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

5 – 7 APRIL 2017
VODAFONE VILLAGEMILAN

BOOK OFABSTRACTS

ORALSESSION 1: SOCIO – ECONOMIC
Higher-order correlations of consumption patterns in social-economic networks

p.5

Mobile phone-based Credit Scoring

p.8

Mobile Wallet Usage Study. Applying CDR models to increase Mobile Wallet Adoption & Activity

p.11

Longitudinal Analysis of Mobile Savings Accounts

p.14

SESSION 2: MOBILITY
Route flow estimation using trips extracted from cellular network data

p.18

Improving human mobility prediction with geo-tagged pictures

p.21

Inferring Commuting Statistics in Greater Jakarta from Social Media Locational Information from Mobile Devices

p.24

An emergent strategy for characterizing urban hotspot dynamics via GPS data

p.27

Inferring customer visitors by means of WiFi crumbles

p.30

SESSION 3: SOCIAL NETWORKAge disparities in ethnic segregation: a study of activity spaces using the Call Detail Record dataset

p.34

The Rippling effect of socil influence on phone communication network

p.36

Cohesive groups in urban area: characterization of p-cliques in mobile phone graphp.39

Personality Traits and Ego-Network Dynamics

p.42

Why people stop calling? The temporal weakness of decaying ties

p.45

SESSION 4: SOCIAL GOOD
A tool for estimating and visualizing poverty maps

p.49

Inside Out: to understand crime mechanisms look at urban fabric first

p.52

LDA Mapping of Regional Socioeconomic Status

p.55

Rapid Assessments of Population Displacement in the 2015 Nepal Earthquake

p.58

Uncovering the Spread of Chagas Disease in Argentina and Mexico

p.62

SESSION 5: HEALTH
Spatially explicit modeling of potential Ebola spread in Senegal

p.66

Anticipatory Monitoring of Depressive States through the Analysis of Multimodal Phone Data

p.67

Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

p.70

Are you getting sick? Predicting Flu-Like Symptoms Using Human Mobility Behaviours

p.72

Impact of Human Mobility on Spread of Dengue in Sri Lanka

p.75

5-7 APRIL 2017 / VODAFONE VILLAGE / MILAN

INDEX

ORAL

Geographical veracity of indicators derived from mobile phone data

p.79

Preserving Mobile Subscriber Privacy in Open Datasets of Spatiotemporal Trajectories

p.83

Detecting the leakage of personal confidential information from mobile datap.85

Biases and errors in the temporal sampling of random movements

p.90

Application of trajectory based models for continuous behavioural user authentication through anomaly detection

p.92

Time Accuracy Analysis of Post-Mediation Packet-Switched Charging Data Records for Urban Mobility Applications

p.95

SESSION 7: SOCIAL NETWORK
What comes first? Social strength or common friends?

p.99

Kernel-based approaches to large social networks

p.102

Familiar Strangers: the Collective Regularity in Human Behaviors

p.105

Impact of university admission on student’s egocentric network

p.108

SESSION 8: MOBILITY
More reliable and more accurate traffic models using mobile phone data

p.112

Understanding Drivers of Short Term Mobility

p.117

Short-Term Traffic Prediction Using Visitor Location Registry Data

p.120

Understanding Multiday Activity Patterns Based on Mobile Internet Usage Behaviour

p.123

The Effect of Pokémon Go on The Pulse of the City

p.126

Traffic Analysis of Railways using Call Detail Records

p.129

SESSION 9: SOCIAL GOOD
A Framework for Evacuation Hotspot Detection after Large Scale Disasters using Mobile Phone Location Data

p.140

Mapping poverty using mobile phone and satellite data

p.142

Estimating population behaviour to describe activity-based land-use in emerging economies using mobile phone event series

p.146

Targeted disease containment strategies based on spatial network analysis

p.149

What does mobile metadata measure? Insights from a pilot study during a sudden emergency

p.152

SESSION 10: TELCO APPLICATIONS
Predict Cellular Network Traffic with Markov Logic

p.156

Towards a data science model for device upgrade

p.159

On Added Value Of Feature Engineering for Churn Prediction

p.162

Layers of social acquaintance for telecommunication churn prediction

p.165

Learning about New Technologies: Evidence from Phone Plans in Rwanda

p.168

5-7 APRIL 2017 / VODAFONE VILLAGE / MILANSESSION 6: DATA QUALITY AND PRIVACYSESSION 1

SOCIO – ECONOMIC

NetMob 5-7 April 2017 – Vodafone Village – Milan

SESSION 1
SOCIO - ECONOMIC

Higher-order correlations of consumption
patterns in social-economic networks
Yannick Leo∗ , Márton Karsai∗ , Carlos Sarraute† and Eric Fleury∗∗ Univ Lyon, ENS de Lyon, Inria, CNRS, UCB Lyon 1, LIP UMR 5668, IXXI, F-69342, Lyon, France† Grandata Labs, 550 15th Street, San Francisco, CA, USA

yannick.leo@ens-lyon.fr, marton.karsai@inria.fr, charles@grandata.com, eric.fleury@inria.fr
I NTRODUCTION
The consumption of goods and services is a crucial element of human welfare. The uneven distribution of
consumption power among individuals goes hand in hand with the emergence and reservation of socioeconomic
inequalities in general. Individual financial capacities restrict personal consumer behaviour, arguably correlatewith one’s purchasing preferences [1], [5], and play indisputable roles in determining the socioeconomic
position of an ego in the larger society [1]–[4]. Consumption behaviour has been addressed from various angles
considering e.g. environmental effects, socioeconomic position, or social influence coming from connected
peers [1]. However, large data-driven studies combining information about individual purchasing and interaction
patterns in a society large population is still rare, although questions addressing higher-order correlation between
consumption and social behaviour are at utmost interest.
In this study [9] we address these crucial problems via the analysis of a dataset, which simultaneously
records the mobile-phone communication, bank transaction history, and purchase sequences over several monthsof millions of individuals living in a developing country. This corpus, one among the firsts at this scale anddetails, allows us to infer the socioeconomic status, consumption habits, and the underlying social structureof millions of connected individuals. Using this information our overall goal is to identify people with certain
financial capacities, and to understand how much money they spend, on what they spend, and whether they spend
like their friends? More precisely, we formulate our study around two main research questions:• Can one associate typical consumption patterns to people and to their peers belonging to the same ordifferent socioeconomic classes, and if yes how much such patterns vary between individuals or different
classes?
• Can one draw relations between commonly purchased goods or services in order to understand better
individual consumption behaviour?
DATA DESCRIPTION
Communication data used in our study records the temporal sequence of ∼ 8B call and SMS interactions of∼ 112M anonymized mobile phone users for 21 months. Using call detailed records (CDRs) we constructeda large social network where nodes are users (whether clients or not of the actual provider), while links are
drawn between any two users if they interacted (via call or SMS) at least once during the observation period.
We also insure that each node has at least one incoming and outgoing call or SMS and remove non-human andcommercial actors from the network. At the same time to calculate individual economic estimators we useda dataset which records financial details of ∼ 6M people assigned with unique anonymized identifiers over 8
months. The data provides time varying customer variables as the amount of their debit card purchases anddemographic attributes such as age and gender. A subset of IDs of the anonymized bank and mobile phonecustomers were matched. The combined data of the bank and mobile datasets (from now called DS1) contained∼ 1M people connected by ∼ 2M links with communication events and detailed bank records available.
To study consumption behaviour we used purchase sequences recording the time, amount, merchant categorycode of each purchase event of each individual during the observation period of 8 months. Each purchase eventsare linked to one of the 281 merchant category codes (MCC) indicating the type of the actual purchase (foodrestaurants, airlines, gas stations). MCCs were grouped into 28 purchase category groups (PCGs) as in [7]. We
decided to remove 11 of the 28 PCGs with extremely low activity (see the 17 remaining categories in Fig.1a).
At the same time we analyze group k1 Service Providers separately from the remaining K2-17 = K17 \{k1 } set

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of 17 groups as it corresponds to cash retrievals and money transfers (70% of the total purchases) and since wehave no further information how the withdrawn cash was spent.
Using only bank transaction traces we build a second data set (DS2), which collects data about the age andgender of ∼ 3.6M individuals together with their purchase sequence recording the time, amount, and MCC ofeach debit card purchase. To estimate the personal economic status we used average monthly purchase (AMP)
of individuals.
 More precisely, in case of an ego u who spent mu (t) amount in month t we calculated the AMP
as Pu = t∈T mu (t)/|T |u where |T |u corresponds to the number of active months of user u (with at least
one purchase in each month). After sorting people by their AMP values we computed the CP (f ) normalized
cumulative distribution function of Pu as a function of f fraction of people. We used the CP (f ) function toassign egos into nine sj (j = 1 . . . 9) socioeconomic classes (smaller numbers assigning lower classes) suchthat the sum of AMP in each class sj was the same.
S OCIOECONOMIC CORRELATIONS IN PURCHASE PATTERNSIn order to address our first research question, for each socioeconomic class sj we take every user u ∈ sj andcalculate the mku total amount of purchases they spent on a purchase category group k ∈ K17 and measure ther% (k, sj ) fractional distribution of spending for each PCGs. In Fig.1a each line shows the r% (k, sj ) distributions
for a PCG as the function of sj social classes, and lines are sorted (from top to bottom) by the total amountof money spent on the actual PCG. Interestingly, people from lower socioeconomic classes are spending moreon PCGs associated to essential needs (Retail Stores (St.), Gas Stations, Service Providers) while richer people
spend more on extra needs (High Risk Personal Retail, Automobiles, Hotels and Airlines).
(a)

(b)

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Car sales and maintenanceHardware stores
Off ce supply stores
IT services
Books and newspapers
State services and education
Home supply stores
Newsstand and duty-free shops

5621
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Amusement and recreation
Travelling
Transportation and commuting
LeisureJewellery and gift shops
Clothing 1
Clothing 2
Personal services
Health and medical services

Fig. 1. Consumption correlations in the socioeconomic network (a) r% (k, si ) distribution of spending in a given purchase categorygroup k ∈ K17 by different classes sj . Distributions are normalized such that they sum up to 1 for each category. (b) Heat-map matrix
representation of dSV (si , sj ) distances between the average spending vectors of pairs of socioeconomic classes considering PCGs in
K2-17 . (c) Heat-map matrix representation of the average LSV (si , sj ) (resp. Lk1 (si , sj )) measure between pairs of socioeconomic
classes considering PCGs in K2-17 . (d) Weighted G>ρ correlation graph with nodes annotated with MCCs. Colors assign 17 communitiesof merchant categories with representative names summarized in the figure legend.

To further investigate these dissimilarities between socioeconomic classes, we build a spending vector SV (u)for each ego u where each item
SVk (u)k assigns the fraction of money what a user u spent on a categoryk ∈ K2-17 out of his/her mu = k∈K mu total amount of purchases. Using these individual spending vectors
we calculate the SV (sj ) average spending vector of a given socioeconomic class sj and use it to quantify
differences between distinct socioeconomic classes. Results in Fig.1b show the pairwise differences between
each class as a heat-map which appears with a strong diagonal green component and with distinct off-diagonalvalues. It indicates that the average spending behaviour of a given class is the most similar to the class itself
and neighboring classes, while dissimilarities increase with the gap between socioeconomic classes. Note that
we observed the same behaviour for the single PCG k1 (results not shown here), and also observed that richer
people tends to be more similar in terms of their purchase behaviour while they used to allocate their purchases
in more PCGs.

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To continue our investigation we characterize the effects of social relationships on the purchase habits ofindividuals through the analysis of DS1. We address this problem through an overall measure quantifying differences between individual purchase vectors of connected egos positioned in the same or different socioeconomic
classes. More precisely, we consider each social tie (u, v) ∈ E connecting individuals u ∈ si and v ∈ sj , andfor each purchase category k we calculate the average absolute difference of their purchase vectors items as
dk (si , sj ) = |SVk (u) − SVk (v)|u∈si ,v∈sj . In order to quantify the effect of the social network we calculate
the average of the same quantity dkrn (si , sj ) measured on corresponding configuration networks. Finally wetake simply the LSV (si , sj ) average of their ratio Lk (si , sj ) = dk (si , sj )/dkrn (si , sj ), averaged over eachcategory group k ∈ K2-17 or respectively k1 . This measure shows whether connected people have more similarpurchase patterns than one would expect by chance without considering any effect of homophily [6], social
influence or structural correlations [4]. Results depicted in Fig.1c for LSV (si , sj ) indicates that the purchasepatterns of individuals connected in the original structure are actually more similar than expected by chance if
the link connects egos from similar socioeconomic groups (diagonal component). Note that we found the same
correlation trends in cash purchase patterns (not presented). These observations do not clearly assign whether
homophily or social influence induce the observed similarities in purchasing habits but undoubtedly clarifies
that social ties (i.e. the neighbors of an ego) and socioeconomic status play deterministic roles in the emerging
similarities in consumption behaviour.
Finally our aim is to obtain an overall picture of the consumption structure at the level of merchant categories
and to understand precisely how personal and socioeconomical features correlate with the spending behaviourof individuals and with the overall consumption structure. Using the spending vectors of individuals we definea ρ(ci , cj ) overall correlation measure between categories. This symmetric measure quantifies how much people
spend on a category ci if they spend on another cj category or vice versa. Using the obtained correlation
matrix we take into account only positively correlated frequently purchased category pairs and we definea weighted correlation network. This network appears with well observable purchase category communities,
what we visualize in Fig.1d by using the Louvain community detection method [8]. Interestingly, each ofthese communities group a homogeneous set of merchant categories, which could be assigned to similar typesof purchasing activities, while inter-community links show how different groups of purchase categories areconnected together.
In this study, from individual level, we manage to draw relations between commonly purchased goods orservices in order to obtain a global consumption picture with 17 merchant category groups (see Fig. 1d). Thanks
to the large scale corpus, offering to infer the socioeconomic status, consumption habits, and the underlying social
structure of millions of connected individuals we derive results that give us the intuition of how consumption
patterns are organized in a society with heterogeneous distribution of wealth and purchase capacities. Whereas
we do not present the results in this abstract, we also studied correlations between average age, SEG and genderof each MCC category. Interestingly we observe the presence of anecdotic purchase patterns characterizing thetwo gender groups, and that wealthier people tend to be older, males, and used to purchase from more expensive
categories in accordance with our intuition.
R EFERENCES[1] A. Deaton, Understanding Consumption Clarendon Press (1992).
[2] T. Piketti, Capital in the Twenty-First Century. (Harvard University Press, 2014).
[3] P. West, Conspicuous Compassion: Why Sometimes It Really Is Cruel To Be Kind. Civitas, Institute for the Study of Civil Society(London) (2004).
[4] Y. Leo, E. Fleury, J. I. Alvarez-Hamelin, C. Sarraute, M Karsai, Socioeconomic correlations and stratification in social-communication
networks J. R. Soc. Interface 13:125 20160598 (2016).
[5] J. E. Fisher, Social Class and Consumer Behavior: the Relevance of Class and Status”, in Advances in Consumer Research Vol. 14,
eds. M. Wallendorf and P. Anderson, Provo, UT : Association for Consumer Research, pp 492–496 (1987) .
[6] M. McPherson, L. Smith-Lovin, J. M. Cook, Birds of a Feather: Homophily in Social Networks. Ann. Rev. Sociol. 27 415–444 (2001).
[7] Merchant Category Codes and Groups Directory. American Express @ Work Reporting Reference (http://tinyurl.com/hne9ct5) (2008)(date of access: 2/3/2016).
[8] V. Blondel, J-L Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks J. Stat.l Mech: theory andexperiment P10008 (2008).
[9] Y. Leo, M. Karsai, C. Sarraute, E. Fleury, Correlations of consumption patterns in social-economic networks IEEE/ACM ASONAM p.
500-507, San Francisco CA, August 18-21 (2016).

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Mobile phone-based Credit Scoring
Skyler Speakman, Eric Mibuari, Isaac Markus, Felix Kwizera
IBM Research – Africa
skyler@ke.ibm.com

INTRODUCTION

Mobile Money platforms are gaining traction acrossdeveloping markets as a convenient way of sending
and receiving money over mobile phones. These systems operate on low-cost feature phones and do not
require users to have a bank account. These low barriers-to-entry make mobile money platforms excellent
tools for financial inclusion of the poor. Additional
financial services, such as saving and lending products, are now being offered over these mobile money
channels. In this work, we demonstrate how boosted
decision trees (Adaboost) may be used to create creditscores (probability of repaying a low-value, shortterm loan) for under-banked populations, allowing
them to access credit that was previously unavailable
due to a lack of financial data. The boosted treemodel demonstrated significant results over the original model used by the bank. We show a 55% reduction in default rates while simultaneously offering
credit opportunities to a million customers that were
given a 0 credit limit in the bank’s original model.
The mobile-based saving and lending product is a
joint collaboration between a mobile network operator (MNO) and a regional bank in East Africa. The
MNO provides access to the customer through theirmobile money platform as well as data on recent customer activity. This data is shared with the banking
partner when a customer signs up for the savings andloan product in order to help determine risk. The features are broken down into three large categories. We
provide a non-exhaustive list. Demographic: Age,
Gender, Account age, etc. Network: Airtime usage,
Top-up amounts, Active days, Airtime borrowing, etc.

Figure 1: Histogram of customer age and payment class.

Mobile Money: Amount(s) sent, received, deposited,
withdrawn, etc.
On top of this rich feature space the bank providedlabeled data of customers who have repaid their mobile loans and those who have defaulted. In particular, the data set contained 295,926 labeled examples
with over 30 categorical and real-valued features.
These records cover customer activity in 2013 andearly 2014. There was a slight class imbalance with23.1% of customers defaulting on a mobile loan.
Figure 1 provides a histogram showing the distributions of “Paid” and “Default” customers across the
Customer Age feature for this data set. The x-axis isin standard deviations away from the mean. Therefore, 0 represents the mean age of borrowing customers. Age is positively skewed and capped at 4 standard deviations for display purposes. As expected,

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younger borrowers are riskier than older borrowers.
However a single feature (histogram) cannot accountfor the interactions involved in calculating an accurate credit score. We therefore turn to one of the best
off-the-shelf classifiers to assist in this textbook supervised learning problem.

BOOSTED DECISION TREES

Decision trees have been a workhorse of data analytics for decades. Their ability to incorporate real andcategorical attributes for both classification and regression problems make them applicable to a wide
range of applications. Additionally they are able togracefully handle missing values that are common in
real-world data sets. In order to increase the accuracyof the binary classification (default or repaid) wetrained multiple, simpler decision trees in sequence.
Each consecutive tree attempts to correct the mistakes
made by the preceding ones. This process is known
as boosting [1,2] and we use the Adaboost implementation in R (gbm package) [3].
Boosting introduces two additional parameters to thetraining process: the depth (complexity) of each individual tree and the number of trees to create in thesequence. We set the value of these parameters
through 10-fold cross validation with the goal of maximizing the area under the receiver operator characteristic curve (also known as AUC). The model isthen built using these parameters. Finally, the values
provided from the boosting process are turned intoclass probabilities using a logistic correction [4].
Figure 2 shows the AUC values for various
combinations of tree depth and number of trees usedin the boosting sequence. 180 trees of depth 2
maximized the AUC around 0.764.
Logistic
regression on the same data set resulted in an AUC of
0.74. We start to see overfitting with large amountsof depth 3 trees. Other depth values were ommitted
for display purposes.

Figure 2: Area-under-curve results for cross validation.

EMPIRICAL RESULTS & DISCUSSION

The method described above takes a customer’s
features (as provided by the MNO) as input andproduces a “probability of repaying” based on thisinformation. We now discuss two straight forwardbusiness implications of increasing the classification
accuracy over the original method used by the bank.
For discussion purposes we assume that a risk
manager at the bank has assigned a threshold of 0.7
for allowing new customers to borrow money on theloan product. Therefore anyone with a probability of
repaying higher than 0.7 would be given a credit limit
whereas customers below that probability would only
be allowed to save money on the product. The
vertical black bar on Figures 3 and 4 represents thiscutoff value.
With this assumption in place, we demonstrate a 55%
reduction in default rate. In other words, the newboosted tree model would have correctly excluded
55% of customers who went on to default on a mobile
loan. This 55% reduction in default rate may also beviewed as the sensitivity or recall of the model. The
same threshold retains 83% of the paying customers
on the product. This 83% retention rate may beviewed as the specificity of the model.

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Figure 3: Histogram of probability of repayment andpayment class.

The precision is 49% which means roughly half of thepeople excluded from the credit portion of the
product were defaulters. Each of these numbers varywith the choice of threshold set by the risk manager.
Figure 3 is a histogram showing the distribution ofcustomers over their probability of repaying forpayers (blue) and defaulters (red). The x-axis is theprobability of repaying the loan and the y-axis is thenumber of customers within each bin. 55% of thedefaulting customers fall below the threshold value
and would have been correctly excluded fromborrowing money.
A reduction in default rate is not the only gain of themodel. We also consider the larger population ofcustomers on the savings and loan product including
those that were initially excluded by the originalmodel used by the bank. The original bank modelallowed 35% of their total customers on the credit
product initially. The boosted tree model (andassumed 0.7 threshold) allws 49% of the customer on
the product; an increase of over 1 million customers.
Figure 4 visually demonstrates this increase inrevenue potential for the bank as well as increased
financial inclusion for the society. The red bars in thehistogram of Figure 4 represent the customers

Figure 4: Histogram of probability of repayment andinitial inclusion on the loan product.

who were initially allowed on the credit portion of thesavings and loan product. The boosted tree model
suggests the original model was too conservative andthat many more customers (blue to the right of the 0.7
threshold) could also have been allowed to initiallyborrow on the mobile credit product.
The boosted tree model has also produced humaninterperable relationships between features of cell
phone usage and mobile money volumes that will
help create additional mobile and banking productsthat support sustainable finanical inclusion.

REFERENCES[1] Y. Freund and R.E. Schapire (1997). “A decisiontheoretic generalization of online learning and an
application to boosting,” Journal of Computer and System
Sciences, 55(1):119-139.
[2] T. Hastie; R. Tibshirani; J. Friedman (2009). The
Elements of Statistical Learning: Data Mining, Inference,
and Prediction (2nd ed.). Springer.
[3] G. Ridgeway (2005). “Generalized boosted models: A
guide to the gbm package”.
[4] A. Niculescu-Mizil and R. Caruana. (2005). “Obtaining
calibrated probabilities from boosting.” In Proceedings of
the Twenty-First Conference on Uncertainty in Artificial
Intelligence (UAI'05).

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Mobile Wallet Usage Study

Apply CDR models to increase Mobile Wallet Adoption & Activity
IFC, Cignifi & Airtel1

This study is the result of a collaborative
project between the International FinanceCorporation (IFC), part of the World Bank
Group; Airtel Uganda, Uganda’s secondlargest mobile operator; and Cignifi, a bigdata analytics company. The developmental
objective of the project was to expand
financial inclusion in Uganda. Due tomultiple factors such as poor
infrastructure, lack of collateral, lack ofproper financial education over existingalternatives, a majority of people over theage of 15 in Uganda do not have access toformal financial services. Among the
population aged 15+ only 17% have aformal savings account and 16% haveaccess to formal borrowing (Findex 2014).
Cell phone penetration is high at over 55%(Uganda Communications Commission,2014 ), which establishes a market for
expanding financial access through digitalfinancial services. To this end we usedpredictive analytics to help Airtel, a mobile
network operator, identify customers most
likely to become regular users of Airtel
Money—a digital financial service offeredby Airtel that allows customers to transfer
money, pay bills, and engage in various
1other financial transactions. When poor
people have access to formal financial
services, they can earn more, build theirassets and cushion themselves against
external shocks. Access to financial
services can support entrepreneurs andmicroenterprises, which in turn promote
job growth and shared prosperity forothers in the community to earn income aswell. That is why the UN’s Sustainable
Development Goals framework explicitly
recognizes the importance of promoting
financial inclusion in to achieve theSustainable Development Goals (SDGs) by
2030.
The project was based on an analysis ofanonymized customer data provided by
Airtel Uganda. This data included
customers’ detailed calling data (voice calls,
text messages, data sessions), customer
payment (or “recharge”) data for theirmobile accounts, and mobile moneytransactions for all Airtel Money users. The
data, approximately one terabyte, covered a
six-month period from November 2014 toApril 2015. It did not include any

Contact Qiuyan Xu, qxu@cinifi.com or Soren Heitmann, sheitmann@ifc.org for further information.

