. IEEE International Conference on, 2008, pp. 4841–4844.
J. Foote, “Automatic audio segmentation using a measure of audio novelty,” in Multimedia and Expo, 2000. ICME 2000.
2000 IEEE International Conference on, 2000.
M. Cettolo and M. Vescovi, “Efficient Audio Segmentation Algorithms Based on the BIC,” IEEE Int. Conf. Acoust. Speech
Signal Process., pp. 537–540, 2003.

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POSTER SESSION 1

Effects of Network Architecture on Model
Performance when Predicting Churn in Telco
Marı́a Óskarsdóttir∗ , Cristián Bravo† , Wouter Verbeke‡ , Bart Baesens∗† and Jan Vanthienen∗∗ Faculty of Economics and Business, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium† Southampton Business School, University of Southampton, United Kingdom‡ Faculty of Economic and Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Belgium

Abstract—Research on social network analytics (SNA) has
advanced significantly in recent years. Before networks can beanalyzed they need to be built from the available data, which is a
non-trivial task. In addition, the results of the analysis can dependon the network architecture itself. In this study, we demonstrate
this effect in the case of customer churn in telco. By building
numerous networks using different definitions of weights andedges and applying a relational classifier to predict churn, we
compare the performance of the resulting models. Our results
imply that the network architecture does in fact have a great
effect on the performance of such models.

I. I NTRODUCTION
Research on social network analytics (SNA) has advanced
significantly in recent years. With applications in both social
sciences and business communities, state-of-the-art techniquesfrom graph theory and machine learning are applied to avariation of social networks that need to be defined given theavailable relational data, which can be both challenging andtime consuming.
In the case of SNA in the telecommunication industry,
the data source is usually call detail records (CDR). Thesephone logs have been a great source for research in various
disciplines over the past years, as an extensive overview of thecurrent literature in large-scale mobile traffic analysis shows[1]. When building networks from CDR data, they need tobe filtered and aggregated in an intelligent way in order torepresent the activity and interactions of customers accurately.
The many possibilities of defining edges and weights together
with other factors, need to be taken into consideration when
the network is built. The impact of the architecture of networks on the techniques which are applied to them, and theresulting findings have been scarcely studied to the best of ourknowledge.
In this study, we explore this potential effect in the case ofcustomer churn in telco, by building more than five hundrednetworks with varying definitions of edges and weights andaggregating them in numerous ways. The number of networks
can increase very rapidly when taking all possibilities into
account, and as a result, it becomes computationally infeasible
to compare all model combinations. Instead, we will exploit
the result of our previous benchmarking study for ’algorithm
selection’ [2] and use the network only link based classifier[3] as a proxy method to predict churn in the different typesof networks and compare the performance of the resulting

TABLE IS EGMENTATION OF N ETWORKS
Day

Part of Week

MondayWorking days
Tuesday
WeekendWednesdayThursdayFridaySaturday
SundayTime of Day
Day
Evening
Night

Combination
Part of Week
1
WD+WE
2
WD+ 12 WE
1
WD+WE
3
WD+ 13 WE

Combination
Time of Day
1
Day+Evening
2
Day+ 12 Evening
1
Day+Evening
3
Day+ 13 Evening

models. As a result, we hope to guide further studies on how to
optimize network architecture when predicting churn in telco.
II. D EFINING THE N ETWORK A RCHITECTURE
The dataset used in this study are call detail records origination from a telecommunication provider in Belgium. The
dataset spans six months of phone logs between 1.2 million
customers with postpaid contracts and churn rate of under apercent.
When building the networks, phonecalls lasting less thana few seconds were removed, as is common in the literature[4]. The edges of the network were defined in three ways:
incoming, representing all phonecalls made to a customer; outgoing, the phone calls made by a customer; and undirectional,
when the previous distinction is not made. The weights of theedges were defined in numerous ways. Firstly, we consider
binary weights, with an indicator of whether a phone call
was made or not. Secondly, length or the total duration of allphonecalls during the period were aggregated. Finally, since
the CDR data had information about the date and time ofeach phone call it was used to segment the data, thus building
separate networks for phone calls made on each day of theweek, during working days (WD) and weekends (WE) andduring the day, evening and night. In addition, parts of the
day and parts of the week were combined in various ways
to build more networks. Table I shows a summary of all
the networks that were built in this way, using duration ofphonecalls. Additionally, since some studies [5], [6] remove
connections that are not reciprocal before applying SNA, we
included this possibility in our exploration, by repeating thewhole setup with non-reciprocal calls removed. In total over
five hundred networks were built for the one dataset.
All networks were built using the same month of data andthe proxy classifier subsequently applied to predict churn in

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POSTER SESSION 1
Incoming

Outgoing

Undirected

Incoming

0.6

Outgoing

Undirected

LegendAUC

0.4

Only reciprocated edges
All edges

Lift at 10%

2
LegendOnly reciprocated edges
All edges

1

0.2

0.0

0
Binary Length

PoD

Binary Length

PoD

Network Architecture

Binary Length

PoD

Binary Length

PoD

Binary Length

PoD

Network Architecture

Binary Length

PoD

Fig. 1. The figure shows the performance of the classifier when applied to the different networks.

the following month. Finally, the predictions were comparedto actual churn labels.
III. R ESULTS OF N ETWORK C ONSTRUCTION
Figure 1 shows a comparison of the performance between
the various networks measured in AUC and lift at 10%. The
two measures show more or less consistent results.
For comprehensibility, we only show three representation ofweights in the networks, namely binary weights, total durationof phonecalls and total duration of phonecalls weighted by thebest combination of time of day. The best combination of timeof day is the first one in table I which is 12 Day + Evening.
The first observation we make, is the performance of networks with and without non-reciprocated edges, as is evident
by the difference between the green and red bars in the figures.
The clear difference between these results is that by removing
the non-reciprocal connections, the performance has decreased
significantly.
A second interesting behavior we can extract, is the difference between performance on undirected, outgoing, andincoming networks. It is clear the undirected outperforms theother two. However, it is not clear whether the outgoing or
the incoming network is better. This result is contradictory to
the findings of [5], where the outgoing edges were shown tohave higher correlation with churn.
When comparing the definition of the weights we seethat sometimes the performance on the whole network tends
to be slightly better than on the time of day combination
network. There is a slight increase in predictive capability bysegmenting the network by part-of-day, with evenings more
important than daytime. These results are consistent with theconclusion from our previous results: calling the close circle ofthe user (family and close friends) tends to be the most relevant
factor when spreading churn influence [2]. These calls arebound to occur with greater chance during the evenings (afterwork). Regarding the weights of the edges, the performanceon the network with binary weights is almost consistently
better or at least as good as on the networks with length.
This indicated that the simplest variant, binary weights, will
result in models that are just as good. This behavior hints at
the importance of the existence of connectivity rather than

the intensity of the communication between two users. As
mentioned before, it is the close circle of the user that are
more at risk of churn when the user does, so the lengthof the call would be a poor proxy of the intensity of therelationship and as such binary weights that reflect whether
there is a connection or not, would be sufficient to represent
this relationship.
IV. C ONCLUSION
When defining network architecture with CDR data manydecisions need to be made. As our results suggest, thesedecisions can have a great effect on the performance of models
which are applied to the networks. We have shown that asignificant increase in performance can be achieved whenusing a best performing network, which, as our results imply,
is one that is constructed with binary weights and undirectededges. As a result, the modeller is responsible for correctly
defining the best network for the problem that is being tackled,
as failure to do so will result in less predictive capability andtherefore less potential gains. In a future study we would liketo take into account more datasets and other classifiers, totest the statistical significance of these observed differences in
performance.
R EFERENCES[1] D. Naboulsi, M. Fiore, S. Ribot, and R. Stanica, “Large-scale mobile
traffic analysis: a survey,” IEEE Communications Surveys & Tutorials,
vol. 18, no. 1, pp. 124–161, 2015.
[2] M. Óskarsdóttir, C. Bravo, W. Verbeke, C. Sarraute, B. Baesens, andJ. Vanthienen, “A comparative study of social network classifiers forpredicting churn in the telecommunication industry,” in Advances in
Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM
International Conference on. IEEE, 2016, pp. 1151–1158.
[3] Q. Lu and L. Getoor, “Link-based classification,” in ICML, vol. 3, 2003,
pp. 496–503.
[4] W. Verbeke, D. Martens, and B. Baesens, “Social network analysis forcustomer churn prediction,” Applied Soft Computing, vol. 14, pp. 431–
446, 2014.
[5] M. Haenlein, “Social interactions in customer churn decisions: The
impact of relationship directionality,” International Journal of Researchin Marketing, vol. 30, no. 3, pp. 236–248, 2013.
[6] K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea,
A. A. Nanavati, and A. Joshi, “Social ties and their relevance to churnin mobile telecom networks,” in Proceedings of the 11th international
conference on Extending database technology: Advances in databasetechnology. ACM, 2008, pp. 668–677.

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26

POSTER SESSION 1

A Neural Network Framework for Next Place
Prediction
Chad Langford1
chad.langford@sbg.ac.at

Euro Beinat1,2
euro@beinat.net

1

Department of Geoinformatics – Z_GIS, University of Salzburg, Austria
CS Research Foundation, Amsterdam, The Netherlands

2

1. Introduction

The goal of this paper is to create aframework for forecasting human mobility
that utilizes the unique architecture foundwithin long short-term memory (LSTM)
neural networks. This particular neuralnetwork architecture is utilized due to its
strength of being able to learn the importanceof long-term dependencies, which within thelens of human mobility prediction means
learning what the next most likely place will
be given the temporal context of locations
observed beyond the recent past. Using adataset of call detail records (CDR) from aEuropean country for roamers, issues of data
structure, sampling, and quality are
considered before determining the temporalextent to which past locations provide
information that is able to improve theaccuracy of next place prediction. The results
will show that prediction accuracy grows
with the window of historical context used,until a point at which the importance of thelong-term
locational
dependencies
diminishes.

2. Data

The problem of forecasting is fundamentally
one of classification. The neural network
learns to classify a past trajectory as the most
likely next location. How the network learns
is partially based on how the training data is

structured and fed into the model, a focus of
this paper. Thus, when constructing a LSTM
model, careful consideration must be given to
the structure, sampling method, and qualityof the data.

2.1 Data structure

The data is transformed from a CDR format
to a list of trips. Each trip is defined asconsecutive hourly data for a user, without abreak in data availability. Each cellular tower
is encoded as a unique Unicode symbol,
which are used to create a string of charactersthat represent a trip. An example trip may
look like: AAABCAADAAAAEFF, where
each character represents a cellular tower theuser was connected to for the most time
during that particular hour. By constructing
the data in this format, we facilitate the
LSTM network’s ability to learn long-term
dependencies between locations across many
trips.

2.2 Data sampling

We choose to predict for the 1000 longest
trips within the data. For each of these test
traces, the first 80% of the trip (considered
the history) is used to sample the appropriate
data, while the latter 20% of the trip
(considered the future) is predicted for. The
sampling method used is a hierarchicalgeographical intersection. For example, if weare interested in using a three-hour window

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POSTER SESSION 1

of past locations to predict the next hour’slocation, then the historic data (the first 80%)is broken into trip segments of length 3+1.
This is necessary to ensure that we sample
data that includes enough information
temporally to use a three-hour window ofpast locations to predict the next location.
The hierarchical geographical intersection
works by finding training traces that have acommon trip segment with the historic test
trace at various temporal scales.
Test trace: AAABCAADAAAAEFF.
The underlined portion of the trace makes up
the first 80%, which is considered the user’s
history. The portion that is not underlined ispredicted for. If we are interested in using atemporal window of three hours to predict for
the next location, then the history would be
broken into the following trip segments oflength four: [AAAB], [AABC], [ABCA],[BCAA], [CAAD], [AADA], [ADAA],[DAAA], and [AAAA]. All training trips,
those that are not part of the 1000 longest
trips, are searched through to find traces that
contain at least one of the trip segments.
Training trips that share a common trip
segment are added to a list of trips that will
be used for training the LSTM. If any trip
segments return zero trips with a matchingsegment, it is further broken down into
smaller trip segments before another search is
performed. If the trip segment [AABC] was
not found anywhere else within the training
data, the segment would be made into twosegments [AAB] and [ABC] that are shorterby one hour, and the search would be
performed again. This hierarchical samplingis done to ensure that the LSTM has as muchinformation as possible to learn from, even if
it does not exactly match the temporal scale
we are interested in. The hierarchicaldownsizing of a path segment will continue

as long as the path segment is not represented
in the training data, until only a single pointis sampled for.

2.3 Data quality

The LSTM is robust against the mostcommon quality flaw found within cellular
telecommunications data: antenna jumping.
The model will not be negatively impacted byapparent shifts in location that occur when acellular tower’s capacity is saturated. This
often appears within a trajectory as a suddenchange of position across a large distance.
The model will give less weight to thesechanges in location since these geographicalshifts are irregular. The structure of the dataalso assists in allowing the model todifferentiate legitimate transitions between
locations and those due to the cellular
network’s protocol for handling capacity
saturation. Since the data is structured into
trips, rather than into 24 hour blocks for eachday, the LSTM will not learn these locational
shifts—even when they’re more common atcertain times of the day.
There is a large number of trips that are notused within the current framework as a direct
result of the historical window size used. If
we are interested in using the last three hours
to predict the next location, then trips that aresmaller than the largest path segment cannot
be used. This is because if a trip has only two
locations, it is not possible to use the past
three hours to determine the last location.
More than 50% of the available data consistsof trips with a length of two. While it isimportant to note this, it does not necessarily
have a negative impact on the results. The
overall volume of data and range ofcomplexity for longer trips ensures that the
information found within the shortest trips is
not lost when only using longer trips.

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POSTER SESSION 1

3. ModelThe training trips gathered by the hierarchicalgeographical sampling method are fed into
the LSTM in batches of segment paths. The
weights within the neural network are
updated after each batch of trip segments.
The experiment will be run for historicalwindows of various sizes (1-10) In order todetermine when the importance of the longterm locational dependencies diminishes. It is
expected that the larger historical window
sizes will predict the next location better thana smaller historical window size. The
maximum window size used must bebalanced with the amount of data available,
since larger window sizes will inherently
have less training data available. After
training, each unique combination oflocations will have its own probability
distribution for the next likely location.
While it is most likely that the location withthe highest probability is chosen as the next
location, there is some chance (depending on
the probability distribution) that a given
unique historical context will not always
produce the same location as a result.

investigation to determine if the results holdtrue for a range of spatial and temporal
aggregations of the mobility information.

Acknowledgements:
This work was supported by the Austrian
Science Fund (FWF) through the Doctoral
College GIScience (DK W1237-N23),
Department of Geoinformatics - Z_GIS,
UniversityofSalzburg,
Austria.
Additionally, this experiment would not havebeen possible without the support of the CS
Research Foundation, Amsterdam, The
Netherlands(www.collectivesensing.org)
who provided the anonymized mobility data
and related support.

4. Results

While the experiment is not yet complete,preliminary results have favorably shownthat it is likely that the long-term dependenceof sequential locations is able to improveprediction accuracy. This experiment focuses
explicitly on the historical window size used
and the subsequent prediction accuracy of themodel, while other important factors such asdata structure, sampling, and quality arecontrolled for in a logical manner. This
experiment is not designed to explore themany variables surrounding the LSTM modelarchitecture. The information gained throughthe results in this experiment will be
generalized, and will require further

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POSTER SESSION 1

Explorative analyse of two Italian cities: Turin and
Venice for diversity
Didem Gundogdu,1,2 , Matteo Moretti,3 and Bruno Lepri,1∗gundogdu@fbk.eu, MatteoMaria.Moretti@unibz.it, lepri@fbk.eu
1
Fondazione Bruno Kessler, Trento, Italy
2
DISI, University of Trento, Italy,
3
Faculty of Design and Arts of the Free University of Bozen-Bolzano, Italy
1.

ABSTRACT2.

INTRODUCTION

In our paper we explore two different Italian cities
for the diversity in population. Turin is the old capital city of Italy, drives Italian automotive industry
and hosts the well known university. Hence Venice is
one of the most visited touristic cities in the world.
Beside from commonly used census data, we analysedmobile phone datasets to examine the distribution ofdifferent communities among the city. The presenceof immigrants and tourists in various locations during day and night would shape the heterogeneity ofa city’s ethnic and cultural dynamics. The diversity
and integrity of different communities may resolve in
healthy economical growth for cities. In that paper
we try to answer if we can foresee the diversity of acity under the light of mobile phone usage.

Contemporary networked cities are “constituted byflows of people, vehicles, and information” [17], andyet data about these flows are increasingly difficult
to collect and analyse using traditional social science
research methods [18]. A possible help come fromthe track and analysis of the mobile phone communications, that allows the gathering of several further
information that open the door on a new paradigmof understandings about the citizens and their behaviours across time and space [3, 4]. The applicability of these methods can provide urban planners, municipalities and public institution with useful information on how different groups behave in the city. Knowing who populate different part of the city at different
times, support and improve the awareness during thepolicy making processes, and consequently the wellbeing of the city and its citizens as well. From thatperspective, understanding the mobility behaviour ofminority and immigrant groups in society, plays a crucial role as agent of change to enable the adoption oftargeted policies. Latter it might helps the preventionof urban segregation phenomena.
According to Lee, migration is defined as a permanent or semipermanent change of residence [11].
Hence, we use word immigrant as, a person who changed
his origin country and move to destination country
and declare his presence to legal authorities.
In this paper we explore two months of spatio-temporal
activity patterns from two cities in Italy, Turin and
Venice, with data collected from the Telecom Italia
Mobile (TIM) phone network. A preliminary assumption led our work, such as the country code of theincoming or outgoing calls defines the nationality of
the citizen. Following this principle we were able toidentify the different ethnic groups in the city, where
they work and commute within the city, similar toGirardin et al. performed with TIM data and Flickr
dataset taken from Rome, Italy [8]. However we used
Census data to understand tourists’ call from immigrants’ call. Apart from Giardini et al. our research
focus on the ethnic groups, not only the tourists, inorder to produce a further detailed portrait of the city,that moves beyond the limits of census data.

3.

RELATED WORKThe urban planners underpinned the importance ofdiversity more than a decade ago [21, 19]. The heterogeneity of the communities foster creativity [14], it canencourage tolerance [7], boost economical growth [15],
could create secure environments to live. According toJane Jacobs the diversity of a city should be evaluatedin different aspects, not only socially but also economically as well, the important thing is that all these parameters have to cohere in a harmonious way [10, 9].
The recent work from De Nadai et al, proved that it
is not just a theory but a fact, by using mobile phone
usage data [5].
The segregation of different ethnic communities among
a city can be a good indicator, to understand how secure and healthy a community is. Beside from thespatial distance, social integrity is also an importantparameter to evaluate [2]. The social integrity of acity can also be increased, if the urban planners create
spaces for different socio-ethnic background people inthe city, such as Trafalgar Square in London [21].
In the detailed work on diversity of the cities from
Fainstein there is a difference in diversity among major cities [7], such as Amsterdam is more successful
to create diversity rather than London and New York.
In means of mobility, immigrants travel patterns arequite different from natives however this behavior assimilate in five year time according to a study by Gil
Tal and Susan Handy [20]. This kind of behavior
change is also in parallel within spatial assimilation
theory, which propose the change of the new incomers
residence according to economical constraints rather
than social [12, 13].
Using mobile phone datasets as a proxy to census
data is proposed by several studies [6, 16], howeverneither of these studies aimed to discover the immigrant populations among the city.

4.

DATA

In that section we introduce the two datasets we use
in our work. The first includes the call detail records
and second the census data from Turin and Venice
Municipalities from 2015 1 .
Telecom Italia organized Big Data Challenge in 2014
and 2015. We are planning to use the recent data from2015. The data collection period covers 2 months from
01 March 2015 to 31 April 2015. The data is collectedin 7 cities of Italy: Milan, Rome, Turin, Bari, Napels,
Venice, Florence [1]. Call detail records (CDR) aremainly used for billing purposes by telecommunication operators.
The dataset includes incoming and outgoing SMS,
incoming and outgoing calls and internet usage of subscribers, with the country code. In order to keep thedata privacy the data is aggregated both spatially andtemporally. Each city is divided into grids, with different in size. Temporal aggregation includes the aggregated activities 15 minute time windows.
The census data are obtained from Municipality ofTurin and Venice, with the precision of administrative
level for each nationality present in the city. Venice ismore cosmopolitan city than Turin with 139 different
nationalities all around the world, comparing to 50 in
Turin.
1

www.comune.torino.it/statistica/dati/stranieriterr.htm

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POSTER SESSION 1

5.

METHODOLOGY

In our paper, we named tourists, as person who
are in that city for a short period of time, either for
touristic or business purposes. Immigrants are defined as, people who lived in that country, originatingfrom other nations, and registered in the community.
Beside unregistered immigrants are the ones who notlegally live in that country therefore not present in thecensus, but possibly present in CDR. We elaborate the
CDR dataset under this perspective and classify thenations either immigrant or tourists.
In CDR dataset, the country code that the call initiated or received are evaluated as the country of originof the user.
First we compare the distributions of each nationality present both in census and CDR data as a bipartite
network, as shown in Figure 1. On the left incoming
calls are grouped according which country code it isinitiated, in the middle the presence of this nation in
the census, and on the right the number of outgoingcalls for the same country. All values are normalized
within itself ranging from 0 to 1. The countries arelisted from top to bottom, as the presence in the corresponding dataset higher to lower respectively, shownin Figure 1. The degree α, corresponds to the degreevalue in the incoming call and population density, βcorresponds to the degree value to population density.
The overall degree (γ) is the sum of α and β.
In Figure 2 shows the degree (γ) and population
densities of each country for Venice and Turin respectively.
We classified tourists and immigrants by applying
linear discriminant analysis (LDA) to the calculateddegrees γ from incoming calls to presence in the population and outgoing calls to presence in the population, as shown in Figure 2. The countries on theupper part of the line are more likely to be tourists,
the ones through the line and below are taken as immigrants. From that formulation nationalities withhigher in incoming and outgoing calls but low presence in census, are likely to be tourist, and the lower
in both incoming and outgoing contrary to their highpresence in census are likely to resident communities
which are immigrants.

6.

RESULTS AND DISCUSSIONIn Turin, the immigrants represents the 15.4% of
the citizenships (ISTAT 2015), in which the majority (39.5%) are from Romania. Their presence in thecall dataset is proportional to their presence in thecensus data, this may be because they prefer using
TIM as mobile phone operators, and use to communicate with their roots. In March 2013 TIM made a
campaign to attract minorities, however this does not
explain the rest of the other nationalities with negative correlation between census presence and numberof calls. On the contrary, France, Poland, Swiss, Germany, Russia, Spain, UK show high presence in CDR
dataset even with low presence in census. The possible explanations for that, there are more people living
unregistered in Turin, or they can be tourists.
According to the CDR data, there are some communities prefer to live together like Romanians, Albanianand Moldavian. However Romanians with very highpresence in the CDR dataset, they prefer to live outskirts of the city. However we do not confirm thesefinding from the census, as it includes only the innercity. Similar spatial sparsity in residence also valid
for Italians. This can be explained by spatial assimilation theory introduced by Massey and Denton [12,13]. Stating that after several years in the destination country immigrants reside in the city according
to economical parameters.
One of the findings from that analysis is that, geographically close communities in origin are prefer tostay closer in the city as well. For African countries
Morocco, Egypt and Nigeria basically present inside

the Moroccan community as seen in Figure 3, on theleft Egypt communities’ night presence is shown for
Turin, on the right Moroccan community is shown.
Similar to African communities, Asian communities(China, Philippines) prefer to be closer as well.

7.

CONCLUSION AND FUTURE WORK8.

REFERENCES

In that work we detect different communities fromtwo different Italian cities by using CDR data. We
found out that similar communities tend to live together. Communities with high presence in census
like Romania and Morocco, the residential areas aresparse all around the city. This may be due to thespatial assimilation theory.
The degree versus population distribution among
the two cities seems to have a similar trend, this analysis can be performed for different cities in different
countries, to generalize this tendency.
Possible future development might include the extension of the research from a market point of view,
addressing how migrant flows affect the residential activity of the city and in which way it affects the native
citizens behaviours, supporting the prediction for instance of possible gentrification phenomena.
A generative mathematical model can be applied
to interpret the spatial distribution of communities,
to avoid the missing data.

[1] G Barlacchi, M De Nadai, R Larcher, A Casella,
C Chitic, G Torrisi, F Antonelli, A Vespignani,
A Pentland, and B Lepri. A multi-source
dataset of urban life in the city of milan and theprovince of trentino. Scientific data, 2, 2015.
[2] Ravi Bhavnani, Karsten Donnay, Dan
Miodownik, Maayan Mor, and Dirk Helbing.
Group segregation and urban violence.
American Journal of Political Science,
58(1):226–245, 2014.
[3] Vincent D Blondel, Adeline Decuyper, andGautier Krings. A survey of results on mobile
phone datasets analysis. EPJ Data Science,4(1):1, 2015.
[4] Joshua Blumenstock, Gabriel Cadamuro, andRobert On. Predicting poverty and wealth frommobile phone metadata. Science,350(6264):1073–1076, 2015.
[5] M De Nadai, J Staiano, R Larcher, N Sebe,
D Quercia, and B Lepri. The death and life ofgreat italian cities: A mobile phone data
perspective. In Proceedings of the 25th
International Conference on World Wide Web,
pages 413–423, 2016.
[6] P Deville, C Linard, S. Martin, M Gilbert, F R
Stevens, A E Gaughan, V D Blondel, and A J
Tatem. Dynamic population mapping using
mobile phone data. Proceedings of the National
Academy of Sciences, 111(45):15888–15893,
2014.
[7] Susan S Fainstein. Cities and diversity should
we want it? can we plan for it? Urban affairs
review, 41(1):3–19, 2005.
[8] F Girardin, F Calabrese, F Dal Fiore, C Ratti,
and J Blat. Digital footprinting: Uncovering
tourists with user-generated content. IEEE
Pervasive computing, 7(4):36–43, 2008.
[9] César A Hidalgo and Ricardo Hausmann. The
building blocks of economic complexity.
proceedings of the national academy of sciences,106(26):10570–10575, 2009.
[10] Jane Jacobs. Thedeath and life of great
american cities. Jonathan Cape. London, UK,
1961.
[11] Everett S Lee. A theory of migration.
Demography, 3(1):47–57, 1966.

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POSTER SESSION 1

Figure 1: The bipartite network is on the left, on the right bipartite network with data from Turin.
On the left the incoming calls coming to Turin from other countries, in the middle the population
distribution from census, and on the right the outgoing calls from Turin to other countries.

Figure 2: On the left hand, CallIn and CallOut degrees versus foreign population densities from
Venice. On the right, Call In and Call Out degrees versus foreign population densities from Turinis shown.

Figure 3: On the left Egyptians night location, on the right Moroccan night locations from Turin.
[12] Douglas S Massey and Nancy A Denton. Spatial
assimilation as a socioeconomic outcome.
American sociological review, pages 94–106,
1985.
[13] John Myles and Feng Hou. Changing colours:
Spatial assimilation and new racial minority
immigrants. The Canadian Journal of Sociology,
29(1):29–58, 2004.
[14] Gianmarco IP Ottaviano and Giovanni Peri.
The economic value of cultural diversity:
evidence from us cities. Journal of Economic
geography, 6(1):9–44, 2006.
[15] John M Quigley. Urban diversity and economic
growth. The Journal of Economic Perspectives,
12(2):127–138, 1998.
[16] Jonathan Reades, Francesco Calabrese, Andres
Sevtsuk, and Carlo Ratti. Cellular census:
Explorations in urban data collection. IEEE
Pervasive Computing, 6(3):30–38, 2007.

[17] Mimi Sheller and John Urry. Tourism mobilities:
places to play, places in play. Routledge, 2004.
[18] Noam Shoval and Michal Isaacson. Tracking
tourists in the digital age. Annals of Tourism
Research, 34(1):141–159, 2007.
[19] Michael P Smith. City, state, and market: The
political economy of urban society. B. Blackwell,
1988.
[20] Gil Tal and Susan Handy. Travel behavior of
immigrants: An analysis of the 2001 national
household transportation survey. Transport
Policy, 17(2):85–93, 2010.
[21] Iris Marion Young. Justice and the Politics ofDifference. Princeton University Press, 2011.

BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 – Vodafone Village – Milan32

POSTER SESSION 1

PyMobility: an open source Python package forhuman mobility analysis and simulation
Luca Pappalardo1 , Gianni Barlacchi2 , and Filippo Simini3
1

Department of Computer Science, University of Pisa, Italy
University of Trento and SKIL-Telecom Italia, Trento, Italy
3
Department of Mathematical Engineering, University of Bristol, UK
2

Human mobility modelling is crucial for urban simulation and what-if analysis [1, 2], e.g., simulating changes in urban mobility after the construction of a
new infrastructure or when traumatic events occur like epidemic diffusion, terrorist attacks or international events. The developing of generative models that
reproduce human mobility patterns in an accurate way is indeed fundamental to design smarter and more sustainable infrastructures, economies, services
and cities. Clearly, the first step in human mobility modelling is to understandhow people move. The recent availability of big mobility data, such as massive
traces from GPS devices and mobile phone networks, allowed the discovery ofthe quantitative spatio-temporal patterns characterizing human mobility such as
the heavy tail distribution in both trip distances [3–5], the characteristic distancetraveled by individuals, the so-called radius of gyration [4–6] and the degree ofpredictability of individuals’ movements [7]. Building upon the above findings,
many generative human mobility models have been proposed so far which try
to reproduce the characteristic properties of human mobility trajectories [8].
The goal of generative models of human mobility is to create a population ofsynthetic individuals whose mobility patterns are statistically indistinguishable
from those of real individuals.
PyMobility is an open-source Python package that provides the implementation of many of the well-known human mobility models and measures. The
long-term vision of the project aims at developing a complete package for human mobility analysis and simulation. PyMobility provides an efficient and easy
to use implementation of the main collective and individual human mobility
models existing in literature, allowing for both the fitting of the parametersfrom real data and the 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, PyMobility
implements the gravity model [9, 10], the radiation model [11], and their recent
improvements [12]. Among the individuals mobility models, i.e., models generating synthetic trajectories of desired length for a set of agents, PyMobility
provides the recent improvements of the classical Exploration and Preferential
Return model (s-EPR, r-EPR, d-EPR and recency EPR) [7, 6, 13] as well as themost accurate spatio-temporal generative human mobility models like DITRAS[14] and TimeGeo [15]. PyMobility also allows to compute a large set of measureson mobility data, both at individual (e.g. radius of gyration and user entropy)
and collective level (e.g. trips per hour, motifs and origin-destination matrix).

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POSTER SESSION 1
2

References
1. S. Meloni, N. Perra, A. Arenas, S. Gómez, Y. Moreno, and A. Vespignani, “Modeling human mobility responses to the large-scale spreading of infectious diseases,”Scientific Reports, vol. 1, 08 2011.
2. C. Kopp, B. Kochan, M. May, L. Pappalardo, S. Rinzivillo, D. Schulz, and F. Simini, “Evaluation of spatio–temporal microsimulation systems,” in Data on Scienceand Simulation in Transportation Research (L. K. D. Janssens, A. Yasar, ed.), IGI
Global, 2014.
3. D. Brockmann, L. Hufnagel, and T. Geisel, “The scaling laws of human travel,”
Nature, vol. 439, pp. 462–465, 01 2006.
4. M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding individual
human mobility patterns,” Nature, vol. 453, pp. 779–782, June 2008.
5. L. Pappalardo, S. Rinzivillo, Z. Qu, D. Pedreschi, and F. Giannotti, “Understanding the patterns of car travel,” The European Physical Journal Special Topics,
vol. 215, no. 1, pp. 61–73, 2013.
6. L. Pappalardo, F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti, and A.-L.
Barabasi, “Returners and explorers dichotomy in human mobility,” Nat Commun,
vol. 6, 09 2015.
7. C. Song, T. Koren, P. Wang, and A.-L. Barabási, “Modelling the scaling propertiesof human mobility,” Nature Physics, vol. 6, pp. 818–823, Sept. 2010.
8. D. Karamshuk, C. Boldrini, M. Conti, and A. Passarella, “Human mobility models
for opportunistic networks,” IEEE Communications Magazine, vol. 49, pp. 157–
165, December 2011.
9. G. K. Zipf, “The p1p2/d hypothesis: On the intercity movement of persons,” American Sociological Review, vol. 11, no. 6, pp. 677–686, 1946.
10. W. S. Jung, F. Wang, and H. E. Stanley, “Gravity model in the korean highway,”
EPL (Europhysics Letters), vol. 81, no. 4, p. 48005, 2008.
11. F. Simini, M. C. González, A. Maritan, and A. L. Barabási, “A universal model
for mobility and migration patterns,” Nature, vol. 484, pp. 96–100, 2012.
12. M. G. Beiró, A. Panisson, M. Tizzoni, and C. Cattuto, “Predicting human mobility
through the assimilation of social media traces into mobility models,” EPJ Data
Science, vol. 5, no. 1, p. 30, 2016.
13. H. Barbosa, F. B. de Lima-Neto, A. Evsukoff, and R. Menezes, “The effect ofrecency to human mobility,” EPJ Data Science, vol. 4, no. 1, pp. 1–14, 2015.
14. L. Pappalardo and F. Simini, “Modelling individual routines and spatio-temporal
trajectories in human mobility,” CoRR, vol. abs/1607.05952, 2016.
15. Y. Yang, S. Jiang, D. Veneziano, S. Athavale, and M. C. Gonzalez, “Timegeo: aspatiotemporal framework for modeling urban mobility without surveys,” PNAS,2016.

BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 – Vodafone Village – Milan34

POSTER SESSION 1

An exploratory analysis of ethnic groups
in the city of Milan through mobile
phone data
Gianni Barlacchi1,2 , Michele Ferretti4 , Bruno Lepri3 , and Fabio Manfredini5
1,2

University of Trento & SKIL, Telecom Italia, Trento, Italybarlacchi@fbk.eu
3
Fondazione Bruno Kessler, Trento, Italy
lepri@fbk.eu
4Department of Geography, King’s College London, London, United Kingdom
michele.ferretti@kcl.ac.uk
5
Politecnico di Milano, Dipartimento di Architettura e Studi Urbani, Milano, Italyfabio.manfredini@polimi.it

January 31, 2017
1 Introduction:
Since 1999 the Municipality of Milan has registered a more than twofold increase in
the number of foreign residents. By the end of 2015 they accounted for 19% of theoverall population, corresponding to circa 260.000 inhabitants. Their spatial distribution
and their country of origin presents specific patterns deriving from historical reasons,availability of low-cost housing and presence of social services.
An inventory of the living population across all the different Nuclei di Identita’ Locale(NIL), Milan’s neighbourhoods, is provided on a yearly basis by the municipal population
register. These recordings, although highly valuable, fail to provide actual informationon the populations’ typologies and densities. They thus necessitate of further integration
with alternative datasets [1]. Further, the spatial and temporal dynamics of foreigners’
concentration within the city, as well as their actual trends and patterns, are difficult tointercept through conventional data sources [2].
Since the beginning of 2010, the availability of mobile phone data has opened a new fieldof research at the intersection of new and traditional disciplinary traditions (city planning,sociology, computational geography, urban informatics etc. . . ) aimed at exploring thepotential use of mobile phone data for understanding mobility patterns and dynamics [3,4], to understand structural conditions underlying the quality of urban life [5], and thus
ultimately to provide new services for urban populations. [6].

1

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POSTER SESSION 1

Nowadays, there are almost 6 billion of mobile phone users worldwide [7]. The world
coverage raised from 12% of the world population in 2000 up to 96% in 2014 [8], with thistrend further accounting for almost the totality of the population in the so-called "developed" countries. Such devices generate an incredible amount of Call Detail Records(CDRs), encompassing informations about how our daily mobile phone usage. Furthermore, they contain location data (e.g. from where we call), a characteristic that makes
CDRs an extremely informative source of mobility patterns, and equally of social connections. Mobile phones are in fact considered to be strictly personal items, in contrast with
fixed-line phones that are usually shared between more users, e.g., different tenants ina house, colleagues in a office. . . This characteristic, coupled with their very fine spatial
and temporal resolution, renders mobile phone data highly suitable for reconstructing acaller’ social network.

2 Challenge:
Mobile phone data could then be applied to study the distribution of ethnic communities
in time and in space. Such study might prove relevant for understanding their growingrole in the city economy, for monitoring potential risks of spatial segregation, providing
new user-centred services or ameliorating the existing. As demonstrated by Hughes
et al. [2] in his review of comparative migration estimates, conducted by the meansof traditional and new data sources, these would prove to be hard-to-reach goals withmethods confined to traditional social research. Other scholars in the field used mobile
phone data, or other novel sources, for studying multicultural diversity of cities [9] andfor analysing segregation with large-scale spatial network data [10, 11].
Following a similar line of inquiry, the present research focuses on the study of theforeigners’ spatial distribution and activity within the city of Milan, via the means oftelecommunications activities provided by TIM [12]. The dataset containing such activity
serves as a measure for the level of interactions occurring between different users within
the mobile phone network. The dataset is spatially aggregated using a regular gridand contains information on SMSs and calls received inside each grid square. A valuable
information provided by this dataset is the country code of the sim-card connecting to themobile network. In particular, the data contains a segmentation of the activity, also basedon the grid square, the time of the day and the country involved in the communication
activities. This information will allow a partial reconstruction of the communications’patterns between users located in Milan and abroad, such as migrant populations.
The data are available on a daily basis for a period of more than two months: from2013-11-01 to 2013-12-20. The number of calls issued or received could then be correlatedwith the interactions of mobile phone users within different nations for the aforementioned
purposes (e.g. personal communications, business. . . ). Lastly, the gridded data will beaggregated according to the NIL boundaries, to contrasting the official residents data
provided by the municipality with the resulting statistics. Such comparison will highlight
any parallelisms or discrepancies between the two data sources, further investigating
if, and under which manner, mobile phone data can be used i) as novel data sources

2

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36

POSTER SESSION 1

to describe phenomena for which official statistics do not collect data ii) as additionalsources complementing the official statistics iii) or as alternatives to replace conventional
ones [13].

References[1]

Pierre Deville et al. “Dynamic population mapping using mobile phone data”. In:
Proceedings of the National Academy of Sciences 111.45 (2014), pp. 15888–15893.

[2]

Christina Hughes et al. Inferring Migrations : Traditional Methods and New Approaches
based on Mobile Phone , Social Media , and other Big Data. Tech. rep. European
Commission, 2016, p. 41.

[3]

Marta C González, Cesar a. Hidalgo, and Albert-László Barabási. “Understanding
individual human mobility patterns.” In: Nature 453.7196 (June 2008), pp. 779–82.

[4]

Cesar a. Hidalgo and C. Rodriguez-Sickert. “The dynamics of a mobile phone network”.
In: Physica A: Statistical Mechanics and its Applications 387.12 (May 2008),
pp. 3017–3024.

[5]

Marco De Nadai et al. “The Death and Life of Great Italian Cities: A Mobile Phone Data
Perspective”. In: Www (2016), pp. 413–423. arXiv: 1603.04012.

[6]

Vincent D. Blondel, Adeline Decuyper, and Gautier Krings. “A survey of results onmobile phone datasets analysis”. In: EPJ Data Science 4.1 (2015), pp. 1–55. arXiv:
arXiv:1502.03406v1.

[7]

Vincent D. Blondel et al. “Data for Development: the D4D Challenge on Mobile PhoneData”. In: CoRR (2012), pp. 1–10. arXiv: arXiv:1210.0137v1.

[8]“The world in 2014 : ICT Facts and Figures. International Telecommunication Union.
http://www.itu.int/ (2014)”. In: ().

[9]Michela Arnaboldi et al. “Studying Multicultural Diversity of Cities and Neighborhoods
through Social Media Language Detection”. In: Tenth International AAAI Conference on
Web and Social Media. 2016, pp. 2–7.

[10]

J Blumenstock and L Fratamico. “Social and spatial ethnic segregation: A framework foranalyzing segregation with large-scale spatial network data”. In: Proceedings of the 4th
Annual Symposium on Computing for Development, ACM DEV 2013 (2013).

[11]

Siiri Silm and Rein Ahas. “The temporal variation of ethnic segregation in a city:
Evidence from a mobile phone use dataset”. In: Social Science Research 47 (2014),
pp. 30–43.

[12]Gianni Barlacchi et al. “A multi-source dataset of urban life in the city of Milan and theProvince of Trentino.” In: Scientific Data 2 (2015), p. 150055.

[13]

Giulio Barcaroli et al. “Dealing with Big data for Official Statistics : IT Issues”. Dublin,
Ireland and Manila, Philippines, 2014.

3

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POSTER SESSION 1

Determining an optimal time window for roaming
data for tourism statistics
Martijn Tennekes

May Offermans

Nico Heerschap

Statistics Netherlands
Email: m.tennekes@cbs.nl

Statistics Netherlands
Email: mpw.offermans@cbs.nl

Statistics Netherlands
Email: n.heerschap @cbs.nl

Abstract—Mobile phone data can be used for making Official
Statistics on various subjects, such as safety, mobility andtourism. One of the major challenges is the protection of privacy
when analyzing and processing these data for publication or
fundamental research. Using sensitive information sparingly is
one of the principles that is often put forward in privacy
discussions. The time dimension is key for analyzing Call Detail
Records (CDRs) but also for privacy. Therefore, in this paper
an optimal time frame is determined for roaming visitors thatprovides sufficient information to create official tourism statistics
of high quality while ensuring privacy. For this study we usedanonymized aggregated CDR data from Vodafone in collaboration with Mezuro. The results show that a time window of 15 days
is sufficient for official tourism statistics. However, a time windowof one month is preferable in order to compare mobile phone
based tourism estimations with the current tourism publication
numbers.

I. I NTRODUCTION
One of the advantages of using Call Detail Records (CDR’s)is the enormous detail of information in time and space. Thisadvantage provides new opportunities for National Statistical
Institutes like Statistics Netherlands for creating statistics on
daytime population and tourism [1-4]. New and more precise
information becomes available making it possible to create
fast, almost real time statistics on a national level with highregional detail. One of the major challenges when making
statistics with an extensive time horizon is the managementof privacy and data-access privileges. Since 2009, Statistics
Netherlands has collaborated with Mezuro and Vodafone inresearch on CDR’s [5]. No data provided by Vodafone maylead to the identification of a person or company. Anonymization, aggregation of data, and remote systems that leave all
personal sensitive data at the providers data center are important measures to ensure privacy. Different privacy rules areapplied to ensure that even if information from space and time
is combined, no identification is possible. One of these privacy
rules in the data provided by Vodafone and Mezuro is thata device receives an anonymized IMSI (International Mobile
Subscriber Identity (IMSI) for a period with a maximumof 31 days. After this period, a new anonymized IMSI iscreated which ensures that the device cannot be tracked fora substantive period of time.
The authors and Statistics Netherlands would like to thank VodafoneZiggo
and Mezuro (www.mezuro.nl) for providing data and processing the CDR’s.
The views expressed in this paper are those of the author(s) and do notnecessarily reflect the policies of Statistics Netherlands.