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personally identifying data (such as
customer names, addresses, or voice or text
content). Cignifi worked with IFC to define
predictive models, identify the primary usergroups, and set a project road map. Cignifi
then proceeded to extract, process, andnormalize the mobile data and develop
analytics models that segmented Airtel
customers into three primary groups:Highly Active Mobile Money Users
Active Mobile Money Users
Non-active Mobile Money Users

Working with the predictive model to divide
the subscriber base into three user
profiles—highly active, active and nonactive—enables a clearer understanding of
the factors influencing mobile money
activity to emerge. These factors can help
Mobile Money service providers identify
subscribers more likely to be active mobile
money users in order to increase theefficiency of marketing and customer
targeting efforts. Social network analysis
and geospatial analysis were applied on calland mobile wallet network to strengthen thefindings and to support strategic growth
beyond urban areas to advance financial
inclusion. The developed model allows to
identify and predict probable active Airtel
Money subscribers from its GSM base withan accuracy rate of at least 85%.
This study identified three main indicators ofa customer’s likelihood to be an active
mobile wallet user:

GSM Activity: A user’s level of GSM
activity strongly predicts theirlikelihood to be actively using
Mobile Money (MM) Voicetransactions in particular emerged
as the most powerful transaction
type for discriminating between
profile groups. As technology

leaders who make most frequent useof their phones and make both more
frequent and larger transactions withAirtel Money, this user group is also
more likely to be smartphoneowners. This increases the ease ofuse for MM since the app version
can be downloaded instead of the
USSD version available for featurephones.
Revenue Generation: Those who
generate higher levels of revenue arealso associated with higher levels of
MM activity. Using revenue
generation as a proxy for acustomer’s income, the study
suggests that those users who may
generate relatively higher incomes
have higher participation in MM
services. This makes sense because
the number of uses for MM andfrequency of applicable use
increases as users have more money
and use it for a variety oftransactions. Additionally, similar to
GSM activity, the probability that
this segment contains a larger
percentage of smartphone users ishigh, as affluence increases so doesbuying power.
Connectedness: How connected auser is to their network, and the sizeof that network, also appear to besignificant factors in determining
propensity for MM activity. As users
talk to and message more uniquecounterparties, their propensity
increases. The behavior of a user’ssocial network also provides
significant discriminating power
between the segments. The more
active a user’s network the more
likely the user is to also be active.
This also makes sense due to thenature of the MM product. The moreconnections a user has already using

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the product, the more useful it will beto the user when they adopt and themore likely they are to use it
regularly.
Visualization of Mobile Money Flow amongst Clusters

The geospatial and cluster analysis of
P2P (person to person money transfer
services) flows further allowed to
identify established and nascenttransaction channels. See graph on theleft in which circle segments represent
origin, destination as well as the size of
P2P transaction flows.
We see a clear concentration of transfer
volumes in the capital city of Kampala.
They represent almost half of all money
sent via interpersonal transfers. In
nearly every case, net money flows gofrom more populated to less populatedareas. Access to mobile phones, theproximity of mobile money agents and amore robust economy in more urban
areas might all play a role in explaining
these results.
Our project’s overall finding - that indicatesmiddle upper income segments as thehighest propensity users - calls into question
the development premise on mobile moneyand DFS (digital financial services) as afinancial inclusion driver amongst
customers at the bottom of the
pyramid. However, results from the social
network analysis and geospatial analysis
also highlight how DFS can be a driver offinancial inclusion by combining highpropensity user models with geospatial
analysis that helps to identify wherestrategic segments are; and where users instrategic segments may already betransacting.
The developmental value of the project
resides precisely in these findings: as longas Mobile Network Operators rely on theirusual business tools for acquiring clients,
DFS will expand but will include non-key
segments of their potential client base. By
including CDR analysis and have it informbusiness decisions, strategic areas andpopulation segments can be identified,
which can maximize development impact.

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Longitudinal Analysis of Mobile Savings Accounts
Isaac M. Markus*, Skyler Speakman, Isaac Waweru, Abdigani Diriye, Jonathan Lenchner
IBM – Africa Research Center
Nairobi, Kenya
ismarkus@ke.ibm.com
The growth of microcredit in developing nations has
accelerated over the last decade with the wide adoption of smart
and feature phones. In conjunction with these mobile loan
products several savings products have been deployed, expanding
the availability of formal financial instruments to previously
unbanked individuals. In this work we examine the utilization ofsuch mobile savings products in eastern Africa, examining
correlations between savings and credit usage. K-meansclustering of savings data reveals the degree of stratification
between users, with typical users (98%) having on average lessthan 1000Ksh ($10) in their account, and the remaining 2%
holding 30% of the total capital in the mobile savings accounts.
Further longitudinal clustering reveals a noticeable balance
decrease preceding a loan request, providing an early indicator ofa pending loan request that is applicable for a large group ofusers.

I. INTRODUCTION
The penetration of feature phones in Sub-Saharan African,
followed by the advent of mobile money has provided criticalinfrastructure, the absence of which had previously hamperedfinancial inclusion efforts on the continent [1,2]. With the
adoption of mobile money, large telcos and banks havecircumvented several of the barriers that prevented offeringformal financial services to large swaths of the population[3,4]. By leveraging cell phone networks, brick and mortar
banking locations are no longer required, which has reduced
service cost and access limitations, particularly in rural areas.
Furthermore, mobile phone data has facilitated risk assessmentof previously unbanked individuals, allowing micro financeinsitutions (MFIs) and banks to provide small loans with astronger foundational understanding of credit worthiness [5].
In eastern Africa the process of applying and qualifying fora mobile based loan has been streamlined, with clear
requirements that are anchored to establishing a transaction
history with mobile money. As part of the qualification process
customers are required to have a positive balance in a mobilesavings account [6]. Although a positive balance is required forassessing credit risk, this financial product is for manycustomers the first opportunity to have an interest yielding
savings account. As an innovative product itself, examining the
behavior of customers in regards to their savings accounts canprovide insights into how traditionally unbanked individuals
will utilize formal banking product, such as a deposit account.
This paper explores cluster behavior of such accountholders over a period of two years. The sample size includes anoverlapping population that also received a mobile based loan.
For the individuals having both savings and credit products,

further analysis of their mobile money usage and repayment
rates was performed. The work seeks to identify activity levelsof deposit accounts, including analysis before and after loan
disbursement in order to correlate with credit worthiness. Long
term goals of this project include providing dynamic credit
scoring capabilities that consider an individual’s savings
history and pattern of deposits and withdrawals leading up to aloan request.
II. METHODOLOGY
The dataset analyzed covers the time window from October
2012 to July 2014, including 4.1 million customers from east
Africa that had signed up for a mobile money deposit account.
Individual customers did not have the same time windows inwhich their accounts were open, with the main difference beingat what time during the analyzed window was the accountopen. The original dataset was formatted as a set oftransactions linked to a member ID, including initial deposit,
and subsequent debits, credits, and interest yields; with eachtransaction accompanied by an action date and a clearance
date. From the initial preprocessing step, the data for eachmember ID was updated on the clearance date, requiring thatthe balance for days with no transaction be filled using the last
known balance. The running balance was calculated as apreprocessing step employing the clearance date using acustom workflow in SPSS modeler.
Subsequent dataprocessing and analysis was performed using the KNIME
analytics platform.
Initial analysis of the data was performed employing Kmeans clustering, by using the average savings per account foreach month over the sample window. Because individuals had
differing time windows in which their accounts were openfrom October 2012 to July 2014, missing information was
assigned a balance of 0. The dataset was cross-referenced witha separate dataset of customers that had also taken out a mobile
based loan, which further included 30 features relating to theirmobile money and airtime usage, for example the number ofmobile money transactions or the average airtime top up
amount. The total number of sample having both a loan
product and a mobile savings account totaled 866229
customers over the two-year window. For the population thattook out loans the K-means clustering for average monthly
savings was repeated including 30 of the features from themobile money usage in order to examine behavior related todefault rates. This set of features was selected since previous
work by our group had found them to be good descriptors for
credit worthiness.

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In order to avoid further artificial distortion of results due tovarying time windows, the overlapping population sample wasreexamined using the loan disbursement date as a reference
point, examining 90 days before and after loan disbursement.
Clustering was performed initially using the average dailybalance. A separate analysis was also performed employing alongitudinal K-means algorithm, where every day in the timeseries constituted its own feature. Subsequent examinations forthe longitudinal data are to be performed using latent classmixed model packages as implemented in R. This latter method
allows for the clustering of a population along prechosenpatterns, facilitating comparison for users that might have
similar behavior but significantly different absolute accountvalues.

Figure 2 shows the result of calculating K-means clusters
for the overlapping sample population that had both savings
accounts and had taken out a loan during the same time
window. The results are calculated using not just the monthly
average saving for each individual, but the full set of 30
features related to airtime and mobile money usage. The
inclusion of these features reveals that there is a correlation
between the running balance and the likelihood of defaulting
on a loan, with lower balance holders being almost twice as
likely to default as the highest balance account holders.
Plotting the data again reveals that the k-means algorithm
simply stratifies the data, with the majority of users belonging
to the low balance clusters.

III. RESULTS
Figure 1 shows the trajectory for 5 calculated clusters,
indicating the different levels of stratification that exist in thedata and the number of users belonging to each cluster. The
initial K-means clustering over the two-year period reveals thatthe majority of users are centered around 1000KsH ($10) in
their account. The data indicates that the typical user will keep
low levels of capital in this type of account, with less than 5%
maintaining balances in excess of $100, and the combinedsavings of this minority representing 30% of the total capital ofin the mobile savings accounts. The initial increase in balance
observed for the clusters is mainly attributed to the inclusion of
new users signing up to the mobile savings accounts.
The initial modeling results clearly indicate that users of
this type of accounts have limited resources invested in them.
Although this can be due to a preference for keeping cash inhand or in other investments, the fact that 90% of the regional
population subsist on less than $10/day suggest that typicalusers have limited resources to begin with. It is also important
to note that this type of account is mandatory for any personthat intends to request a mobile based loan; having to maintaina minimum deposit of 1KSh.

Figure 1 Average monthly balance for clusters calculated using Kmeans. The number of users per cluster is shown in the legend.

Figure 2 Average monthly balance for clusters calculated using kmeans with 30 added features relating to mobile money and airtime
usage. The number of users and default rate for each cluster is shownin the legend.

Because the prior modeling relied on average monthly
savings with no distinction for the length of time that accounts
were open for, the loan disbursement date was used a reference
point for further analysis (Figure 3 and 4). Initially k-meansclustering was performed employing the daily average savingsfor the 90 days prior to taking out a loan. Using this reference
point reduced the differences in history lengths, but the results
still indicate that a significant number of users do not have an
established deposit account of the requisite 90 days. This lackof established deposit account is identifiable as the steep
difference in the balance 90 days prior to having taken out aloan before a relative plateau is reached. From figure 3, it isimportant to note that prior to taking out a loan the accountbalance of all clusters decreases. With the time to onset andsharpness of decline potentially giving banks an early warningthat a customer will take out a loan. This clustering was
performed without considering the additional features ofmobile money usage, but post cluster analysis indicates that thedifference in default rates was not as marked as in Figure 2,
with the exception that the lower balance clusters still have thehighest default rates.

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Figure 3 Average daily balance for the 90 days prior to taking out amobile based loan for clusters calculated using k-means. The legendshows the cluster size and default rate for each cluster.

K-means longitudinal analysis spanning 90 days before andafter a loan disbursement further clarify the savings behavior ofindividuals that took out a loan. Figure 4 shows that low
balance account holders can be separated into three main
clusters with varying history lengths. The analysis indicatesthat individuals with the shortest deposit history prior to taking
out a loan tend to maintain the lowest account balances, while
also having the highest default rates. Furthermore, the results
clarify that the balance dip prior to taking out a loan is linked tousers that have over 40 days of account history. Examining thebalance after loan disbursement indicates that on average
account activity is minimal, with no significant change
observed for any of the clusters. Lastly, exclusion of features
related to mobile money or airtime usage results in small
differences in the default rate between clusters. This suggestthat rigorous modeling of credit worthiness still depends on alengthier list of features about customer behavior, with saving
accounts usage providing some weaker insights for default
rates.

Figure 4 Longitudinal k-means clustering for 90 days before andafter a loan disbursement of mobile deposit account holders.
Associated cluster size and default rates are displayed in the legend.

Ongoing work focuses on understanding how savings data
can be leveraged for predicting credit worthiness andassociated credit scores. The longitudinal k-means method is tobe repeated with additional inclusion of mobile money andairtime usage. Further work will also explore patterns of
behavior based on relative account balance changes, instead of
the absolute account balances and will employ additional
longitudinal methods, specifically latent class mixed models.

REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]

Nick Hughes and Susie Lonie “M-PESA: Mobile Money for the“Unbanked” Turning Cellphonrd inyo 24-hour Tellers in Kenya.”Innovations: Technology, Governance, Globalization 2007 2:1-2, 63-81
Etzo, Sebastiana, and Guy Collender. "The mobile phone ‘revolution’in
africa: Rhetoric or realty?." African affairs (2010): adq045.
Porteous, David. "The enabling environment for mobile banking in
Africa." (2006).
Asongu, Simplice A. "How has mobile phone penetration stimulated
financial development in Africa?." Journal of African Business 14.1
(2013): 7-18.
Donovan, Kevin. "Mobile money for financial inclusion." Information
and Communications for Development 61 (2012): 61-73.
Cook, Tamara, and Claudia McKay. "How M-Shwari works: The story
so far." Consultative Group to Assist the Poor (CGAP) and Financial
SectorDeepening(FSD)(2015).

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Route flow estimation using trips extracted from cellular network
data
Nils Breyera , David Gundlegårda , Clas Rydergrena
a Department of Science and Technology (ITN)

Linköping University
601 74 Norrköping, Sweden∗ Corresponding author. Email: nils.breyer@liu.se, Phone +4611-36 32 80

routes from cellular network data. For example
just matching to a road because it is close to abase station tower that a user connected to is very
unrealistic given the big area that base stations
cover.
The evaluation of route inferring algorithms is
usually made for individual routes or single ODpairs only. For practical applications, like estimating infrastructure use from cellular network data,
it is important to understand the impact of routeestimation algorithms when embedded into a complete network assignment process. We therefore
propose a pipeline to estimate flows for the individual links in a transportation network from cellular
network data. We use the pipeline to evaluate the
Keywords: cellular network data; route estimation; effects of different algorithms for estimating the
network assignment
used routes in a transport network from CDRs.
We compare the characteristics of the estimatedroutes and the impact of different route estima1 Introduction
tion algorithms on traffic flows.
Abstract. The signalling data in cellular networks
provide means for analyzing the use of the transportation system. We propose methods that aim to reconstruct the used route through a transportation networkfrom call detail records (CDRs) which are spatially andtemporally sparse.
The route estimation methods are compared regarding the characteristics of the routes estimated. We
also investigate the effect that the use of different routeestimation methods has when used in a complete network assignment for a larger city. Using CDR data for
Dakar, Senegal we show that the choice of the routeestimation method can have a significant impact on
resulting link flows. The data was made available by
Orange/Sonatel within the framework of the D4D challenge (de Montjoye et al., 2014).

Using cellular network signaling data like Call Detail Records (CDRs) is a promising way of getting
insights about the usage of the transportation system including all modes of travel. In order to gain
insights about travel patterns, the raw data has
to be filtered and processed. A big challenge with
CDR data is that a record including a timestamp
and the basestation that the user was connected
to is only saved when the user actively makes acall or writes or receives a text message.
As only the movements of users are of interest, a necessary first step is to extracting trips
from the data. The information which cells auser connects to during a trip gives an indication about which mode and which route that has
been used. To infer the route for road-boundtraffic, Fillekes (2014) discovered problems withusing simple map-matching techniques to recover

2

Computational pipeline

Several steps are necessary in order to estimatetransport infrastructure use from cellular network
data. In Gundlegård et al. (2016) a computational
pipeline was proposed similar to the one in Figure
1. The first step is to extract trips from the data.
Each trip has an origin and destination cell anda cellpath. The cellpath p = (b1 , ..., bn ) is the listof base stations that a user connected to during
the trip. The trip extraction method used for theexperiments in Section 4 is the same as describedin Gundlegård et al. (2016). The extracted trips
are used in order to calculate a travel demand ineach pair of cells, which is stored in an origindestination matrix (OD-matrix). Currently, we
use an unscaled OD-matrix generated by aggre-

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gating the detected trips for one day. In order toestimate the actual flows in terms of vehicles or
people, scaling would be necessary assuming thatthe data does not cover the whole population.
The cellpath routing estimates a route throughthe transportation network for a given cellpath. In
the final flow distribution step, the travel demand
given in the OD-matrix is distributed among theestimated routes and assigned to the transportation network. The cellpath-routing is implemented
for road-bound traffic, though similar algorithms
might be applicable to other modes as well. The
result of the pipeline can be expressed either inform of route flows or link flows.

Cellulardata

Tripextraction

OD-matrix
estimation
Cellpathrouting

shortest path calculation on the given transportation network.
• Waypoint Routing: By selecting a waypointin each Voronoi cell that appears in the cellpath, the route has to visit each cell thatappears in the cellpath. The waypoints are
selected such that the impact on the cost of
the total travel time is minimized.
• Magnetic Routing: The link costs are lowered for links close to cells in the cellpath.
The route between s and t is a shortest pathcalculation but using the modified link costs,
which makes the route more likely to follow
the cellpath. This algorithm is a simplified
version of the idea proposed by Leontiadis
et al. (2014).

Flow
distribution route/linkflows• Magnetic Waypoint Routing: A waypoint isplaced in a few selected cells of the cellpath
to ensure that the route follows the shape ofthe cellpath approximately. Between waypoints Magnetic Routing is used in order tomake the route likely to follow the cellpath
between waypoints.

Figure 1: Computational pipeline to estimate traffic
flows from cellular network data. The gray parts are
not covered in this article.

3

Cellpath routing

We formulate the cellpath routing problem as follows: Given a cellpath p = (b1 , ..., bn ) consisting of
the base stations that a user connected to alonga trip, find the route through the road network,that the user most probably took. Furthermore,
the base station postions are known. As no otherinformation about the base station configuration
is available, a Voronoi diagram is used as an estimation of the coverage area of each base station.
The link costs on the road network correspond to
the free-flow travel time. The route estimation canbe divided into two steps: First, a startnode s end
an endnode t for the route inside the Voronoi cellsof b1 respectively bn is selected. Second, a route
through the road network is estimated between s
and t. The selection of the start- and endnode fora route is the same for all algorithms presented
in this section. We propose the following 4 algorithms that differ in the way the route between s
and t is calculated:

Table 1 shows a summary of the characteristics of the different algorithms. For the algorithms that require parameters, two parameter setups have been tested. Variant 1 heavily reduces
the link costs within cells that appear in the cellpath but not around the cells in the cellpath, whilevariant 2 only cuts the cost by half and lowers thecost around the cells slightly.

4

Experiments & results

To compare the cellpath routing algorithms described in Section 3, we used CDR data for thecity of Dakar, Senegal from the D4D challenge(de Montjoye et al., 2014) provided by Orange.
Direct Routing generates the cheapest and also
shortest routes (see Figure 2). This is because Direct Routing uses the shortest path between startand endpoint, while all other algorithms potentially prolong the route in order to match the cellpath. Most impact on both route cost and route
length had Waypoint Routing. Compared to Di• Direct routing: The route estimation be- rect Routing, the average travel time increased bytween startnode s and endnode t is a single 26.7 %. Comparing a network assignment using

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Improving human mobility prediction with geo-tagged pictures
M. G. Beiró, A. Panisson, M. Tizzoni, C. Cattuto1
1. ISI Foundation, Turin, Italy

1.

Introduction

Predicting human mobility flows at different spatial scales and aggregation levels has interesting applications in areas such as urban planning, resource allocation and disease spreading control, for example. In thissense, the vast amounts of geolocated data currently generated by mobile devices as cellphones or digital
cameras provide a unique opportunity for improving our understanding of human mobility processes, withimmediate benefits.
In this work we analyze a set of 18 million timestamped, georeferenced pictures from Flickr, taken by
40,000 users in the U.S, which are part of the Yahoo Flickr Creative Commons 100M public dataset [1].
We show that by assimilating these data into well-established mobility models as the gravity model or theradiation model we can improve their predictions significantly.

2.

Methodology and results

We use the Flickr georeferenced pictures to model two different types of human mobility flows: (a) daily
commuting at the county level and; (b) flows in the air-transportation network. For each case, we have real
flow patterns provided by the U.S. Census Bureau [2] (for the commuting flows), and the U.S. Bureau ofTransport Statistics [3] (for the air-travel flows), which we use as ground-truth for our models.
We process the geotagged pictures of each user, ordered by the time in which they were taken. Each ofthese pictures is assigned to a geographical basin according to its coordinates. In the commuting network
the basins are the counties, while in the air-travel network we define the basins from a Voronoi tessellationof the airports network. Then, we consider that a user makes a trip between two basins i and j whenever hetakes two consecutive pictures in those basins. After this process, the network of Flickr users’ flows involves
350,000 trips between airport basins and 520,000 trips between counties.
Now, we evaluate two well-known models of human mobility: (a) the radiation model [4], which considers
human movements as diffusion processes that depend on the population distribution over the space, reproducing Stouffer’s theory of intervening opportunities and; (b) the gravity model [5], in which the amount ofpeople travelling between two locations is computed as directly proportional to some power of their populations, and decays as some power of the distance between them. We combine these models and our Flickr
users flows into a hybrid model, using a stacked regression procedure expressed as:
H(α, β, γ, A, B; P) = A · B(α, β, γ; P) + B · F,
where H = (hij ) represents the matrix of flows between basins in our hybrid model, B represents the flows
in the base model (gravity or radiation) and F represents our Flickr users flows matrix; α, β, γ, A, B are
real-valued fitting parameters (α, β, γ only apply for the gravity model). We will fit the model by minimizing
the following loss function L:
L = Y − H(α, β, γ, A, B; P) ,

where  ·  denotes the Frobenius norm.
In order to evaluate the predictions we adopted a machine learning approach. The model was fitted andcross-validated using a 10-fold cross validation procedure. In Table 1 we showed the performance of our
1

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hybrid model compare to that of the base gravity or radiation models, in terms of the Pearson correlation
between the real and predicted flows, and in terms of the determination coefficient.
Model
Gravity model
Radiation model (*)
Flickr modelHybrid model (Gravity+Flickr)
Hybrid model (Radiation+Flickr)

Commuting
ρ
r2
0.69
0.41
0.78
0.60
0.69
0.47
0.79
0.62
0.85
0.73

Air travelρ
r2
0.68 0.40
0.47 -0.21
0.78 0.62
0.84 0.72
0.80 0.64

Cuadro 1: Cross-validated model performance (10-fold cross-validation). The table shows the performance of the hybrid models in terms of the Pearson correlation coefficient and the determination coefficient.
We also display the results for the gravity model, the radiation model and the Flickr model alone. All thevalues were produced under a 10-fold cross-validation scheme, except for the radiation model (as noted by
the asterisk), which is parameter free.
We observe that the assimilation of the Flickr traces into a hybrid model produced a substantial increasein the predictive performance both for daily commuting as for air travel in the U.S., as shown in the last
two rows of Table 1. In particular, we observed that the incorporation of Flickr traces solved one biases of
the gravity model, i.e., the underestimation of large flows across distant cities, as previously observed in [6].
A comparison between the real and estimated flows for the gravity model and the hybrid model assimilating
the Flickr traces is shown in Figure 1.

3.

Conclusions

In this work we investigated the quantitative effect that the use of geolocalized traces may have in theprediction of human mobility flows at different scales. We showed that the assimilation of geolocalized traces,
as those collected from Flickr, can significantly improve the predictions of well-established human mobility
models as the gravity model and the radiation model.
Additionally, we evaluated the out-of-sample true predictive power of the models by using a learning
approach, and we assessed their applicability in two different spatial scales.

4.

References[1] Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, and Li L (2016) Yfcc100m:
The new data in multimedia research., Comm.ACM 59(2), 64-73.
[2] Bureau of Transportation Statistics. Commuting (journey to work).
URL http://www.rita.dot.gov/bts/dataa nds tatistics/index.html, Dateof access : 11/04/2016.
[3] US Census Bureau. Airline origin and destination survey.
URL http://www.census.gov/hhes/commuting/, Date of access: 11/04/2016.
[4] Stouffer SA (1940) Intervening opportunities: A theory relating mobility and distance. Am Soc Rev 5(6),845-867.
[5] Alonso W (1976) A Theory of Movements: I, Introduction. Working paper No. 266. Institute of Urban
Regional Development, University of California, Berkeley.
[6] Simini F, González MC, Maritan A, Barabási A-L (2012) A universal model for mobility and migration
patterns. Nature 484(7392), 96-100.