This ensures privacy and provides enough information for
statistical research purposes. However, devices that have roaming switched on in the Netherlands, the period was set at 24hours. The reasons for this shorter, more strict period is the factthat there is no legal framework on collecting data from foreigndevices that roam on the Dutch network. Each tourist receives
an SMS when it starts roaming on the network. However, forma legal point of view it is impossible to obtain full informed
consent or to provide an opt-out option. Therefore, the 24
hour IMSI-hash is introduced. The disadvantage of this 24hour privacy rule is that the roaming data does not meetthe requirements needed for tourism statistics. For example,
we can roughly estimate the number of Japanese tourists in
Amsterdam within a 24-hour timeframe, but it is not possible
to see how long these tourists stay and where they consecutively go when visiting the Netherlands. These statistics
contain important information on, for example, national andlocal tourism planning, public transport and (international)
festivals. Determining an optimum period for this privacy
rule is therefore important. On the one hand, one wants to
produce useful statistics for the general public, government,
and industry, and on the other hand, only use information thatis necessary to produce these statistics.
II. DATAFor this specific analysis, we used two aggregated andanonymized data tables that were extracted from CDRs at
the Vodafone data center from all roaming customers on the
Vodafone network in the Netherlands during the period fromMay 1st 2014 to May 31st 2014. The minimum cell count ofthese CDR tables is 15. Table cells with values lower than
15 are coded as missing to prevent identification. For this 31
day period, the anonymized IMSI numbers were not renewed.
Instead, all geolocation data has been left out. The specificdescriptions of the two tables that we have used are as follows:
1) The first table contains three columns: first date, lastdate, and number of foreign roaming devices. The number of foreign users refers to the number of users forwhich an CDR event is logged every day between (andincluding) the first and the last date. We refer to this date
range as consecutive stay. Note that we do not know thelength of the consecutive stay if the first date is May 1stor the last date May 31st, because foreign users could

BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 – Vodafone Village – Milan38

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between (and including) the first and last date, but notnecessarily every day in between. Therefore, we refer tothis date range as non-consecutive stay. To prevent low
cell volume, the countries of origin were aggregated asfollows: Belgium, Germany, France, United Kingdom,
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POSTER SESSION 1

7

14

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Non−consecutive stay length28

Fig. 3. Number of foreign users per consecutive stay length. Absolute
numbers are shown above, cumulative below.

III. R ESULTS AND D ISCUSSION
In this study, we focus on the length of stay of foreign
roaming devices in the Netherlands. For tourism statistics, it
is important to classify foreign users into standard categories,
such as tourists, workers that cross the border on a regularbasis, and normal border crossings (for example to do shopping). However, it was not possible to classify foreign users
in this study, because it is unclear from the used data whether
foreigners stay overnight in the Netherlands, or travel backand forth. It can generally be assumed that many people from
Belgium and Germany, and, to a lesser extent France, will
make day trips, whereas people from other countries will stayovernight in the Netherlands.
Figure 1 shows the number of users per consecutive staylength. For users who were logged on the 1st or the 31st of
May, the consecutive stay can be longer, since they could havebeen in the Netherlands earlier or later than May.
The vast majority of users have only stayed for one day
in the Netherlands. The number of users who have stayed
between 15 and 30 consecutive days is relatively low. However, there are 38373 regular foreign users (almost 2 percent),
probably people who live abroad and work or study in the
Netherlands, or people who temporarily live in the Netherlands
to work or study.
One of the aims for tourism statistics is to determine thetotal number of (overnight) stays and day trips of tourists in
the Netherlands. In order to obtain those, the absolute numbers
shown in Figure 1 will have to be multiplied by the active staylengths. This will put more emphasis on foreign users who stayfor longer periods.
Figure 2 shows the number of (overnight) stays during theobserved period, grouped by the total consecutive stay length.
For instance, the left-most green dot means that there are23617 foreign users who have stayed between the 1st andthe 2nd of May 2014 in the Netherlands (which can either

BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 – Vodafone Village – Milan

39

POSTER SESSION 1

Belgium

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Number of days between (and including) first and last dateFig. 4. Consecutive period of foreign users.

Small local peaks are visible at 8, 15, and 22 days, which
probably corresponds to standard holiday stays of one, two,and three weeks. There are differences visible per country oforigin. Germany and Belgium show for example higher rates
of visitors that stay short, usually one day , but we will notdescribe them in detail.
The total number of foreign users per (grouped) country
are depicted in Figure 5 shows as expected that the numberof German and Belgium users is relatively high. Also, thenumber of Inter Standard Roaming user is high, especiallyamong regular users.
This dataset contains several limitations. First, in the Netherlands there are four network providers and Vodafone is oneof them. In this study, no corrections or extrapolations wereTotal foreign users

Country of origin

be an overnight stay or back and forth traveling) and 3 or 4
consecutive days in total.
Figure 2 also shows a typical tourism pattern. First, there
is a holiday period from the 26th April 2014 until the 5th
May 2014 of which the first days are visible in this datasetwith high frequency of roaming data. These are mainly touriststhat stay for 2 days or 3-4 days. Second, there is a weekend
pattern (grey area) with again mainly tourists that stay for
2 days and 3-4 days. Many tourists stay from Thursday to
Sunday. Third, there are the one-day events. The 29th of May
is Ascension Thursday (a National day), and a lot of people
also take holiday on the Friday after this Ascension Thursday,again 2 days or 3-4 days. The 5th of May is called Liberation
Day (WWII) but is not visible, showing that tourists fromother countries are not participating. Also for mothers day (formost countries on the 2nd Sunday of May), no differences arevisible.
The number of users per non-consecutive stay length isshown in Figure 3. A stay length of 20 only means thatthe difference between the first and last date equals 20 days.
It does not say anything about how many days in between
the user actually stayed in the Netherlands. Observe that 30
percent of the foreign users have stayed non-consecutively for
longer than 1 week in the Netherlands. An explanation for this
could be the large amount of Belgium and German foreigners
who work in the Netherlands or who regularly make day trips
to the Netherlands.
Figure 4 shows the number of foreign users per nonconsecutive stay length by (grouped) country of origin. The
color of the dots correspond to the continent: Europe (green),other (purple) and Inter Standard Roaming Roaming (orange).

Regular foreign users

Germany
BelgiumInter−Standard Roaming
Eastern Europe
Europe other
Southern Europe
United Kingdom
FranceNorth America
Asia other
CHN, JPN, AUS, NZL
AfricaMiddle−East
South America
0

200 400 600 800 0(x 1000)

20

40

60

Fig. 5. Consecutive period of foreign users.

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40

POSTER SESSION 1
made. Second, a time window of 31 days was used. Therefore,
the stay length of foreign users that were already in the
Netherlands at May 1st or were there at May 31st cannot
be determined exactly, since they could have been in the
Netherlands earlier or later than May. Third, there was nogeolocation provided in this dataset. Therefore, it was notpossible to make a clear distinction between tourists on the one
hand and border traffic and foreign workers in the Netherlandson the other hand.
IV. C ONCLUSION
Based on the results presented in Figures 1, 2, and 3, it canbe concluded that a time window of 15 days is sufficient forcovering almost 98% of consecutive stays and 80% of nonconsecutive stays. It is advisble to choose the time window
larger than the desired stay length to be measured, in order todecrease the probability of overlap.
For tourism statistics, a time window of 1 month is preferable in order to compare the mobile phone based estimationsof tourists with the current tourism statistics.
For future research, it is necessary to classify foreign users
as tourists, day trip visitors, and cross border workers. For
tourism statistics, also the geospatial information is important.
Once roaming data is available for time window of 15 days or
longer, geospatial information can be used to classify foreign
users based on the places they visit.
R EFERENCES[1] Raun, J. Ahas, R., 2013. Distinguishing tourism destinations withbehavioural data. Brussel, New Techniques and Technologies for
Statistics, NTTS.
[2] European Commission, 2014. Feasibility Study on the Use of Mobile
Positioning Data for Tourism Statistics, Eurostat.
[3] Altin, L., Tiru, M., Saluveer, E., Puura, A., 2015. Using Passive Mobile
Positioning Data in Tourism and Population Statistics, Brussel, New
Techniques and Technologies for Statistics, NTTS.
[4] Meersman, F. de, Seynaeve, G., Debusschere, M., Lusyne, P., Dewitte,
P., Baeyens, Y., Wirthmann, A. Demunter, C., Reis, F., Reuter, H. I.,
2016. Assessing the Quality of Mobile Phone Data as a Source of
Statistics, Paper for the European Conference on Quality in Official
Statistics (Q2016).
[5] Offermans, M, Priem, A, Tennekes, M., 2013. Rapportage project
Impact ICT mobiele telefonie. Technical report (in Dutch). Statistics
Netherlands.

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41

POSTER SESSION 1

Measuring transnational population mobility with roaming data
Rein Ahas1, Siiri Silm1, Margus Tiru1,2
1

Department of Geography, University of Tartu, Estonia

2

Positium LBS, Tartu, Estonia

Transnational population mobility can be defined as living and working in two or more countries(Schiller et al 1995). A transnational lifestyle is becoming more and more commonplace in various
parts of world thanks to rising population mobility, changing labor market, opening borders, anddeveloping information and communication technologies. Transnationality confronts the historicalconcept of ‘Nation State’, which approaches the world from an idealistic, citizenship and territorybased perspective which insists that all permanent residents should be citizens and all citizens
should be permanent residents (Mayer et al, 1997). Today we can see that many transnational
communities and floating population segments live and work outside of their homelands or apartfrom their families and homes. Measuring, understanding, and the management of such
communities is complicated; we can see that traditional statistics and register data sources are notsuitable to describe the transnational community.
In this paper, we are proposing a methodology for measuring the transnational mobility of a
population with the help of the roaming databases of mobile network operators. We developed aconceptual and methodological framework for detecting tourists, cross-border commuters,
transnationals, and foreign workers from roaming CDR datasets (Eurostat, 2014). We use thismethodology for measuring outgoing mobility from Estonia in the databases of the two largest
mobile network operators with market shares of 39% and 34%. Methodological challenges are
related to integrating data from two mobile network operators; defining trips; and proposing theory
based and distribution based parameters for algorithms. The work is related to the development of
migration statistics within the framework of the Eurostat BIG Data Task Force.
Table 1. The distribution of outbound visitors according to visitor segments, data from the twobiggest mobile network operators in Estonia on 2015.
Numberof
Visitor segment persons
Tourists592,335

Share fromoutboundvisitors

Number ofcountries
visitedNumber of
trips

98.2

200

3.6

Annual average
Number of
Duration of
days
trips
16.7

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5.0

42

POSTER SESSION 1
Transnationals
Cross‐bordercommuters

23,587

3.9

101

15.7

158.1

13.3

1,518

0.3

14

85.3

144.7

1.9

Foreign workers

4,366

0.7

61

9.6

306.7

89.4

200

4.4

25.3

6.0

Total

603,355

Results so far show that, in methodological terms, it is possible to distinguish between different
transnational segments of outbound travelers. An average outbound traveler carried out a total of
4.4 trips with an average duration for each trip of six days annually, but this averaged result issignificantly influenced by high share of tourists in outbound travel (Table). Transnationals carriedout 15.7 trips with a duration of each trip lasting 13.3 days. A total of 2.7% of transnationals wereconnected to two or three foreign countries. Cross-border commuters carried annually out 85.3
trips with average duration of trip 1.9 days and foreign workers 9.6 trips with average duration 89
days. Results show that the most popular countries to be visited by transnationals from Estonia are
Estonia’s closest neighbors in Scandinavia and Western Europe. One unexpected result was therelatively small proportion of transnationals going to Russia, even if Russia is a very important
tourism destination and is closely connected to the Russian minority living in Estonia (forming
27% of the total population). The travel behavior parameters and the social profile of visitors
allows us to differentiate between tourists, cross-border commuters, foreign workers, andtransnationals.
The results are interesting, but validating our results is a rather complicated business as it is also
complicated when it comes to finding alternative sources of data for validation. We discuss thestrengths and weaknesses of such data and the methodology from the point of view of theinternational migration research and statistical system.
The phenomenon of transnational commuting is closely related to the development of ICT and theinformation society (Nedelcu & Wyss, 2016). Ubiquitous mobile communication data issomething that can be a starting point when it comes to broadening the concept of the ‘Nation
State’ with citizens and residents having activities in several countries. The concept of e-residency(Estonian e-Residency 2017) can be one way of developing concept of transnational citizenship.
References
Estonian e-Residency 2017. https://e-estonia.com/e-residents/about/

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POSTER SESSION 1

Eurostat 2014. Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics,
Consolidated Report Eurostat Contract No 30501.2012.001-2012.452, Positium LBS, 31p.
http://ec.europa.eu/eurostat/web/tourism/methodology/projects-and-studies
Meyer, J. W., Boli, J., Thomas, G. M., & Ramirez, F. O. (1997). World society and the nation‐state. American Journal of sociology, 103(1), 144-181.
Nedelcu, M., & Wyss, M. (2016). ‘Doing family’ through ICT‐mediated ordinary co‐presence:transnational communication practices of Romanian migrants in Switzerland. Global Networks,
16(2), 202-218.
Schiller, N. G., Basch, L., & Blanc, C. S. (1995). From immigrant to transmigrant: Theorizing
transnational migration. Anthropological quarterly, 48-63.

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6 APRIL 2017

POSTERSESSION 2

NetMob 5-7 April 2017 – Vodafone Village – Milan

POSTER SESSION 2

CLIMATE CHANGE INDUCED MIGRATIONSFROM A CELL PHONE PERSPECTIVE
Sibren Isaacman​1​, Vanessa Frias-Martinez​2​, Lingzi Hong​2​, Enrique Frias-Martinez​3​
Loyola University Maryland; 2​ Collegeof Information Studies, University of Maryland;
3​
Telefonica Research, Madrid, Spain1​​isaacman@cs.loyola.edu​{vfrias, ​lzhong}@umd.edu​​enrique.friasmartinez@telefonica.com

ABSTRACT
Cell phone traces have been successfully used to study human mobility during natural disasters such as
earthquakes and flooding [1,2,3]. Climate change, understood as the change in weather patterns for a long
period of time, also has the potential of causing changes in human mobility and cause migrations thathave a wider and long standing impact. A ​climate migrant is an individual that is forced to leave theirlocal environment due to long or sudden weather changes. In this study we present initial results of the
migrations caused by the severe drought that happened in La Guajira, Colombia, in 2014. Our initial
results indicate a linear reduction of the population of 10% during the 6 months considered.
La Guajira is a department of Colombia located in the northwest tip of the country and borders
Venezuela. According to the UN Office for the Coordination of Humanitarian Affairs (OCHA)[4], since
the beginning of 2014 an extreme drought has affected La Guajira. The drought caused a declaration of
the state of public calamity in the municipality of Uribia (La Guajira) in February of 2014[5]. It isestimated that around 65,000 people have been affected by the severe droughts in La Guajira.
Consequences to the population are mainly regarding malnutrition, especially for infants, with extreme
consequences for the agricultural and livestock sectors.
Using a 6-month (December 2013 through May 2014) anonymized CDR dataset from Colombia we
wanted to measure the impact, if visible, in the number of inhabitants of both Uribia and La Guajira
during the drought period. A home detection algorithm based on when phone calls were made wasapplied weekly for any cell phone that was present in La Guajira during the period of study. Note that all
calls independently of where they were made have been considered when applying the home detection
algorithm, i.e. homes were assigned anywhere in Colombia. If there was not enough information to assign
a home tower in a given week we assumed the last known home. Figure 1 presents the number of cell
phones whose home location was assigned in the towers that cover Uribia and La Guajira from January toJune during each week, showing a linear decrease in both cases of 10% of the population during theperiod of study. Both cases can be model with a linear regression showing an r​2 of 0,78 and 0,93 for
Uribia and La Guajira respectively.

Figure 1. Number of Homes detected in (left) municipality of Uribia and (right) the department of La Guajira from December 2013
to June 2014.

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POSTER SESSION 2

Regarding where those climate migrants go, 90% of them stay in La Guajira relocating to othermunicipalities where access to help, food and water is probably easier. The other 10% move to otherdepartments. In general, the closer the department to La Guajira the higher the number of people
relocating is, i.e. climate migrants move to neighboring departments, with the exception of Bogota. Figure
2 presents in a red color scale where the migrants move from the second week of January 2014.

Figure 2. Departments where climate migrants move in Colombia for the second week of January 2014.

References[1] Moumni, B., Frias-Martinez, V., & Frias-Martinez, E. (2013, September). Characterizing social response to urban
earthquakes using cell-phone network data: the 2012 oaxaca earthquake. In ​Pro0. 2013 ACM Conf. Pervasive andUbiquitous Computing (pp. 1199-1208). ACM.
[2] ​Pastor-Escuredo, D., Morales-Guzmán, A., Torres-Fernández, Y., Bauer, J. M., Wadhwa, A., Castro-Correa, C.,... & Oliver, N. (2014, October). Flooding through the lens of mobile phone activity. In ​Global HumanitarianTechnology Conference (GHTC), 2014 IEEE (pp. 279-286).
[3] Frias-Martinez, V., Soguero, C., & Frias-Martinez, E. (2012, August). Estimation of urban commuting patterns
using cellphone network data. In ​Proceedings of the ACM SIGKDD International Workshop on Urban
Computing (pp. 9-16). ACM.
[4] UN Office for the Coordination of Humanitation Affairs: Colombia Disaster Snapshot 2014
http://reliefweb.int/sites/reliefweb.int/files/resources/DisastersSnapshot_Colombia_April2014_1.pdf[5] Declaration of Public Calamity in Uribia (in Spanish) February 14​th​ 2014:
http://uribia-laguajira.gov.co/apc-aa-files/62353738366136663032666631393236/decreto-calamidad-publica-uribia.p
df

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POSTER SESSION 2
Abstract Submission to NetMob17
Fifth conference on the Analysis of Mobile Phone Datasets

High speed analysis of volatile mobile data applied to road safety
Gómez Castaño, JoséCTO INSPIDE jgcasta@inspide.com
GIS Specialist Dpto. Astrofísica y CC de la Atmósfera;
Univ. Complutense de Madrid jgomez03@pdi.ucm.es

Cabrera García, Juan José
CEO INSPIDE jjcabrera@inspide.com

The mobile data analysis focuses on the extraction of information using large amounts of data.
These come not only of the CDRs, but from other sources such as social networking or
connections from mobile applications. In most cases, this analysis is carried out over a variety
period of time and they use the dataset accumulated implementing batch processes. These bear acertain amount of run time depending on the algorithm complexity .
If we want to obtain quality results in certain environments, the processing of the data must be
more agile and it have to spend less time. In the field of road safety, for instance, you cannot wait
for a process that takes a long time, due to the fact that the traffic is changing, and the information
should be provided as soon as possible.
This work explores the analysis of the mobile data taking into account two aspects: High Speed
Analysis and Volatility of the Information. The purpose is to gather georeferenced information
provided by mobile data, determine the position in relation to the traffic and computes traffic
congestion situations. The result is sent to the drivers concerned and to inform Traffic Authorities,
in real time. Congestions are bottlenecks and the are a traffic hazard because a reduction of theunexpected speed by drivers and they may be due to multiple causes.
The origin of the information, in this work, are anonymous Twitter messages, and they are obtainedusing the API provided to developers [API Twitter 2016]. This API allows us to get all the messages
with location information associated. We have chosen this set of data for its easy access and its
proven usefulness in works such as the of Llorente, et al 2015, Carley, K et al.(2014) and Abassi,
A. et al (2015)
Regardless the origin of the messages, the treatment of the information, their volatility and theresults are independent of the source data, and you can apply this methodology to any other data.
The concept of volatility of the information is related to the use of the information. So we define
time of volatility of the information as the period of time during which a data is useful for a particularpurpose. This period depends not only on the data, but also on the processes of calculation
required to obtain a useful outcome from them. High Speed Analysis are the processes
implemented to achieve this. After this period, the data is considered as expired.
In this work applies this concept of volatility of the information to improve road safety. The receiptof messages is carried out by a series of processes developed in Python that run on a distributed
Spark architecture implemented on a Amazon AWS EC2 environment.
The location of every message received is compared with the geometries of roads stored in a
PostGIS spatial database. These geometries (2.6 million) have been previously charged and theycome from OpenStreetMap. The set of messages is translated in a geometry of roads for the
stretch of the same affected by congestion. This new geometry is used as a final result as a trackover the road geometry. The messages are grouped into short periods of time, after which they are
discarded and are not valid for a good result. The are expired.
Because the aim is to improve the information to drivers, the system presented in this paper allows
us to locate the information panels that are closest to the drivers in the direction of its motion, and it
is possible to issue alerts of congestion to these panels. These panels are located over the road to
inform the drivers with different notifications abut traffic. We have developed a preprocessing thatlocates the panels for every of the roads of study.