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Figura 1: Predictions for the gravity model and the hybrid (gravity+Flickr) model. These 2Dhistograms compare the predicted values against the real flow values for (A, C) the gravity model; (B, D)
the hybrid model. Each point represents flows with some real/estimated flow value relation. Points color ina gray scale represents the frequency values. The boxplots correspond to the interquartile range within abin. (A, B) U.S. Commuting network. (C, D) U.S. air travel network.

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Inferring Commuting Statistics in Greater Jakarta fromSocial Media Locational Information from Mobile Devices
Imaduddin Amin, Ni Luh Putu Satyaning P.P, Yulistina Riyadi, and Jong Gun Lee
Pulse Lab Jakarta - United Nations Global Pulse
Email: {imaduddin.amin, ni.paramita, yulistina.riyadi, jonggun.lee}@un.or.id
Setia Pramana and Robert Kurniawan
Institute of Statistics, Jakarta, Indonesia
Email: {setia.pramana, ​robert}@stis.ac.id

ABSTRACTJakarta is the biggest city in Indonesia with a populationof more than 10 million and a megacity with 1.38 million
people commuting around Greater Jakarta [1]. In order for
the Government of Indonesia to understand the commuting
behaviors of citizens and better plan the transportation
system in Greater Jakarta, the Indonesian Central Bureauof Statistics first conducted its commuting survey in 2014.
In this paper, we produce commuting statistics fromlocational information on social media and show that the
information from mobile phone apps is a promising sourceof data. It implies that the commuting behaviors from big
data can allow the public sector more frequent statistics.

1 INTRODUCTION
It is known that about 1.38 million people commute daily
in Greater Jakarta or Jabodetabek, which includes ​Ja​karta,
Bo​gor, ​De​pok, ​Ta​ngerang and ​Bek​asi. The scale of thecommuting population along with the population of Jakarta(about 10 million) has caused Greater Jakarta to face manyurban challenges, including transportation. This led theGovernment of Indonesia to conduct the first commuting
survey by the Indonesian Bureau of Statistics (Badan Pusat
Statistik or BPS) in 2014.
Among many development issues, transportation is oneof the key issues that directly affect citizens’ daily lives. In
many cases, a survey-based data collection method is
widely used but one of its inherent drawbacks is that suchan approach takes some time from the design of a survey tothe release of its result. For instance, the first commuting
survey in Greater Jakarta took longer than one year.
Consequently, there are many attempts to use other typesof data to produce similar statistics using different kinds of
geo-located information such as GPS devices, sensors,social media and mobile phone data, in a shorter cycle [2, 3,
4, 5, 7, 8, 9].
In Indonesia, social media is recognized as a promisingdata source to understand the behavioral patterns ofpeople, specifically as about 88.1 million people use theInternet and among them about 79 million people use theInternet for accessing social media1 . Jakarta is often named
as the Twitter capital of the world with 10 million tweets
every day. As another example, in Greater Jakarta,
between January and May 2014 for five months, about 40
million tweets were posted with GPS information.
In this paper, we test how the locational information fromsocial media on mobile devices can reveal commuting
patterns in Greater Jakarta. First we produce Origin Destination statistics given 10 cities in Greater Jakarta fromthe entire GPS-stamped tweets posted by mobile devices,by identifying a set of people who commute in these areas.
Second, we calibrate the initial result based on the
population distribution and Twitter user distribution. Finally,
we verify the result with the official commuting statistics.
Our result confirms that geo-located tweets has thepotential to complement official commuting statistics to fill
information gaps.

2 DATA SET
2.1 Official Commuting Statistics

The Indonesian Central Bureau Statistics (BPS)
conducted the first commuting survey in household level
covering 13,120 households from 1,312 census blocks. Thissurvey aims to understand the behavior of commuting users
in 13 cities or regencies in Greater Jakarta which includes
five Jakarta cities (Central, South, North, East, and West
Jakarta), five satellite cities (Bogor, Bekasi, Depok,
Tangerang, and South Tangerang), and three regencies(Bogor, Bekasi, and Tangerang). Greater Jakarta is aloosely defined term with few definitions. For instance, one
defines it with 13 cities and regencies but another
understands Greater Jakarta only with 10 cities. We use thelatter, which is widely perceived by citizens, excluding three
1“E-Government Indonesia” (Source: Ministry of Communication and ICT)
http://www.detiknas.go.id/wp-content/uploads/2016/08/PAPARAN-SNGIDC-2
016-Bambang-Dwi-Anggono.pdf

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SESSION 2
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regencies, i.e., Kabupaten (Regency) Bekasi, Kabupaten
Tangerang, Kabupaten Bogor, which rather connect otherclose provinces such as Banten and West Java.
Table 1 shows a part of the commuting survey results
particularly showing commuters from the 10 cities in Greater
Jakarta to five cities in Jakarta. For instance, among thetotal number of commuters to the five cities in Jakarta,
2.31% of people commute from South Jakarta to East
Jakarta.
DESTINATIONORIGIN
SJ

EJ

CJ

WJ

NJ

South Jakarta (SJ)

-

2.31%

4.69%

1.88%

0.97%

East Jakarta (EJ)

5.33%

-

4.66%

1.62%

3.34%

Central Jakarta (CJ)

1.83%

0.86%

-

1.70%

1.20%

West Jakarta (WJ)

2.65%

0.46%

4.65%

-

4.19%

North Jakarta (NJ)

0.87%

1.14%

3.17%

1.82%

-

Bogor (Bo)

0.34%

0.27%

0.39%

0.25%

0.05%

Bekasi (Be)

3.36%

7.27%

3.48%

1.10%

1.73%

Depok (De)

7.40%

2.35%

2.37%

0.72%

0.54%

Tangerang (Tg)

2.62%

0.17%

1.62%

4.23%

0.45%

South Tangerang (ST)

6.13%

0.33%

1.89%

1.30%

0.27%

Table 1 Commuting Survey 2014 - Statistics (%) from 10
cities in Greater Jakarta to 5 cities in Jakarta

2.2 Twitter Data
We collect all GPS-stamped tweets posted in Greater
Jakarta from a data firehose and specifically for this study,
we use a set of tweets posted between January 1st 2014
and May 30th 2014, for five months, considering that theofficial commuting survey was conducted during the firstquarter of 2014. Given all tweets in the Greater Jakarta
area, we first filter tweets posted using mobile devices only
and then map and use the locations of tweets to theadministrative resolution. Finally, we use 38,491,430 tweets
from 1,456,927 unique users.
​3

METHODOLOGY
3.1 Inferring Origin-Destination LocationsFirst, per user, we infer two locations, Origin (Home) and
Destination. It is worth noting that even though we finallyproduce city-level statistics, we process the locations of
one’s tweets at sub-district level, when determining theOrigin and Destination cities.

Home Location ​is inferred as the most tweeted
sub-district location between 9pm and 7am inorder to avoid using the locational information
during commutes.
● Destination Location is determined as the most
tweeted sub-district location during weekdays,
excluding Home Location.
Using this approach, among 1,456,927 unique users
who posted tweets in Greater Jakarta for five months fromJanuary 2014, we find the origin information of 877,054
users, and the origin and destination information of 305,761
users in sub-district level, which is about 2.8% (14%) of thewhole population (the commuting population, respectively)
in Greater Jakarta.
●

3.2 Calibrating the Initial Result
Once we map the Origin-Destination information at
sub-district level to city level, we do a calibration based on
the population data from 10 cities, due to the unequal
penetration rates of Twitter in the cities. For instance,
people in South Jakarta post tweets much more than people
who are based in other cities in Greater Jakarta. After thecalibration with Twitter penetration information2 , thecross-correlation score between two forms of statistics,
official statistics and the statistics from our approach
improved from 0.92 to 0.97.

4 RESULTS

The final result is presented in Table 3(a) and its chord
diagram is shown in Figure 1. In Table 3(b) we show therank difference between Table 2 and Table 3(a). For
instance, the value for SJ ⇒ CJ is calculated as ‘0’ because
two ranks from two statistics are same as people in South
Jakarta most commute to Central Jakarta than other threecities. Table 3(b) shows that our simple approach produces
good results without significant differences. Even though
three origin cities have three different rank pairs, it is worth
mentioning that the values in the official statistics are
originally close each other, and our results also present
similar values. For instance, the values of De ⇒ EJ and De⇒ CJ are 2.35 % and 2.37% in the official statistics (2.46%
and 2.39%, respectively and in our results).

5 SUMMARY

We have shown that social media is a promising sourceof data to infer commuting statistics in Greater Jakarta. For
future work, we will improve our simple approach with othercalibration methods utilizing further information, such as
demographic information.
2​It is worth mentioning that we tried a calibration based on a basic
gravity model but it does not produce a significant improvement.

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SESSION 2
MOBILITYREFERENCES
[1]
[2]

[3]

[4]

Jakarta Commuting Survey
http://www.bps.go.id/index.php/publikasi/4416
Palchykov V, Mitrović M, Jo HH, Saramaki J, Pan RK. Inferring
human mobility using communication patterns. Scientificreports. 2014;4. doi: 10.1038/srep06174. pmid:25146347
A Kurkcu, K Ozbay, EF Morgul, "Evaluating the Usability of
Geo-Located Twitter as a Tool for Human Activity and Mobility
Patterns: A Case Study for New York City"
B Hawelka, I Sitko, E Beinat, S Sobolevsky, P Kazakopoulos,
C Ratti, Geo-located Twitter as proxy for global mobility
patterns

[5]
[6]

[7]

[8]

[9]

Liu, W., Zamal, F.A., & Ruths, D., (2012) Using social media toinfer gender composition of commuter populations.
Swier, N., Komarniczky, B., & Clapperton, B. (2015) Using
geolocated Twitter traces to infer residence and mobility. GSS
Methodology Series No 41
Gonzalez MC, Hidalgo CA, Barabási A-L. Understanding
individual
humanmobility
patterns.
Nature.
2008;453(7196):779–782.
doi:
10.1038/nature06958.
pmid:18528393
Palchykov V, Mitrović M, Jo HH, Saramaki J, Pan RK. Inferring
human mobility using communication patterns. Scientificreports. 2014;4. doi: 10.1038/srep06174. pmid:25146347
Jiang S, Fiore GA, Yang Y, Ferreira F Jr, Frazzoli E, González
MC. A review of urban computing for mobile phone traces:
current methods, challenges and opportunities. In Proceedingsof the 2nd ACM SIGKDD International Workshop on Urban
Computing (p. 2). ACM. (2013, August).

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An emergent strategy for characterizing urban hotspot dynamics
via GPS data.
Antonio L. Alfeo1, Mario G.C.A. Cimino1, Sara Egidi1, Bruno Lepri2 and Gigliola Vaglini1
1

Department of Information Engineering, Università di Pisa, largo Lazzarino 1, Pisa, Italy
1
Bruno Kessler Foundation, via S. Croce, 77, Trento, Italyluca.alfeo@ing.unipi.it, mario.cimino@unipi.it, s.egidi1@studenti.unipi.it, lepri@fbk.eu, gigliola.vaglini@unipi.it.
Keywords: Urban mobility, taxi-GPS traces, stigmergy, emergent paradigm, hotspot.

INTRODUCTION AND MOTIVATION
The increasing volume of urban human mobility data arises
unprecedented opportunities to monitor and understand city
dynamics. Identifying events which do not conform to the
expected patterns can enhance the awareness of decision
makers for a variety of purposes, such as the management ofsocial events or extreme weather situations [1]. For this purpose
GPS-equipped vehicles provide huge amount of reliable data
about urban dynamics, exhibiting correlation with humanactivities, events and city structure [2]. For example, in [3] the
impact of a social event is evaluated by analyzing taxi traces
data. Here, the authors model typical passenger flow in an area,
in order to compute the probability that an event happens. Then,the event impact is measured by analyzing abnormal trafﬁc
ﬂows in the area via Discrete Fourier Transform. In [4] GPS
trajectories are mapped through an Interactive Voting-based
Map Matching Algorithm. This mapping is used for off-line
characterization of normal drivers’ behavior and real-timeanomalies detection. Furthermore, the cause of the anomalies isfound exploiting social network data. In [5] the authors employ
a Multiscale Principal Component Analysis to analyze Taxi
GPS data in order to detect traffic anomalies. The most of themethods in the literature can be grouped into four categories:
distance-based, cluster-based, classification-based, andstatistics-based [6]. Typically, due to the complexity of thiskind of data, the modeling and comparison of their dynamics
over time are hard to manage and parametrize [7]. In this paper,
we present an innovative technique aimed to handle suchcomplexity, providing a study of urban hotspot dynamics.

dynamics while removing insignificant activity levels. At aperiodic time instant, a mark is released in a computersimulated spatial environment (stigmergic space). The markintensity is proportional to the number of people. Marks
aggregation forms a trail, which progressively evaporates. For agiven threshold of trail intensity, areas with intensity higher
than the threshold correspond to hotspots. As an example, Fig.
2 shows the hotspots identified in Manhattan (New York).
Their locations correspond to: East Harlem - Upper East Side(A), Midtown East (B), Broadway (C), East Village -Gramercy- MurrayHill (D), Soho - Tribeca (E), Chelsea (F) and Time
Square – Midtown West - Garmet (G).

Figure 1. Positional stigmergy for hotspot identification.

APPROACH DESCRIPTION
The developed approach is based on stigmergy, a mechanism
belonging to the emergent paradigms. Emergent paradigms
allow to avoid the explicit modeling of a system, which works
only under the assumption formulated by the designer.
Emergent paradigms offer model-free computational approach,characterized by adaptation, autonomy and self-organization ofdata [8]. In particular, with the emergent mechanism based oncomputational stigmergy, each sample position is associated toa digital pheromone deposit (mark). Marks are defined in athree-dimensional space, and characterized by evaporation overtime, i.e. a progressive decay of mark intensity. Marks
aggregate according to their spatio-temporal proximity, forminga stigmergic trail. As an effect, the evaporation is counteracted
while new marks arrive in a given region. Thus, aggregationand evaporation can produce an emerging mechanism whichacts as an agglomerative spatio-temporal clustering withhistorical memory. Employing the principle of stigmergy, we
exploit positioning data to identify high-density areas (hotspots)
within a city and to analyze their activity (presence of people)
over time. Fig. 1 shows the positional stigmergy applied tohotspot identification. First, input data undergo the smoothing
process, which focuses the analysis, highlighting relevant

Figure 2: Hotspots identified in Manhattan (2015).

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In order to characterize the dynamics of the identified hotspots,
the activity of each hotspot generates a mono-dimensional timeseries for every observation day. In a given time series, what is
actually interesting is not the continuous variation of theactivity over time, but the transition from one type of behavior
to another. Each type of hotspot activity behavior should be
defined by an expert in the field, in order to be general andreusable for many hotspots and many cities. More formally,each type is called an archetype, since it is an ideal time series
segment defining a behavioral class of the hotspot activity. An
example of archetype is rising activity, which means that the
hotspot is moving from an ordinary activity level to its highest
activity level. Fig. 3a and Fig. 3a' show such archetype and areal example of time series, respectively.

receptive fields, providing a value between zero and N-1, where
N is the number of archetypes, called activity level.

Figure 4: System architectural overview.

Figure 3. Transformation of an activity time series into a similarity
time series with respect to an archetype.

In order to assess the match between an archetypal behavior
and the current hotspot behavior, the time series are processedin sequential stages (Fig. 3). The initial samples first undergo
the clumping process, which is a soft discretization of samples
with respect to a set of levels of interest for any archetype (Fig.
3b and Fig. 3b'). Subsequently, in correspondence of eachsample value, a trapezoidal mark is released (Fig. 3c and Fig.
3c'). Marks aggregate over time in a trail. Trail intensity is
subject to evaporation. As an effect, a trail captures thespatiotemporal behavior of the time series in the short-term. A
degree of similarity between the archetype trail and actual
activity trail is then computed (Fig. 3d). Finally, to better
sharpen the similarity value against the other archetypes, anactivation function is applied to enhance only relevant
similarity values, while removing insignificant values.
The overall processing schema is called stigmergic receptive
field (receptive field for short), because it is receptive to a
specific archetype, and it takes inspiration from theneurocomputing domain. A receptive field should be properly
parameterized to effectively assess the archetypal behavior. For
example, short-life marks evaporate too fast, preventing
aggregation and pattern reinforcement, whereas long-life marks
cause early activation. For this purpose, the receptive field isequipped with a parametric adaptation mechanism, based on the
differential evolution algorithm [9]: parameters are adjusted byminimizing the mean square error over a set of annotatedsample signals.
Since any real signal is usually similar to more than one
archetype, a collection of receptive fields specialized on
different archetypes is arranged to make a stigmergic
perceptron, i.e., a connectionist topology whose architecture isrepresented on the left of Fig. 4. More specifically, thearchetypes are ordered for increasing activity of the hotspot: a)asleep, b) falling, c) awakening, d) flow, e) chill, f) rising, andg) rush-hour hotspot activity. The stigmergic perceptron
combines linearly the similarity values provided by all

As a result, the stigmergic perceptron provides a new timeseries of activity levels for a given time series of activity
samples. In order to compute the overall similarity between twodays of hotspot activity, a further receptive field is used. Such
receptive field compares the time series of two activity levels
corresponding to two days. This measure of similarity is used to
identify anomalies in the hotspot activity. For this purpose, we
assume that during days characterized by an anomaly, thehotspot activity level is dissimilar with the activity level of
equivalent days. Two days are considered equivalent if they fallon the same day of the week and in the same month. In order todetect the anomaly, we define the normality index of a given
day as the average similarity of that day with its equivalentdays. The normality index is 1 (or 0) if the day is perfectly
similar (dissimilar) to its equivalent days.
The overall architecture allows the incremental detection of
perturbations on the city routine that can involve the hotspots
for a variety of reasons. As an example, the next section showssome experimental results on anomalies caused by adverse
weather conditions.

EXPERIMENTAL RESULTS AND DISCUSSION
The analysis is based on taxis OD (Origin-Destination) traces
provided by Taxi and Limousine Commission of New York
City [10], which contains information about all medallion taxi
trips from 2009 to 2016. Data are spatio-temporally discretizedin bins characterized by 10 ft. wide and 5 minutes duration. In
order to focus on urban hotspot dynamics the number ofpassengers in each bin, in terms of both pick-up and drop-off is
first considered. Subsequently, the min-max normalization isapplied. As a case study we focus on anomalies caused by anadverse weather condition. Specifically, we analyze Manhattan
in January 2015. In January 26-27, 2015 many mobility issues,caused by a blizzard, were reported [11]. We refer thisknowledge as the ground truth. In Fig. 5 the activity level andthe corresponding stigmergic trail have been generated
analyzing the activity in hotspot G in every Tuesday of January
2015. Here, it is apparent that the January 27 is characterized
by an anomaly, for the notable differences with the other days
both in terms of activity level (left side) and stigmergic trail(right side). A measure of these differences is based on thesimilarity values supplied by the receptive field, used to
calculate the normality index of each hotspot. In Fig. 6 andFig.7, a radar chart shows the normality indexes for all hotspots
in each Tuesday and Monday of January, respectively.

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1 shows, for equivalent days, the average normality index
calculated between all hotspots. Again, it is apparent that thelowest values are related to the blizzard.
Table 1. The average of the normality index, over all city hotspots.
Mondays05 Jan12 Jan19 Jan26 JanAverage Normality

0,45

0,42

0,38

0,28

Tuesdays

06 Jan13 Jan20 Jan27 JanAverage Normality

0,46

0,48

0,45

0,35

Although the study focuses on a blizzard, the analysis can be
carried out on other events. Moreover, our approach provides asimilarity measure that may be used with clustering techniques.
Clustering can discover a structure in data and may generate
data-driven prototypes of normal days, which are more
effective than calendar-driven days. For this reason, to adopt aclustering technique is considered a key investigation task for
future work.
Figure 5. The activity levels on Hotspot G (on the left) during each
Tuesday of January 2015 and the corresponding trails (on the right)
which feed by the second level receptive field.

REFERENCES[1] Sagl, G., Loidl, M., & Beinat, E. (2012). A visual analytics approach forextracting spatio-temporal urban mobility information from mobile network
traffic. ISPRS International Journal of Geo-Information, 1(3), 256-271.
[2] Veloso, M., Phithakkitnukoon, S., & Bento, C. (2011, September).
Urban mobility study using taxi traces. In Proceedings of the 2011
international workshop on Trajectory data mining and analysis (pp. 23-30).
ACM.
[3] Zhang, W., Qi, G., Pan, G., Lu, H., Li, S., & Wu, Z. (2015). City-scalesocial event detection and evaluation with taxi traces. ACM Transactions
on Intelligent Systems and Technology (TIST), 6(3), 40.
[4] Pan, B., Zheng, Y., Wilkie, D., & Shahabi, C. (2013, November).
Crowd sensing of traffic anomalies based on human mobility and social
media. In Proceedings of the 21st ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems (pp. 344353). ACM.

Figure 6. Normality index of January Tuesday for each Hotspot.

[5] Kuang, W., An, S., & Jiang, H. (2015). Detecting traffic anomalies inurban areas using taxi GPS data. Mathematical Problems in Engineering,2015.
[6] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A
survey. ACM computing surveys (CSUR), 41(3), 15.
[7] Castro, P. S., Zhang, D., Chen, C., Li, S., & Pan, G. (2013). From taxi
GPS traces to social and community dynamics: A survey. ACM Computing
Surveys (CSUR), 46(2), 17.
[8] Vernon, D., Metta, G., & Sandini, G. (2007), A Survey of Artificial
Cognitive Systems: Implications for the Autonomous Development of
Mental Capabilities in Computational Agents. IEEE Transactions on
Evolutionary Computation, 11(2), 151-180.
[9] Cimino, M. G., Lazzeri, A., & Vaglini, G. (2015, June). Improving theanalysis of context-aware information via marker-based stigmergy anddifferential evolution. In International Conference on Artificial Intelligenceand Soft Computing (pp. 341-352). Springer International Publishing.
[10] NYC.gov, the official website of the City of New York: Taxi andLimousine Commission (TLC) Trip Record Data, http://www.nyc.gov/
html/ tlc/html/about/trip_record_data.shtml

Figure 7. Normality index of January Mondays for each Hotspot.

Since a blizzard is a city-wide event which affects urban
activity in the whole city, any hotspot during 26 and 27 January(purple line) results in the lowest normality index values. Table[11] Weather NYC: Thousands of transatlantic travellers face serious
disruption caused by New York winter storm 'Juno'. The Independent.
January 26, 2015.