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48

POSTER SESSION 2This information is also used by the Singularity platform, which provides the logic of road safety for
Comobity application. This is a road safety applications developed for the General Directorate ofTraffic [DGT 2015] and Gomez, J, Cabrera, JJ 2016, and it provides road safety information tomore than 20,000 drivers in real time since November of 2015.
As a result is to be able to issue notifications and information that are visible by drivers in advanceand automatically. As a result, below there is a graph indicating warning levels at different times,
and an example of the tweets used. More than 400,000 locations and tweets has been taken into
account to generate a near real environment as a test for the scenario over Spain, and more than
2,6 geometries have been used. The expiration time of the volatile data has been evaluated from 1
minute to 15 minutes.
Better information to the drivers has an impact on an improvement in the road safety

tweets distribution at 8:45 over Madrid and Guadalajara provinces

Information level on panels at 7:00 over Madrid and Guadalajara provinces

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POSTER SESSION 2

Information level on panels at 8:45 over Madrid and Guadalajara provinces
References
Abassi, A., Hossein T, Maghrebi, M., Travis, S. 2015, “Utilising Location Based Social Media in
Travel Survey Methods: bringing Twitter data into the play” Proceedings of the 8th ACM
SIGSPATIAL International Workshop on Location-Based Social Networks, Bellevue, WA, USA, Nov
2015 ISBN: 978-1-4503-3975-9
API Twitter 2016 https://dev.twitter.com/rest/reference/get/geo/search
Carly, K, Kathleen M. Pfeffer, Jürgen Morstatter, Fred Liu, Hua (2014) “Embassies burning: toward
a near-real-time assessment of social media using geo-temporal dynamic network analytics” Social
Network Analysis and Mining, 2014, vol 4 issue 1 pag 195
DGT 2015 “Comobity: La app que conecta y protege a los conductores, ciclistas y peatones”
http://www.dgt.es/es/prensa/notas-de-prensa/2015/20151112-App-Comobity-DGT.shtml
Gómez, J,Cabrera, JJ, 2016 “Phii y Comobity. Movilidad y Seguridad vial colaborativa” X Jornadasde SIG Libre, Universidad de Girona 2016
Llorente A, Garcia-Herranz M, Cebrian M, Moro E (2015) Social Media Fingerprints of
Unemployment. PLOS ONE 10(5): e0128692. doi: 10.1371/journal.pone.0128692

BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 – Vodafone Village – Milan

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POSTER SESSION 2

Context-Aware Recognition of Physical Activities
Using Mobile Devices
Gabriele Civitarese

Claudio Bettini

EveryWare Lab, Università degli Studi di Milano
Email: {gabriele.civitarese, claudio.bettini}@unimi.it
I. I NTRODUCTION
Context-awareness in mobile apps has been the focus ofseveral research efforts in the last years. The activity that amobile user is performing while using an app is an essential
component of context and an enabler for innovative functionalities [1]. While activity recognition is a wide area, in thiswork we focus on the recognition of simple physical activities(e.g., walking, running, riding a bicycle, . . . ) by processing
data provided by the many sensors that we find inside modern
smartphones and smartwatches. This task is not new andsolutions are typically based on machine learning applied
to data obtained from inertial sensors (e.g., accelerometer,gyroscope, and magnetometer) [2]. The techniques have been
available to Android developers as well in the form of theGoogle Activity Recognition API. Despite the amount of workin the area there is still a lot of space to improve since the set of
activities is very restricted, precision is sometimes inadequate,
some activities are easily confused with others, and there is adelay in the recognition.
The EveryWare Lab has introduced hybrid recognition techniques and designed COSAR [3], a system that exploited thesemantics of the locations where the activities are performed(e.g., home, work, park, street, . . . ) to refine the statisticalclassification. Essentially, an ontology which describes therelations between locations and activities is used to exclude
from the classification results those activities which have a
low probability of being performed in a particular location. In
this work we present new results in line with that approach byusing state of the art smartphones and smartwatches insteadof wearable prototype sensors as in previous work, and weapply new algorithms. We collected a real dataset consisting ofseveral activities performed by different subjects in different
locations. The technological setup allows us to evaluate thequality of our recognition, the added value of consideringsmartwatch data in addition to smartphone, and to comparewith the predictions obtained through the Google Activity
Recognition API, showing the effectiveness of our approach.
II. M ETHODOLOGY
The general methodology consists in continuously acquiringand preprocessing raw data from sensors in order to classify the performed activities using standard machine learningtechniques. Symbolic reasoning is then applied consideringcontextual information in order to refine the statistical classification. We assume that the user carries the smart-phonein the pants front pocket and the smart-watch at the wrist of
the non-dominant arm. It is important to note that our method
works even if only one of the two devices is used by the user
(obviously with different accuracy levels).
A. Sensing
At the base of our infrastructure we consider a continuously
collection of data from sensors equipped in nowadays smartphones and smart-watches using dedicated apps. In particular
we consider inertial sensors (e.g., accelerometer, magnetometer, gyroscope, . . . ) to monitor the physical movements of theuser and context sensors (e.g., GPS, location, light sensor, . . . )to capture the context of the surrounding environment.
B. Preprocessing, segmentation and feature extraction
First of all, a median filter is applied on raw signals ofinertial sensors to reduce the noise. Magnetometer data isthen analyzed to infer the smartphone’s orientation in the
pocket. We consider “normal position” when the screen isagainst user’s thigh, and when we detect that the magnetometer
indicates a different orientation we swap the axis to bring all
the smartphone’s sensors to the “normal position”.
After this preprocessing phase, we align and segment preprocessed data from the different inertial sensors of both devices using temporal sliding windowing. A feature vector consisting of several statistical features on smartphone/smartwatch
inertial sensors data is then computed from each window.
C. Hybrid activity recognition
A multinomial classifier (which has been previously trained
off-line) is used to obtain, for each feature vector, the probability distribution over all the considered activities. Since
statistical classification accuracy is affected by intrinsic noise,
we propose a symbolic refinement which aims to improve therecognition accuracy by considering the surrounding context
where the activities are performed. In particular, we exploit
semantic location information (e.g., home, office, park, street,. . . ) to exclude from the probability vector generated by themultinomial classifier all the activities which have a low
probability of being performed according to the current context(e.g., taking the elevator in the street is very unlikely). The
final result of classification is the most probable activityof the refined probability vector. Semantic location can beinferred using reverse geocoding techniques. However, in our
preliminary implementation we annotated in the dataset the

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POSTER SESSION 2
semantic locations and we used those information for theexperimental evaluation of the method.
III. P RELIMINARY RESULTS
A. Experimental setup
Our experimental setup consists in a LG Nexus 5x smartphone equipped with 9-axis accelerometer, GPS sensor andseveral environmental sensors. This device allowed us toacquire data at a 200HZ frequency. We also adopted a LG Gwatch R smart-watch which includes the same sensors of theabove mentioned smart-phone (except for GPS). We developed
two Android applications (one for the smart-phone and one
for the smart-watch) capable to continuously collect the datafrom the sensors, allowing the user to introduce activities
annotations.
B. Dataset acquisition
We designed the acquisition of a large dataset, in orderto obtain a reasonable variability in activity execution. The
considered activities are walking, running, go upstairs, go
downstairs, still, sitting, elevator up and elevator down. We
accurately planned four different scenarios for those physicalactivities to be executed in different locations: office, home,park and street. Having different locations it is also useful tocollect different ways of performing the same activity. Consider, for instance, the activity “walking”. This activity can be
performed differently indoor and outdoor. We asked to sixteen
different volunteers (aged between 20 and 27) to perform theactivities of a particular scenario wearing the smartwatch on
the non-dominant arm and the smartphone in the pants front
pocket (any direction, face-up or face-down). Every activity
in the scenarios has been performed for around 2 minutes(in total each scenario duration was 20-25 minutes). In order
to collect the ground-truth, the users annotated the activities
using a dedicated smartwatch application. We collected in total
6 hours of sensor data. While performing the activities we also
had active on the smartphone an app based on Google activity
recognition APIs.
C. Results
1) Evaluating the context impact: In Table I it is possible
to analyze the comparison between using or not the context
information to refine the statistical classification. The metrics
indicated with the letter C are computed by consideringcontext (the semantic location). The influence of the symbolic
TABLE IC OMPARISON OF RECOGNITION WITH AND WITHOUT CONTEXTREFINEMENT

Activity
Riding Bicycle
Walking
Running
Going upstairs
Going downstairs
Still
Sitting
Elevator
Total Average

Precision
0.98
0.87
1.00
0.73
0.72
0.93
0.97
0.94
0.91

Recall
0.92
0.85
0.99
0.80
0.71
0.98
0.99
0.73
0.86

F1
0.95
0.86
0.99
0.77
0.72
0.95
0.98
0.82
0.88

Precision C
0.99
0.89
1.00
0.82
0.85
0.94
0.97
0.90
0.91

Recall C
0.94
0.92
0.99
0.77
0.76
0.97
0.99
0.82
0.89

F1 C
0.96
0.90
0.99
0.78
0.80
0.95
0.98
0.86
0.90

reasoning module is clearly most relevant for those activities
which are strongly related with a location (for instance using
elevator is very unlikely when being at the park). The improvement in recognizing “walking” is due to the fact that
it is often confused with “going upstairs” activity. Since we
improved the recognition of the latter, we also recognize betterthe former.
2) Comparison with Google Activity Recognition API: We
finally compare our results with the ones provided by an app
using Google APIs. For this comparison we considered thesubset of our activities which are also considered by the APIs:
walking, running, riding a bicycle, and still. Since the APIs
only rely on the smartphone, we compared with our method
excluding smartwatch sensor data. In Table II it is possible
to analyze the comparison between the results obtained with
Google Activity Recognition API (indicated with the letter G)
and the ones obtained with our approach. It is easily seen that
our methods significantly reduce the number of false positives.
Overall, our method improved Google results by 10%.
TABLE IIC OMPARISON OF OUR METHOD WITH G OOGLE AR API
Activity
Riding Bicycle
Walking
Running
Still
Total Average

Precision G
0.99
0.91
0.96
0.90
0.93

Recall G
0.70
0.83
0.85
0.73
0.77

F1 G
0.82
0.87
0.90
0.81
0.84

Precision
0.88
0.95
0.94
0.97
0.94

Recall
0.97
0.95
0.96
0.97
0.94

F1
0.95
0.95
0.95
0.97
0.94

IV. C ONCLUSIONS AND FUTURE WORKIn this work we presented new experimental results on
refining statistical activity recognition using current mobile
device sensors by considering the semantic location of theuser. The results show how the contextual information ofsemantic location improves activity recognition. A preliminary
comparison with Google Activity Recognition API shows
how our approach is significantly superior for some activities.
However, the comparison results need to be validated on muchlarger datasets including a large variety of users and situations
and removing the assumption that we currently have on theuser wearing the smartphone in the pants front pocket.
ACKNOWLEDGMENTS
The authors would like to thank Chiara Mariani and Riccardo Presotto for their excellent work in software implementation and data acquisition.
R EFERENCES[1] J. W. Lockhart, T. Pulickal, and G. M. Weiss, “Applications of mobile
activity recognition,” in Proceedings of the 2012 ACM Conference on
Ubiquitous Computing, ser. UbiComp ’12. ACM, 2012, pp. 1054–1058.
[2] J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using
cell phone accelerometers,” SIGKDD Explor. Newsl., vol. 12, no. 2, pp.
74–82, Mar. 2011.
[3] D. Riboni and C. Bettini, “Cosar: hybrid reasoning for context-aware
activity recognition,” Personal Ubiquitous Comput., vol. 15, no. 3, pp.
271–289, Mar. 2011.

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POSTER SESSION 2

Mining the Air -- for Research in Social Science and Networking Measurement
Scott Kirkpatrick, Hebrew University kirk@cs.huji.ac.il
Ron Bekkerman and Adi Zmirli, Haifa University ron.bekkerman@gmail.com
Francesco Malandrino, Politecnico de Torino francesco.malandrino@polito.it
Smartphone apps provide a vitally important opportunity for monitoring human mobility, humanexperience of ubiquitous information aids, and human activity in our increasingly wellinstrumented spaces. As wireless data capabilities move steadily up in performance, from
2&3G to 4G (today's LTE) and 5G, it has become more important to measure human activity inthis connected world from the phones themselves. The newer protocols serve larger areas thanever before and a wider range of data, not just voice calls, so only the phone can accurately
measure its location. Access to the application activity permits not only monitoring theperformance and spatial coverage with which the users are served, but as a crowd-sourced,
unbiased background source of input on all these subjects, becomes a uniquely valuable
resource for input to social science and government as well as telecom providers.
The public also stands to beneﬁt. National and regional regulators tasked to ensure that
consumers are getting the communications bandwidth, coverage and capability that were
advertised and they paid for, are beginning to use crowd-sourced measurements from the edge
to provide public "report cards" of communications quality. We have been working with data
captured by applications based on the phones, authorized by their users to capture location
information and share it to build a public database of internet access performance. We haveused most extensively results from an Israeli startup called WeFi, which observes the categoryof application in use during some of its measurements and determines upload and download
data volumes and rates. We have, in all, about 3 billion measurements from ﬁve US cities andtheir surroundings, Atlanta, Boston, Brooklyn, Los Angeles and San Francisco, for several
months in each location during 2014 and 2015.
Our data has been presented in several publications that address issues in mobile network
planning and management. One surprising result is the range over which each cell antenna is
received. Earlier studies in which the data source is a carrier have used the cell tower locations
as a proxy for user location. In this study, which sees all carriers in each city, we ﬁrst estimatedthe locations of the cell towers, which were named in each measurement record, as the centroidof all the observations where a tower was seen. Cell dimensions of several km are observed.
By contrast, the phone locations are known to GPS accuracy, with errors as little as 10m.

Fig. 1. Number of LTE and 3G cells deployed by each operator (a); distance from user to cell
tower in Boston (b) and Brooklyn (c). Solid lines are LTE connections, dashed lines are 3G.

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Extended Abstract

POSTER SESSION 2

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NetMob 2017

In this report we use unbiased observations made in the background in the course of the daily
lives of over a hundred thousand people in and around Los Angeles, sampling roughly 1 percent of the population, from all economic levels and demographics. The WeFi application
monitors location, data connections, and application usage on Android smartphones, but does
not capture any content exchanged or any information relating to phone calls. Personallyidentifying information is removed from the data by hashing the identiﬁers of the phones. We
observe the activities of individual phones, but collect them into aggregated communities
sufﬁcient in number to prevent re-identiﬁcation of individuals. One purpose of this study is tounderstand how much data is required in order to observe the social behaviors relevant to wellfunctioning cities. One observation is that the more data we can consider, the ﬁner the scale
which we can study without danger of compromising individuals' privacy.
Measurements based on the cellphone can occur whenever there is activity, either data transfer
or motion of the user, and thus are much more frequent than the monitoring normally seen indatasets which record the metadata of phone calls. In mobile CDRs and related carrier data thetowers see each phone typically 5-20 times a day, with phones in cars seen more often as theychange towers during a call. Some of the WeFi data is taken much more frequently. The
Android operating system allows the WeFi app to request a measurement when the phone
position changes by as little as .0001 in the Lat or Lon coordinate, or on any change in thesystem's connectivity. As a result we are presented with position information as often as morethan a thousand times an hour.
Using Location Data
In employing this data for more traditional social science ends we follow a methodology ofsuccessive reduction to isolate distinctive communities, then extract their characteristic patternsof commuting, working, shopping, and leisure activities. Our ﬁlters are simple and fairly strong.
We have used two data sets. In the ﬁrst, we observe over 131K users, recording their positionas lat/lon to a precision of .0001, with a timestamp giving days, minutes and seconds. The
835M measurements in this data set take up about 20 GB. We refer to this data as the location
data set. It was gathered during Marchthrough May 2015,. The second data set,
3 months ofcollected in February, 2015 consisted of 130
locality data(> 131k users)
GB with 422M measurements, eachcontaining location information plus details of
the applications in use, the data connection
Consistent data(> 57k users)
used, and the amounts of data uploaded or
downloaded since the previous
Home datameasurement. We refer to this data as the(> 51k users)
application data set. Because thesemeasurements were grouped an hour at a
LA residents
Suburbanites(> 10k users)(> 36k users)
time to simplify their retrieval, the timestamps were only given to the precision of an
LA suburban
LA neighborhood
census data
census data
hour. The application data set contained
10k rich users (>$75k)
over 91K distinct users. Each user is only
10k poor users (<$45k)
known to us by a random hash of themachine identiﬁcation of their Android phone.
The same hash function was used in≥ 30 days of data
≤ 36 hours of gaps

Same location≥ 2 hours a night≥ 15 nights

Google Maps API

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Extended Abstract

3

POSTER SESSION 2

NetMob 2017

gathering the two data sets, so we can determine that over 67K of the UIDs (the user's
randomized identiﬁer) are present in both data sets. This allows us to combine information
gathered in each of these two ways. We start our characterization of interesting communities
with the location data set.We reﬁne the 57K UIDs by determining the towns or neighborhoods inwhich they reside. The criterion applied was that a residence is identiﬁed when the UID is seenat the same location to an accuracy of .001 in lat/lon two or more hours a night for 15 or morenights within our sample. For more than 51K of our consistent UIDs we can identify such homelocations. More than half of the UIDs ( >36K) live outside the city of LA proper, and are called
suburbanites in this discussion. However, the city of LA is rather porous. Thus quite a few of our
suburban districts are governed as separate towns but are contained within the city of LA. Morethan 10K UIDs reside in neighborhoods in the city proper. We also omitted some UIDs forwhich the suburb or neighborhood is not unambiguously deﬁned, or for which the residential
area population is <5000 people. This left us with 36,531 users who live in 232 independent
towns and 10,573 users who live in 83 different neighborhoods within the city.
Next, using census demographic tables of population and median income, we assign to each
UID the median income of their home district. A concern is that usage of smart phones mightskew our sample of users within the populations of each home town, but with smart phone
usage now passing 60-70% of all mobile telephone customers, we do not think this will causesigniﬁcant bias. We next identify a population of 10,094 UIDs (and their users) from the bottomof the demographics, living in areas with median income < $45K, and another 9780 UIDs,
whose users live in the wealthiest areas, with median incomes > $75K. We will distinguishthese "rich" and "poor" users when identifying further details of their activities. The two setsmake up 20.8% (the "poor" cohort) and 21.5% (the "rich" cohort) of our total sample of users.
We next determine where our users work. We again start with the users seen more than 30days, with few gaps, the consistent user set. We identify a stable daytime location, or work
location, as a place deﬁned to .001 accuracy in lat and lon, and seen for at least 4 fours per day,on at least 30 workdays.
For almost 25K users, we
can ﬁnd such locations, but
for 14K users, this was also
their home location. This
leaves us with 10,596
commuters, for whom wealso know their homelocation. We still have
21.4% of our sample ofusers to study. Of these,
2263 (or 20.9% of thecommuters) live in our
wealthier districts, thus are
members of the rich cohort,and 1902 (0r 17.5% of the commuters) live in the poorer neighborhoods. We notice that thesecond group has dropped by 4% or about one ﬁfth, in their participation, as these members of
the poorest cohort do not have ﬁxed locations in which they work during the day. The
distribution of hours worked also shows an important difference. Our richer cohort works
shorter hours than the poor cohort.

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Extended Abstract

4

POSTER SESSION 2

NetMob 2017

Adding Application Data
To ﬁnd larger populations and address larger distinctions in behavior, we looked into two things
that affect almost everyone: shopping and fast food restaurants. This required some manual
effort. Shopping malls were identiﬁed with the help of Google Maps API, and resolved into 418
shopping areas. Over 37,000 of our consistent users spent at least 10 minutes and up to 6
hours at one of our shopping areas during the study period. They averaged 1 hour and 7
minutes per visit. Similarly, MacDonald's restaurants were screened to identify 553 withoutdoor, separated locations, not inside some larger shopping center. McDonald's visitors spentfrom 5 minutes to two hours, for an average of 24 minutes in their fast food breaks. In order toknow more about who goes to these, when, and why, we need some information about theusers' interests. Here the Application data set is useful.
An indicator that serves todifferentiate populations are thedifferences between two
applications, Pinterest and Yahoo
Sports. Pinterest has been foundelsewhere to attract about 85%
female users. Frequenting Yahoo
Sports for results would seem togive a high probability that theuser is a male. Almost all of theusers we can separate in thisway are seen at some point inour data set, shopping at a mall.
The Fig at right summarizes thesizes of the communities that we
extract in this fashion. We see
1.5K Pinterest users (each ofwhom has invoked the app >100 times) and 1.6K Yahoo sports users (also calling for the latest
results at least 100 times). Many of these users, about 1000 of the likely female UIDs andalmost 700 of the sports fans were seen at one of the 418 shopping centers.
Looking at the activity patterns, we see that our female shoppers are seen at the malls almost
three times per week, while the males appear less than twice per week. In the longer work fromwhich these examples are drawn
we also separate younger andolder users, analyze fast foodconsumption, commuting times
and distances.
We have been able to resolve
our data down by two and eventhree levels of ﬁltering, but only
because we have a lot of it.
Imagine how much could besafely learned if all phones
contributed to this information!

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POSTER SESSION 2

User Authentication with Neural Networks Based on CDR Data
Dominik Filipiak

Bartosz Perkowski

Agata Filipowska

Department of Information Systems, Poznań University of Economics and Business
The paper describes an attempt to measure the performance of neural networks in the
behaviour-driven user authentication task. The goal of this experiment was to minimise theloss of a binary classifiers and check how well neural networks perform while authenticating auser based on his actions, namely calls and messages exchanged).