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Inferring customer visitors by means of WiFi
crumblesFabio Pinelli, Carlo Ceriotti and Emilia Traficante Cloud4wi.com Inc., 363 Clementina Street
San Francisco, CA 94103
Email: {fpinelli,cceriotti,etraficante} @cloud4wi.com

In the online world, it is easy for businesses to gather
valuable information about their customers, such as: whothey are, what theyre searching for, and how much timethey spend browsing. Brick-and-mortar locations have traditionally been at a disadvantage when it comes to knowing
customers, however, because its nearly impossible to knoweach person that walks into a venue. But as the demand forguest Wi-Fi grows, businesses are realizing they can achieve
insights comparable to what theyd get online through analytics
collected from Wi-Fi networks something that was neverbefore possible. Businesses can make data-driven operationaland strategic decisions and cultivate customer relationships
by analyzing foot traffic, evaluating customer behavior, andunderstanding where visitors spend most of their time and how
they move within the physical space of a venue. By having
this information, businesses can:• Monitor marketing campaign performance• Position displays and products where there is significant
foot traffic• Optimize business hours• Allocate support and security services more effectively
For these reasons, it is becoming very crucial to enhance
the analysis of the data gathered from a guest Wi-Fi network
infrastructure. Once this has be done, then retailers andstore managers can deliver new accurate services to the rightcustomers at right time. In particular, among all the detected
devices is necessary to understand which ones belong to areal customer and which ones belong to a passerby. In thiscontext, we propose a new machine Learning framework,
Smart Presence ANalyicts (SPAN) that leverages the Wi-Fi
data logs in order to distinguish visitors from passersby, so thatnew services can be designed and adopted using the insights
extracted by means of SPAN.
Several technologies are already available to count and trackpeople in store, such as Monocular (Single Lens) and 3D
Stereo Video, Thermal Imaging, Infrared Beams, and BLE
beacons. However, the Wi-Fi opens an unique opportunity forretailers to establish a direct communication channel with theircustomers.
Each technology has its own set of challenges and benefits.
The difference between the solutions also includes the relationship between the capture technology and data integration
requirements. Moreover, each solution has its own uniqueconcepts of accuracy and data quality.
Monocular devices capture images through a single lenscamera. The image can be processed within the sensor itself,

sent to an in-store server for analysis, or processed at a
central server. Due to costs and bandwidth requirements, mostcounters are At-the-Edge (computer and camera) devices andsend only metric data to the central server. Some methods
adopting this methodology are discussed in the following
papers: [6], [7], [9], [2].
The principle behind 3D Stereo Vision is the assumptionthat accuracy starts with capturing the most high-resolution
camera and creating a three-dimensional view of the tracked
object. The key challenge for stereo vision is cost, since
the counting solution requires special devices and cannot use
standard cameras. Some works investigating such technology
are presented in [8], [10], [1].
Thermal imaging works by detecting emissions from moving targets, by locking on to the targets, and then tracking
them within the sensors field of view. Thermal imaging ignores
the background features and focuses only on the moving
object. As a result, thermal technology is not sensitive tolight and allows the sensors to function well in challenging
conditions such as the fluctuations from darkness to bright
light. Examples of methods are described in: [4], [3], [5].
Infrared beams are mounted either sideways on the gate
door or top-down from the ceiling. These low-cost devices are
simple to install and setup. The device sends a direct infraredbeam, and counts the person moving across the doorway oncethe beam is broken. Unfortunately, beam counters suffer fromaccuracy challenges [11].
Differently from these methods, the insights can be extracted from the Wi-Fi data gathered costless by the network
infrastructure. This opens an unique opportunity for retailers,and store managers to enable a direct communication channel
with their customers.
In our case study, data are collected by several access points(APs) installed inside the venue. Each request probe, received
by an AP, is logged into the system with this information:
MAC address of the sending device, the signal strength, the x,yestimated location, the status of the device: associated or not.
The association is the process that enables the client the actual
access to the WLAN network. It is the same like plugging thecable into the wired network. When a device is associatedto one AP, it does not indicate necessarily that the user islogged into the Guest Wi-Fi network. Notice that the system
receives as raw data log separately from each AP, therefore, anaggregation step is needed to recover information generated at
device level.
The framework work-flow is reported in Figure 1. SPANBOOK OF ABSTRACTS: ORAL 5 - 7 April 2017 – Vodafone Village – Milan

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Fig. 1. The SPAN data work-flow, from the raw data to the predictions based on models extracted from historical data.

is composed of two main components, one working on(near)real-time and the other working in a batch mode. The
former receives logs from the APs and it performs an ETL
step. It aggregates probes sent by a certain MAC received
contemporary by more than one AP and it extracts the features
used by the machine learning module. For each aggregatedprobe, we extract cumulative and instantaneous features. The
cumulative intend to describe the behavior of a specific MAC
until that instant. For example, the number of probes sentby that MAC, the number of probes with associated status,
etc. In this way, we want to model the historical behaviorof the users in a sort of visit sessions. Instead, the set of
instantaneous features gives a snapshot of the current status of
the device, the APs which have received the probe, the RSSI
level, etc. The aggregated data with relative features are stored
in the Processed Data database. In the real-time component,
the processed data are given in input to the prediction step. For
each new aggregated probe, the classification model is applied
to classify the probe as visitor or passerby.
The SPAN batch component includes 3 steps: labeling,learning, and evaluating. The labeling takes in input theprocessed data (aggregated and with features) and it uses theground truth to label – if possible – each probe as passerby
or visitor. In the ground truth we know which devices are
passersby, visitors or stationaries. The ground truth is not give,
but it is based on some assumptions. The stationary devices
are the ones with probes for 10 hours in at least half of thedays in the historical dataset. We consider the best archetype of
passersby the users with probes sent only during the closing
time of the store, i.e. from 0 to 7 and from 21 to 24. All
the probes of these devices are labeled as passerby. Instead,as visitor prototype we use the behavior of users connectedto the guest Wi-Fi. We labeled as visitor the device probe
requests sent during a Guest Wi-Fi session. The definitionof visitor is quite restrictive, and it covers few users but it
represents a starting point to evaluate the effectiveness ofthe methodology. The labeling defined as such generates anunbalance datasets. Moreover, not all the received probes geta label, only the ones that belong to night, and guest Wi-Fi
devices. The labeled data are given as input to a learning loopthat includes the training and the evaluation steps. The training
phase creates prediction models. In this paper we used decision
trees. The evaluation step computes some quality measuresof the predictions, for instance: precision, recall, f 1 score,among the others. When the performance of the model is toopoor then another training session is executed.
For our tests, we used a dataset of a retail store with 7
distinct Access Points. The store is located in the city center.
The dataset covers a period of two months: July and August
2016. During this periods, we counted 5966731 aggregatedprobes sent by 750176 devices. In the experiments we used
July as training set and August as test set.
We detect 15 stationary devices, such as devices observedin at least 50% of the days for at least 10 hours each day.
We extracted the users observed only during the night with adaily basis. The results of this process are reported in Figure
2a where we can observe a sort of weekly pattern with evident
drops on Sunday nights. All the request probes of night users
are labeled as passerby.
The guest Wi-Fi logged-in users are reported in Figure
2b. It is easy to see the different order of magnitude of thetwo sets. We recall that the probes of a user falling into a
guest Wi-Fi session are labeled as visitor, passerby otherwise.
This difference is reflected into an unbalanced training set.
In fact, we have 10522 probes labeled as visitor, 153339
as passerby, and 2881709 probes without any label. To deal
with the unbalance nature of the dataset, we tested different
down-samples of the passersby probe set proportionally to thenumber of visitor probes. The input parameters of the training
phase are: the proportion between the number of visitors andpasserby; the minimum depth of the decision tree; and thenumber of features that can be used contemporary to split thetree. Preliminary results are shown in Figures 3a where we
reported the precision, recall and f 1 score values for the

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31

SESSION 2
MOBILITY(a) Night users(a)(b) Guest Wi-Fi users(b)

Fig. 2. The figure a) reports the daily number of night users detected tocompute the ground truth. The second shows the number of users using theguest Wi-Fi in each day.

Fig. 3. The figure a) reports the performance of different models in terms
of precision, recall, and f 1 score. Figure b) shows the daily number ofusers detected with our method and a manual counter.

top 5 models in terms of f 1 score testing the models againstlabeled data (i.e. only the part of the data getting a label). The
input parameters of the model are reported as labels on thegraph. Moreover, we evaluate the performance of best modelon the totality of the data. We compute the Pearson correlationand Median Average Percentage Error (MAPE) of the daily
number of users with at least one probe predicted as visitor,
against a manual counter. The results are reported in Figure 3b.
The MAPE has been computed as follow: i) we computed anaverage ratio between the predicted values and the real ones;i) we scale the predicted values accordingly to the ratio; iii)
we computed the MAPE. We obtained a Pearson Correlation
equal to 0.76 and a MAPE equal to 12.12%.
As general trend, figures show very promising results considering the simple adopted model and the loose definition ofvisitors.
The presented methodology leverages Wi-Fi probe request
data to distinguish passersby customers from real visitor. The
results confirm that goodness of the approach. Nonetheless,several directions are still open to be investigated: i) adopt
a more complex machine learning model; ii) adopt a moreaccurate definition of visitors, iii) test the methodology on
multiple locations.

[2] Homa Foroughi, Nilanjan Ray, and Hong Zhang. Robust people counting
using sparse representation and random projection. Pattern Recogn.,
48(10):3038–3052, October 2015.
[3] Weiming Hu, Tieniu Tan, Liang Wang, and S. Maybank. A survey on
visual surveillance of object motion and behaviors. Trans. Sys. Man
Cyber Part C, 34(3):334–352, August 2004.
[4] Miklas S. Kristoffersen, Jacob V. Dueholm, Rikke Gade, and Thomas B.
Moeslund. Pedestrian counting with occlusion handling using stereo
thermal cameras. Sensors, 16(1):62, 2016.
[5] Suren Kumar, Tim K. Marks, and Michael Jones. Improving person
tracking using an inexpensive thermal infrared sensor. 2014 IEEE
Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 00(undefined):217–224, 2014.
[6] Damien Lefloch, Faouzi A. Cheikh, Jon Y. Hardeberg, Pierre Gouton,
and Romain Picot-Clemente. Real-time people counting system using asingle video camera, 2008.
[7] Bin Li, Jian Zhang, Zheng Zhang, and Yong Xu. A people counting
method based on head detection and tracking. 2014 International
Conference on Smart Computing (SMARTCOMP), 00(undefined):136–
141, 2014.
[8] Xiaobai Liu. Multi-view 3d human tracking in crowded scenes, 2016.
[9] Venkatesh Bala Subburaman, Adrien Descamps, and Cyril Carincotte.
Counting people in the crowd using a generic head detector. In
Proceedings of the 2012 IEEE Ninth International Conference on
Advanced Video and Signal-Based Surveillance, AVSS ’12, pages 470–
475, Washington, DC, USA, 2012. IEEE Computer Society.
[10] Xiaogang Wang. Intelligent multi-camera video surveillance: A review.
Pattern Recogn. Lett., 34(1):3–19, January 2013.
[11] Hong Yang, Kaan Ozbay, and Bekir Bartin. Investigating the performance of automatic counting sensors for pedestrian traffic datacollection. In Proceedings of the 12th World Conference on Transport
Research, Lisbon, Portugal, volume 1115, 2010.

R EFERENCES[1] S. Bahadori, L. Iocchi, G. R. Leone, D. Nardi, and L. Scozzafava.
Real-time people localization and tracking through fixed stereo vision.
Applied Intelligence, 26:83–97, 2007.

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SESSION 3

SOCIAL NETWORKNetMob 5-7 April 2017 – Vodafone Village – Milan

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SESSION 3
SOCIAL NETWORKAge disparities in ethnic segregation: a study of activity spaces using the Call Detail
Record dataset

Siiri Silm, Veronika Mooses, Rein Ahas
Department of Geography, University of Tartu, Estonia
E-mail of corresponding author: Siiri.Silm@ut.ee

Ethnic segregation or, in other words, the spatial separation of ethnic groups is a widespread
phenomenon. The emergence and persistence of segregation is influenced by various
geographical, cultural, political, and economic factors (Musterd 2005). In theory, thedifferences between a minority group and the majority population should decrease with time
and the minority group should assimilate into the surrounding society. The excessive attention
paid to segregation studies has concentrated on places of residence and workplaces, while inrecent years the interest has been in expanding into people’s entire activity space (Farber et al,2012; Järv et al, 2014; Silm and Ahas, 2014; Wang and Liu, 2016; Tammaru and van Ham,
2016).
The aim of this study is to determine the differences in ethnic segregation between
generations; the differences between the levels of segregation for different generations should
express the progress of assimilation over time. We use census data and passive mobile
positioning data (CDR) from Estonia. Two dimensions are used for the assessment ofsegregation level in different age groups. Firstly, we use the place-based approach using
segregation indexes and density patterns for different ethnic groups (Massey and Denton,
1988; Johnston et al, 2004). We differentiate between places of residence, workplaces, andout-of-home non-employment activity places. This approach offers the opportunity of being
able to discover which place offers the highest integration potential. Nonetheless, the placespace approach ignores the personal experience, geographical context, and spatial mobility(Kwan 2012). In order to counter this, we also use the activity space approach (Farber et al,2012; Järv et al, 2014; Silm and Ahas, 2014; Wang and Liu, 2016), in which ethnic
differences in space-time behaviour have been assessed with individual activity spacemeasures. We measure the number, randomness, and ethnicity of visited districts, and theextent of the activity space.
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34

SESSION 3
SOCIAL NETWORKThe study area is Estonia, which is an interesting country when it comes to studying the
temporal dimension of ethnic segregation. The migrant population here is formed by the
Russian-speaking minority, whose massive immigration ended in 1991 when Estonia wasliberated from Soviet occupation. Since the number of new immigrants from Russianspeaking countries has been very low since 1991, we have been able to observe the results of
the assimilation process for immigrants of the Soviet era across different generations. This
also provides a unique experimental picture of the potential assimilation process for
immigrants and also a picture of the changes in segregation following the end of immigration.
The place-based approach shows that segregation in places of residence and out-of-home nonemployment activity districts is higher in younger age groups than it is in older age groups.
The geographical distribution of places of residence and out-of-home non-employment
activity districts across different age groups is quite similar both in Estonians and Russianspeakers. Greater differences can be seen in the distribution of workplaces in the 30-39 age
group. The activity space approach shows that the difference between the activity spacemeasures for Estonians and Russian-speakers generally decreases with age, which refers to
the higher ethnic segregation in younger age groups. Younger people are more connected with
districts which are more heavily populated by their own ethnic group. Thanks to thesefindings, the result of the study may show that the spatial behaviour of Russian-speakers does
not became similar over time to that of Estonians. Our results for ethnic differences in activity
spaces by age groups does not follow the pattern described in the spatial assimilation theory,
which dictates that minority group disparities decrease with time.

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35

SESSION 3
SOCIAL NETWORKThe rippling effect of social influence on phone
communication network
Yan Leng,1 Xiaowen Dong,1 Esteban Moro,1,2 and Alex “Sandy” Pentland1
1. MIT Media Lab, Cambridge, MA, USA
2. Departamento de Matematicas & GISC, Universidad Carlos III de Madrid, 28911 Leganes, Spain
Abstract—What is the reach of the social influence of ouractions? Although many works have studied how influence
diffuses within direct neighbors at local level and mostly on
online social networks, little is known of the fingerprints that
our actions leave in the network as a whole. Leveraging the data
set of mobile phone records, we use state-of-the-art methods todetect social influence and distinguish it from homophily. We
find that although most influence disappears after one hop of
distance in the social network, it persists up to six degrees
of separation. Our results suggest that people’s actions yield
to a network-wide ripple effect which could have important
implications in understanding phenomena such as mobilization,marketing campaigns, or opinion formation.

We live in a connected world and are increasingly closer
to each other thanks to the emerging information technologies[1]. From “six degrees of separation” discovered by Milgram
in 1967 [2], it has been recently found that the average degreeof separation between two people via Facebook is reduced to
4.74 [3]. Furthermore, individuals are not merely connected; asa series of experiments in domains such as obesity and happiness has demonstrated, connectivity also indicates behavioral
similarities of up to three degrees of separation [4].
The recent availability of large-scale communication andnetworked data, such as emails, phone records and social media, enables, at an unprecedented level of detail, resolution, andscale, studies of not only information diffusion and correlationsof adoption behaviors, but also social contagion processes [5],[6], [7]. In particular, the understanding of the phenomenon ofand the mechanism that drives the social contagion process
helps to promote behavioral change for commerce, publichealth, politics and social mobilization at both local and global
scales [8], [9], [10].
However, most of the previous works focus on onlinesocial networks, and measure influence between direct contacts
involving either long-term habits or low-cost decision-making
in virtual space. In this study, we are interested in how social
influence propagates over a nation-wide offline communication
network and how it manifests in short-time decision-making
and social mobilization that are more costly than merely
information diffusion or online production adoption. More
importantly, while most literature pays attention to social
influence between immediate neighbors, we are particularlyinterested in the subtle and invisible influence that extends
beyond direct contact.
Leveraging a data set of mobile phone records with highresolution in the European country of Andorra, we construct anation-wide communication network and mirror the contagion

process of social influence, which is measured by the likelihood of attending a large-scale international cultural eventin the capital city. We found that the degree of separationin phone communication from attendees of the event is astrong indicator of the likelihood of attendance. In order tocontrol for the bias caused by homophily and quantify the
impact of social influence on decision-making, we utilize amatching method to mimic random assignment of treatments[8], [11]. Our results show that influence decays across social
distances from initial attendees, but persists, surprisingly, up
to six degrees of separation.
P ROBLEM FORMULATION
Mobile phone logs provide a proxy for human mobility andsocial interactions at a societal scale [5], [12]. In this study, we
use the countrywide mobile phone logs in Andorra to study
how the likelihood of an individual attending a local Cirque
Du Soleil performance, which was held repetitively on July,
2016, is affected if someone in his social circle receives phone
calls from past attendees of the event.
We assume that people who connected to a nearby cell
tower of the performance venue, as shown in the left panel of
Figure 1, during the performance period (± 30 min as buffertime) attended the events and are named as attendees. We
construct influence cascades, as shown in the right panel of
Figure 1, by adding links between the caller and receiver if:1) at least one of them are linked directly or indirectly withthe attendees. 2) and the calls happened within 24 hours after
the performance started. We use hop to capture the nearest
social distance to the attendee on the influence cascade. We
observed 16,043 attendees across the observational periods.
161,857 individuals are connected directly or indirectly to theattendees via the communication network. Meanwhile, 71,337
users are not connected to the attendees via the influencecascades.

Fig. 1.

Illustration of attendee, influence cascade and hop

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36

SESSION 3
SOCIAL NETWORKM ETHOD
In order to quantify social influence, the key challenge isto distinguish social influence and homophily to avoid overestimation. We use matched sample estimation to mimic theassignment of treatment as in a randomized experiment. Conditioning matches on a vector of historical mobility patterns
yields influence estimates. In particular, the matching results
establish an upper bound to which extent social influence,
rather than homophily, explains the attendance [8].
In order to purge homophily, which creates upward selection bias, and quantify social influence through phone
calls, we quantify the similarities among individuals in theinfluence cascades based on their behavioral characteristics,
specifically the historical mobility patterns [13]. Then, we
segment individuals into two groups: the treatment group andthe control group. One individual in the treatment group ispaired with another individual in the control group who aremost similar in terms of interests. This constructs the casewhere the differences between paired individuals are due tothe treatment of social influence [14]. Pairs in the control andtreatment groups are matched based on nearest Mahalanobis
distance, calculated as:
md(Xj , Xk ) = [(Xj − Xk )T S −1 (Xj − Xk )]1/2 ,(1)where Xi and Xj are the covariates for individual j andindividual k and S is the sample covariance matrix for X.
In our study, we use the first fifteen principle components ofthe mobility frequency matrix, capturing the historical mobility
patterns during the weekends for the past six months, as theobserved covariates.

Fig. 2. Average treatment effects of social influence. X-axis is hop distanceto the nearest attendee and Y-axis is the average treatment effect of social
influence. The blue and red dash lines represent Mahalanobis Distance Matching and random matching. The shaded regions represent the 95% confidence
intervals. The decay pattern of social influence exhibits “ripple effect”.

and social mobilization via offline communication. Recent
works have demonstrated the success of social mobilization
via Internet-based services [15], but also shown that suchmechanisms are not without limitations [16]. Our findings
suggest that an alternative would be to exploit the hidden andoften overlooked influence between people that are caused bychains of offline communication, thus leading to more effective
strategies in marketing or political campaigns.
R EFERENCES

The difference in the attendance rates of the two groups isthe average treatment effect of social influence in our setting:

[1]

(2)

[2]
[3]

AT Eg = E(Yig − Yic ),where AT Eg is the average treatment effect of the treatmentgroup on hop g. Yig and Yic are the outcome for matched pair iin the treatment group g and in the control group respectively.
R ESULTS AND D ISCUSSIONSIn this study, we quantify how our decision-making are
influenced by, and how the social network propagates our influence to, people that are several degrees away from us in thecommunication network by analyzing the pattern of attendanceof a large-scale cultural event in Andorra using nation-widemobile phone data. The blue and red dash lines in Figure 2
represent the average treatment effect of social influence ofMahalanobis Distance Matching and random matching acrossdifferent hops. The general decay pattern of social influence
originated from the attendees reveals the existence of a “ripple
effect” across information cascades in the communication
network. Specifically, the average treatment effect of social
influence is 11% on the first hop, drops dramatically to ahalf at the second hop and decays slowly afterwards. Yet,
surprisingly, our results show that social influence persists up
to six degrees of separation. This result highlights the hidden
relationship and influences between individuals that are notnecessarily acquaintances to each other.
The ripple effect demonstrated through our study has applications that are important for viral marketing, public health

[4]
[5]

[6]

[7]

[8]
[9]

[10]

[11]
[12]

D. J. Watts and S. H. Strogatz, “Collective dynamics of smallworldnetworks,” nature, vol. 393, no. 6684, pp. 440–442, 1998.
S. Milgram, “The small world problem,” Psychology Today, 1967.
L. Backstrom, P. Boldi, M. Rosa, J. Ugander, and S. Vigna, “Fourdegrees of separation,” in Proceedings of the 4th Annual ACM Web
Science Conference, pp. 33–42, ACM, 2012.
N. Christakis and J. Fowler, “Connected: The surprising power of oursocial networks and how they shape our lives,” 2009.
P. Deville, C. Song, N. Eagle, V. D. Blondel, A.-L. Barabási, andD. Wang, “Scaling identity connects human mobility and social interactions,” Proceedings of the National Academy of Sciences, p. 201525443,
2016.
J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K. Kaski,
J. Kertész, and A.-L. Barabási, “Structure and tie strengths in mobile
communication networks,” Proceedings of the National Academy of
Sciences, vol. 104, no. 18, pp. 7332–7336, 2007.
G. Miritello, E. Moro, and R. Lara, “Dynamical strength of social ties
in information spreading,” Physical Review E, vol. 83, no. 4, p. 045102,
2011.
S. Aral, “Social science: Poked to vote,” Nature, vol. 489, no. 7415,
pp. 212–214, 2012.
J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg, “Structural
diversity in social contagion,” Proceedings of the National Academy of
Sciences, vol. 109, no. 16, pp. 5962–5966, 2012.
R. M. Bond, C. J. Fariss, J. J. Jones, A. D. Kramer, C. Marlow,
J. E. Settle, and J. H. Fowler, “A 61-million-person experiment insocial influence and political mobilization,” Nature, vol. 489, no. 7415,
pp. 295–298, 2012.
G. King and R. Nielsen, “Why propensity scores should not be used
for matching,”
J. L. Toole, C. Herrera-Yaqüe, C. M. Schneider, and M. C. González,“Coupling human mobility and social ties,” Journal of The Royal SocietyInterface, vol. 12, no. 105, p. 20141128, 2015.

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SOCIAL NETWORK

[13]
[14]
[15]
[16]

Y. Leng, L. Rudolph, A. Pentland, J. Zhao, and H. N. Koutsopolous,“Managing travel demand: Location recommendation for system efficiency based on mobile phone data,” CoRR, vol. abs/1610.06825, 2016.
E. A. Stuart, “Matching methods for causal inference: A review and alook forward,” Statistical science: a review journal of the Institute ofMathematical Statistics, vol. 25, no. 1, p. 1, 2010.
G. Pickard, W. Pan, I. Rahwan, M. Cebrian, R. Crane, A. Madan,and A. Pentland, “Time-critical social mobilization,” Science, vol. 334,
pp. 509–512, 2011.
A. Rutherford, M. Cebrian, S. Dsouzaa, E. Moro, A. Pentland, and
I. Rahwan, “Limits of social mobilization,” Proceedings of the National
Academy of Sciences, vol. 110, no. 16, pp. 6281–6286, 2013.

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SESSION 3
SOCIAL NETWORK

Cohesive groups in urban area: characterization of
p-cliques in mobile phone graphMatteo Zignani, Christian Quadri, Sabrina Gaito, and Gian Paolo Rossi
Department of Computer Science, University of Milan, Milan, Italy
firstname.lastname@unimi.it

Social networks built on top of mobile phone data are one of the main sources facilitating the understanding of the interplay between people’s sociality and proximity. At
the dyad level, it is well established that people connected by a social tie are more likely
to share city’s locations than unrelated ones [1]. On the contrary, more complex social
structures such as communities remain quite loose organizations that only sporadicallygather in a place to carry common activities out [2].
In this work we focus on cohesive groups expressed through calls and text-messages
and on their spatial characterization. To this aim we exploit the localization provided by
the phone data to reconstruct the proximity patterns of the group members. In particular,
we proceed by i) identifying and characterizing cohesive groups through the extractionof maximal pseudo-cliques (p-cliques); ii) studying the different member roles (leaders
or followers); and finally iii) mapping a p-clique to the places it visits.
Dataset. Our mobile phone dataset [3] is based on Call Detail Records (CDRs) registering voice-call, text and data activities of 1 million mobile subscribers in the Milan
metropolitan area over 67 days in the spring of 2012, before the era of OTT services,
when text messages were significantly used by mobile users. For each issued activity,
a CDR entry is created as a 6-ple tCDR = s, r,tstart , d, cell, area, where s and r represent the sender and the receiver of the call/SMS, tstart is the start time of the activity,d is its duration and cell is the serving cell the user s is attached to. The field area
indicates the city’s zone representing a coarse grain division of the city region. Unlike
previous studies where cell tower may cover a zone as wide as a few kilometers [4], thedataset reports data about cell towers inside a city space where small coverage radius isadopted. This characteristic, combined with the knowledge of p-cliques, is a powerfulenabler to study the off-line social life of tight-knit groups.
Methods. Starting from the mobile phone data, we construct an undirected graph
G(N, E) where N is the set of the mobile operator subscribers and E is the set of ties
between them. In order to discard occasional interactions, we required that users had atleast 3 interactions (calls or texts) or a total calls duration above 1 minute. In this work,
the notion of cohesive subgroup corresponds to the definition of p-clique presented in[5]. Using the concept of p-clique instead of clique allows us to better capture the structure of close groups by relaxing the constrain of the existence of all communicationedges. To this aim, we apply the Pseudo Clique Enumeration (PCE) algorithm [5] tofind all the maximal p-cliques in an undirected graph. We use 0.8 as value of edge density threshold θ and we impose 5 as minimum p-clique size. Moreover, we exploit thelocation information to evaluate the co-location of the p-clique. We adopt the following
methodology to detect clique co-location. For each clique we reconstruct the mobility
trace of each user starting from the CDRs, and we transform it as follows: we convert
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SESSION 3
SOCIAL NETWORK

0.5

1.0

10 0
0.4

0.8

0.3

10

Presence probability

CCDF

Probability

10 −1
−2

10 −3
10 −4

0.2

10 −5
10 0

10 1

10 2

10 4

10 3

Pseudo-clique per user

0.1

0.6

0.4

0.2

0.0

0.0
4

6

8

10

12

14

16

18

1

2

Pseudo-Clique size

3

4

5

6

7

Member rank

(a)

(b)

(c)
Fig. 1. a) The histogram of the p-clique size (k). The inset figure shows the distribution (CCDF)
of the number of p-clique per user. b) The boxplot of the presence probability of each member
for p-cliques of size 7. c) The city map with the places where p-cliques meet.

each point, identified by triplet t, cell, area, in a time interval assuming that if the user
was in a cell at time t she/he was in that cell from t − ∆ until t + ∆ . In this paper weuse ∆ = 30 minutes in line with [6]. Each time interval maintains the same location
attributes (cell and areas) of the trace point from which is generated. Then we merge
all the traces of the members of the clique, by retaining only the time overlapping intervals that share the same location attributes. In the following we only consider clique
co-location in area. We consider a p-clique to be co-located if at least the 60% of its
members are co-located.
Results. We observed 43, 646 maximal p-cliques whose size (k) is in the range 5 and18. 25, 422 people, 8.8% of all users, are involved in at least one clique, a very high percentage if we mind the limits of mobile phone data. The same holds for links since the
15% of network ties are intra-p-cliques. This result confirms the presence of strongly
cohesive groups in mobile phone graphs. Small p-cliques (k = 5, 6, 7) are predominant,Fig.1(a), and we can also observe that most people belong to few p-cliques (mean andmedian values are 10.7 and 2.0 respectively), as shown in the inset of Fig.1(a). As forthe localization of the p-cliques, our findings show that 83.4% of p-cliques meet at least
once in the considered time frame. Also for cohesive groups, the result confirms that
there is a strong correlation between on-phone interactions and off-line encounters. We
also observed that the probability that a member is present at a meeting is not equallydistributed across all members; in Fig.1(b) we report the box plot of the presence probability aggregated on all p-cliques of size 7 (similar results can be observed for all the
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1.0

0.8

Gini index

0.6

0.4

0.2

0.0
1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

Number of locations visited by p-cliquesFig. 2. The boxplot of Gini-index of visited areas by p-cliques.

other p-clique size). This result suggests the existence of two kind of members, theleaders who are almost always present when the p-clique meets and represents the coreof the p-clique and the followers who participate less frequently to the off-line social
aggregation of the p-clique. The map in Fig.1(c) highlights the fact that some city locations are preferred than others for p-clique encounters and therefore, indirectly, thatcities have places more social than others. Finally, we observe that p-cliques meet infew favorite locations. Fig. 2 reports the Gini index distribution grouped by the numberof locations visited by p-cliques. As we can observe, the values of Gini index are far
from 0 (equality condition), meaning that almost all p-cliques prefer to meet in few
favorite locations.

References
1. Calabrese, F., Smoreda, Z., Blondel, V.D., Ratti, C.: Interplay between telecommunications
and face-to-face interactions: A study using mobile phone data. PloS one 6(7) (2011) e20814
2. Zignani, M., Quadri, C., Gaito, S., Rossi, G.P.: Calling, texting, and moving: multidimensional
interactions of mobile phone users. Computational Social Networks 2(1) (2015) 1
3. Quadri, C., Zignani, M., Capra, L., Gaito, S., Rossi, G.P.: Multidimensional human dynamics
in mobile phone communications. PLoS ONE 9(7) (07 2014) 1–12
4. Nanavati, A.A., Singh, R., Chakraborty, D., Dasgupta, K., Mukherjea, S., Das, G., Gurumurthy, S., Joshi, A.: Analyzing the structure and evolution of massive telecom graphs.
Knowledge and Data Engineering, IEEE Transactions on 20(5) (2008) 703–718
5. Uno, T.: An efficient algorithm for solving pseudo clique enumeration problem. Algorithmica
56(1) (2010) 3–16
6. Wang, D., Pedreschi, D., Song, C., Giannotti, F., Barabasi, A.L.: Human mobility, social ties,
and link prediction. In: Proceedings of the 17th ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining. KDD ’11, New York, NY, USA, ACM (2011)
1100–1108

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Personality Traits and Ego-Network Dynamics
Simone Centellegher, Eduardo López, Jari Saramäki, Bruno Lepri
09 January 2017

1

Abstract

We interact with a wide network of people on a daily basis, and these social
relationships play an important functional role in our lives. A large number ofstudies has shown that having strong and supportive relationships is essential
for health and subjective well-being [1]. However, there are costs to their maintenance [2, 3], resulting in a tradeoff between quality and quantity, a typicalstrategy being to put a lot of effort on a few high-intensity relationships while
maintaining larger numbers of less close relationships. It has also been shownthat there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others
strongly focus on their closest relationships [4, 5, 6]. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how
personality traits of individuals affect this picture, using data from the Mobile
Territorial Lab study [7]. In particular, we look at the relationship between
personality traits, alter rank dynamics, and social signatures [8] that measure
how communication efforts are allocated to personal network members.
We observe that some traits have effects on the stability and homogeneity ofsocial signatures as well as network turnover and ego-networks rank dynamics.
As an example, individuals who score highly on the Openness to Experience trait
tend to have more diverse social signature shapes and higher levels of network
turnover, with open to experience individuals showing higher rank variations
with respect individuals more closed to experience (Figure 1). On broader terms,
our study provides one possible explanation for the uniqueness and stability of
the individuals’ social signatures. As pointed out by Saramäki et al., [8] social
signatures’ characteristics reflect the fact that ego networks are typically layered
into a series of hierarchically inclusive subsets of relationships of different quality.
One of the constraints shaping the social signatures seems to be the one arisingfrom differences in personality traits, with some individuals preferring to havea few, intense, and stable relationships and others preferring more diverse, butless intense ones [9].

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SOCIAL NETWORKOpen to experience

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matrices for the subgroups of individuals open and closed to experience. It is
possible to observe that the open-to-experience subgroup shows a higher spread(large rank variation) with respect to the closed-to-experience subgroup where
the “heat” is more concentrated around the diagonal (lower rank variation).

References[1] Lyubomirsky S, King L, Diener E. The benefits of frequent positive affect:
Does happiness lead to success? Psychological Bulletin. 2005;131(6):803–
855.
[2] Winship C. The allocation of time among individuals. In: Schuessler K,
editor. Sociological Methodology. vol. 9. Wiley; 1978. p. 75–100.
[3] Dunbar RIM. Neocortex size as a constraint on group size in primates.
Journal of Human Evolution. 1992;22(6):469–493.
[4] Gonçalves B, Perra N, Vespignani A. Modeling users’ activity on twitter
networks: Validation of Dunbar’s number. PloS one. 2011;6(8):e22656.
[5] Miritello G, Lara R, Cebrian M, Moro E. Limited communication capacity
unveils strategies for human interaction. Scientific reports. 2013;3.
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SESSION 3
SOCIAL NETWORK[6] Miritello G, Moro E, Lara R, Martı́nez-López R, Belchamber J, Roberts SG,
et al. Time as a limited resource: Communication strategy in mobile phone
networks. Social Networks. 2013;35(1):89–95.
[7] Centellegher S, De Nadai M, Caraviello M, Leonardi C, Vescovi M, Ramadian Y, et al. The Mobile Territorial Lab: a multilayered and dynamic viewon parents’ daily lives. EPJ Data Science. 2016;5(1):1.
[8] Saramäki J, Leicht EA, López E, Roberts SG, Reed-Tsochas F, Dunbar RI.
Persistence of social signatures in human communication. Proceedings of
the National Academy of Sciences. 2014;111(3):942–947.
[9] Swickert RJ, Rosentreter CJ, Hittner JB, Mushrush JE. Extraversion, social support processes, and stress. Personality and Individual Differences.
2002;32(5):877–891.

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Why people stop calling? The temporal weakness of decaying ties
Henry Navarro,1 Giovanna Miritello,2 and Esteban Moro1, ∗1

Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganés, Spain
2
Telefónica Research, 28050 Madrid, Spain

Social networks are made out of strong andweak links having very different structural anddynamical properties. Social scientists have longrecognized the value of weak links in information discovery but also their relative structural
weakness that makes them more likely to decay
in the future. What what features of humaninteraction build a strong tie? Here we approach
this question from an practical way by finding
what are the properties of social interactions thatmake ties more persistent and thus stronger tomaintain social interactions in the future. Usinga large longitudinal mobile phone database webuild a predictive model of tie persistence basedon intensity, structural and temporal patternsof social interaction. While our results confirm
that structural (embeddedness) and intensity(number of calls) are correlated with tie persistence, we find that daily temporal featuresof communication events in a tie are betterand more efficient predictors for tie persistence.
Specifically, although communication between
links is always bursty we find that links that are
more bursty than the average are more likely todecay, signaling that the strength of the tie is
not only reflected in the intensity or topology ofthe network, but also on how we distribute intime our interactions with our relationships. Our
results not only are important to understandthe strength of social relationships but also to
unveil the entanglement between the different
temporal scales in networks, from microscopic
tie burstiness to network evolution.
Social networks are dynamic objects, they grow andchange over time through the addition of new ties or theremoval of old ones, leading to an ongoing appearance
and disappearance of interactions in the underlying social structure [1, 2]. Identifying the different mechanisms
by which a link form or decay is a fundamental and challenging question of individual human behavior, but also
it can unravel the processes behind group, communityand network dynamics that shape our social fabric and,in turn, how that network evolution impact important
processes in our society like economy, cooperation or information diffusion. On the other hand, understanding
under what condition a tie is more or less likely to decay may shed light on the circumstances under which an∗ Corresponding author emoro@math.uc3m.es

observed tie can be actually considered a genuine social
relationship [3, 4].
Link evolution is usually studied in the context of themuch more general problem of link prediction in data
science [5]. In general, link prediction in social networks
seeks to determine what are the endogenous and exogenous factors to the network that predict the appearance
and persistence of a link, or even to infer the existenceof a link when missing or anomalous data about the network is given [6]. In fact, endogenous factors, i.e. those
properties that can be extrapolated from the network
itself, are very good predictors for tie creation, persistence or decay. They reflect the inherent mechanismsthat lead people to tie together and/or maintain a social
relationships or to destroy it, such as triadic closure, homophily or geographical proximity [7]. For this reason,
the majority of approaches to this issue focus to identify tie properties such as common friends or communities properties similarity among individuals or frequency
of interaction, which have been observed to capture theemotional or trust intensity of a tie [8]. Another conclusion that emerges from previous studies is that ties without this structural similarity and/or intensity are more
likely to decay [9, 10]. In the context of Granovetter’s
theory of strength of weak ties, strong ties are those which
are more likely to persist, since they are structurally embedded (common friends) are are more intense (numberof interactions), while bridges between communities are
weak and, as Burt found [9], they are more likely to decay
in the future.
Despite this understanding of some processes behindsocial interactions has helped to build very good models for link formation, the problem of link persistence has
not received the same attention, largely due to the lack ofquality data. Although some online social networks have
explicit mechanisms to “unfollow ” (Twitter) [11] or ‘unfriending” (Facebook) [12] other users, access to structural or intensity data is limited. On the other hand,most studies infer link decay from absence of tie activity
in large databases [3, 10]. This is a potential problem,since the burstiness of human interaction could rendermost of the detected decay events are simply large inactivity periods of the interaction. Thus, although thesestudies of link decay agree on the general importanceof structural embeddedness, intensity or reciprocity of atie to predict its future persistence, they still provide an
incomplete picture of what are the main tie properties
that make them strong (persistent) and if, as was donein the problem of link prediction, we can build an efficient model based on endogenous properties of links topredict if a social relationship is bound to decay.

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SESSION 3⌦before⌦after

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SOCIAL NETWORK

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FIG. 1. Definition of observation periods and examples of call activity for 4 given ties. Any vertical segment is a call between
the users in a particular tie. Our 19 months database is divided in three periods, where the 7 months in the middle Ω is ourobservation period where all the tie features will be measured. The period Ωafter is used to asses if ties are persistent, i.e. if
there is activity in the tie. For example, ties A) and D) are persistent, while ties B) and C) are said to have decayed in Ωafter .
All links have similar values of number of calls in the observation period with wij ∈ [30, 40]. We also show specific examples of
one inter-event time δij (tie B) and freshness fij (tie C).

In this communication we overcome some of these difficulties by studying tie persistence in human communication using a large longitudinal database of 19 month ofmobile phone calls. The large duration of the databaseallow us to accurate assess the presence of a tie by using
the method introduce by Miritello et al. [13] which splits
the observation period in different time windows and use
each of them to characterize and assess the presence ofthe tie (see figure 1). But more importantly, having adetailed and large longitudinal database for human communication allow us to characterize better the patterns ofcommunication within a tie and see if temporal properties of human interaction are predictors of tie persistencein the future. Although simple temporal properties havebeen considered before in the problem of tie prediction[14] and strength estimation [10, 15], here we show thatthe persistence of a tie is also encoded in the bursty patterns of communication between people. Furthermore,by building a high accurate predictive model based on
different tie features (structural, intensity, intimacy andtemporal) we are able to show that temporal properties
are indeed as important as intensity and much more thanstructural properties in predicting tie persistence (see figure 2). Our results show that it is possible to build simple
predictive models of network evolution based on temporal and intensity properties of the human interaction.

[1] J. Saramaki and E. Moro, The European Physical Journal B 88 (2015).
[2] P. Holme and J. Saramaki, Physics reports 519, 97(2012).
[3] C. A. Hidalgo and C. Rodriguez-Sickert, Physica A: Statistical Mechanics and its . . . 387, 3017 (2008).
[4] G. Kossinets and D. J. Watts, Science 311, 88 (2006).
[5] P. Wang, B. Xu, Y. Wu,
and X. Zhou, CoRR
abs/1411.5118 (2014).
[6] D. Liben Nowell and J. Kleinberg, Journal of the American Society for Information Science and Technology 58,
1019 (2007).
[7] M. T. Rivera, S. B. Soderstrom, and B. Uzzi, Annual
Review of Sociology 36, 91 (2010).
[8] J. Saramaki, E. A. Leicht, E. López, S. G. B. Roberts,
F. Reed-Tsochas, and R. I. M. Dunbar, pnas.org (2012).
[9] R. S. Burt, Social Networks 22, 1 (2000).

[10] T. Raeder, O. Lizardo, D. Hachen, and N. V. Chawla,
Social Networks 33, 245 (2011).
[11] H. Kwak, S. Moon, and W. Lee, “More of a receiver thana giver: Why do people unfollow in twitter?” (2012).
[12] D. Quercia, M. Bodaghi, and J. Crowcroft, in Proceedings of the 4th Annual ACM Web Science Conference,
WebSci ’12 (ACM, New York, NY, USA, 2012) pp. 251–
254.
[13] G. Miritello, Temporal Patterns of Communication in Social Networks, Springer Theses (Springer, 2013).
[14] L. Tabourier, A.-S. Libert, and R. Lambiotte, EPJ Data
Science 5, 1 (2016).
[15] E. Gilbert and K. Karahalios, in Proceedings of the
SIGCHI Conference on Human Factors in Computing
Systems, CHI ’09 (ACM, New York, NY, USA, 2009)
pp. 211–220.

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FIG. 2. A) Importance of the variables in the model to predict tie persistence. Importance is measured as the normalized %of the t-statistics for each model parameter. B) Density plot of the average persistence of ties as a function of the two mostimportant variables in A), namely, the coefficient of variation (that measures the burstiness of ties) and total number of calls.
The dashed line shows the 1/2 probability for the simplified models in table.

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NetMob 5-7 April 2017 – Vodafone Village – Milan

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SOCIAL GOOD

A Tool for Estimating and Visualizing Poverty Maps
Myeong Lee1 , Rachael Dottle2 , Carlos Espino2 , Imam Subkhan3 , Ariel Rokem3 , Afra Mashhadi3
1 iSchool, University of Maryland, College Park, USA
2 Columbia University, New York, USA
3 University of Washington, Seattle, USA
myeong@umd.edu, rcd2127@barnard.edu, carlos.espino@columbia.edu, imams@uw.edu, arokem@gmail.com, afra.mashhadi@gmail.com

ABSTRACT"Poverty maps" are designed to simultaneously display thespatial distribution of welfare and different dimensions ofpoverty determinants. The plotting of such information onmaps heavily relies on data that is collected through infrequentnational household surveys and censuses. However, due to thehigh cost associated with this type of data collection process,poverty maps are often inaccurate in capturing the current
deprivation status. In this paper, we address this challenge bymeans of a methodology that relies on alternative data sourcesfrom which to derive up-to-date poverty indicators, at a veryfine level of spatial granularity. We validate our methodology
for the city of Milano and demonstrate how it could be used
to implement a poverty mapping tool for policy makers.
INTRODUCTION

Small area estimation poverty maps are a recent innovation thatprovide detailed estimates of poverty levels in highly disaggregated geographical units [2]. The visualization of deprivation
information in this form has been shown to be extremely effective in empowering policy makers and local municipalities to
identify those areas in most need of interventions and revitalization programs. Poverty maps not only improve readability
compared to traditional tabular data format by simultaneously
preserving the spatial distribution of welfare, but also are powerful tools for capturing relations between deprivation andgeographical factors such as city infrastructure and offerings.
In order for poverty maps to be impactful in determining anddesigning interventions, the poverty data ought to be up-todate and presented in disaggregate level of granularity. However, due to the high costs associated with the household survey
and censuses, National Statistical Institutes and the like often
possess social and economic well being information that is
out-of-date (i.e., collected infrequently) and only inclusive ofa rather small sample of the population.
Within the remit of ‘Data for Development’ there have been anumber of promising recent works, whereby researchers haverelied on alternative sources of data to estimate deprivation.
For example, models exploiting Call Detail Records (CDRs)
from mobile phones have shown to be good indicators of thespatial distribution of socio-economic status in developing
countries [7, 4]. Other sources of data including readily available open datasets such as those of Volunteer Geographical
Information (VGI) have been shown to successfully predict thepoverty level based on the offering advantages of the cities [8].
While both these alternative sources have been shown to suc-cessfully estimate deprivation level, their results have only
been presented in isolation rather than in comparison. Furthermore, each has various shortcomings. The CDR are often hardto obtain due to their commercial and privacy sensitive nature.
The Open Data sources on the other hand are readily available
but could suffer from biases in coverage [5].
In this paper, we propose a methodology that leverages both
open source and proprietorial datasets to compute and offer amore complete spatial distribution of deprivation at the city
scale. In so doing, we extract features corresponding to thefunctional offerings and connectedness of the urban areas fromOpenStreetMap (OSM), and network and activity related features from the CDR for the city of Milano. We quantitatively
draw a comparison between the poverty estimation offered byeach source with respect to poverty indicators derived fromcostly census data (ground truth). Our results, based on a
Random Forest Classifier, indicate that the combinations offeatures that were extracted from CDR and OpenStreetMap
data predicted poverty level better than the baseline models
and complement each other. Based on the proposed methodology we built a visualization tool that displays the estimatedpoverty level for each area of the city as well as the determinants that contribute to the predicted poverty level.
FEATURE EXTRACTION AND BASELINES

For the purpose of this study we used CDR dataset made available by Telecom Italia [1] and freely available OSM data. We
extracted features from these datasets following the measuresthat are often considered as proxy of the poverty level. Insights for the dynamics of a city from CDR data are usuallybased on individual-level mobility, people’s communication
network, and the amount of activity. Due to the anonymizationof the CDR data, however, it is not possible to extract mobility
information. We thus used network advantages and activity
signatures as major predictors from the dataset. Network advantages include (1) call volume (the extend to which people
are active in each region); (2) introversion (the amount of calls
made inside of a region divide by the outgoing call, which isrelated to access to resources and social capital outside of aneighborhood); (3) PageRank and eigenvector centrality (relative popularity based on communication network structure);and (4) entropy (the diversity of a region that may imply thepotential capability to reach diverse resources). Furthermore,
we generated different CDR activity signatures by separating
weekdays and weekends due to different human behavioral
patterns. As a result, a vector with 288 elements (144 forweekdays and 144 for weekends in 10-minute interval) was

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Figure 1. Average F1-score for models based on CDR features (left), OSM features (middle) and combined features (right) for varying train/test set.

constructed for each region. By conducting K-means clustering for these vectors, we classified every census tract into
three categories, i.e., business, residential, and mixed regions.
The number of clusters, K, was determined by using DaviesBouldin cluster index [3] (K = 3).
While CDR provides information about hidden communication structure and dynamic human activities, OSM allows to
extract features about physical resources (i.e., offering advantage) and access structure (i.e., street network) in a city. For
offering advantage, we extracted amenity nodes for the city of
Milano and grouped them by their categories. We then calculated the offering advantage metric for each sub-category tag
based on the distribution of the city. The offering advantage
metric was utilized to understand what PoIs are present in agiven neighborhood, distinct from other neighborhoods [8].
This metric weighs each category by its presence, so that categories that are not very popular are more significant in theanalysis of a neighborhood compared to a category that occurs
frequently. These features enable a greater understanding of
the distribution of resources across the city, and the extent towhich areas of the city have less access to such amenities as
hospital, police station, and bicycle parking, as opposed toother PoIs, like bars and fast food restaurants. Additionally,
street layers were extracted for the city of Milano alongside
OSM amenity data. These street layers were converted into
graph networks, representing the intersections as nodes andstreet segments as edges. Two centrality measures, closenessand betweenness, were calculated to understand each urban
area’s global and local centrality, which is an indicator of thatspace’s social, economic and spatial prosperity and accessibility. These features further contribute to our model as indicativeof the spatial distribution of resources and access in the city.
In order to compare the performance of our models against
benchmarks, we borrow from the methodology proposedby [6] and implement the two baselines: popularity density
and spatial-lag based on past poverty level. Population density
is a well-known indicator of poverty as it has been shown tonegatively correlate with socio-economic level, especially indeveloping countries. That is, the populated areas in a city
are more likely to exhibit a lower welfare. This measure is
calculated from the population of a SEZ region, a smallest
census block in Milano, divided by its area. For the secondbaseline and as an alternative to the above, we target scenarios
where no current census information exists and we are limited
to the older information (e.g., previous census). For this baseline we exploit the poverty information provided by ISTAT
corresponding to 2001 Italian census. While not suitable forprediction due to the changes of census block boundaries aswell as urban landscapes, the past poverty data could help us
understand the spatial autocorrelation in a given city as poverty
often contains a strong degree of spatial autocorrelation. We
thus create a second baseline model based on the spatial-lagof this independent variable.
PREDICTION MODEL

In order to test the predictability, we first examined the correlation between poverty level of Milano and each extracted
feature by using linear regression. The Spearman’s Rho correlation value and Mean Absolute Error (MAE) suggest that eachfeature by itself is very weakly correlated with the poverty
level. Furthermore the baseline models also suffer from low
accuracy (rho = 0.35 for population density, rho = 0.15 for
spatial lag regarding 2001 poverty level). This observation
indicates that prediction of poverty in such a fine grain levelof granularity in a developed city is fundamentally a difficult problem even when we possess knowledge about how
the poverty is spatially distributed (spatial-lag baseline) andstrong determinant of the poverty (population density). We
thus treat this problem as a classification problem where rather
than predicting exact numerical poverty level, we aim to accurately classify which areas fall within different poverty levels.
For this purpose we categorise the poverty distribution into
7 bins based on standard deviation, σ . We then use Random
Forest classification with cross-fold validation. To ensure therobustness of our models, we run each model 20 iterations ofrandom train/test splits with varying training proportion.
We used F1 score to measure the performance of the models.
By combining the concepts of precision and recall, it provides
an intuitive measure for prediction power. Figure 1 reports
the F1 scores for models based on different features against
varying proportions of train sets. As it can be observed, theperformance of models are consistently better than the baselines but with varied differences depending on features andcombinations. Based on these figures we can observe that