Introduction

Mukhopadhyay, 2015). Recently, researchers’ attention isdrawn by an emerging field of behavioural profiling, which

It is possible to identify various patterns based on a fre-can be described as a dynamic profiling approach. The num-quent behaviour for active users. Daily call duration, a num-

ber of domains in which such methods are applicable is con-ber of text messages sent, BTS stations visited etc. define astantly growing. The aforementioned research activities canuser profile. However, are these features enough to tell an

be noticed in the domain of online communities (Fernandez,

identity of a given user? How many activities are needed

Scharl, Bontcheva, & Alani, 2014), social networks (Abel,to perform a robust authentication? These questions con-Gao, Houben, & Tao, 2011; Xu, Zhang, Wu, & Yang, 2012)

stitute the fist research problem addressed within the paper.

or telecommunication (Hohwald, Frías-Martínez, & Oliver,

The second research problem concerns the minimal timespan2010).

needed for a robust user authentication (the shorter required,

Neural networks have already been used to solve various

the better). This paper describes an attempt to measure theproblems with authentication of users. Bagnall (2015) pre-performance of neural networks applied for these tasks. The

sented an approach, which helps solving the problem of au-

goal of this experiment was to minimise the loss of a binary

thentication of authors in documents of an unknown author-

classifier and check how well neural networks perform withship. Another example of employment of neural networks

a different day granulation while authenticating a user. The

is user authentication in the Tor network (Ishitaki, Oda, &higher granulation is, the faster an anomaly can be detected,

Barolli, 2016).

i.e. after shorter time, what is desirable.
The problem of user authentication in various domains(e.g. telecommunication, social networks) relates to profiling. It may be associated with Knowledge Data Discovery
model – many steps in KDD model resemble stages associated with the user profiling process (Kanoje, Girase, &In this paper, we present an approach to the user authentication based on CDR data mimicking data that may be inferred from phone logs using neural networks.
Methodology and Results
The goal of the presented experiment was to check
whether it is possible to develop classifiers capable of authentication of different users based on call and message logs.

dominik.filipiak@ue.poznan.pl

Each classifier was tested against authenticating a single user

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57

POSTER SESSION 2
2

DOMINIK FILIPIAKBARTOSZ PERKOWSKI

behaviour in a different day granulation setting.

AGATA FILIPOWSKA

replaced by more recent Adam optimiser (Kingma & Ba,

Data. The sample used in the experiment was a Call2014). Each neural network has six layers, each of which is

Detail Records database, containing actions performed bya densely connected layer (which means that every output of

100,000 users in six consecutive months. For an average

the layer is connected with each input in the next layer). The

user, the mean number of activities performed is 1150. There

first layer is an input layer with 10 neurones with Rectified

are three groups of users: these with an average activity

Linear Unit (ReLU) activation, each of which corresponds(650-1650 actions, 50 randomly chosen users), high activity

to a specific input variable (a single daily, weekly, monthly(1650-5000 actions, 50 randomly chosen users), very highstatistics related number). The second layer has 30 neurones

activity (20 randomly chosen users from 2000 the most ac-with ReLU activation. Dropout technique (p = 0.5) wastive ones). We used 10 input variables, representing accord-

used between the first and second layer. The third one has

ingly: time of the day, total number of calls, total calls’

50 neurones with ReLU activation. The fourth one has 30

duration, number of text messages, number of visited baseneurones with Sigmoid activation. The fifth one consists oftransmission stations (BTS), number of BTS used when call-

50 neurones with Sigmoid activation. The output layer has

ing, number of BTS used when texting, number of different

only a one neurone, which value ranges between 0 and 1.

contacts, number of contacts called, and number of contacts

The closer to 1, the more probable is the data sample is true

texted. The last position in a vector represents whether a(which means). Each model was trained during 250 epochs

given record is a true one or a fake one. Since our data sample

with 10 items in one batch.

consists of (arguably) true records, we came up with a simple

Results.

We have tested different 960 models (8 differ-idea to extend our dataset and enrich with fake records. To

ent granulation times for 120 users in three groups). As it

produce fake data, we simply randomly swapped user labels

turned out, the Dropout technique significantly reduced over-and combined original daily actions with signatures from an

fitting, since the difference between training and test accu-

another user. A standard approach in the dataset division wasracy is relatively small. Increasing the d value generally re-used: for the each user and a given granulation, 70% goes tosults in a drop in accuracy (see Figure 1). Whereas the best

the training sets, whereas the rest is for the testing process. Inscore is reached for d = 1 (no day partitioning), the othercalculations, days were partitioned to equal parts in several

scores smoothly drop, with no strong tendency to plummet

manners (d = {1, 2, 3, 4, 6, 8, 12, 24}). For example, d = 1

in accuracy. It is worth to mention that the fake data aremeans no granulation at all. All the hours of a given day are

made-up and the results might be different in the real world

placed in a single data row. In other example, d = 3 means

usage. On the other hand, such data are extremely hard to

dividing day into three equal parts with regard to hours (0-8,obtain in the appropriate amount and this is perhaps the only

8-16, 16-24).

possible option. However, taking the results into account the

Methodology. The algorithm behind every model isdata need to be combined e.g. with the data on user trajecto-

Feed-Forward Artificial Neural Network with Backpropaga-ries or phone environment to enable for authentication withtion as a learning model and the sigmoid and ReLU acti-

a higher level of confidence.

vation functions, Dropout (Srivastava, Hinton, Krizhevsky,

References

Sutskever, & Salakhutdinov, 2014) and cross-entropy costfunction. Classic Stochastic Gradient Descent algorithm was

Abel, F., Gao, Q., Houben, G.-J., & Tao, K. (2011). Analyzing user

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58

POSTER SESSION 2
3

0.85

USER AUTHENTICATION WITH NEURAL NETWORKS BASED ON CDR DATA

0.60

0.65

0.70

0.75

Mean accuracy

0.80

Very high activity
High activity
Average activity

1

2

3

4

6

8

12

24

Granulation

Figure 1. The difference between mean testing accuracies for different granulation (d) values in the tested activity groups
Table 1
Mean accuracy scores for different granulation (d values)
User activity group

Hohwald, H., Frías-Martínez, E., & Oliver, N. (2010). User modeling for telecommunication applications: Experiences and practical implications. In International conference on user modeling,

d

Avg.

High

V. High10.69390.75000.83862

0.68510.72130.7725

30.66460.70280.7631

man test. In 2016 30th international conference on advanced in-

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0.65880.68300.7507

formation networking and applications workshops (waina) (pp.

60.64050.69000.73037–13).

8

0.63330.65940.7073120.64430.66240.7062

profiling trends, techniques and applications. arXiv preprint

24

0.61380.62800.6950arXiv:1503.07474.

adaptation, and personalization (pp. 327–338).
Ishitaki, T., Oda, T., & Barolli, L. (2016). A neural network baseduser identification for tor networks: Data analysis using fried-Kanoje, S., Girase, S., & Mukhopadhyay, D.

(2015).

User

Kingma, D., & Ba, J. (2014). Adam: A method for stochastic
modeling on twitter for personalized news recommendations. In
International conference on user modeling, adaptation, and personalization (pp. 1–12).
Bagnall, D. (2015). Author identification using multi-headed recurrent neural networks. arXiv preprint arXiv:1506.04891.

optimization. arXiv preprint arXiv:1412.6980.
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., &Salakhutdinov, R. (2014). Dropout: a simple way to preventneural networks from overfitting. Journal of Machine Learning
Research, 15(1), 1929–1958.

Fernandez, M., Scharl, A., Bontcheva, K., & Alani, H. (2014).

Xu, Z., Zhang, Y., Wu, Y., & Yang, Q. (2012). Modeling user

User profile modelling in online communities. In Proceedings

posting behavior on social media. In Proceedings of the 35th in-of the third international conference on semantic web collabo-

ternational acm sigir conference on research and development

rative spaces-volume 1275 (pp. 1–15).

in information retrieval (pp. 545–554).

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POSTER SESSION 2

Analysis of Tourist Activity from Cellular Network Data
Marco Mamei

Massimo Colonna

marco.mamei@unimore.it

massimo.colonna @telecomitalia.it

Dipartimento di Scienze e Metodi dell’Ingegneria
University of Modena and Reggio Emilia, Italy

1.

Engineering & Tilab
Telecom Italia, Italy

INTRODUCTION

In the following we better describe our training approach and some experimental results.

In this work, we explore the use of anonymized Call
Detail Records (CDRs) from a cellular network to classify users according to different mobility-behavioral profiles (i.e., residents, commuters, user in-transit, tourists
and excursionists). Our focus is primarily on the touristclass and in estimating the tourist presence in an area
over a specific period of time.
A number of previous works addressed tourists identification via the analysis of the SIM card registration
country and on the basis of the number of days spentin the region [1]. More recent approaches try to detectalso national tourists by the classification of their behavioral patterns [2]. In general, classification of users
from CDR data is problematic since groundtruth classes
are completely missing. Therefore, supervised learning,in which the system learns a classification mechanism
from training samples, cannot be applied directly. To
tackle this issue two approaches are possible:

2.

METHODS

We analysed anonymized CDR data comprising SMSs
and calls exchanges from multiple tourist cities in Italy.
In this abstract we focus on results obtained in Venice.
We monitored Venice province (800’000 inhabitants)
with the goal of classifying users in Venice historic center (60’000 inhabitants). The monitored period consistsof the months of July 2013 and March 2014. The approximate number of CDR users in each period is 600K.
In order to set up a classification approach, we processed CDR data to extract a feature vector compactly
describing users’ pattern of visit. Specifically, for eachuser we computed features comprising: (i) whether theuser is roaming (likely to be foreigner) or not, (ii) number of days in which the user generates at least one
CDR, (iii) number of days in which the user generates
at least one CDR from the city (i.e., excluding events
generated in the city’s neighborhood), (iv) number ofnights, (v) number of weekends, (vi) max gap in hours
between two subsequent CDR generated in the city
Then we assign profile labels to a subset of the users
on the basis of domain knowledge (this will form ourtraining set). Then to train a classifiers on this data
and to extend the labeling to all the other users.
Focusing on the standard classes adopted in the analysis of tourists’ and visitors’ behavior (www.unwto.org),
we consider 5 main user profiles: residents: users living in the city. They show a continuous presence over
all the monitored period during the whole day, there included night hours and weekends, commuters: people
living outside of the city, but working or studying in
the city, people in-transit: people passing by/driving
though the city without stopping for a visit, tourists:
people traveling to and staying in places outside theirusual environment for leisure, business and other purposes, for a limited amount of time and excursionists:
people visiting the area for leisure, business and otherpurposes, but who does not stay overnight.
On the basis of domain knowledge, we defined the set

1. Apply a clustering algorithm to feature vectors
describing users’ behavior. Then, attach a label(e.g., tourist) to each cluster on the basis of domain knowledge. This is basically the approach
adopted in [2].
2. On the basis of domain knowledge identify classlabels for a subset of the feature vectors. Train asupervised classifier on these labeled data. Use theclassifier to assign a label to the remaining feature
vectors. This is the approach used in this work.
While the two approaches are similar from a functional viewpoint, there are some differences that areat the basis of our contribution: (i) supervised classifiers tend to produce better results than clustering
algorithms. Moreover, some algorithms (e.g., decision
trees) tend to produce more understandable decisions
than clustering approaches. (ii) From our perspective,
it is easier to identify class labels for individual users
than to entire clusters. The domain expert in chargeof assigning the labels can in fact better analyze thebehavior of individual users and establish more precise
rules for classifying some of them.
1

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POSTER SESSION 2
of rules to translate the above profiles’ descriptions incomputable rules, and assign profile labels to a subset ofthe users. For example, we defined Tourists those users
in roaming – i.e., foreigner and stay in the city for morethat 1 and less than 4 days. We deliberately set up arather stringent set of rules (e.g., there are also Italian
tourists) in order to limit the number of misclassified
users. Overall, these rules allow to assign a label toabout 30% of the users. This labeled set will be thebasis for instrumenting our classification mechanism.
We then set up a supervised classification approach.
We used the labeled instances to train and test (in crossvalidation) the classifier. We then use the trained classifier to assign a label to the users that were not classified
with the “domain knowledge” approach described in theprevious section. The classifier will generalize the rules
to label all the users.

3.

supervised mechanisms largely outperform classification
based on clustering supports the approach adopted inthis work over clustering-based approaches [2].
Analysis of descriptive statistics. In this section
we analyze the resulting classes of users obtained by
the classification. Our aim is to find in the statistics
describing those classes evidences supporting the goodness of the obtained results. We report some statisticsof the users classified as tourists in Venice in July 2013.
Figure 2(a) illustrates how the algorithm classifies
users, dividing between ITalian and FOREigner users.
Looking at this graphs, it is possible to see that ourclassifier identifies residents, commuters and people intransit mainly in Italian people (it is worth noting that
Venice geographic position very peculiar, and transit
through Venice might be biased by cruise ships that
visit Venice in the summer). Although the lack of actual groundtruth information impede sound evaluation,
the results well conform to commonsense expectations.
Something that might be puzzling at the beginningis the huge number of tourists and excursionists being
present. The wrong conclusion would be to think thaton a typical day in Venice there are more tourists andexcursionists than residents. In fact, to consider a typical day, we should divide the tourists’ and excursionists’
numbers by about 15-30 (as they typically visit the city
for 1 or 2 days), while leaving the resident column unchanged (as they are always there).
Figure 2(b) illustrates the number of days of presencein the area for a given percentile of tourists. It is possible to see that the majority of tourists remains in thearea for less than 2 days, associated to a typical tourist
destination (official statistics estimate that tourists stayin the city for 2-3 days).
Figure 2(c) illustrates tourists composition by nationality. It is possible to see that although Italians are themajor cut (15%), the remaining 85% of the tourists arelikely to be foreigner.
An important aspect of these statistics is that they
well illustrate the generalization effect of the classifier.
For example, there are indeed Italian tourists and tourists
staying in the city for more than 3 days (contrarily togroundtruth rules).
In another analysis we report experiments cross analyzing users from different months and regions. Figure2(d) compares users from Venice in July 2013 to users
from Venice in March 2014. We try to analyze how
many users of a given class from July 2013 are in thedata also in March 2014. The histogram shows the percentage of users of a given class who are in the city also
in the other month. The main assumptions is that most
tourists in July 2013 will not visit Venice again in March
2014. Vice versa residents in July 2013 will remain residents also in March 2014. Results are roughly in line

EXPERIMENTS

Although the lack of actual groundtruth prevents soundevaluation of our framework, we conducted several experiments to partially evaluate and validate our results.
Classifier Evaluation We run classification experiments on the labeled subset of the users. For computation efficiency we sampled only 5% of the labeled population to create a training set. We trained a model,
then used the trained model to classify the whole labeled population.
In Figure 1 we report results comparing different classifiers. We used 1R (i.e., one-level decision tree) as abaseline. Then, we compare C4.5, logistic regression,classification via clustering (using k-means - with 5 clusters - as the clustering algorithm, and also using selforganizing maps - SOM as in [2]). It is possible to seethat even the baseline produces good classification results, and more sophisticated algorithms like C4.5 andLogistic regression further improve on that. Viceversa,
classification via clustering do not produce good results.
On the basis of this result, in the following we adopt
C4.5 as our classification mechanism.
Three comments are important with regard to theseresults: (i) These results are obtained with an artificial
groundtruth. By setting groundtruth with a rule-based
procedure like the one described, it is not hard to obtain extremely high accuracies. (ii) These results are
obtained considering only 5% training data. So results
are at least stable across our dataset. (iii) The fact that
VeniceJul 2013
Mar 2014

C4.5
99.91
99.96

1R
91.29
91.96

Logistic
99.94
99.5

KMeans
70.83
38.74

SOM
81.58
61.72

Figure 1: Accuracy with artificial groundtruth.

2

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61

POSTER SESSION 2

cdf

number

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Figure 2: Tourists in Venice in July 2013 . (a) Daily average number of CDR. (b) Number of daysin the area. (c) Tourists by country. (d) Cross month analysis: the fraction of users from a given
profile in one setting that are also present in the other setting
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Figure 4:
Correlation between official(groundtruth - GT) data and classification
results. Pearson = 0.97

with expectations. Only a tiny fraction of July 2013 tourists and excursionists are in Venice also in March
2014. While a large fraction of residents and commuters
are still there.
A careful analysis of these results raises some issues.
Considering the “Resident” data, one might ask “Whythe resident bar is not 100%?”. These users are not in
the data from March 2014. This is rather surprising:
they were users who stayed in Venice in July 2013 foralmost a month – including nights and weekends, are
not there anymore in March 2014. This can be associated to network operator churn and to the fact thatour definition of “Resident” includes long-time staying
in the city other than persons permanently living in
the city. Probably an analysis involving a longer time
period (some months) would reduce the percentage of
missing residents.
we visualize tourist presence in the area from a spatial perspective. Figure 3(left) shows tourists presencein the area. Figure 3(right) shows tourist entry and exit
approximate location (location of the first and of thelast CDR in the area). It is possible to see that tourists
clusters their stay around Rialto and San Marco areas.
It is also possible to see tourists arrivals from Mestre
and Venice airport.

Comparison with Other Datasources. In a final
experiment, we computed the correlation that exists between the number of tourists from official (i.e., statistical offices) data and our classification. We run our
analysis over multiple cities (Venice, Florence, Turin,
Lecce) and months and compared the results (see Figure 4). On the basis of this strong correlation, it ispossible to create a linear regression model to estimate
the number of tourists on the basis of our classification. Fitting the model with least mean square errors,
we obtain: nT ourists = 5.39 ∗ estimated + 21.162 anda median relative absolute error of 27%.

4.

REFERENCES[1] R. Ahas, A. Aasa, A. Roose, U. Mark, S. Silm,
Evaluating passive mobile positioning data for
tourism surveys: An estonian case study, Tourism
Management 23 (3) (2008) 469–486.
[2] B. Furletti, L. Gabrielli, C. Renso, S. Rinzivillo,
Analysis of gsm calls data for understanding user
mobility behavior, in: IEEE Big Data, Santa Clara(CA), USA, 2013.

3

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POSTER SESSION 2

A neural embedding approach to recommender systems intelecommunication
Nikolaos Lamprou1 , Giovanna Miritello1
1

Vodafone UK

February 6, 2017
Abstract
With the advent of online retail at scale, the increasing demand for improved, personalised and relevant
product recommendations has resulted in the development of new exciting techniques in recommender systems. At heart, recommendation tasks rely on some form of similarity measure between products, customers
or both. In this work we explore how a product-to-product similarity can be learned by embedding items ina low dimensional space, without any consideration of product or customer details but solely on customer
interaction with the products. We then discuss ways of utilising the learned similarities for generating recommendations along with the suitability and limitations of the method.
Different recommendation systems have been developed over time, each with its strengths and weaknesses.
Popular methods, such as nearest neighbours or clustering rely on generating quantifiable features based on
the characteristics of either the products or the customers, such as screen size or customer age. Product-toproduct or customer-to-customer similarity can be computed using these features. However, such methods
fail to capture the impact of cultural and social influence, customer predispositions or existing trends. An
illustrative example is how unlikely a customer is to switch to a Samsung device when he possesses an iPhoneand vice versa, no matter how similar the device features are. More recent approaches, such as collaborative
filtering, address this issue by learning a low dimensional embedding of users and items simultaneously.
Implicit information about customers and products is captured in the form of latent features. In general,
however, recommendations generated by such systems are for specific customers.
The methodology to extend the notion of latent features to a latent representation of products can beborrowed from the field of natural language processing (NLP). Recent research in this field has shown thatlearning a latent representation of words using neural embedding algorithms is particularly effective in identifying word-word relations and similarities. Although word embeddings were originally introduced by Bengio
et al.[1] [2] more than a decade ago, they are one of the most exciting area of research in deep learning at
the moment.
A popular algorithm for word embeddings is the Skip-Gram with Negative Sampling (SGNS), commonly
known as Word2Vec, a neural word embedding method introduced by Mikolov et. al in [3]. This algorithm
builds the embedding using a shallow neural network that is trained to predict a word given its neighbouring
words, i.e. its context. The hidden layer of the neural network is a lower dimensional space with respect
to the entire word space. Once the neural network is trained, the word embedding is built by using the
projection into the hidden layer, where words are transformed into vectors.
This simple embedding is able to generate complex structures in which words similar in meanings and in
use are mapped close together. Moreover algebraic relations between words are surprisingly captured suchas the famous ”king - man + women = queen”.
By adapting the SGNS methodology, we construct a framework that produces embedding for items in a
latent space. The method is capable of inferring product-to-product relations without considering details
about either the product or the end users. A similar approach has been proposed before for computing
artist similarity [4] although the nature of the data available is different. Specifically, we have applied thisapproach to various telco related domains such as the mobile tariff, device and mobile application spaces,
to explore whether this technique can shed light on similarities and relations between those products thatcan be used for recommendations.

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POSTER SESSION 2

Figure 1: t-SNE embedding for the tariff vectors where
each has subsequently been coloured based on release
date. Clear clusters are observed to have formed containing tariffs released in different years.

Figure 2: The tariffs are considered separately by
release year and coloured according to price. This
demonstrates that there is an inherent substructure
within the observed main clusters.

In the context of the mobile tariff space, a word corresponds to a tariff and a sentence is made up by thehistorical transitions a customer has made between different tariffs. Using the corpus built by the overallcustomers for training the neural network, we create the embedding. Overall, 3.37 million customer transitions were observed between 519 tariffs since 2015.
In order to explore the obtained embeddings we use t-SNE, a powerful dimensionality reduction techniquefor visualising high-dimensional data. When we inspect these visualisations it becomes apparent that thevectors capture some general, and in fact quite useful, semantic information about tariffs and their relationships to one another. For example, some of the structures that emerge in the tariffs space can be related toprice band, type of contract or time since the tariff has become available (see Fig.1 and Fig.2)
Techniques from Word2Vec have been used in some of the recommendation methods to represent text
based features. For example, in [5] authors aim to make recommendation to users about which Tumblr
blogs to follow by using side features (i.e. likes, re-blogs and tags) as well as past preferences of users.
The method does not directly use techniques from natural language processing, but employ Word2Vec tocompute vector representation of tags, which are word based features. In [6] authors empirically evaluate
three word embedding techniques, namely Latent Semantic Indexing, Random Index- ing and Word2Vec, tomake recommendation. They evaluate their proposed method on MovieLens and DBbook datasets. Finally,in [7] authors apply Word2 Vecs skip-gram to recommend check-in locations from Foursquare data to thetarget users. In this case, non-textual features, namely the past check-ins of the users, are used. They
mapped the items in the datasets to textual contents using Wikipedia and used the textual contents formaking recommendation.
Our findings on telco related product spaces open up new possibilities of using this embedding technique togenerate product recommendations also in the telecommunication world.

References[1] Bengio Y., Ducharme R., Vincent P., Jauvin C. A Neural Probabilistic Language Model. Advances in Neural
Information Processing Systems 13 (NIPS’00), MIT Press, 2001.
[2] Bengio Y., Ducharme R., Vincent P., Jauvin C. A Neural Probabilistic Language Model. Journal of Machine Learning
Research 3 (2003) 1137-1155.
[3] Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and theircompositionality. In Advances in neural information processing systems, 2003 (pp. 3111-3119).
[4] Oren Barkan, Noam Koenigstein. Item2Vec: Neural Item Embedding for Collaborative Filtering.

2

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POSTER SESSION 2[5] D. Shin, S. Cetintas, and K. Lee, Recommending tumblr blogs to follow with inductive matrix completion, in Poster
Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA,
USA, October 6-10, 2014, 2014.
[6] C. Musto, G. Semeraro, M. de Gemmis, and P. Lops, Word embed- ding techniques for content-based recommendersystems: An empirical evaluation, in Poster Proceedings of the 9th ACM Conference on Recommender Systems,
RecSys 2015, Vienna, Austria, September 16, 2015., 2015.
[7] Makbule Gulcin Ozsoy, From Word Embeddings to Item Recommendation arXiv:1601.01356.

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POSTER SESSION 2

Drivers of spatial heterogeneity of HIV prevalence in Senegal: disentangling key features of humanactivity and mobility
L. Righetto, L. Mari, M. Gatto and R. Casagrandi
The human immunodeficiency virus (HIV) is still the second cause of mortality among infectious diseases at the
global scale, with an estimated 1.1 million people dying from AIDS-related illness in 2015; around 36.7 million
people are currently infected, with 2.1 new infections in 2015 (UNAIDS Global Statistics 2015). Sub-SaharanAfrica alone accounts for more than a half of these figures. Although, in the African scenario, Senegal emerges as
one of the countries where HIV has been controlled quite successfully, the spatial variability of the prevalence of
this disease is still remarkable. For this reason, in particular, it would be important to single out the main driversof the diffusion of HIV in the country, including socio-economic, behavioral and cultural factors, as well as
human mobility patterns.
In terms of the characterization of spatial processes and social structure, mobile communication data have beenused fruitfully in the field of epidemiology. We thus aim to analyse a call detail record (CDR) dataset, acquired
via the D4D Senegal Challenge, which was organized by Orange and Sonatel in 2014. We extract an extensive setof spatially aggregated variables that may explain the variability of HIV prevalence at the Health District scale
throughout Senegal. These variables encompass several measures of user activity, but they also focus on their
spatial displacement and long-term movement. To analyze the data, we plan to use multivariate regression andfeature selection methods for problems with high multicollinearity among predictor variables (such as lasso
regression). Studies using a similar methodology have been recently performed for Ivory Coast, however they arebased on a relatively limited set of mobile communication data (CDRs from the D4D-Ivory Coast challenge2013).
In addition, we aim to use the available information gathered from the Demographic and Health Survey (DHS)
carried out by the United States Agency for International Development (http://www.dhsprogram.com/), in
order to bridge knowledge inferred from remotely acquired mobile communication cues with locally collected
data on socio-economic structure and behavior. The comparison of these two different data sets is interesting
per se, as it provides information of the relationships between communication and mobility features and sociocultural indicators at local level. When we evaluate their respective significance in defining patterns of HIV
prevalence, we also assess whether at least some of the CDR-extracted information can be actually more
informative than social, economic and cultural indicators.
To the best of our knowledge, such an endeavour has never been undertaken so far in the context of HIV/AIDS
research. Therefore, it might provide essential information on the large-scale drivers of disease transmission,
especially regarding the controversial role of human mobility in defining patterns of HIV prevalence. This might
help guide future efforts in disease control in Senegal and in other disease-stricken countries.

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POSTER SESSION 2
1

Automatic stress assessment using smartphoneinteraction data
Matteo Ciman and Katarzyna Wac
Center for Informatics, University of Geneva
1227 Carouge, Switzerland
Email: name.forename@unige.ch
Abstract—An increasing presence of stress in people’ lives has motivated much research efforts focusing on continuous stressassessment methods in individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., theyuse invasive external devices or several privacy-related information. This paper presents an approach for stress assessment based on
smartphone usage analysis, evaluated with 25 participants, with a final F-measure of 77-88% for a within-subject model and 63-83%
with a global population model.
Index Terms—Human-smartphone interaction, stress, smartphone, affective computing, mobile sensing, pervasive computing.

1

I NTRODUCTION

In the last few decades, the amount of stress experienced
by individuals in daily life has dramatically increased. Stresshas been strongly correlated with several chronic diseases
and health risks, such as hypertension and coronary arterydisease [1], cardiovascular disease [2], diabetes and obesity [3]. It is correlated with Heart Rate (HR), Respiration
Rate (RR) and Galvanic Skin Response (GSR) values [4][5]. The development of mobile wearable devices permits
to continuously and unobtrusively monitor these psychophysiological parameters of individuals, enabling practitioners to acquire objective data of their patients [6] [7] [8],without relying on self-reports that are intrinsically biased
and prone to errors [9]. However, wearable devices may be
still perceived as burden for individuals, hence not being
used in regular practice.
In this paper we present a method and associated computational model for stress assessment based solely onhuman-smartphone interaction analysis, e.g., which does
not rely on invasive wearable external sensors, but only on
the smartphone data. This method analyses data like whichkind of applications are used, for how long, what are theinteractions with the screen, etc. We involved 25 participants for a 4-weeks long data acquisition, using Experience
Sampling Method (ESM) to gather information about theirstress level. The classification models achieved an average Fmeasure of 77-88% when building a within-subject computational model, or between 63-83% when building a globalone.

2

S TRESS A SSESSMENT I N -T HE -W ILD

2.1

Methodology

We collected data about daily life routine of the participants,
to analyze their behavior in a free environment, without any
constraints or particular tasks to perform, installing a stressassessment service on their own smartphone. This service
runs in background and gathers information about all theinteractions and events that take place on the smartphone.
Collected information include:• events related to the screen (when it is turned on or off,
unlocked, etc.);• the applications used and each interaction duration;• the physical activity performed by the person (running,
walking, etc., using the Google API);• the number of touches on the screen when the smartphone is unlocked and• the value of the lightning ambient condition measured
by the phone’s light sensor.
The data collection lasted four weeks. During this month,
we used the Experience Sampling Method (ESM) [10] tocollect data about the participants’ stress condition, with anotification prompted on the smartphone asking to evaluate
stress level in a 5-Likert scale. ESMs were provided every
three hours, from 10.30 AM to 8.30 PM, asking for an
evaluation of the past three hours. In total, 25 individuals
affiliated with the Quality of Life (QoL) Living Lab [11]participated in the experiment and provided their data.
2.2

Results

We analyzed the collected data in two different ways. The
first one (Section 2.2.1) evaluated the statistical significanceof the extracted features for the different stress levels. The
second one (Section 2.2.2) evaluated the accuracy of aclassification model build with the collected data and theextracted features. For every feature, we calculated both theaverage and the standard deviation values for each 3-hours
time range. Features values were normalized with a linear
transformation inside the interval ]0, 1], to combine in the
global model values from different participants. The total
number of features was 182.
Participants that took part in the data collection provided
in total 1630 answers (over a maximum of 2800, representing the 58% of the expected value). On average, each

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POSTER SESSION 2
2

TABLE 1. D ISCRIMINATION POWER : D URATION OF OnOffScreen
EVENTS .

TABLE 2. D ISCRIMINATION POWER : D URATION OF UnlockedScreen
EVENTS

Stress Level

1

2

3

4

5

Stress Level

1

2

3

4

5

1
2
3
4
5

-

25%
-

24%
8%
-

28%
12%
6%
-

50%
50%
50%
33%
-

1
2
3
4
5

-

21%
-

15%
12%
-

33%
25%
20%
-

60%
20%
40%
25%
-

TABLE 3. D ISCRIMINATION POWER : D URATION OF THE USAGE SESSION

participant answered 65.20 ± 20.76 times. The response
rate is slightly over 50%. Even if this percentage could
appear low, it is necessary to remind that we did not
impose to participants to answer each ESM, in order toavoid stressing them, thus collecting biased answers. Eachanswer to an ESM is the evaluation of the perceived stress,
and relates to the previous three hours of the day. Each 3hours range contains different usage sessions (the number
changes depending on the behavior of the individual), andother data recorded in the files presented in the previous
section. Answers provided by participants were distributed
as follows, where 1 means “Very Relaxed” and 5 “VeryStressed”: 1=> 46.44% (757/1630); 2 => 32.76% (534/1630);
3 => 16.01% (261/1630); 4 => 4.11% (67/1630); 5 => 0.67%(11/1630).
As we can see, the collected data is strongly unbalanced.
This unbalance can reduce performances in the classificationtask, hence provide biased models. We applied the SMOTE
filter [12] to the dataset to resolve the problem. This filter,
used before training the classifier, is used to increase thenumber of instances of the less occurring classes. In thisway, we add random examples to the less frequent classes,
to obtain a final dataset that is balanced and to perform amore precise training. We applied the SMOTE filter until thedifference between the number of instances of all the classes
was less than 10. Therefore, we have added 4 instances forclass 1, 225 instances for class 2, 499 instances for class 3,689 instances for class 4 and 749 instances for class 5. The
final distribution was: 1: 761, 2: 759, 3: 760, 4: 756, 5: 760. We
defined a 3-class problem, where class 1 and class 2 werecombined in “Relaxed”, class 3 in “Normal” and class 4 andclass 5 in “Stressed”. The final distribution of the instances
was: “Relaxed”: 1296, “Normal”: 1288 and “Stressed”: 1289.
2.2.1 Statistical Analysis
The first step of data analysis aimed at understanding whichfeatures alone are statistically significant for stress conditionof the user. A two-sample, one-tailed t-test was performed
for each of the calculated features (significance value set at
p = 0.05). To obtain a broad overview of the significancepower, we evaluated all the features against all the possible
stress levels (1÷5, thus not considering the 3-class problem).
We present the results in the form of a table, where eachcolumn and each row represents a stress value. Calling i
the index for the rows and j the index for the column, thecell xij indicates the percentage of participants for whichthat particular feature was statistically significant, i.e., hada discriminatory power for stress state assessment. The
analysis was made comparing all the possible stress levels
reported by participants.
Tables 1, 2, 3 and 4 show how some features had
statistically significant discriminatory power to distinguishbetween the five stress levels. In particular, the higher

Stress Level

1

2

3

4

5

1
2
3
4
5

-

25%
-

15%
4%
-

21%
22%
17%
-

60%
40%
40%
25%
-

percentages of discrimination are achieved when analyzing statistical significance between stress level 1 and 5,
or between stress level 2 and 5. This means that, given
the extremity ranks of stress level, there is a statisticallysignificant difference on how people interact with theirsmartphone, i.e., how many times they turn on and off thescreen without unlocking it (Table 1) or how much time they
use the smartphone (Table 2 and Table 3), or for how muchtime the Social application category is used (Table 4).
2.2.2

Classification Task Analysis

We used all the features together to build a model for stressassessment. We used standard classification algorithms: Decision Tree (DT), k-Nearest Neighborhood (kNN), Support
Vector Machine (SVM) and Neural Network (NN) algorithms available with the Weka Software [13]. We compared
the results of these algorithms with the baseline calculatedusing the ZeroR classifier, which merely predicts the majority category. We used 10-Fold Cross-Validation to evaluate
the within-subject model, and Leave-One-Out for the global
one, and we calculated the micro F-measure to evaluate
the goodness of the classification model [14]. In Tables 5
and 6 and Figure 1 we present the micro F-measure of theclassification task for the 5-class and the 3-class classification
problem. All the classification performances achieved by thelearning algorithms outperformed the results of the ZeroR
algorithm baseline. The results show that for the 5-class classification problem, it is possible to assess stress level with
an F-measure on average between 77-80% for the withinsubject model, or between 69-74% for the global model.
On the other side, with the 3-class classification problem,
F-measure is on average between 86-88% for the withinsubject model, or between 63-83% for the global model.
TABLE 4. D ISCRIMINATION POWER : T IMING OF S OCIAL APPLICATIONCATEGORY
Stress Level

1

2

3

4

5

1
2
3
4
5

-

17%
-

21%
9%
-

15%
8%
23%
-

33%
67%
67%
100%
-

TABLE 5. F- MEASURE FOR THE 5- CLASS PROBLEM .
Model

ZeroR

DT

kNN

SVM

NN

Average
Global0.27 ±0.06
0.20

0.77 ±0.13
0.69

0.79 ±0.12
0.730.78 ±0.10.740.80 ±0.12
0.72

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68

POSTER SESSION 2
3

TABLE 6. F- MEASURE FOR THE 3- CLASS PROBLEM .
Model

ZeroR

DT

kNN

SVM

NN

Average
Global0.39 ±0.09
0.39

0.86 ±0.12
0.810.88 ±0.09
0.83

0.86 ±0.09
0.63

0.88 ±0.09
0.70

Figure 1. F-measure of the algorithms for all the classification models.

To create a rank of the classification algorithm and tounderstand which is the one with the best performances,a paired, one-tailed t-test was performed between all theclassification models for all the participants, both for the
5-class and the 3-class classification problem, with the adjustment of the p-value for multiple comparisons. For the
5-class problem, NN was statistically significantly better
than DT (p-value=0.004) and than SVM (p-value=0.03). All
the other models were not statistically different. For the
3-class classification problem, NN was significantly better
than SVM (p-value=0.009) and kNN was significantly better
than SVM (p-value=0.04). Looking for a generalization ofthese results, NN and kNN are the two learning algorithms
with better performances, followed by DT and SVM.
Finally, we analysed the most predictive features whenbuilding the classification models, evaluating the Information Gain Ratio. Table 7 reports the five most predictive features both for the 5-class and the 3-class classification task.
Casual, Word and Puzzle relate to mobile game applications,while Music&Audio relates to applications used to listen tomusic.
TABLE 7. M OST SIGNIFICANT FEATURES FOR THE CLASSIFICATION .
Rank

5-class problem

3-class problem

1
2
3
4
5

Casual Influence
Casual Timing Influence
Puzzle Influence
Word Influence
Word Timing Influence

Word Influence
Score sum activity
Music&Audio Timing Influence
Word Timing InfluenceMusic&Audio Influence InfluenceThis ranking shows that the type of application individuals use is strongly correlated with their stress level. Decision
Tree construction gives an idea of how stress is related tofeature values and individual’s behavior. In particular, it
is possible to notice that the higher frequency usage ofthe Word gaming application category is associated withlower stress values. The same happens with the amountof physical activity performed during the day, where asmaller amount is frequently associated with higher stressvalues. Moreover, lower Puzzle game application category
frequency, in combination with low usage of Word andCasual gaming application categories, is associated withhigher stress levels.

3

C ONCLUSIONS AND F UTURE WORKS

The presence and influence of stress in individuals’ life
strongly increased in the last years. Prolonged exposure
to stress situations can lead to several adverse effects, e.g.,
cardiovascular diseases, diabetes, and obesity. In this paper
we propose a method for stress assessment that relies only
on the analysis of how people use the smartphone, anddo not acquire personal, privacy-sensitive information. The
models reached an average F-measure between 77-88% for
the within-subject model, or between 63-83% for the global
model. Most predictive features for stress related to the useof games applications (Word, Puzzle and Casual) and Music&Audio applications. Given the conducted experiments,
we showed that it is possible to reach accurate classification for stress assessment using only interaction data andwithout relying on privacy-related or invasive information,
thus potentially increasing user acceptance. In the future,
we plan to develop a background service that continuously
and in real-time assesses stress state of the user.

R EFERENCES[1]

T. Pickering, “Mental stress as a causal factor in the developmentof hypertension and cardiovascular disease,” Current Hypertension
Reports, vol. 3, 2001.
[2] P. L. Schnall, P. A. Landsbergis, and D. Baker, “Job strain andcardiovascular disease,” Annual review of public health, 1994.
[3] M. F. Dallman, N. Pecoraro, S. F. Akana, S. E. la Fleur, F. Gomez,
H. Houshyar, M. E. Bell, S. Bhatnagar, K. D. Laugero, and S. Manalo, “Chronic stress and obesity: A new view of “comfort food”,”
Proceedings of the National Academy of Sciences, 2003.
[4] A. de Santos Sierra, C. Avila, J. Guerra Casanova, and G. del Pozo,“A Stress-Detection System Based on Physiological Signals andFuzzy Logic,” IEEE Transactions on Industrial Electronics, 2011.
[5] T. G. Vrijkotte, L. J. Van Doornen, and E. J. De Geus, “Effects ofwork stress on ambulatory blood pressure, heart rate, and heart
rate variability,” Hypertension, 2000.
[6] L. Salahuddin, J. Cho, M. G. Jeong, and D. Kim, “Ultra Short Term
Analysis of Heart Rate Variability for Monitoring Mental Stress in
Mobile Settings,” in Proceedings of the IEEE EMBS, 2007.
[7] A. Muaremi, B. Arnrich, and G. Trster, “Towards measuring stresswith smartphones and wearable devices during workday andsleep,” BioNanoScience, 2013.
[8] E. Jovanov, A. O’Donnell Lords, D. Raskovic, P. Cox, R. Adhami,
and F. Andrasik, “Stress monitoring using a distributed wirelessintelligent sensor system,” IEEE EMB Magazine, 2003.
[9] R. A. Bryant, M. L. Moulds, and R. M. Guthrie, “Acute stressdisorder scale: a self-report measure of acute stress disorder.”Psychological Assessment.
[10] R. Larson and Csikszentmihalyi, “The experience sampling
method,” New Directions for Methodology of Social & Behavioral
Science, 1983.
[11] K. Wac, M. Gustarini, J. Marchanoff, M. A. Fanourakis, C. Tsiourti,
M. Ciman, J. Hausmann, and G. P. Lorente, “mQoL: Experiences ofthe ’Mobile Communications and Computing for Quality of Life’
Living Lab,” in Proceedings of The Living Lab.
[12] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer,“Smote: Synthetic minority over-sampling technique,” Journal of
Artificial Intelligence Research, 2002.
[13] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, andI. H. Witten, “The weka data mining software: An update,”
SIGKDD Explorations, 2009.
[14] M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Information Processing andManagement: an international Journal, 2009.

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POSTER SESSION 2

A New Point Process Model for the Spatial
Distribution of Cell Towers
Ezequiel Fattori∗ , Pablo Groisman∗ , Martin Minnoni† Carlos Sarraute†∗ FCEyN, Universidad de Buenos Aires, Argentina. Email: ezequieltori@hotmail.com, pgroisma@dm.uba.ar† Grandata Labs, 550 15th Street, San Francisco, CA, USA. Email: {martin, charles}@grandata.com

Abstract—We introduce a model for the spatial distribution ofcell towers in the city of Buenos Aires (CABA). After showingthat the Complete Spatial Randomness (homogeneous Poisson
distribution) hypothesis does not hold, we propose a model inwhich each site is distributed according to a bivariate Gaussianvariable with mean given by the barycenter of its neighbors in the
Delaunay triangulation. We show that this model is suitable, andcan be used to generate a synthetic distribution of cell towers.

I. I NTRODUCTION
The analysis of mobile phone datasets, which contain information about the mobility and interactions of individuals
at unprecedented scales, is a recent field of study with published works starting in 2005 and an increasing number ofpublications from 2012 onwards [1].
An important problem of the data collection process isthe preservation of subscriber privacy. Mobile traffic datasets
contains sensitive information on subscribers, whose privacy
needs to be protected [2]. Limitations to the current approaches
to the anonymization of datasets are shown by [3].
One promising approach to avoid privacy issues is to generate synthetic datasets. Realistic records that do not containpersonal information can be freely distributed, and used byany entity needing to perform analysis [4]. In this work, wetackle the problem of generating a synthetic distribution ofcell towers (also called cell sites or base stations), which canbe used as a foundation for a synthetic mobile phone traffic
generator.
Several articles deal with the complete spatial randomness hypothesis [5], [6] to model the distribution of cellsites, mostly due to its tractability. Although, it has beenshown that this kind of point processes is not suitable inrealistic situations [7]. Typical alternatives suggest the use ofMatérn processes, determinantal processes, Poisson hard-coreprocesses, Strauss processes, and Perturbed triangular latticemodels, but few have been contrasted with real data [7].
In this article we use real data to test the homogeneous
as well as non-homogeneous Poisson point process (PPP)
hypothesis and we conclude that none of them are realistic
models. We propose a new model based on the Harmonic
deformation of the Delaunay triangulation of a PPP [8] andwe contrast the model with real data. This model turns out tobe suitable.
II. C OMPLETE S PATIAL R ANDOMNESS
The mathematical object used to model the distribution ofcell towers is a point process. A point process is a randomvariable X: Ω → N that draws finite points in each boundedregion of the space. When we face an instance of a point
process we compare it with a benchmark model which exhibits
complete spatial randomness (CSR). Given a point process X
and a subset S ⊂ R2 , N (S) denotes the number of pointsof X contained in S and µ(S) := E(N (S)) is the intensity
measure. We say that X is distributed according to a PPP ofintensity ρ: R → R≥0 if the following properties are satisfied:1) µ(S) = S ρ(x) dx for any square S = [a, b] × [c, d].
2) For any square S with µ(S) < ∞, N (S) ∼ P(µ(S)).
The number of points in S is distributed according to a
Poisson distribution with mean µ(S).
3) For any n and any square S with 0 < µ(S) < ∞,conditional on N (S) = n, the n points within S are
independent and have density ρ(·)/µ(S).
When ρ(x) ≡ ρ > 0 is a constant function we say that X isan homogeneous Poisson point process.
III. N ONPARAMETRIC E STIMATION OF I NTENSITYF UNCTIONSIn the case of a homogeneous Poisson process in a bounded
set S, there is an unbiased estimate of its intensity, the maximum likelihood estimate given by ρ̂ = N (S)/Area(S). The
case of our network is different, since a greater concentrationof cell sites is observed at least in the city centre. Whenhomogeneity is suspected not to hold, a non-parametric kernel
estimate of the intensity function should be used:kb (x − ζ)(1)ρ̂(x) =
ζ∈X

Here kb represents a volume preserving scaling of a kernel
function k (which could be a multivariate Gaussian).
This estimate is usually sensitive to the choice of the
bandwidth b, while the choice of k is less important. We
choose the bandwidth b in order to improve the estimation. The
distance ρ − ρ̂b 2 which measures the dissimilarity between
the real intensity ρ and the estimate ρ̂b is to be minimized.
A good choice for b is the one that minimizes the following
quantity:ρ(x)ρ̂b (x)dx.
(2)
L(ρ̂b ) = ρ̂b 22 − 2
R2

Since this formula involves ρ, which is unknown, it can only
be estimated. The estimator will be:1  ζ 2L̂(ρ̂b ) =ρ̂b 2 − 2ρ̂ζb (ζ)(3)|X|

BOOK OF ABSTRACTS: POSTER 5 -7 April 2017 – Vodafone Village – Milan

ζ∈X

70

POSTER SESSION 2
where ρ̂ζb denotes the intensity estimator built from X − {ζ}.
This estimator is known as Left One Out Cross Validation. The
variance and the bias of the estimator can be approximately
calculated by bootstrapping. We will settle for simply choosing
the value of b with less estimated risk.
Our experiments show that the estimated optimal value of blies around 1.6 in the chosen scale units. Fig. 1 displays threedifferent bandwidth choices for the city of Buenos Aires.