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Figure 3. A screenshot of our poverty mapping tool visualizing the urban
elements and infrastructure of a given area.
Figure 2. A screenshot of the Poverty Mapping Tool illustrating disaggregated distribution of poverty estimated by our model (left) and thedeterminants contributing to the estimated poverty (right).

while CDR and OSM features individually provide an improvement over the baseline prediction, they best perform
when combined together and boosted with the knowledge ofpoverty autocorrelation (i.e., spatial lag).
POVERTY MAPPING VISUALIZATION TOOL

In order to put our methodology into actual use, we collected a
set of requirements through discussions with the Global Pulse,
UN research lab. These requirements led us to the designand development of a poverty mapping tool that leverages our
prediction model. Figure 2 (RQ1) presents a screenshot of thistool which enables the user to select a city and view the estimated poverty level presented by a simple chloropleth color
ramp. Furthermore, it visualizes the density distribution ofpoverty across the city as a whole by a bar chart. This representation captures the requirement of simultaneously preserving
the spatial distribution of welfare, and provide this information in a highly disaggregated geographical unit. Therefore
it enables easy interpretation and comparison across different
areas and in relation with the geographical distribution of theareas (e.g., suburban areas vs central). The tool also allows
the user to view the poverty level at various spatial granularity
catering for diverse information needs of different users. For
example, a district commissioner would be interested in subdistrict poverty and require a more abstract view of the poverty
map. This is illustrated in Figure 2 (RQ2) where poverty isdisplayed in the sub-municipal area level, in contrast to thefinest resolution, SEZ level.
Furthermore the tool also caters for the need for transparency.
Indeed many policy makers in the past have viewed the poverty
estimation methodology as a black box [2], and thus have often
lacked trust in the poverty mapping algorithms behind thescene. Based on this observation we have created a transparent
design where the determinants and indicators that contributeto the estimated poverty values are clearly communicated tothe user. As illustrated in Figure 2 our tool allows the user toselect a poverty determinant from the drop down menu (e.g.,community services) and explore the spatial distribution of theselected determinant across the city (right map).
Finally, the last design requirement that our tool caters foris the presentation of the urban infrastructure and elements,
allowing the stakeholders to interpret the potential impactof their policies in relation to the existing elements. This
information could help with the placement of amenities thatare vital but missing in a neighborhood. For example, as partof gentrification interventions one could decide where to create
third places that would encourage a sense of community inisolated neighborhoods. Our tool allows the user to selectan area and display the spatial distribution of existing PoIs
and street connectivity for that selection. Figure 3 illustrates
this feature for one of the poorer areas of Milano, where themap indicates the sheer presence of bars and fast food places,
resembling the previous findings reported in [8].
CONCLUSION

We proposed a methodology for estimating poverty levels
that relies on alternative data sources than census and thus
is relatively low in cost. We have demonstrated how thesepoverty estimates could be presented as poverty maps andoffer spatially fine grained and up-to-date information that iseasy to interpret. Our results indicate that our model is able topredict poverty more accurately than the known baselines.
REFERENCES
1. Gianni Barlacchi, Marco De Nadai, Roberto Larcher, Antonio
Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli,
Alessandro Vespignani, Alex Pentland, and Bruno Lepri. 2015.
A multi-source dataset of urban life in the city of Milan and theProvince of Trentino. Sci Data 2 (27 Oct. 2015), 150055.
2. Tara Bedi, Aline Coudouel, and Kenneth Simler. 2007. Morethan a pretty picture: using poverty maps to design better
policies and interventions. World Bank Publications.
3. David L Davies and Donald W Bouldin. 1979. A cluster
separation measure. IEEE transactions on pattern analysis andmachine intelligence 2 (1979), 224–227.
4. Vanessa Frias-Martinez and Jesus Virseda. 2012. On therelationship between socio-economic factors and cell phone
usage. In Proceedings of the fifth international conference on
information and communication technologies and development.
ACM, 76–84.
5. Giovanni Quattrone, Afra Mashhadi, and Licia Capra. 2014.
Mind the map: the impact of culture and economic affluence on
crowd-mapping behaviours. In Proceedings of the 17th CSCW.
ACM, 934–944.
6. Chris Smith-Clarke and Licia Capra. 2016. Beyond the Baseline:
Establishing the Value in Mobile Phone Based Poverty
Estimates. In Proceedings of the 25th International Conferenceon World Wide Web. 425–434.
7. Christopher Smith-Clarke, Afra Mashhadi, and Licia Capra.
2014. Poverty on the cheap: Estimating poverty maps using
aggregated mobile communication networks. In Proceedings of
CHI. ACM, 511–520.
8. Alessandro Venerandi, Giovanni Quattrone, Licia Capra,
Daniele Quercia, and Diego Saez-Trumper. 2015. Measuring
Urban Deprivation from User Generated Content. In
Proceedings of the 18th ACM CSCW. ACM, 254–264.

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Inside Out: to understand crime mechanisms look at urban
fabric first
Marco De Nadai∗1,2 , Emmanuel Letouzé†4 , Marta C. González‡3 and Bruno Lepri§2
1

1

University of Trento. 2 FBK. 3 MIT. 4 Data–Pop Alliance

Introduction

The rapid growth of cities and the increase of population mobility have challenged our ability tounderstand how to understand crime. The primary focus of criminology research has been on people
that commit crimes, and the reason they are involved. For crime to happen three conditions aresupposed to be present and interact: the presence of a motivated offender who is willing to commit
a crime, a suitable target, and the absence of guardians that would otherwise prevent the crimefrom taking place [2]. In this equation crime offenders, victims and guardians are all affected bysocio-economic conditions, the social disorganization (e.g. unemployment) of community [4] andthe place where they intersect. Thus, place matters.
Environmental criminology suggests that place not only is logically required, but also influences
the likelihood of becoming a crime hotspot through its physical characteristics. Accordingly, placeis one of the five necessary and sufficient components that constitute a criminal incident, namely
place, time, law, offender and victim [1]. Thus, environmental criminologists are interested in landuse, street design, traffic patterns and daily activities of people. However, scholars have virtuallyignored other theories (e.g. social disorganization), and bounded their discussion to macro-areas offew cities.
Urban planners and sociologists argue that cities are not a mere artificial construction thatgroup people. A city is a vital process of the people who compose it; and its neighborhoods are
the elementary form of cohesion in urban life. One of the seminal books in city planning is Jane
Jacobs’ The Death and Life of Great American Cities [6]. In this book she introduced the conceptof eyes-on-the-street, which suggests that safety can be maintained by citizens through urban
surveillance. For this to work, some physical qualities need to be present in the neighborhoods(i.e. a mix of residential, commercial and recreational land uses) to guarantee the diversity andcontinuous presence of people throughout the day. It is thus clear the tight coupling of environmental
criminology and urban planning theories.
Traditional approaches on describing crime have failed to provide a clear and broad descriptionof the desirable characteristics the different parts of the city should possess to keep crime eventslow.
In the present study we seek to fill this gap, formalizing the hypothesis that physical characteristics of the city not only are related to better life conditions and vitality [3], but also greatly
influence crime. We leverage open data, web data and call detail records (CDRs), which are used to
extract mobility networks. Then, we create two types of models. One is focused on describing how
physical characteristics influence crime in each part of the city. The other predicts crime eventsin a city from the structural features, and answers to the question “can we predict crime fromthe physical characteristics of the city?”. Thanks to several new sources of data and a Negative
Binomial model, we model crime through physical characteristics and socio-economic, spatial androutine-activity information.

2

Methods and results

Crime is considered a rare occurring phenomenon: a small proportion of people are victimized,with also few unreported and undiscovered crimes. Crime offenses are not distributed evenly inspace; they tend to cluster in parts of the city that exhibits similar characteristics, favorable tothe spread of crime. Rare events, expressed through count variables, have been widely modeled∗ denadai@fbk.eu† eletouze@datapopalliance.org‡ martag@mit.edu§ lepri@fbk.eu

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on Ordinary Least Squares (OLS) through a logarithmic and square root transformation of the
response variable. However, this introduces extra estimation bias, homoschedability assumptions
and impossible predictions. Moreover, spatial auto-correlation is rarely accounted for. For thisreason, we use a Negative Binomial regression (NB) with an eigenvector spatial filter (ESF) [5] that
introduces a set of independent variables that account for the spatial relationship of the variables.
Our hypothesis is that Jane Jacobs’ diversity conditions (e.g. small blocks, concentration ofdiversified enterprises, non-standardized buildings, land use mix) have an impact on crime measures.
Thus, we consider the metrics we created and validated [3] to operationalize the Jacobs’ theory.
Thus, for each neighborhood we measure the entropy of land uses, the average block size, thepopulation density, the walk-ability and the distance to vacuums (e.g. big parks).
Mobility network. As aforementioned, daily routines are supposed to influence the presenceof offenders, victims and guardians in a place. Moreover, frequent trips between two places aresupposed to influence the criminality of these places. Wang et al. [8] used the taxi flow networkwith a spatial lag regression. We instead use the same ESF method used to take into accountthe spatial-autocorrelation. Firstly we compute the total number of trips made in a typical day
between spatial units. Thus, we utilize anonymized CDRs with an observation period of six months(2013-2014) to estimate origin-destination (OD) matrices for a typical day. For each hour andspatial unit we compute the flows to all other spatial units of the city, using the method describedby Toole et al. [7]. The result is a N × N × 24 weighted mobility network denoting the number oftrips made by people during a typical day, where N is the number of spatial units in the city. We
define the symmetric weight matrix Wt from this mobility network, by applying this transformationTto the original mobility network Wm as Wt = Wm
Wm . The eigenvectors inserted in the NB model
are supposed to be a proxy for the mobility network dependencies between spatial units.
Covariates. The number of committed crimes is mainly influenced by the number of residents,
their social disorganization and routine activity. Social disorganization is the inability of theneighborhood to maintain effective social control. Social disorganization is higher in deprived areas,social heterogeneous units and in places with high unemployment rate, which is also a proxy for
motivated offenders. Social heterogeneity is accounted by the entropy of the buildings social class.
We first employ a descriptive model for Bogota where it is possible to understand the interactionsof each component of the model to describe crime events. Then, we create a predictive model
validated with a 5-fold Cross-validation with 1000 repetitions (to avoid overfit). This allows toanswer to the question ”can I predict crime events from the characteristics of the city?”.

2.1

Descriptive model

From the NB regression fit we observe the β coefficients of the features to understand the importanceof each variable, holding the others as constant. The most important variables to describe crime arebuilding density, population density and closeness to daily-use buildings. Particularly, we observethat the higher the building density is, the less crime events happen. On the contrary, population
density has a negative correlation with crime events. We also see that, as Jane Jacobs’ argued, thedistance of buildings from the nearest street has a positive correlation with crime events. Thismeans that her eyes on the street has a positive role on the decrease of crime in a neighborhood.
We also find that the number of inhabitants’ routine movements between the neighborhoods isclosely related to crime, with highly-connected points of the city experiencing a higher number ofcrimes, as suggested by the routine-activity theory [2].

2.2

Predictive model

Our preliminary findings (see Table 1) indicate that structural characteristics of the city, namely
Jacobs’ diversity conditions, are a better predictor of the target variables (the number of homicides
and robberies) than socio-economic conditions such as unemployment and deprivation. Mobility
networks, and thus the routine activity theory, improve the prediction of the model by 15%. The
combination of structural and socio-economic information provides better predictions than each on
its own. Nevertheless, to avoid the descriptive bias of describing just one city, we plan to apply
this method to Bogota, Los Angeles, Boston and Providence, where we have all the data we need.
Moreover, we plan to aggregate the smallest spatial-unit in multiple ways using the K-medoid
clustering algorithm on the centroids of the units. The evaluation of the descriptive and predictive

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(a)

(b)

(c)

Figure 1:(a) Crime counts in Bogota for each neighborhood; (b) Crime rate(|crimes|/(βp |population|) in Bogota for each neighborhood; (c) Stochastic signal component
Be E representing the spatial auto-correlation for each neighborhood in Bogota..
model on these multiple and new aggregated spatial units will reduce the MAUP problems previous
approaches did not consider.
Together, these observational results suggest that the city structure has a strong connection
with crime, and that improving its qualities can discourage criminality.

McFadderPseudo-R2 †
RMSE

S

C

D

S+D

C+D

S+C

Full

0.077
231.930.113
145.040.085
312.700.106
181.760.120
133.360.141
143.350.143
127.76† This is not a true measure of fit, and not bounded to 1. It indicates the degree to which

the model parameters improve upon the prediction of the null model.

Table 1: Negative Binomial regression models that predict the number of crime in each spatial
unit. The results are average across 1000 iterations of a 5-fold Cross-validation. S: demographic
and social disorganization variables only; C: Jane Jacobs’ diversity variables only; D: daily routine
variables only; Full: model with all the variables.

References[1] P. J. Brantingham. Environmental Criminology. Waveland Press, 1991.
[2] L. E. Cohen and M. Felson. Social change and crime rate trends: A routine activity approach.
American sociological review, pages 588–608, 1979.
[3] 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 WWW, 2016.
[4] C. Graif, A. S. Gladfelter, and S. A. Matthews. Urban poverty and neighborhood effects oncrime: Incorporating spatial and network perspectives. Sociology Compass, 8(9):1140–1155,
2014.
[5] D. A. Griffith. Spatial autocorrelation and spatial filtering: gaining understanding through theory
and scientific visualization. Springer Science & Business Media, 2013.
[6] J. Jacobs. The death and life of great American cities. Vintage, 1961.
[7] J. L. Toole, S. Colak, B. Sturt, L. P. Alexander, A. Evsukoff, and M. C. González. The pathmost traveled: Travel demand estimation using big data resources. Transportation ResearchPart C: Emerging Technologies, 58:162–177, 2015.
[8] H. Wang, Z. Li, D. Kifer, and C. Graif. Crime rate inference with big data. In KDD, 2016.

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LDA Mapping of Regional Socioeconomic Status
Lingzi Hong

Vanessa Frias-Martinez

Enrique Frias-Martinez

College of Information Studies
College of Information Studies
Telefonica Research
University of Maryland
University of Maryland
Madrid, Spainlzhong@umd.edu enrique.friasmartinez@telefonica.com vfrias@umd.edu

Abstract
The socioeconomic status of a region provides an understanding of the access of its citizens to basic services.
While socieconomic maps are key for policy makers, its
compilation requires extensive resources and becomes
highly expensive. As a result, traditional methods are
now using pervasive datasets (such as cell phone traces
for example) to infer regional socio-economic characteristics in a cost efficient manner. In this paper we use
mobility information derived from cell phone records toidentify socioeconomic levels by using a Latent Dirichlet Allocation that extracts recurring patterns of cooccurring behaviors across regions.

Introduction

Socio-economic maps contain information that characterizes various social and economic aspects like the educational level of the citizens or the access to electricity. The
accuracy of these maps is critical given that many policy
decisions made by governments and international organizations are based upon such information. National Statistical Institutes (NSIs) compute these maps every five to ten
years, and typically require a large number of enumeratorsthat carry out interviews gathering information pertaining
the main socio-economic characteristics of each household.
All these prerequisites make the computation highly expensive, especially for budget-constraint emerging economies.
The ubiquitous presence of cell phones worldwide is generating datasets of spatio-temporal data across large groups
of individuals. As previous research has shown, cell phonedata can offer a detailed picture of how humans move andinteract with each other (Becker et al. 2013). Recent results
found that cell phone-based behavioral patterns might becorrelated to specific socio-economic characteristics (Eagle,
Macy, and Claxton 2010; Soto et al. 2011; Frias-Martinez etal. 2013; 2012). For example, higher socio-economic levels
have been associated to stronger social networks or longer
distances traveled (Blumenstock and Eagle 2010). Framing the problem as a supervised learning setting, these approaches use the spatio-temporal data to compute a set ofpre-determined behavioral features per region and attempt
to predict the regional socio-economic levels manually collected by the NSIs. Rather than pre-determined features, regions might be better characterized by probabilistic models

SEL	
  Map	
  To:	
  R1…Rm	
  

	
  	
  	
  	
  Spa)o-­‐temporal	
  Data	  e2,d4R3,R5,23:11,02/03	
  f4,a3,R6,R1,13:12,02/05	
  …	
  a5,g8,R10,R2,16:32,02/12	
  From:	
  
R1	
  

.	
  
.	
  

Rm	
  

Popula)on	
  Mobility	
  Behavior	
  (Topics)	
  R1:{PMB1,…,PMBn}	
  …	
  Rm:{PMB1,…,PMBn}	
  
LDA	
  
	
  
Discrimina)ve	
  Classiﬁers	
  
SVM,	
  RF	
  

e.g.,	
  Words	  in	
  Region	
  R1:	  [out,R2,T],[R5,in,T’]	
  …[out,R2,T’’]	
  

	
  	
  Mobility	  	  Mo)fs	  	
  (Words)	
  Popula)on	
  Mobility	  Behavior	
  (Topics)	
  R1:{PMB1,…PMBk},SEL	
  	
  
…	
  
Rm:{PMB1,…PMBk},SEL	
  
	
  
sLDA	
  

Figure 1: General Approach. sLDA and LDA plate notationfrom (Mcauliffe and Blei 2008).
of latent behaviors not obvious through observation. Suchlatent recurring patterns might best reflect the complex nature of human behaviors and the impact that geography andtime might have on that behavior. We propose a novel approach to the problem of inferring regional socio-economic
levels from spatio-temporal data by using a topic modelingframework based on Latent Dirichlet Allocation (LDA).

Learning Architecture
Figure 1 shows the general approach proposed in this paper. We start with a socio-economic map where each region
is represented by a pair (SEL, Spatio − temporal; ; data).
Although the SEL is a continuous variable, it is often times
expressed as a discrete value through a letter (A, B, C, etc.).
On the other hand, the spatio-temporal data of a given region contains all calls in that region for a given period oftime. Since this information is collected by telecommunication companies for billing purposes, it contains behavioral information about millions of users. As a result, and as
shown in previous research (Vieira et al. 2010), the mobility
patterns extracted from such data can be representative of
the regional population at large, and thus of the underlying
socio-economic level.

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In this paper, we use the spatio-temporal data as a proxyof the mobility across regions, which will in turn be used
as a predictor of regional socio-economic levels. With thisapproach in mind, we organize the spatio-temporal data into
geographic data structures amenable to represent mobility aswords. We define the mobility motifs as individual transitions
containing origin and destination regions together with thetime range at which that event happens. As a result, eachregion will have a set of such motifs every time a transition
is observed for the time period under study.
With these regions and their motifs, we propose three
approaches: (1) the use of supervised Latent Dirichlet Allocation to do both latent population mobility behaviors(PMB) extraction and SEL prediction over the mobility motifs, (P M BSEL − sLDA) (Mcauliffe and Blei 2008); (2)use unsupervised LDA to reveal the population mobility behavior (topic) proportions across regions which are in turnused as input to discriminative regression and classification
algorithms to predict the SELs, PMB-LDA; and (3) Predetermined Features (PF), which represents each region asa vector of pre-determined mobility motifs where each component shows the number of times a given motif happens. By
comparing the accuracy of the three approaches, PMBSELsLDA, PMB-LDA and PF, we expect to quantify the impactof using latent topics to predict socio-economic levels fromspatio-temporal data.

Mobility Motifs and LDA approaches
The proposed approach uses large-scale spatio-temporal
data collected from cell phones to model mobility. Each
record collected is of the type (i, j, Ri , Rj , T, D where i, j
are encrypted phone numbers, Rz the regions where the individuals were when the phone call was made and T, D aretime and date of the call. We use such records to computethe mobility motifs of a region Ri as the set of individual
continuous transitions that depart or reach that region for agiven period of time.
Given two call records from the same individual i, we
can build a transition as follows. If i was in region Ri andcalled individual j in region Rj at time T and date D i.e.,(i, j, Ri , Rj , T, D) and next the individual i moved to region Ri and called to k in region Rk at time T  and dateD ≥ D i.e., (i, k, Ri , Rk , T  , D ) we can extract a mobility
motif for region Ri as the tuple mm = (out, Ri , T ) meaning that we observe an individual outgoing transition fromregion Ri to region Ri at time T ; and a mobility motif forregion Ri as the tuple mm = (in, Ri , T  ) meaning that we
observe an individual incoming transition from region Ri toregion Ri at time T  . The average time between visited regions is 3.2h, thus, we discretize the time into six four-hour
ranges i.e., T ∈ {[0 − 4), [4 − 8), ...[20, 24)}. Repeating theprocess for all transitions observed, we can build a collection of regions (documents) each containing a specific set ofmobility motifs modeled from the observed calling data as
Ri = j=1∈1...6∗R2 (out, Rj , T ) ∨ (Rj , in, T ) where 6 ∗ R2
is the size of the vocabulary accounting for all possible bidirectional transitions between any two given regions in thearea under study at any four-hour time range.

PMBSEL-sLDA
In this approach, we assume that the mobility motifs in eachregion arise from a set of latent topics or population mobility behaviors (PMB) at large scale i.e., a set of unknown
distributions over the mobility motifs. The set of PMBs iscommon to all regions, but each region will have a different
combination of them.
We propose to use a supervised latent Dirichlet allocation(sLDA) in such a way that the generative process also includes the socio-economic label for each region as part ofthe model (Mcauliffe and Blei 2008). As a result, the inference is based on model estimates that take into account thesocio-economic labels ı.e., the empirical PMB frequencies
put together and non-exchangeably both mobility motifs and
SELs.

PMB-LDAThis second approach focuses on using topic modeling to
extract the population mobility behaviors (PMB) in an unsupervised manner i.e., topics are identified with the regions
treated as unlabelled. Next, we use the PMBs as features topredict SELs either as continuous values or as classes. In this
scenario, the LDA is used for dimensionality reduction.

Pre-determined Features
In this approach, each region Ri is represented by a vector containing all possible mobility motifs as features. We
refer to the features as pre-determined because they are defined from behavioral hypothesis about human behavior andsocio-economic levels rather than from latent topics directly
extracted from the features which add the possibility of finding more complex behaviors. Instead of using population
mobility behaviors extracted with LDA, here the regions
have hard-coded all possible mobility motifs.

Results
To evaluate the accuracy of the approaches proposed, we use
two datasets: a large-scale spatio-temporal dataset containing one month of calling activity for three cities from thesame country, and the socio-economic map for those threecities containing regional SEL information. The spatiotemporal dataset contains a total of 134M calls and 1.8M
individuals; while the SEL map contains a total of 186 regions distributed across the three cities.

SEL Inference
We first compute the mobility motifs from the spatiotemporal dataset. We obtain a total of ≈ 4.4M mobility motifs across all 186 regions, with an average of ≈ 24K motifs
per region (σ =≈ 32K).
Table 1 shows the results for the four approaches using
regression to infer SELs as continuous values. The results
reported are for 20 topics for PMB-LDA (RF and SVR) and
25 for PMBSEL-sLDA, which turned out to be the number of topics that had the best results in terms of accuracy(R2 ) as shown in Figure 2. For Support Vector Regression,
we used a Gaussian RBF kernel and the parameters (C, γ, )were selected using 5-fold cross validation to minimize the

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CLASSIFICATION

0.9

PMBSEL-sLDA

R-squared value

0.8

PMB-LDA

0.7

ldaSVR

0.6

ldaRF

0.5

sLDA

0.4

5

8

15

20

25

Figure 2: R2 per number of topics for PMB-LDA (SVR andRF) and PMBSEL-sLDA approaches.
REGRESSIONPMBSEL-sLDA
SVR
PMB-LDA
RF
SVR
PF
RF
SVR
PF2
RF

R2
0.78020.70500.71880.25730.69270.57210.6288PF2

AVG.F10.75650.62370.71300.45220.70040.62000.64400.75260.63020.72120.45100.71000.63740.6567A
0.72730.66090.7786
0.7856
0.78560.74090.7468F1
B0.72830.55190.65720.62830.62830.5586
0.5847C
0.80230.67770.7276
0.71600.71600.61280.6387

Table 2: Accuracy (ACC), average F1 and per-class F1 scorewith topic models and pre-determined features.