Fig. 2. Distribution of cellphone sites before and after homogenization.

Fig. 1. Contour plots of intensity function ρ̂b in logaritmic scale.

IV. S IMULATION OF P OISSON P OINT P ROCESSES
By simulation of a point process, we understand the generation of an instance of the process. For the same process there
may exist various possible algorithms that simulate it.
The simplest case is the simulation of a homogeneous
Poisson process in a box. Let ρ be its intensity and [a, b]×[c, d]a box in R2 . The number of point occurrences must be firstdrawn from a Poisson distribution with mean ρ.(b−a).(d−c).
Once the number n of occurrences is established, the process
is the result of putting together n independent draws from theuniform distribution U ([a, b] × [c, d]).
For the simulation of non-homogeneous point process, thebox is first partitioned into a grid of m×m rectangles, in eachof whom the intensity function is assumed to be approximately
constant. These rectangles form m × m independent Poissonprocesses, each of whom is simulated like the case handled
above.
V. C OMPLETE S PATIAL R ANDOMNESS T ESTING
Testing the CSR hypothesis is an important part of theanalysis. If the hypothesis is accepted, then it is not possible
to find interesting interactions between the points based on thegeometry observed. This analysis provides also information on
the direction of the deviation from CSR.
In our case, we suspect that the process of cell sites
is not CSR, because it has a clear non-homogeneity. It is
still necessary to determine if the non-homogeneous Poisson
hypothesis holds. Under the Poisson hypothesis, if we apply
a thinning transformation to the process, the resulting process
should be Poisson homogeneous, that is, CSR. Then we canapply a test for CSR, and if the test rejects CSR for thetransformed data, then it also rejects the Poisson hypothesis
for the original data.
A thinning transformation consists of a random drop of
points of the process. A function p(x) with range [0, 1] isdefined, and each point x is dropped with probability 1−p(x).

A thinned Poisson process is also a Poisson process. If theintensity of the original process is λ(x), the new intensity
becomes λ(x).p(x). So that if we apply p(x) = 1/λ(x),assuming λ(x) > 1 everywhere, the process becomes homogeneous.
Many statistics for testing CSR are available, one is Ripley’s
K function. The K function is defined as K(t) = λ−1 E[N0 (t)]where N0 (t) represents the total count of points of X at
distance less than t from the origin, which in the Poisson
homogeneous case should be the same as Np (t) for any p.
To correct biases caused by edge-effects, we use the following estimate due to Ripley:
1 −1I(rij < t)wij(4)
K̂(t) =λn ij=iwhere w(x, r) stands for the proportion of the disk D(x, r)
contained in S, and wij represents w(xi , rij ). The K functionof our thinned process is compared with the (theoreticallycalculated) expectation of a Poisson homogeneous process.
Our experiments show that the process lacks small distances
between points, which indicates a departure from the Poisson
hypothesis. Our process shows repulsion between points.

VI. H ARMONIC D EFORMATION OF D ELAUNAYT RIANGULATION
Ferrari, Groisman and Grisi proved the existence and constructed a point process model that fits well the distribution ofcell sites [8].
Let X be an homogeneous PPP in R2 . The Voronoi cell
associated to each point p of X is the region made up of all
points in R2 which are closer to p than to any other point of
X. The Voronoi neighbors of p are all the points of X ownersof the adjacent cells. The Delaunay triangulation of X is thegraph built from all points of X and all edges made up ofpairs of Voronoi neighbors.
Let H : X → R2 be a function such that for each p in
X, H(p) is located at the barycenter of {H(q)|q ↔ p}. Hereq ↔ p denotes that p and q are Delaunay neighbors. Such afunction is called a “Harmonic function”, and its existence wasproved in [8]. It represents a position change made to every
point of X such that every point is relocated at the barycenterof its, also relocated, Delaunay neighbors (as illustrated by
Fig. 3).

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POSTER SESSION 2
every x ∈ X, conditioned on the position of its neighbors, theposition of x is Gaussian with mean the barycenter µ of theneighbors and variance .
Fig. 5 displays the final result and assessment via the
Ripley’s K function. As can be seen, there is no significant
deviation between the K function of the actual distribution ofcell towers and the final simulated model.

Fig. 3. Delaunay triangulation of an homogeneous PPP (left) and its harmonic
deformation (right). The star indicates the origin (left) and H(0) (right).

We simulate a non-homogeneous PPP according to ourestimated intensity ρ̂ for the city of Buenos Aires, calculate
its Delaunay triangulation and finally apply a function similar
to H to locate every point at the barycenter of its neighbors.
Since our region is bounded, it is necessary to allow somepoints to stay fixed by H.
Which points should be kept fixed? Ultimately, we would
like to perform the harmonic deformation over our boundedregion S, without fixing any point and without altering theoriginal intensity function. Our proposed solution is therefore
to extend the intensity function smoothly over a box B
containing region S; simulate a PPP according to this intensity
in B \ S; join these points with the ones in S; and finallyperform an harmonic deformation in B leaving the points in
B \ S fixed.
In order to smoothly extend the intensity function, we divide
the box B into a grid and assign to each intersection of gridlines a variable ρij . We generate and solve a system of linear
equations to obtain the extended function (see Fig. 4).

Fig. 5. Example of the final model (left). Upper and lower envelopes of
Ripleys K function for 100 simulated (and homogenized) processes from theharmonic deformation with noise model (right).

VII. C ONCLUSIONS
Using real cell tower location data, we showed that a Poisson point process is not a realistic model for the distributionof cell sites in cellular phone networks. We proposed analternative point process which fits the real distribution.
We used real data for the location of cell towers in the city of
Buenos Aires to show that this model is adequate. This model
can be used to generate a realistic spatial distribution of cellsites, or to simulate the growth of the network. The generated
distribution contains no sensitive or proprietary information,and can thus be freely shared with research groups, fostering
further research on the subject.
R EFERENCES

Fig. 4. Smooth extension of the intensity function to a box containing thecity of Buenos Aires.

The harmonic deformation can be achieved by an iterative
procedure. First the Delaunay triangulation is calculated for thewhole box B. Secondly the points within S are copied into asequence. We iterate over this sequence, in each step relocating
the present element at the barycenter of its neighbors. This
iteration is performed until a prescribed degree of convergenceis achieved.
This model is better than the Poisson model but not realistic
enough. In the actual distribution, each cell tower is not locatedexactly at the barycenter of its neighbors. In order to makethe model fit better, we add a noise parameter  so that for[1] Diala Naboulsi, Marco Fiore, Stephane Ribot, and Razvan Stanica. Mobile
Traffic Analysis: a Survey. PhD thesis, Université de Lyon; INRIA
Grenoble-Rhône-Alpes; INSA Lyon; CNR-IEIIT, 2015.
[2] President’s Council of Advisors on Science and Technology. Big Data
and Privacy: A Technological Perspective. Technical report, Executive
Office of the President, 5 2014.
[3] Yves-Alexandre de Montjoye, César A Hidalgo, Michel Verleysen, andVincent D Blondel. Unique in the crowd: The privacy bounds of humanmobility. Scientific reports, 3, 2013.
[4] Eduardo Mucelli Rezende Oliveira, Aline Carneiro Viana, Kolar Purushothama Naveen, and Carlos Sarraute. Measurement-driven mobile
data traffic modeling in a large metropolitan area. In Pervasive Computing
and Communications (PerCom), 2015 IEEE International Conference on,
pages 230–235. IEEE, 2015.
[5] Martin Haenggi, Jeffrey G Andrews, François Baccelli, Olivier Dousse,
and Massimo Franceschetti. Stochastic geometry and random graphs
for the analysis and design of wireless networks. Selected Areas inCommunications, IEEE Journal on, 27(7):1029–1046, 2009.
[6] Martin Haenggi. Stochastic geometry for wireless networks. Cambridge
University Press, 2012.
[7] Anjin Guo and Martin Haenggi. Spatial stochastic models and metrics
for the structure of base stations in cellular networks. Wireless Communications, IEEE Transactions on, 12(11):5800–5812, 2013.
[8] Pablo A Ferrari, Rafael M Grisi, and Pablo Groisman. Harmonic
deformation of delaunay triangulations. Stochastic Processes and their
Applications, 122(5):2185–2210, 2012.

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POSTER SESSION 2

Evolving connectivity graphs in mobile phone data
Olivera Novović, Sanja Brdar, Vladimir Crnojević
BioSense Institute
University of Novi Sad, Serbia
onovovic@gmail.com, {brdars, crnojevic}@uns.ac.rs

1. INTRODUCTION
Connectivity graphs inferred from mobile phone data
uncover pulse of human interaction. In the recent years many
innovative applications based on this rich data emerged, suchas urban sensing, transport planning, social analysis andmonitoring epidemics of infectious diseases [1][2].
Anonymous mobile communication data from telecom
operators can be utilized for sensing activities occurring
within a city and can further fit into wider vision of smart
cities that aims at monitoring and optimizing urban
landscapes. Several studies explored mobile phone data in thecontext of urban sensing. Cici et al. analyzed aggregated cell
phone activity per unit area that allowed them to detectseasonal patterns (weekday/weekend), anomalous activities
and to segment a city into distinct clusters [3]. In anotherstudy, authors examined interactions among city inhabitantsand visitors and identified the city’s hotspots [4]. Mobile
phone data can be also used to derive city land use
information [5].
Here we utilized graph theory to study connectivity
patterns on a city scale. We focused on the dominant
backbone of networks - the most significant part of overall
communication interaction. Pairwise communication wasaggregated over spatial units of a city and one day timeintervals and analysed throughout two months period. In ourgraphs nodes are spatial units and links were drawn ifcommunication strength between units was significant. This
allows us to study the backbone connectivity graphs as
evolving structures and to examine temporal and spatial
dynamics within a city. We measured global and local graphproperties and here present a part of obtained results.
1.

DATA

Mobile phone service providers collect large amount ofdata for every user interaction. Every time a user makes
interaction using mobile phone (SMS or call), one Call Detail
Record (CDR) is created in Telecom operator database.
CDRs used in our research are provided by the Semantics andKnowledge Innovation Lab (SKIL) of Telecom Italia [6]. The
records refer to the communication inside city of Milan for atime period of two months (November and December 2013).
Telecommunication interaction between mobile phone users
is managed by Radio Base Stations (RBS) that are assignedby the operator. Every RBS has unique id, location andcoverage map that provide approximate user's geographicallocation. CDRs contain the time of the interaction and the
RBS which handled it. In available data collection CDRs are
spatially aggregated on the grid containing 10 000 cells andtemporally aggregated on time slots of ten minutes. We used
telecommunication interactions set that comprises measured

intensities between different cells. Only cells that spatiallyintersect with administrative area of Milan city were selected.
The city is divided into 88 administrative zones [7]. The mapwith zones is presented in Fig 1, where each is represented byunique id placed in its center.

Fig. 1. Centres of 88 adninistartive zones of Milan.

2. CONNECTIVITY GRAPHS
To create connectivity graphs, we further aggregatedcommunication from grid cells to 88 zones of Milan. Eachgrid cell is assigned to corresponding zone, thus our graphs
refer to zones interactions. The connectivity matrices for 61
days were made in pairwise manner. Matrix element on theposition [i, j] represents aggregated communication strength
between zone i and zone j. After creating the connectivity
matrices the filtering was performed to eliminate weak links.
The strong and weak links distinction was made by equation
1 by calculating the significance of links αij [8]:αij = 1 – (k – 1),

(1)

where α denotes significance threshold, pij is the probabilityof having link between nodes i and j, and k is the number ofnodes. In our case k = 88 and α was set to 0.05. After the
weak links were eliminated, graph structure for each day wascreated from remaining links in connectivity matrix. The
final graphs are sparse and suitable for visual analytics. We
presented links with QGIS and selected four typical graphs(Fig 2.). The first is typical weekday, where the strongest
communication links tend to appear and concentrate near city
center and maximum communication strength is much higher
than observed on the weekends. The second is the holyday(Friday 2013-11-01), where the strongest communication

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POSTER SESSION 2

Fig. 2.

Bacbone connectivity graphs between zones of Milan

links tend to disperse and the maximum communication
strength is very low. The third graph presents typicalweekend. The strongest communication links tend todisperse across city but the maximum communication
strength is higher than on the holyday. Finally, the Christmas
day is presented in the fourth graph. Its structure is similar as
the one presented for another holiday. The strongest links are
dispersed across residential parts of the city and the overall
communication strength is low, which is typical for
holydays.
3. GRAPHS PROPERTIES
Along with visual inspection of graphs across two month
period we quantified graphs properties and did deeper
analysis of their changes during time and identified
interesting weekday/weekend distinctions. We performed
both, global and local, graphs analysis [9].
Global graph properties provide information on a global
structure and further allow comparisons among graphs. We
calculated the number of edges, maximum weight, radius,
diameter, max clique size, average clustering for all inferredgraphs. We compared global graph properties on different day
types: weekdays and weekends and discovered differences.
We observed that number of edges is higher on the weekdays

than on weekends (Fig 3). Distributions of measured
diameters unveil that weekday graphs have lower diametercompared to weekend, indicating faster information flow
during work days and larger “connectivity distance” inweekends.

Fig. 3. Distribution of number of edges and diameter in different day types

Local graph properties uncover localized patterns in thegraphs. We measured numerous local properties such as
clustering coefficient, node degree, page rank, betwenness
centrality, etc. Here we selected three nodes (Zone 1, 71 and23, see Fig 1.) and presented changes in time of betwenness

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POSTER SESSION 2

centrality (Fig 4.) and PageRank (Fig 5.). Betwenness
centrality calculates the number of shortest paths that passthrough examined node and PageRank determines therelevance or influence node in graph. Zone 1 thatencompasses city center has the largest betwenness centrality
and PageRank and we can notice clear weekday-weekend
pattern. Zone 71, has opposite pattern, it increases on
weekends and drops on workdays. The strong links involving
Zone 71 are also visible in Fig 2. during weekends andholidays. Interestingly Zone 23, that covers part of Lambrate
district has unusual jump in betweenes centrality at 15th
December, that could be due to event The Lambrate Bicycle
Film Festival. PageRank pattern for Zone 23 differs
considerably form betweenes centrality implying thatdifferent graph properties can provide complementary
information.

Fig. 5. Pagerank of zones 1, 71 and 23

REFERENCES[1]

Fig. 4. Betwenness centrality of zones 1, 71 and 23

4. CONCLUSIONS AND FUTURE WORK
Our analysis of the connectivity backbone networks in thecity provided new insights into social interactions and theirchanges across the city zones in different day types. Throughthe lenses of graph theory we discovered properties that canserve for detecting the patterns and deviations from typicalobservations. Our future work will include more graph-basedformalism in identifying strong temporally consistent links,
patterns of change and evolving graph sequences [10] as wellas unveiling underlying social pulse that is reflected in mobile
phone data.

V.D. Blondel, A. Decuyper, and G. Krings. "A survey of results onmobile phone datasets analysis." EPJ Data Science vol. 4, no. 1, 2015.
[2] D. Naboulsi, M. Fiore, S. Ribot, S and R. Stanica,. Large-scale mobile
traffic analysis: a survey. IEEE Communications Surveys & Tutorials,
vol. 18, no.1, pp. 124-161, 2015.
[3] B. Cici, M. Gjoka, A. Markopoulou, and C. T. Butts, On thedecomposition of cell phone activity patterns and their connection withurban ecology. In Proceedings of the 16th ACM International
Symposium on Mobile Ad Hoc Networking and Computing, pp. 317326, June 2015.
[4] P. Bajardi, M. Delfino, A. Panisson, G. Petri & M. Tizzoni. Unveiling
patterns of international communities in a global city using mobile
phone data. EPJ Data Science, vol. 4, no. 1, 2015.
[5] T. Pei, S. Sobolevsky, C. Ratti, C., S. L. Shaw, T. Li, and C. Zhou. A
new insight into land use classification based on aggregated mobile
phone data. International Journal of Geographical Information Science,
vol. 28, no. 9, pp. 1988-2007, 2014.
[6] G. Barlacchi et al. “A multi-source dataset of urban life in the city of
Milan and the Province of Trentino,” Scientific data, vol. 2, 2015.
[7] Milan's public data. http://dati.comune.milano.it/, September 2016[8] M. Á. Serrano, M. Boguná, & A. Vespignani, Extracting themultiscale backbone of complex weighted networks. Proceedings ofthe national academy of sciences, vol. 106, no. 16, pp. 6483-6488,
2009.
[9] L. D. F., Costa, F. A. Rodrigues, G. Travieso, and P. R Villas Boas..
Characterization of complex networks: A survey of measurements.
Advances in physics, vol. 56, no. 1, pp. 167-242, 2007.
[10] A. Kosmatopoulos, K. Giannakopoulou, A.N. Papadopoulos, and K.
Tsichlas, An Overview of Methods for Handling Evolving Graph
Sequences. In Algorithmic Aspects of Cloud Computing, pp. 181-192,
2016.

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1

Abstract: Using Mobile Phone Signalling Data For
Estimating Urban Road Traffic States
Thierry Derrmann, Raphael Frank, Francesco Viti, Thomas Engel
University of Luxembourg
E-Mail: {firstname.lastname}@uni.lu
I. I NTRODUCTION
It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks.
Among these events, not only the initiation of calls and data
sessions can be used in analyses, but also handovers between
different locations that reflect mobility. Various studies havebeen performed on Location/Tracking Area Updates, which
concern idle phones (in a disconnected state) and are usefulat larger distances, e.g. on Interstate highways [1], [2], [3].
However, there are – to the best of our knowledge – no studies
on the relationship handovers of mobile phones and traffic
states in urban environments. In this work, we investigate
if handovers can be used as a proxy metric for flows in
the underlying road network. More precisely, we show thatcharacteristic profiles of handovers within and between mobile
cell clusters exist. We base these profiles on models from roadtraffic flow theory, and show that they can estimate traffic
states in an urban context using a distinct linear relation. The
presented traffic state estimation model can be beneficial inareas with strong mobile network coverage but low road traffic
counting infrastructure, e.g. in developing countries, but also
complement existing transportation systems.
In the following sections, we will present our datasetsand the necessary mapping between both domains. Then we
introduce the clustering technique to partition both networks
jointly and finally present the results of estimating road traffic
state with this model.
II. DATASETS
A. Mobile Network Signaling Dataset
The mobile dataset contains aggregate data from 1839 3G(UMTS) cells within the country of Luxembourg, 611 ofwhich are located in and around its capital, Luxembourg City,
the region relevant to this study. More specifically, the dataconsists of:• the count of handovers between cell pairs per hour
• the count of calls initiated from each cell per hour
This data was made available for a single week at the endof September 2016. We organize the handovers in a handover
matrix, i.e. the weighted, directional adjacency matrix of 3G
cells in the study area.
B. Floating-Car Dataset
As ground truth data, we use Floating-Car Data (FCD)
collected during the same study week. This is a set oftime-stamped location updates and travel speeds which wascollected in the area of Luxembourg City and its highway
ring, and consists of 600 trips and 220000 location updates.
In particular, we are interested in traffic states, i.e. the ratiov
between actual speeds and the speed limit ( vmax).
C. Mapping FCD to the Mobile Network
In order to enable the use of FCD for validation purposes,
we need to map the most likely associated mobile network cellto each GPS location entry. We can easily find each location
entry’s nearest cells by distance. Usually, this is a set of cells,
as multiple cells are co-located at a common base station site.
From an FCD trajectory, we can thus identify a sequenceof these sets of potentially associated cells, matching to theroad path taken. Now, in order to identify the single, most
likely visited cell sequence, we use the handover matrix. We
choose the most frequent cell transition to be the likely cellpair visited, thus building a chain of visited cells over the entire
trip. This means that we get a single likely associated cell foreach Floating-Car Data entry, i.e. the cell that the car could
have most probably been connected to at its location. This
allows to compute road traffic states relative to the connected
cell.
III. C LUSTERING
We want to consider internal and exiting handovers fordifferent partitions of the mobile network, and thus have topartition it into mobile cell clusters. In previous work [4],spectral clustering was identified as an adequate method
for partitioning the handover matrix into densely connectedclusters.
Ji et al. have shown in [5] that partitioning by normalizedgraph cuts is a valid starting point for finding homogeneousroad network partitions. Spectral clustering is a relaxation of
normalized cuts, and was effective for clustering the mobile
network in a previous study [4] that we performed on simulated handover data.
In this work, we apply spectral clustering to the handover
matrix (i.e. the weighted adjacency matrix) of the mobile
network cells. The weights in the matrix correspond to thenumber of handovers between cell pairs. Spectral clustering
allows defining the desired number of clusters. We identified
the ideal number of clusters with respect to the error in traffic
state estimation (cf. next section).
We have found the prediction error resulting from therespective clustering to be lower for the square root of thehandover matrix, i.e. by increasing the amount of potential
cuts by bringing the weights closer together.