0.3
3

PF

SVM
RF
SVM
RF
SVM
RF

ACC

RMSE
0.09020.10880.10580.17310.11560.12900.1195

Table 1: Accuracies for Regression with topic models andpre-determined features.
mean squared error. For Random Forest, the results are reported for 8 random trees in PMB-SEL; 146 trees in P F and
14 trees in P F 2.
We can observe that both topic model approaches,
PMBSEL-sLDA and PMB-LDA, have the best R2 values
together with the lowest error. In the case of PMB-LDA
the best results are obtained using RF although SVR gave
results with R2 only ≈ 1% worse. As hypothesized, thetopic model approaches outperform the pre-determined feature approaches by ≈ 9% in the best case. In fact, R2 =0.7802 for PMBSEL-sLDA while R2 = 0.6927 for P F .
These results show that the topic models reveal latent population mobility behaviors (PMB) that appear to characterize
SELs (and the complex behaviors associated to them) better
than the mobilility motifs in which the PMBs are based on.
Comparing both topic model approaches, the supervised approach gave ≈ 6% better R2 than the unsupervised approachcombined with RF. A similar result was also reported by(Mcauliffe and Blei 2008) in an experiment inferring movie
ratings with sLDA.
Table 2 shows the accuracies and F1 scores for all four
approaches when SELs are defined as three discrete classes:
A, B and C (from high to low socio-economic level). The
results are reported for 25 topics for PMBSEL-sLDA and
15 topics for PMB-LDA, which are the topics that gave thehighest F1 scores. For SVM, we used an RBF Gaussian Kernel and for RF the number of trees were 8, 146 and 2 for
PMB-LDA, PF and PF2, respectively.
In general, the findings and trends are similar to the ones
already discussed for the regression results. Here again, we
observe that both topic models appear to improve the average F1 score obtained with the pre-determined features approach by ≈ 4% in the best case scenario (PMBSEL-sLDA

vs. PF). It seems that LDA-based approaches might be doinga better job at extracting more complex population mobility
behaviors than just the mobility motifs. Similarly, the predetermined mobility motifs approach is slightly better than
the simpler features of PF2 when RF are used. Moving onto the per-class F1 scores, we observe that the results acrossclasses are quite balanced, specially when topic models areused. Interestingly, this fact reveals that regions are not simply being classified as the most frequent class which would
be B in this case.

References
Becker, R.; Caceres, R.; Hanson, K.; Isaacman, S.; Loh, J.;
Martonosi, M.; Rowland, J.; Urbanek, S.; Varshavsky, A.;
and Volinsky, C. 2013. Human Mobility Characterizationfrom Cellular Network Data. In CACM.
Blumenstock, J., and Eagle, N. 2010. Mobile divides: Gender, socioeconomic status, and mobile phone use in rwanda.
Eagle, N.; Macy, M.; and Claxton, R. 2010. Network diversity and econmic development. Science 328.
Frias-Martinez, V.; Soto, V.; Virseda, J.; and Frias-Martinez,
E. 2012. Computing cost-effective census maps from cell
phone traces. Workshop on Pervasive Urban Applications,
PURBA.
Frias-Martinez, V.; Soguero-Ruiz, C.; Frias-Martinez, E.;
and Josephidou, M. 2013. Forecasting socioeconomic trends
with cell phone records. ACM Symposium on Computing forDevelopment.
Mcauliffe, J. D., and Blei, D. M. 2008. Supervised topic
models. In Advances in neural information processing systems, 121–128.
Soto, V.; Frias-Martinez, V.; Virseda, J.; and Frias-Martinez,
E. 2011. Prediction of socioeconomic levels using cell
phone records. In Proceedings of the Int. Conference on
User Modeling, Adaption, and Personalization.
Vieira, M.; Frias-Martinez, E.; Bakalov, P.; Frias-Martinez,
V.; and Tsotras, V. 2010. Querying spatio-temporal patterns
in mobile phone-call datasets. International Conference ofMobile Data Management.

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Rapid Assessments of Population Displacement in the2015 Nepal Earthquake
Robin Wilson​1,2​, ​Elisabeth zu Erbach-Schoenberg​1,2,*​, Maximilian Albert​1​, Daniel Power​1​, Simon Tudge1​​ , Miguel​ , Christopher Brooks1​​ , Christopher Hughes1​​ , Lenka Pitonakova1​​ ,Gonzalez1​​ , Sam Guthrie1​​ , Heather Chamberlain1,2​
1,3,4​
1,5,6​
1,7​​ , and Linus Bengtsson1,8​
Caroline Buckee​ , Xin Lu​ , Erik Wetter​ , Andrew Tatem1,2​
1​

Flowminder Foundation, Stockholm, Sweden
Geography & Environment, University of Southampton, Southampton, UK
3​
Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, USA
4​
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
5​
Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden
6​
College of Information System and Management, National University of Defense Technology, Changsha, China
7​
Stockholm School of Economics, Stockholm, Sweden
8​
Dept. of Public Health Sciences, Karolinska Institute, Sweden.
*Corresponding author for this abstract, ​elisabeth.zu.erbach@flowminder.org
2​

Introduction
Disasters, such as hurricanes, floods, landslides or
earthquakes lead to displacement of populations
from the areas affected. These populations arevulnerable, as they frequently do not have access toshelter, food, clean water and other necessities.
Relief efforts aiming to provide these necessities rely
on up to date information on locations and sizes ofdisplaced populations for both assessment of
resource needs as well as prioritisation of resource
allocation. In the later stages of relief efforts,
assessing which areas are recovering well and which
are lagging behind in terms of recovery compared toother areas, becomes important.
Information on population movements following
disasters is often not available or has poor spatial
coverage, due to lack of suitable data sources andinterruption of existing data collection systems.
Mobile phone call detail records (CDRs) have beenused to study population flows in a variety of
research settings [1-6]. They have been shown toallow accurate estimation of displaced population
flows in the context of the 2010 Haiti earthquake in a
retrospective analysis [6]. Here, we describe how
CDRs were used to assess displacement resulting
from the 2015 Nepal earthquake, which happened
on 25 April 2015. We describe the methods used toestimate magnitude and destinations of population
flows related to displacement as well as measure
return rates. The methods described were used toprovide these estimates in near-real time, whichmeant key findings could be included in reports by

the UN resident Coordinator, adding to the data
evidence base available for relief agencies.

Data and Methods
Mobile phone data for this project were provided by
Ncell, the leading mobile phone services provider in
Nepal with 12.9 Million subscribers. The Flowminder
Foundation had signed an agreement with Ncell
before the earthquake and was in the process ofsetting up the technical infrastructure to process
CDRs according to the GSMA privacy guidelines atthe premises of the operator. This setup had notbeen completed by the time the earthquake hit,
however, Ncell were able to provide access todeidentified records within 6 days of the earthquake.

Preprocessing
Preprocessing
includedremoving
unusedinformation from the data, data compression as wellas calculating a ‘daily location’ for each user. We
defined the daily location as the location of the lastcall made by each user. This definition aims at
capturing the nighttime location of individuals, whichis generally the place of residence or shelter and thismethod was chosen following preliminary analyses.
The daily locations on tower level, were mapped toadmin level locations (admin 3 (district) for the
displacement flows and admin 4 level (Village
Development Committee, VDC) for the return rates).
Admin units were used instead of tower level

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estimates to match other information used by aid
agencies and to further increase anonymity.

Estimation of population flows
Population flows can be estimated by calculating auser’smostfrequent
locationfortwo
non-overlapping, subsequent periods and comparing
these two locations. This can be formalised as amatrix of location pairs and associated flows ofusers from an origin to a destination location, called
an origin-destination matrix (OD matrix). This method
has been used in a number of settings to measure
population flows [7-10]. The aim for this application
was more specific: to measure mobility resulting
from the disaster, and with mobility under normal
conditions being high in Nepal, we have to accountfor this high baseline mobility to measure movement
in excess of this baseline.
To account for baseline mobility, we normalised thepost-earthquake flows using pre-earthquake mobility
estimates. Pre-earthquake estimates were calculated
as an OD matrix using two periods before theearthquake. More specifically we compared abenchmark period from the beginning of the year (1stJanuary until 7th April 2015) to a comparison period
immediately before the earthquake (20th-24th April,note that this period is shorter, chosen to exclude
population flows resulting from travel around the
Nepali New Year festival) to obtain the
pre-earthquake mobility estimate.
Post-earthquake flows were calculated as changes
in location between the benchmark period (seeabove) and a focal period, which consisted of themost recent week of data at any point in time during
the response period.
This normalisation provides us with a measure of
how much traffic volumes following the earthquake
deviate from the volumes that are a result of normal
mobility.

Quantifying return rates
At later stages of relief efforts, an important measureis the rate of return to certain areas. Low return rates
might indicate that recovery in an area is not
sufficient yet to allow return of residents. To estimate
return rates, we extract user’s usual residence as themost frequent daily location over the benchmark
period. A user is considered displaced if they spent a
period of at least 7 days away from their usual
residence in the two weeks following the earthquake.
We can then calculate how many of the users
considered displaced return to their place of usual
residence in the weeks following.

Results
In the first weeks following the earthquake, large
above normal flows were observed going out of the
Kathmandu area into the surrounding areas. Figure 1
shows the districts that received above normal flows
from Kathmandu for the period of 10-14 May 2015.
Figure 2 shows the lower level admin 4 districts(VDCs) coloured by the percentage of usual
residents who remain after being displaced following
the earthquake. This highlights that there is variationin recovery of areas.
The full paper [11] including additional results can befound at:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779
046/

To provide and allocate aid appropriately,
humanitarian agencies require information on
population flows. The flows extracted from the CDRs
are numbers of users travelling in excess of the flows
observed under normal conditions. To estimate
population flows from user flows, we calculated
scaling factors to be used to transform the flows fromuser estimates to approximate population estimates.
The scaling factors were calculated by relating user
and population numbers (using population counts
providedbytheWorldPop
project)(http://www.worldpop.org.uk/) for each district.

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Figure 1: Outflows out of Kathmandu with above normal
rates compared to baseline mobility. Boundaries shown
are admin 3 (districts)

Figure 2: Percentage of people displaced by theearthquake who remain away as of 19 August 2015.
District boundaries shown in grey, VDC (admin 4)
boundaries shown in white.

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References[ 1 ] Le Menach, A., Tatem, A. J., Cohen, J. M., Hay, S. I., Randell, H., Patil, A. P., & Smith, D. L. (2011). Travel risk, malaria
importation and malaria transmission in Zanzibar. ​Scientific reports, ​1.
[ 2 ] Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., & Ratti, C. (2011). Real-time urban monitoring using cell phones: A
case study in Rome. ​IEEE Transactions on Intelligent Transportation Systems, ​12(1), 141-151.
[ 3 ] Wesolowski, A., Metcalf, C. J. E., Eagle, N., Kombich, J., Grenfell, B. T., Bjørnstad, O. N., ... & Buckee, C. O. (2015).
Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. ​Proceedings of the
National Academy of Sciences, ​112(35), 11114-11119.
[ 4 ] Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. ​Nature,453(7196), 779-782.
[ 5 ] Sevtsuk, A., & Ratti, C. (2010). Does urban mobility have a daily routine? Learning from the aggregate data of mobile
networks. ​Journal of Urban Technology, ​17(1), 41-60.
[ 6 ] Bengtsson, L., Lu, X., Thorson, A., Garfield, R., & Von Schreeb, J. (2011). Improved response to disasters andoutbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti.
PLoS Med, ​8(8), e1001083.
[ 7 ] Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., & Buckee, C. O. (2012). Quantifying
the impact of human mobility on malaria. ​Science, ​338(6104), 267-270.
[ 8 ] Wesolowski, A., Buckee, C. O., Bengtsson, L., Wetter, E., Lu, X., & Tatem, A. J. (2014). Commentary: Containing theEbola outbreak–the potential and challenge of mobile network data. ​PLOS currents outbreaks.
[ 9 ] Calabrese, F., Di Lorenzo, G., Liu, L., & Ratti, C. (2011). Estimating origin-destination flows using mobile phone location
data. ​IEEE Pervasive Computing, ​10(4), 0036-44.
[ 10 ] Bengtsson, L., Gaudart, J., Lu, X., Moore, S., Wetter, E., Sallah, K., ... & Piarroux, R. (2015). Using mobile phone data
to predict the spatial spread of cholera. ​Scientific reports, ​5.
[ 11 ] Wilson, R., zu Erbach-Schoenberg, E., Albert, M., Power, D., Tudge, S., Gonzalez, M., ... & Pitonakova, L. (2016).
Rapid and near real-time assessments of population displacement using mobile phone data following disasters: the 2015
Nepal earthquake. ​PLoS currents, ​8.

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Uncovering the Spread of Chagas Disease inArgentina and Mexico
Juan de Monasterio∗ , Alejo Salles∗† , Carolina Lang∗ , Diego Weinberg‡ , Martin Minnoni§ , Matias Travizano§ ,Carlos Sarraute§∗ FCEyN, Universidad de Buenos Aires, Argentina. Email: {laterio, carolinalang93}@gmail.com† Instituto de Cálculo and CONICET, Argentina. Email: alejo@df.uba.ar‡ Fundación Mundo Sano, Buenos Aires, Argentina. Email: dweinberg@mundosano.org§ Grandata Labs, 550 15th Street, San Francisco, CA, USA.

Email: {martin, mat, charles}@grandata.com

Abstract—We use mobile phone records for the analysis ofmobility patterns and the detection of possible risk zones of
Chagas disease in two Latin American countries. We showthat geolocalized call records are rich in social and individual
information, which can be used to infer whether an individual
has lived in an endemic area. We present two case studies, in
Argentina and in Mexico, using data provided by mobile phonecompanies from each country. The risk maps that we generate
can be used by public health campaign managers to target specificareas and allocate resources more effectively. Finally, we show thevalue of mobile phone records to predict long-term migrations,which play a crucial role in the spread of Chagas disease.

I. I NTRODUCTION
Chagas disease is a parasitic disease of global reach, spread
mostly across 21 Latin American countries. Caused by the
Trypanosoma cruzi parasite, its transmission occurs mostly
in the American endemic regions via the Triatoma infestansinsect family. In recent years and due to globalization andmigrations, the disease has become an issue in other continents [1], particularly in countries that receive Latin American
immigrants such as Spain and the United States.
A crucial characteristic of the infection is that it may last 10
to 30 years in an individual without presenting symptoms [2],which greatly complicates effective detection and treatment.
Long-term human mobility (particularly seasonal and permanent rural-urban migration) thus plays a key role in the spreadof the epidemic [3].
In this work, we discuss the use of Call Detail Records(CDRs) for the analysis of mobility patterns and the detectionof possible risk zones of Chagas disease in two Latin Americancountries. We generate predictions of population movements
between different regions, providing a proxy for the epidemic
spread. We present two case studies, in Argentina and in
Mexico, using data provided by mobile phone companies fromeach country.
II. C HAGAS D ISEASE IN A RGENTINA AND M EXICO
A. Endemic Zone in Argentina
The Gran Chaco, situated in the northern part of the country,
is hyperendemic for the disease [4]. The ecoregion’s low sociodemographic conditions further support the parasite’s lifecycle,
where domestic interactions between humans, triatomines andanimals foster the appearance of new infection cases, particularly among rural and poor areas. This region is considered as
the endemic zone EZ in the analysis described in Section IV.
Recent national estimates indicate that there exist between
1.5 and 2 million individuals carrying the parasite, with morethan seven million exposed. National health systems face manydifficulties to effectively treat the disease. In Argentina, lessthan 1% of infected people are treated (the same statistic holds
at the world level). Even though governmental programs havebeen ongoing for years now, data on the issue is scarce or
hardly accessible.
B. Endemic Zone in Mexico
The Mexican epidemic area, which includes the states
having the top 25% prevalence rates nationwide [5], covers
most of the South region of the country and includes thestates of Jalisco, Oaxaca, Veracruz, Guerrero, Morelos, Puebla,
Hidalgo and Tabasco. This region is considered as the endemiczone EZ for the Mexican case.
Despite the lack of official reports, an estimate of the number of Trypanosoma cruzi infections by state in the country
indicates that the number of potentially affected people in
Mexico is about 5.5 million [6]. In recent years there has beena focus on treating the disease with two available medications,benznidazole or nifurtimox, with less than 0.5% of infectedindividuals receiving treatment in Mexico [7].
People from endemic areas of Chagas disease tend to
migrate to industrialized cities of the country, mainly Mexico
City, in search of jobs [8]. Therefore, the study of long-term
mobility is crucial to understand the spread of the Chagas
disease in Mexico.
III. M OBILE P HONE DATA S OURCES
Our data source is anonymized traffic information fromtwo mobile operators. The Argentinian dataset contains CDRs
collected over a period of 5 months, from November 2011 toMarch 2012. The Mexican dataset contains CDRs for a periodof 24 months, from January 2014 to December 2015.
For our purposes, each record is represented as a tuple
i, j, t, d, l, where user i is the caller, user j is the callee,t is the date and time of the call, d is the direction of the

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call (incoming or outgoing), and l is the location of the towerthat routed the communication. The dataset does not include
personal information from the users.
We aggregate the call records for a five month period into anedge list (ni , nj , wi,j ) where nodes ni and nj represent users
i and j respectively and wi,j is a boolean value indicating
whether these two users have communicated at least once
within the five month period. This edge list represents ourcommunication graph G = N , E where N denotes the setof nodes (users) and E the set of communication links. We
note that only a subset NC of nodes in N are clients ofthe mobile operator. Since geolocation information is available
only for users in NC , in the analysis we considered the graph
GC = NC , EC  of communications between clients of theoperator.
IV. R ISK M APS FOR C HAGAS D ISEASE
A. Methodology for Risk Map Generation
The first step it do determine the area in which each user
lives. For each user u ∈ NC , we compute its home antenna
Hu as the antenna in which user u spends most of the time
during weekday nights [9]. The users such that Hu is locatedin the endemic zone EZ are considered the residents of EZ .
The second step it to find users highly connected with theresidents of the endemic zone EZ . To do this, we get the listof calls for each user and then determine the set of neighbors
in the social graph GC . For each resident of the endemic zone,
we tag all his neighbors as vulnerable.
The third step is to aggregate this data for every antenna.
For every antenna a, we compute: the total number of residents
Na , the total number of residents which are vulnerable Va , thetotal volume of outgoing calls Ca , and the number of outgoingcalls whose receiver lives in the endemic area V Ca (vulnerable
calls).
We generated heatmaps to visualize these antenna indicators, overlapping these heatmaps with political maps of theregion taken for study. Each antenna is represented by a circle
whose area depends on the population living in the antenna Na
and whose color depends of the fraction Va /Na of vulnerable
users living there. We used two filtering parameters: eachantenna is plotted if its fraction of vulnerable users is higher
than β, and if its population is bigger than mv .

(a) β = 0.01(b) β = 0.15

Figure 1. Risk map for Argentina, filtered according to β.

special focus on the metropolitan area of Greater Buenos Aires
whose heatmap is shown in Fig. 2.
High risk antennas were separately listed and manually
located in political maps. This information was made available
to the Mundo Sano Foundation collaborators who used it as
an aid for their campaign planning and for the education ofcommunity health workers.
V. P REDICTION OF L ONG - TERM M IGRATIONSIn this section, we describe our work on the prediction of
long-term mobility. The CDR logs available in the Mexican
dataset span 24 months, from January 2014 to December 2015,
making them suitable for this study.

B. Results and Observations
Fig. 1 shows the risk maps for Argentina, generated withtwo values for the β parameter and fixing mv = 50 inhabitants
per antenna. After filtering with β = 0.15, we see thatlarge portions of the country harbor potentially vulnerable
individuals. Namely, Fig. 1(b) shows antennas where morethat 15% of the population has social ties with the endemic
region EZ .
Advised by Mundo Sano Foundation’s experts, we thenfocused on areas whose results were unexpected to the epidemiological experts. Focused areas included the provinces ofTierra del Fuego, Chubut, Santa Cruz and Buenos Aires, with

Figure 2. Risk map for the metropolitan area of Buenos Aires, filtered with
β = 0.02.

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We divide the available data into two distinct periods: T0 ,from January 2014 to July 2015, considered as the “past” inour experiment; and T1 , from August 2015 to December 2015,
considered as the “present”. Knowing which users live in theendemic region EZ and how they communicate during period
T1 , we want to infer whether they lived in EZ in the past(period T0 ).
A. Model Features
The features constructed reflect calling and mobility patterns. Each week is divided into 3 time periods: (i) weekday
is from Monday to Friday, on working hours (8hs to 20hs);(ii) weeknight is from Monday to Friday, between 20hs and8hs of the following day; and (iii) weekend is Saturday and
Sunday. The model consists of the following features, which
can be classified in 4 categories:
1) Used and home antennas: For each user u ∈ NC , weregister the top ten most used antennas, considering all calls
or only calls made during the weeknight period. Users were
tagged as ‘endemic’ if their home antenna is in the endemiczone EZ and ‘exposed’ if any of the top ten antennas is in
the risk area.
2) Mobility diameter: The user’s logged antennas define a
convex hull in space and the radius of the hull is taken to be
as the mobility diameter. We generate two values, considering(i) all antennas and (ii) only the antennas used during theweeknight.
3) Communications graph: We enrich the social graph GC
built from the CDRs. For each edge ni , nj  ∈ EC , we gather
the number of calls exchanged, the sum of call durations(in seconds), the direction (incoming or outgoing), segmented
according to the periods weekday, weeknight, and weekend.
Since the samples in our dataset are users, we aggregate
these variables by grouping interactions at the user level. The
combination of different variables amounts to a total of 130
features per user.
We also compute the user’s degree and the total countof endemic neighbors, labeling each user i as vulnerable
whenever he has an edge with another user j who lives inthe endemic region EZ .
4) Validation data: We perform an analysis similar to thehome antenna detection previously described, but considering
the time period T0 (from January 2014 to July 2015), in orderto determine the home antenna of users during T0 .
B. Supervised Classification
We used most common techniques for this task: Support
Vector Machines, Random Forest, Logistic Regression, andMultinomial Naive Bayes. The data was split into 70% fortraining and 30% for testing.
The Multinomial Bayes classifier has a linear time complexity, and thus serves as a fast benchmark. Support Vector
Machines (SVM) and Logistic Regression performed better
than Multinomial Bayes. We tuned the standard hyperparameters: L2-penalty regularization for Logistic Regression andkernel bandwidth for the Gaussian Kernel SVM. Both learning

routines were executed in parallel and in each iteration 5%of the training set was sampled for cross validation. The bestmodel was a Logistic Regression Classifier with an L2-penalty
value of 0.01. The scores obtained by the selected model on theout-of-sample set are F1-score: 0.964537; accuracy: 0.980670;
AUC: 0.991593; precision: 0.970838; recall: 0.958316.
High values across all scoring measures are achieved. These
results can be explained by the fact that communication andmobility patterns are in essence highly correlated across time
periods. In this case, a user being endemic in T1 is correlated
to being endemic in T0 , and the same holds with a user’s
interaction with vulnerable neighbors during T1 .
VI. C ONCLUSION
The heatmaps shown in Section IV expose an expected“temperature” descent from the endemic regions outwards.
We also found out communities atypical compared to theirneighboring region, which stand out for their strong communication ties with the endemic region EZ . The detectionof these communities is of great value to health campaign
managers, providing them tools to target specific areas andprioritize resources and calls to action more effectively.
In Section V, we tackled the problem of predicting longterm migrations. In particular, we showed that it is possible touse the mobile phone records of users during a bounded period
in order to predict whether they have lived in the endemic zone
EZ in a previous time frame.
To conclude, we showed here the value of generating risk
maps in order to prioritize effectively detection and treatment
campaigns for the Chagas disease. The results stand as a proof
of concept which can be extended to other countries withsimilar characteristics.
R EFERENCES[1] Gabriel A Schmunis and Zaida E Yadon. Chagas disease: a Latin
American health problem becoming a world health problem. Acta tropica,
115(1):14–21, 2010.
[2] Anis Rassi and Joffre Marcondes de Rezende. American trypanosomiasis(Chagas disease). Infectious disease clinics of North America, 26(2):275–
291, 2012.
[3] Roberto Briceño-León. Chagas disease in the Americas: an ecohealthperspective. Cadernos de Saúde Pública, 25:S71–S82, 2009.
[4] OPS. Mapa de Transmisión vectorial del Mal de Chagas. Organizacion
Panamericana de la Salud, 2014.
[5] A Cruz-Reyes and José M Pickering-López. Chagas disease in Mexico:an analysis of geographical distribution during the past 76 years-A review.
Memorias do Instituto Oswaldo Cruz, 101(4):345–354, 2006.
[6] Alejandro Carabarin-Lima, Marı́a Cristina González-Vázquez, Olivia
Rodrı́guez-Morales, Lidia Baylón-Pacheco, José Luis Rosales-Encina,
Pedro Antonio Reyes-López, and Minerva Arce-Fonseca. Chagas disease (american trypanosomiasis) in Mexico: an update. Acta tropica,
127(2):126–135, 2013.
[7] Jennifer M Manne, Callae S Snively, Janine M Ramsey, Marco Ocampo
Salgado, Till Bärnighausen, and Michael R Reich. Barriers to treatment
access for Chagas disease in Mexico. PLoS Negl Trop Dis, 7(10):e2488,
2013.
[8] Carmen Guzmán-Bracho. Epidemiology of Chagas disease in Mexico:
an update. TRENDS in Parasitology, 17(8):372–376, 2001.
[9] Balázs Cs Csáji, Arnaud Browet, VA Traag, Jean-Charles Delvenne,
Etienne Huens, Paul Van Dooren, Zbigniew Smoreda, and Vincent D
Blondel. Exploring the mobility of mobile phone users. Physica A:
Statistical Mechanics and its Applications, 2012.