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POSTER SESSION 2
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IV. ROAD T RAFFIC S TATE R EGRESSION
We evaluate the predictive power of mobile network signaling data using Floating-Car Data (our ground truth). In
particular, we want to estimate for each road network partition
the traffic state variable, i.e. the ratio between the actual
observed link speeds and their respective speed limits.
We want to evaluate if there are profile functions of themobility inside and between mobile network cell clusters,
similar to Macroscopic Fundamental Diagrams (MFD) in road
networks. The MFD describes the relationship between outgoing flows and internal density of vehicles in a homogeneouspartition of a road network. Similarly, we want to make useof exiting and internal handovers of mobile cell clusters tobuild such profile functions for mobile cell clusters. Using
these functions, we then want to estimate the current degreeof saturation of the underlying road network, as more or fewer
handovers happen within clusters or across their boundaries.
The final goal then is to find a single regression equation thatuses these (previously trained) profile function of mobile cellclusters, along with each cluster’s handover and call counts,
to estimate the road traffic states. If that is possible, then we
have sufficiently characterized the mobility inside the clusters,
as the same distinct regressive relation holds for all clusters.

Figure 1: MAPE by number of clusters

A. Training set
In order to prove the above conjectures, we need to build
a training set with the road network traffic state (the response
variable) as well as each cluster’s hourly statistics and profile
function coefficients (as the dependent variables). From this,we can then estimate a global equation for all clusters relating
the inputs to the traffic state. Thus, we construct the following
training dataset using FCD and mobile network data from
Monday through Wednesday of the study week:
To each FCD entry we have associated the current traffic
state variable and most likely associated cell. This cell, in
turn, maps to a single mobile network cluster as defined by
the spectral clustering of the handover matrix. This means that
we have a mapping of each FCD entry to its respective mobile
network cluster, i.e. we can associate the response variable(traffic state) to the predictors (mobile network statistics).
For each cluster and hour, we compute:• entering, internal and exiting handovers• aggregate amount of calls emitted from this cluster• quadratic fit coefficients of the profile function• the derivative of the profile functionv, response variable)• the average traffic state ( vmaxUsing these features, we can then evaluate our approach:
We study whether the aggregate mobile network statistics andthe previously learned profile functions (i.e. coefficients ofthe convex quadratic curve) can serve as sufficient predictors
for the underlying road network traffic state. We found that aquadratic polynomial best fits the relationship between innerand exiting handovers.
B. Validation Set
The validation dataset consists of FCD data and mobile
network data from Thursday. The values for a,b and c –i.e.

Figure 2: Test set validation with 28 clusters
the profile function coefficients– are adopted as learned fromthe training dataset. They represent the learned profiles frompast observations.
C. Regression Results
As described above, we train our profile functions of eachclusters on the data from Monday through Wednesday, andevaluate them on data from Thursday. We consider 20 through130 clusters of the mobile network, all obtained by spectralclustering. We performed a set of 10 clustering and regression
runs on every cluster count, to account for the randomness ofthe clustering initialization. Fig. 1 shows the Mean Absolute
Percentage Error (MAPE) with respect to the number ofclusters in question, averaged over 10 test runs for each cluster
count. The noisy aspect of the scatter is due to the way therespective clustering partitions the road network. We havefound a local optimum at 28 clusters. Hence we will continue
our study with this value. Fig. 2 shows the scatter between
estimated and actual traffic states for the Thursday data withfor 28 clusters. Individual points represent a clusters’ mean
traffic state during an hourly time slot. The green line is theidentity line, while the blue line represents the regression trend
between prediction and true values. The proximity of bothlines indicates a good fit The MAPE amounts to 11.8, which– given the constraints of our data sets – shows that mobile

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Figure 3: Example profile functions (left) and corresponding road network partitions covered by the cell clusters (right)
network data can be used for estimating road network traffic
conditions.
D. Mobile Network profile functions
In Fig. 3, we see three example profile functions andthe underlying data scatter (left) and a map of the FCD
locations that correspond to the same clusters. We can seethat the different clusters correspond to coherent regions of
the road network. They were not yet optimized in termsof homogeneity of the underlying road network partitions.
However, the resulting clusters’ profile functions exhibit low
variance, and distinct ratios of internal and exiting handovers
that are characteristic of each region.
E. Limitations and Possible Extensions
The main limitation of this work is the temporal aggregationof the mobile phone data set (1 hour granularity). However,
we believe that the results above are sufficiently good toshow that traffic state estimation is feasible using only mobile
phone data. In particular, there is room for improvement of
the predictions, using more fine-grained data, a longer training
period and multiple radio technologies (2G, 3G and 4G).
Another limitation is the low degree of congestion in thedata, which prevents us from directly comparing our profile
functions to other models. Generally speaking, the functions
follow the free-flow and ’sweet-spot’ parts of a Macroscopic
Fundamental Diagram (MFD), as known from traffic flow
theory [6]. However, for a lack of very severe congestion in theroad network, we do not observe the negative slope of the flow
ratio characteristic of grid-lock and spill-back phenomena.
V. C ONCLUSION AND FUTURE WORK
We have shown that profile functions of partitions of mobile
networks exist, and that they exhibit predictive power for
estimating the road network traffic state. Future work consists in evaluating whether or not these profile functions andMacroscopic Fundamental Diagrams possess a theoretical link,
and if that link holds for extreme congestion conditions, i.e.
grid-lock. This could be studied further in a simulation setting.

We made a first step in this direction with our work in [4], but
a more extensive study and comparison to FCD-based MFDs
is necessary.
As mentioned above, there are various extensions anddirections for future work regarding the results we found. We
believe that by using multiple radio technologies and finergrained data can lead to better models of the underlying topology.Further improvements can be expected from improved
clustering algorithms of the mobile network that lead to morehomogeneous road network partitions.
The goal of this work was to get a single regression equation
mapping to all the clusters, in order to show that mobile
network clusters have characteristic profiles with predictive
power. We have shown that this is indeed possible, and havereached a MAPE of 11.8% with respect to the true traffic
states. It is possible to improve the resulting error by using
fixed temporal effects for the typical daily road patterns, moreradio access technologies and longer periods of data. Finally,
comparing mobile network profile functions directly to theunderlying road network fundamental diagrams would be ahelpful next step, because this would tighten the theoreticallink between both domains.
R EFERENCES[1] A. Janecek, K. A. Hummel, D. Valerio, F. Ricciato, and H. Hlavacs,“Cellular data meet vehicular traffic theory: Location area updates
and cell transitions for travel time estimation,” in Proceedings of the2012 ACM Conference on Ubiquitous Computing, ser. UbiComp ’12.
New York, NY, USA: ACM, 2012, pp. 361–370. [Online]. Available:
http://doi.acm.org/10.1145/2370216.2370272[2] K. Hui, C. Wang, and A. Kim, “Investigating the use of anonymous
cellular phone data to determine intercity travel volumes and modes,”2017.
[3] H. Bar-Gera, “Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from israel,”Transportation Research Part C: Emerging Technologies, vol. 15, no. 6,
pp. 380–391, 2007.
[4] T. Derrmann, R. Frank, and F. Viti, “Towards estimating urban macroscopic fundamental diagrams from mobile phone signaling data: A
simulation study,” 2017.
[5] Y. Ji and N. Geroliminis, “On the spatial partitioning of urban transportation networks,” Transportation Research Part B: Methodological, vol. 46,
no. 10, pp. 1639–1656, 2012.
[6] N. Geroliminis and C. F. Daganzo, “Existence of urban-scale macroscopic
fundamental diagrams: Some experimental findings,” Transportation Research Part B: Methodological, vol. 42, no. 9, pp. 759–770, 2008.

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POSTER SESSION 2
3.3.1. Evaluation in predictive accuracy

f04
f05
f03
f02
f00
f23
f01
f08
f16
f06
f17
f07
f22
f10
f09
f18
f15
f12
f19
f11
f21
f13
f14
f20

We conducted five-fold cross validation. For each fold,randomly selected examinees are tested. The test data arelabelled data of them and the training data are labelled dataof the rest of examinees.
Evaluation metrics are precision, recall and F measuredefined as follows.
Precision =#Corretly_Predicted_and_Labelled_Significant_Places#Extracted_Significant_Places#Corretly_Predicted_and_Labelled_Significant_Places
Recall =#Labelled_Significant_Places
F measure =2 ∙ Precision ∙ Recall
Precision + Recall

0

accuracy of 3-7th class is still low.
Table 1. Results of accuracy for each stay purpose
Method b) classify home or0

Recallmeasure

4

6

8

10

Fig 2.2. Ranking of important featuresFmeasure

1

0.788

0.898

0.839

0.814

0.877

0.845

2

0.646

0.800

0.715

0.754

0.714

0.733

3

0.000

0.000

0.000

0.082

0.020

0.032

4

0.083

0.002

0.005

0.185

0.163

0.173

5

0.000

0.000

0.000

0.125

0.035

0.055

6

0.000

0.000

0.000

0.150

0.317

0.204

7

0.000

0.000

0.000

0.095

0.022

0.036

(Classifier b-2: classifies the other classes)

4. Conclusion• • In this paper, we evaluated methods to extract
significant places from CDRs and characterize thembased on their temporal features. For further study, we
will refine our classifiers to improve the predictive
accuracy of stay purposes.

References[1]

3.3.2. Ranking of important features• • Fig 2.1, 2.2 show ranking of important features thescores of which are calculated using Mean Decrease Gini.
Here, we chose randomly one trial of cross validation forevaluation.
Fig 2.1 shows results of the first classifier of method b)
which classifies home, office / school or the others. It
shows top features are those of early morning (f[00-05,23]). Those reflect temporal features of home well.
Fig 2.2 shows results of the second classifier of methodb) which classifies the residual classes. It shows top
features are those of daytime (f[08-09, 15-16, 21-22]).
Those reflect time intervals when users often move for
shopping or the other purposes.

2

MeanDecreaseGini

office / school first
Precision

250

f09
f16
f15
f21
f08
f22
f18
f19
f14
f10
f20
f17
f13
f11
f12
f23
f07
f05
f06
f01
f03
f02
f04
f00

(home, office / school or the others). However, theF

200

Fig 2.1. Ranking of important features

reason is effect of grouping classes with high frequency

Recall150(Classifier b-1: classifies home, office /school or the others)

outperforms the method a) for all classes. We think thePrecision

100

MeanDecreaseGini• • Table• • 1 shows the results. It indicates the method b)

Method a) classify at once

50

J. H. Kang, W. Welbourne, B. Stewart, G. Borriello,“Extracting Places from Traces of Locations”, Mobile
Computing and Communications Review, Vol. 9, No. 3,
pp.58-68, 2005.

[2] M.A. Bayir, M. Demirbas, and N. Eagle, “Mobility profiler:
A framework for discovering mobility profiles of cell phoneusers,” Proc. of the International Conference on Pervasiveand Mobile Computing, Vol.6, No.4, pp.435–454, 2010.
[3] Y. Cheng, “Mean Shift, Mode Seeking, and Clustering”,
IEEE Trans. Pattern Anal. Mach. Intell, Vol. 17, No. 8, pp.
790-799, 1995.
[4] S. Isaacman, R. Becker, R. C´aceres, S.G. Kobourov, M.
Martonosi, J. Rowland, and A. Varshavsky, “Identifying
Important Places in People’s Lives from Cellular Network
Data,” Proc. of the 9th International
Conference on
Pervasive Computing, pp.133–151, 2011.
[5] Y. Zhu, Y. Sun, Y. Wang, “Predicting Semantic Place andNext Place via Mobile Data,” the Mobile Data Challenge
2012 (by Nokia) Workshop, June 18-19, 2012.
[6] L. Breiman, “Random Forests,” Machine Learning 45 (1):
5–32, 2001.

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POSTER SESSION 2

Estimating the Indicators on Education and Household
Characteristics and Expenditure from Mobile Phone Data in Vanuatu
Zakiya A. Pramestri, Muhammad Subair, Imaduddin Amin, Dikara Alkarisya, Muhammad Rheza, Ni Luh Putu
Satyaning P.P, Yulistina Riyadi, Mahdan A. Fauzi, Jong Gun Lee
Pulse Lab Jakarta - United Nations Global Pulse
Email: {zakiya.pramestri, muhammad.subair, imaduddin.amin, dikara.alkarisya, muhammad.rheza, ni.paramita,
yulistina.riyadi, mahdan.hasan, jonggun.lee}@un.or.id

ABSTRACT
Vanuatu is a developing country and one of the fastest
growing economies in the Pacific region. The government
has implemented policy initiatives for developing its
education and socioeconomic sector so it is important tomonitor how the policy actually impacts the livelihood of
its citizens. In this study, we aim to test the potential ofusing mobile phone data as proxies for education,
household characteristics, expenditure and income
diversity in Vanuatu as a data source to help monitor suchproxy indicators in a timely manner.

1 INTRODUCTION
Vanuatu is located in the South Pacific Ocean and has
a population of about 290,000. Vanuatu is a Y-shaped
archipelago with four main islands and 79 small islands. It
has six provinces (​Malampa​, ​Penama​, ​Sanma​, ​Shefa​,
Tafea​, and ​Torba​). The capital city ​Port Vila on
Efate island in ​Shefa province and ​Luganville on
Espiritu Santo island in ​Sanma are the largest andthe second largest cities, respectively.
With regard to education, the government has been
providing free primary education since 2010 in response
to decreasing enrolment rates. This policy resulted in an
increase in the enrolment rate which in 2012 was 88% forprimary education. In terms of socioeconomic
development, policy initiatives were introduced to promote
the growth of agricultural production and tourism as themainstay of the economy, and telecommunications, airline
industries and infrastructure as supporting industries [1]. It
was reported in 2010 that households spent 56% of
expenditure on food, 14% on household operations and
5% on miscellaneous expenses. There are differences in
the categories of expenditure, e.g., people in urban areas
spend considerably more cash on food, clothing andtransport vs. households in rural areas consume much
more of their own home produce.
As one of the fastest growing countries economicallyin the region, it seems obvious that Vanuatu should
understand the dynamics of its society. Among others, aneed to monitor key development aspects such as
education, housing characteristics and expenditure, hasbeen addressed, but the national census in Vanuatu iscarried out every 10 years, while the household
expenditure and income survey is conducted every four

years. More frequent information is essential to monitor
and evaluate policy development to improve the social
welfare system and protect vulnerable populations indeveloping countries such as Vanuatu but this is often
quite difficult to achieve.
Recent studies have shown that mobile phone data
can be used to understand socioeconomic conditions in
the absence of official statistics. Researchers have foundthat Call Detail Records (CDRs) and airtime credit
purchases can be used to infer economic parameterssuch as wealth, economic diversity, and economic
segregation within communities [2]. Another study reveals
that poverty indices have good correlation with several
indicators derived from mobile phone activity [3, 4].
Furthermore, the authors of [5] confirmed that top up
purchases can potentially be used for inferring poverty
levels and measuring food consumption at quite a
granular level.
In this paper, we investigate how mobile phone data
can be used to produce a set of proxies for education,
household characteristics, expenditure and income
diversity in Vanuatu by comparing the features measured
from mobile phone data, such as CDRs and airtime
purchase records, with the ground truth data from official
statistics. Our preliminary result shows that mobile phonedata is highly correlated with many indicators on
education, household characteristics and expenditureboth at province and island level in Vanuatu. This implies
that mobile phone data could be used as a source of nearreal-time proxy indicators to complement the information
obtained from census which would be helpful insupporting the mapping of education and household
demographics in Vanuatu.

2 DATAThis section briefly explains how to prepare a list of
indicators from our primary and secondary data and Table
2 shows the entire indicators we used in this paper.

2.1

Official Statistics (Ground-truth Data)

We use two different forms of official statistics, (a)National Census of Population and Housing 2009 and (b)Household Income and Expenditure Survey 2010, bothconducted by the National Statistics Office in Vanuatu.
The first covers all households and individuals throughout

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POSTER SESSION 2

Vanuatu, while the coverage of the second isapproximately 82.5% of all households.
● Education: ​We extract all education-related
statistics at island level from the National Census
to investigate the population segregation byeducation attainment, education completion, andliteracy.
● Household Characteristics: We extract household
electricity usage data and its segregation by themain source of electricity, electronic appliances
ownership, motor vehicle and boats ownership andmain source of water from the national census andalso prepare the data per province.
● Household Expenditure: We extract all relevant
statistics from the household expenditure survey atprovince level to investigate average education
expenditure per household, total housing, fuel &lighting, and food expenditure.
● Household Income: We use total monthly income
data and its segregation based on the income
source including from wages & salaries, cash
crops, fruits and vegetables sales, and fishingactivities from the household expenditure survey atprovince level.

2.3

Mobile Phone Data

We produce nine indicators at province and islandlevel, as shown in Table 2, using two types of anonymized
mobile phone data from a mobile operator in Vanuatu, (a)3-week CDRs and (b) 7-week airtime purchase records,
collected between November 2016 and January 2017. It
is worth noting that, when producing the indicators, perprovince, we use the mobile information up to the third or
fourth most populated cities.

3 PRELIMINARY FINDINGS
Using the Pearson correlation method, we investigate
which indicators on the education, household condition,and spending at province and island level from official
statistics, could be estimated by the mobile phone data.

3.1

Province Level

One natural pattern from official statistics in Vanuatu is

Shefa > Sanma > Malampa > Penama > Tafea > Torba

which is the order proportional to population size. It is not
surprising that we find the same pattern from mobile
phone data as well, but it is interesting to note that any
combination of two indicators, one from the official
statistics and another from the mobile data from Table 1,
is highly correlated each other (> 0.8).
Official Statistics
Working population
Internet usage
English literacy

Mobile Phone Data
Total amounts of top-up
Total number of top-up
Total num. calls and SMSs

French literacy
Bislama literacy
Avg. edu. expenditure per H.H.
Generator usage
Freezer ownership
Telephone ownership
Mobile phone ownership
Boat ownership
Total Monthly Income (TMI)
TMI from wages/ salaries

Num. unique customers
Total number of SMSs
Total number of calls
Total duration of calls

Table 1 A set of indicators from official statistics andmobile phone data which shows the pattern of

Shefa > Sanma > Malampa > Penama > Tafea > Torba

and is highly correlated each otherAn interesting indicator from mobile data showing a
different pattern from the indicators from official statistics
is ​M3 ​or the number of unique customers (SIM cards) who
purchased prepaid phone credits, with the following order,

Sanma > Shefa > Malampa > Penama > Tafea > Torba

where the highest record is ​Sanma​, while the province
with the highest values from the other indicators is ​Shefa​.
It would be explained with the fact that the ​Sanma
province is the most popular tourist destination based on
Department of Immigration in Vanuatu [6], expecting that
not only residents but also tourists top up phone credits,
while tourists are not actively calling phones or sending
messages.

3.2

Island Level

Among three types of official statistics indicators,
Education indicators at island level are most correlatedwith the indicators from mobile phone data. The averagecorrelation coefficient value of all combinations between
18 Education Indicators and nine Mobile Data Indicators
is 0.92, while the one between 16 Household
Characteristics (20 Household Ownership) and nine
Mobile Data Indicators is 0.76 (0.72, respectively).
Table 3 shows the correlation results (up to 10 mostcorrelated indicators from mobile phone data at islandlevel) but some highlights are like the following.
● Education○ The indicators most correlated with mobile
phone indicators are the literacy rates ofBislama and English, which are two of threenational languages in Vanuatu.
○ The population who completed secondary
education or above (10-year certificate,college, university, vocational) generally has
higher correlation with the mobile data
indicators than the population with primaryeducation only.
● Household Characteristics and Expenditure○ Mobile phone data is highly correlated with
the number of households (a) whose main
source of light is generator or electricity main
grid, (b) whose main source of cooking
2

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energy is electricity, and who owns electricity
appliances, such as radio, TV, and DVD.
Considering the fact that the electricity supply
is dominated by main grids in urban areas,
while it is dominated by diesel generation inrural areas, mobile phone data may be able
to be used for assessing the electrification
profiles in Vanuatu but we will leave this as
our future work.

REFERENCES
[1]
[2]

[3]

4 SUMMARY
We have presented that mobile phone data could be
used for producing a set of proxies for some indicators on
education and household characteristics, expenditure,and income diversity in Vanuatu. This is particularly
valuable in developing economies, where traditionalsources of population data are often scarce but mobile
phones are increasingly common as these methods may
provide a cost-effective option for measuring thecharacteristics of populations.
We plan to extend our findings by applying otherstatistical methods to map those education andsocioeconomic features at quite a granular level, forinstance at city level where official statistics data are not
yet available.

[4]

[5]

[6]

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