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Spatially explicit modeling of potential Ebola spread in Senegal
Lorenzo Mari*, Francesca Dagostin, Letizia Raffa,
Manuela Ciddio, Lorenzo Righetto, Marino Gatto, Renato Casagrandi*
Politecnico di Milano
Piazza Leonardo da Vinci, 32 – 20133 Milano, Italy* corresponding authors: lorenzo.mari@polimi.it, renato.casagrandi@polimi.it

Abstract
Ebola virus disease (EVD) is a viral hemorrhagic fever with severe, and often fatal,consequences for the infected human hosts. In 2014, several West African countries were hit by
the largest EVD epidemic ever recorded, which exerted a toll of thousands of victims in Liberia,
Sierra Leone and Guinea. Only one case occurred in Senegal (a person who traveled by road to
Dakar from Guinea), but a prompt sanitary response successfully contained the outbreak andprevented onward transmission. A thorough understanding of the main potential propagation
pathways of EVD in each country of the area remains a crucial goal of sanitary policies,
inasmuch as it can guide the optimization of the surveillance infrastructure, as well as
emergency management should an EVD outbreak occur in the future.
We propose a spatially-explicit model for EVD transmission in Senegal accounting for the
dynamics of the human population suitably divided into the compartments of susceptible,
exposed, infectious and deceased people. The model is developed at the spatial scale of healthdistricts. It is parameterized with literature data and georeferenced indicators available fromdemographic surveys describing the spatial heterogeneity of socioeconomic conditions
throughout the country. Also, a large dataset of anonymous mobile phone traces obtained
through the D4D-Senegal Challenge organized by Orange and Sonatel in 2014 is used todescribe human mobility and social links (through aggregated source-destination matrices
based on the mobile phone users' communication patterns), which are the key drivers for the
propagation of EVD at various spatial scales. Conditions for epidemic outbreaks are determined
via the linear stability analysis of the model, while numerical simulations are carried out tooutline the ensuing spatiotemporal patterns of disease spread. The analysis of the model isperformed under several scenarios describing different outbreak intensities and initial epidemic
sources. Also, different strategies for epidemic control (involving e.g. early quarantine andtreatment measures) are assessed, and their outcomes are quantitatively evaluated.
We show that the spatial heterogeneity induced by population distribution, human mobility andsocioeconomic conditions play nontrivial, important, and possibly counterintuitive roles indetermining the likelihood of EVD epidemics in Senegal. The densely populated region ofDakar, the main hub of human mobility within the country, is key in the early phases of an
epidemic. Model simulations show in fact that outbreaks originating close to Dakar would be
likely to produce a high number of cases, and that prompt and timely localized interventions
might reduce the odds of large-scale epidemic spread. We thus conclude that spatially-explicit
modeling, usefully assisted by the analysis of mobile phone traces, can elucidate the main
propagation pathways of EVD in Senegal and anticipate the potential patterns of spread in theunfortunate case of an epidemic outbreak. Results from our analysis can be used to informsanitary policies, and to guide the optimization of surveillance infrastructure, resource
allocation and emergency management.

Keywords: call detail record analysis, spatially explicit modeling, epidemiology

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Anticipatory Monitoring of Depressive States through theAnalysis of Multimodal Phone Data
Mirco Musolesi
University College London
United Kingdom
m.musolesi@ucl.ac.uk

Abhinav Mehrotra
University College London
United Kingdom
a.mehrotra@ucl.ac.uk
ABSTRACT

Remarkable advances in smartphone technology, especially interms of passive sensing, have enabled researchers to passively
monitor user behavior in real-time and at a granularity thatwas not possible just a few years ago. Recently, different approaches have been proposed to investigate the use of different
sensing and phone interaction features, including location, call,
SMS and overall application usage logs, to infer the depressive
state of users. In this paper, we propose an approach for monitoring of depressive states through the analysis of multimodal
phone data. More specifically, we present the initial results ofan ongoing study to demonstrate the association of depressive
states with the indicators extracted from mobile phone traces.
Author Keywords

Mobile Sensing; Depression; Anticipatory Computing.
INTRODUCTION

Today’s smartphone comes with an array of sensors and highperformance computing power. They are also carried by theirowners all the time. These characteristics have not only enabled researchers to build very effective systems for passively
monitoring numerous physical-context modalities such asusers’ location [5], physical activity [4] and mobile phoneinteraction [10], but also cognitive context [12], such as moodand well-being states.

Instead, in this paper, we argue that depressive states should
be monitored via the analysis of multimodal phone data. Thismight also contribute to the improvement of the performanceof the machine learning algorithms for predicting depressive
states.
Our focus instead is on monitoring depressive states that arequantified by means of a PHQ-8 questionnaire [7, 8] asked
over a period of 14 days. Moreover, the authors of [9] do notconsider the role of micro-interactions such as clicks, scrolls
and many others, and reactions to notifications. Preliminary
results of this study are presented in [11].

MEASURING MOBILE PHONE USAGE AND DEPRESSIONSCORES

In order to study the association of users’ depressive states on
their mobile interaction behavior, we conducted a longitudinal
field study. More specifically, we developed an Android app
and collected smartphone interaction data from 25 participants
for a time period of 30 days. The collected data include logs
for notification handling and phone usage.
We use several smartphone interaction metrics to analyze thecollected data, capturing various dimensions, from application
usage to the number of clicks on the screen. The metrics are
summarized in Table 1.

However, cognitive context is inferred mostly by employing
ESM techniques, according to which users are prompted with aseries of questions that are required to be responded repeatedly.
Past studies have shown the potential of exploiting mobile sensing data to learn and, potentially, predict the user’s cognitive
context [1, 2, 3]. For example, Canzian et al. have used mobility data to monitor depressive states [3] and Alvarez-Lozano
at al. have exploited application usage logs to monitor patientsaffected by bipolar disorder [1].

We also collect the responses to the PHQ-8 questionnairefrom the users via an ESM approach. This data is then used
to compute the depressive scores for each users. Finally, we
compute the Kendall’s Rank correlation coefficients to analyze
the relationship between the severity of the depressive state andphone interaction metrics (based on notification, applicationand phone usage).

The key limitation of these approaches is the fact that they
rely on a single data source for monitoring depressive states.

PRELIMINARY RESULTS

Past studies have used mobility, activity, application usage andcommunication data for inferring depressive state of users [6,1, 3]. We hypothesize that there are additional features (which
can be captured via mobile phones) that are associated withthe changes in the user’s depressive state. Therefore, in thispaper we present the initial findings of our ongoing study
to investigate the impact of depressive state on the microinteraction data including notification and phone interaction
data.

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Notifications

MetricCount
Acceptance %% Handled (Other Device)
Average Seen Time (ST)Average Decision Time (DT)Average Response Time (RT)

Launch CountApp Count
App Usage Time
Sig Launch Count
Sig App Unique Count
Sig App Usage Time
Phone Usage Non-Sig Launch Count
Non-Sig App Count
Non-Sig App Usage Time
Phone Usage Time
Click CountLong Click Count
Unlock Count

Description
Total number of notifications clicked.
Percentage of notifications clicked out of total arrived.
Percentage of notifications that are not handled on phone out of total notifications arrived.
Average of the seen time of all notifications. Here, seen time is the time fromthe notification arrival until the time the notification was seen by the user.
Average of the decision time of all notifications. Here, decision time is thetime from the moment a user saw a notification until the time they acted uponit (by clicking, launching its corresponding app or swiping to dismiss).
Average of the response time of all notifications. Here, response time is thesum of seen and decision times.
Number of times applications are launched.
Number of applications launched.
Time duration for which applications were used.
Number of times significant applications are launched.
Number of significant applications launched.
Time duration for which applications were used.
Number of times non-significant applications are launched.
Number of non-significant applications launched.
Time duration for which applications were used.
Time duration for which phone was used.
Number of clicks on the phone screen.
Number of long clicks on the phone screen.
Number of times the phone was unlocked.

coefficients are indicated by the white boxes in the correlation
plots.

Average RT

Average DT

Average ST% Handled(Other Device)

Acceptance %Count

Table 1. Description of phone interaction metrics.

Depressive State and Notifications
1

−14

0.8
0.6

−7

0.4
0.2

0

0

Figure 1. Results for correlation between depressive state and notification metrics.

In Figure 1 we show the correlation coefficients that are computed to assess the relationship between depression score andnotification metrics. The results show that users’ depressive
state moderately correlates with all metrics that are computedby using the past 14 days of data. The correlation results are
the same for the metrics that are computed with past 7 days ofdata, except that the average DT has a weak correlation. On
the other hand, users’ depressive state does not correlate withthe average ST and DT of notifications arriving on the current day when the user responded to the PHQ-8 questionnaire.
Moreover, other metrics computed with the current day’s data
have a weak correlation with the depression score.
Depressive State and Phone Usage

Interpretation of Correlation Plots

The correlation results are presented as a plot of the correlation
matrix. In this matrix the x-axis indicates the notification andphone interaction metrics and y-axis indicates the days forwhich the metrics are computed. For instance, in Figure 1 thebox in the first column (Count) and first row (-14) presents
the absolute value of the coefficient for the correlation of thedepression score with the acceptance percentage of notifications, computed by using data from the current day to 14 days
before the current day. Here, the current day refers to the
day in which users reported their depressive state via PHQ-8
questionnaire. Moreover, we set the significance level α forthe correlation results to 0.001 and non-significant correlation

In order to quantify the association between users’ depressive
state and their phone usage pattern we compute the correlation
coefficients and present the results in Figure 2. The results
show that users’ depression score moderately correlates withall the metrics that are computed by using the past 14 daysof data. On the other hand, users’ depressive state weakly
correlates with most of the metrics computed with the data of
past 7 days and there is non-significant correlation with themetrics computed with the data of the current day.
SUMMARY

In this paper, we have presented our initial analysis of mobile
phone usage traces for monitoring depressive states. The
results are based on the data collected from 25 participants for

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68

SESSION 5

Unlock Count

Single ClickLong Click

NSig App Use Time

NSig App Count

NSig Launch Count

Sig App Use Time

Sig App Count

Sig Launch CountApp Use TimeApp Count

Launch Count

Phone Use TimeHEALTH

1

−14

0.8
0.6

−7

0.4
0.2

0

0

Figure 2. Results for correlation between depressive state and phone usage metrics.

a period of 30 days. Our results suggest that by using sensed
data (including both notification and phone interaction data)of the past 14 days, it is possible to improve the accuracy ofthe prediction of the current day’s depression score of a user.
We envision this work to contribute in the direction of building
a technique to learn and predict users’ depressive state throughthe logs of their interaction with smartphones.
REFERENCES

1. Jorge Alvarez-Lozano, Venet Osmani, Oscar Mayora,
Mads Frost, Jakob Bardram, Maria Faurholt-Jepsen, andLars Vedel Kessing. 2014. Tell me your apps and I willtell you your mood: correlation of apps usage withbipolar disorder state. In PETRA’14.
2. Jakob E Bardram, Mads Frost, Károly Szántó, Maria
Faurholt-Jepsen, Maj Vinberg, and Lars Vedel Kessing.
2013. Designing Mobile Health Technology for Bipolar
Disorder: a Field Trial of the Monarca System. In
CHI’13.
3. Luca Canzian and Mirco Musolesi. 2015. Trajectories ofdepression: unobtrusive monitoring of depressive statesby means of smartphone mobility traces analysis. In
UbiComp’15.
4. Sunny Consolvo, David W McDonald, Tammy Toscos,
Mike Y Chen, Jon Froehlich, Beverly Harrison, Predrag
Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby,
and others. 2008. Activity Sensing in the Wild: a Field
Trial of UbiFit Garden. In CHI’08.
5. N. Eagle, A. Clauset, and J. Quinn. 2009. Location
Segmentation, Inference and Prediction for Anticipatory
Computing. In AAAI’09.

6. Agnes Grünerbl, Patricia Oleksy, Gernot Bahle, ChristianHaring, Jens Weppner, and Paul Lukowicz. 2012.
Towards smart phone based monitoring of bipolar
disorder. In mHealthSys’12.
7. Kurt Kroenke, Robert L Spitzer, Janet B. W. Williams,
and Bernd Löwe. 2010. The patient health questionnaire
somatic, anxiety, and depressive symptom scales: asystematic review. General hospital psychiatry 32, 4(2010), 345–359.
8. Kurt Kroenke, Tara W. Strine, Robert L Spitzer, Janet
B. W. Williams, Joyce T. Berry, and Ali H. Mokdad.
2009. The PHQ-8 as a measure of current depression in
the general population. Journal of Affective Disorders
114, 1 (2009), 163–173.
9. Robert LiKamWa, Yunxin Liu, Nicholas D Lane, and Lin
Zhong. 2013. Moodscope: Building a mood sensor fromsmartphone usage patterns. In MobiSys’13.
10. Abhinav Mehrotra, Robert Hendley, and Mirco Musolesi.
2016a. PrefMiner: Mining User’s Preferences forIntelligent Mobile Notification Management. In
UbiComp’16.
11. Abhinav Mehrotra, Robert Hendley, and Mirco Musolesi.
2016b. Towards Multi-modal Anticipatory Monitoring ofDepressive States through the Analysis of
Human-Smartphone. In Adjunct UbiComp’16.
Heidelberg, Germany.
12. Kiran K. Rachuri, Mirco Musolesi, Cecilia Mascolo,
Jason Rentfrow, Chris Longworth, and Andrius Aucinas.
2010. EmotionSense: A Mobile Phones based Adaptive
Platform for Experimental Social Psychology Research.
In UbiComp’10.

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SESSION 5HEALTH

Assessing the use of mobile phone data to describe recurrent
mobility patterns in spatial epidemic models
Cecilia Panigutti1,2 , Michele Tizzoni2 , Paolo Bajardi3 , Zbigniew Smoreda4 Vittoria Colizza5,2
1 Dipartimento di Fisica, Università degli Studi di Torino, via Giuria 1, Torino 10125, Italy
2 ISI Foundation, via Alassio 11/C, Torino 10126, Italy
3 Aizoon Technology Consulting, Str. del Lionetto 6, Torino, Italy
4 Sociology and Economics of Networks and Services Department, Orange Labs,
Issy-les-Moulineaux, France
5 Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d’Epidémiologie
et de Santé Publique (IPLESP, UMR−S 1136), Paris, France
Email: CP: cecilia.panigutti@gmail.com MT: michele.tizzoni@isi.it, PB:
paolo.bajardi@aizoongroup.com, ZB: zbigniew.smoreda@orange.com, VC:
vittoria.colizza@inserm.fr

Abstract
The recent availability of large-scale call detail record data has substantially improved our abilityof quantifying human travel patterns with broad applications in epidemiology [1–6]. Notwithstanding a number of successful case studies, previous works have shown that using different
mobility data sources, such as mobile phone data or census surveys, to parameterize infectious
disease models can generate divergent outcomes [7–9]. Thus, it remains unclear to what extent
epidemic modelling results may vary when using different proxies for human movements.
In this study, we present an extensive side-by-side comparison of simulated epidemics in France
based on two commuting networks: one extracted from an official census survey and one froma large-scale mobile phone dataset. We have previously examined the two networks in terms of
their statistical features, comparing their topology and distributions of travel flows, and found agood statistical agreement between the two [8]. In contrast, previous results based on simulated
epidemics on the two networks have shown that simulation outcomes may vary substantiallywhen using one dataset or the other, depending on the specific outbreak location and disease
parameters.
Here, we thoroughly assess the adequacy of the mobile phone network to match epidemic patterns that have been generated by simulations using the census data. Our goal is to test the
goodness of the mobile phone mobility network to replace the census survey mobility network,
which is explicitly assumed to be the best representation of commuting patterns in France. To
this aim, we compare the spatio-temporal properties of 658,000 simulated outbreaks originatingfrom every possible seed of the mobility networks and quantify their similarity in terms of theepidemic invasion tree and arrival time of first infection.
We identify the features characterizing the outbreak seed nodes that best correlate the similarity between epidemic patterns and discuss how these results can help to assess the adequacyof mobile phone data to describe recurrent mobility patterns in spatial epidemic models. We
find that similarity of simulated epidemics is significantly correlated to connectivity, traffic andpopulation size of the seeding nodes, suggesting that the adequacy of mobile phone data for
infectious disease models becomes higher when epidemics spread between highly connected and

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70

SESSION 5HEALTH

Figure 1: Distributions of similarity measures of epidemic simulations and their correlation.
Frequency distributions of the Spearman’s rank correlation coefficient measured between arrival
times on the two networks (a) and the Jaccard similarity index between the infection trees
on the two networks (b). Each value of rs and J(s) is computed over a statistical ensambleof 1,000 simulations for a given outbreak seed s. Both histograms correspond to 329 binned
values, one for each node of the commuting networks, and solid lines indicate the average of the
distributions. Panel c shows the relation between J(s) and rs for each node of the networks.

heavily populated locations, such as large urban areas.

References[1] A. Wesolowski et al., Proceedings of the National Academy of Sciences 112, 11114 (2015).
[2] A. Wesolowski et al., Proceedings of the National Academy of Sciences 112, 11887 (2015).
[3] A. Wesolowski et al., Science 338, 267 (2012).
[4] L. Bengtsson et al., Scientific Reports 5, (2015).
[5] F. Finger et al., Proceedings of the National Academy of Sciences 201522305 (2016).
[6] A. Lima, M. De Domenico, V. Pejovic, and M. Musolesi, Scientific reports 5, 10650 (2015).
[7] A. Wesolowski et al., PloS one 8, e52971 (2013).
[8] M. Tizzoni et al., PLoS Comp. Biol. 10, e1003716 (2014).
[9] A. Wesolowski et al., Scientific reports 4, (2014).

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71

SESSION 5HEALTHAre you getting sick?
Predicting Flu-Like Symptoms Using Human Mobility
Behaviours
Gianni Barlacchi

University of Trento,
SKIL, Telecom Italia
Trento, Italy

Christos Perentis

Fondazione Bruno Kessler
Trento, Italy

perentis@fbk.eu
barlacchi@fbk.eu
Mirco Musolesi
Bruno Lepri
Abhinav Mehrotra

University College London, UK
London, UK

a.mehrotra@ucl.ac.uk

University College London, UK
London, UK

m.musolesi@ucl.ac.uk

ABSTRACTThe mobility of individuals is of paramount importance forpublic health, particularly mobility characterization is key topredict the spatial and temporal diffusion of human-transmitted
infections. However, the mobility behavior of a person canalso reveal relevant information about her/his health conditions. In this paper, we study the impact of people mobility behaviours for predicting the future presence of flu-like
symptoms (i.e. fever, cough and cold). To this end, wecollect the mobility traces from mobile phones and the daily
self-reported flu-like symptoms. First of all, we demonstratethat daily flu-like symptoms of a user can be predicted by using his/her mobility trace characteristics (e.g. total displacement, radius of gyration, number of unique visited places,etc.). Then, we present and validate models that are able tosuccessfully predict the future presence of flu-like symptoms
by analyzing the mobility patterns of our individuals.

Keywords
Computational Health, Human Mobility, Predictive Models

1.

INTRODUCTION

The link between the health of a person and its behaviours
is a well established connection and previous works haveshown the possibility to predict health and well-being conditions using different sources of information from social media and mobile phones. In particular, recent studies have
proved that human mobility represents a good proxy for understanding people’s mental health conditions like depressive states [1]. These results basically suggests that, similarly to the depression case, human mobility can impact
also on the physical health conditions of a person. In our

Fondazione Bruno Kessler
Trento, Italy

lepri@fbk.eu

paper, we focus on investigating the effectiveness of individual mobility behaviors for predicting the health conditions ofa person. Specifically, we address the challenging problem ofpredicting future presence of influenza-like symptoms (e.g.
fever and cough) by exploring the past mobility activities
of a user, thus trying to answer the following question: canmobility behaviours reveal something about the future healthconditions of a person?.

2. DATAIn this work we use a subset of the data collected during the
Mobile Territorial Lab (MTL) study (for a more detailed
description of the study, see [2]).
The study was conducted on 29 individuals. They havebeen selected among the participants in the MTL project(12 males and 17 females), with an age ranged from 28 to
50 years old. We use a combination two different type oflocation data and survey data with daily information about
the health of the user. We focus on data from February 20,
2013 and March 20, 2013 since in this period we have anhigh presence of influenza-like symptoms.
Location Data. The software installed in the smartphone
continuously keeps track of: (i) the communications events(e.g. call and SMS), and (ii) the user’s location by means of
the Global Positioning System (GPS), which recorded 82%
of positions with an accuracy within 20 meters. In addition,
to increase the number of location points we also used thecell towers to which the phone is connected. For each location point we kept track of the user ID, the latitude, thelongitude, the accuracy and the timestamp.
Daily Symptoms Symptoms data were collected using adaily self-report survey instrument, designed by an experienced epidemiologist. The survey instrument consisted ofsix questions with yes/no responses for each of the following symptoms: fever, shivers, sore throat, cough, gaspingbreath, headache, muscular pain, malaise, cold.

3.

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72

SESSION 5HEALTHOur main goal is to study the relationship between mobility behavior and the self-reported flu symptoms. To do so,
we need a set of characteristics that systematically describe
human mobility behaviour. Canzian et al. [1] have recently
introduced mobility metrics able to capture both presence
and absence of human mobility. Such features appear tobe promising in identifying health conditions, since many ofthem are related with the nature of the movement. For instance, in [1] they focus on depressive symptoms which could
go along with decreased movement patterns and increased
spending time at home for a long-term period. In our case,
we expect to identify similar signals, but in a short-term
context. We computed all the mobility features proposed by
Canzian et al. [1]: total distance traveled, standard deviation of the total distance traveled, total displacements, standard deviation of the displacements, maximum displacement
between two visited places, radius of gyration of the visitedplaces, maximum displacement from home, number of different places visited, number of different significant places
visited, number of moving geo-location points, routine index, unique number of visited places, diversity of the visitedplaces, aggregated mobility features (rolling statistics computed for each of the aforementioned mobility features). Unfortunately, due to space limitations we remind the reader
to the original paper [1] for more details.

3.1

Classification Model

We model our problem as a binary classification task, where
the target variable is called Symptoms Presence and the possible values of the label is {Yes/No}, that is if a user has or
not the symptoms. Given a target date, our ultimate goal is
to understand if a user will present or not flu-like symptoms
in the forthcoming days by looking into its very recent mobility behavior. We expect to capture even slight changes
in the mobility behavior (e.g. changes in covered distancecovered) that can testify an upcoming flu. Formally, given
a date t we define:• thist as the number of days we go back in user history
data from the date t;• historical time interval as the time interval [t − thist , t];• thor as the number of days ahead we answer our Symptoms Presence: Yes/Not question.
To sum up, we classify with the label Yes a user who presents
flu-like symptoms at time thor , by using historical data in
the interval [t − thist , t].
Due to the limited size of the dataset, we decide not to build
a specific model for each user. Instead, we design a relatively
general machine learning framework that can work for every
user u. A sample for the model is built using data whenmore than three consecutive days are available. Thus, given
a date t, we consider valid a time window of five days if
the following conditions are satisfied: (i) mobility data forthist ∈ [0, 2]; and (ii) symptoms data for the time thor . To
give an example, with t = Wednesday we want to know if auser u will present influenza-like symptoms at t+2 = Friday
considering her/his previous mobility behaviours from thetime interval t-2 = Monday, t = Wednesday.

In order to carry out our experiments, we split the dataset
in two parts: train and test. In the first step, we extract
the features described in Section ??. Due to the high number of features and the limited number of samples, we perform a feature selection step. First, we fit one of the classifier models. Then, we rank the features by the weightassigned in the model. For the classification task, we test
four different machine learning models: Logistic Regression(LR), Conditional Random Forest (CRF), Gradient Boosted
Trees (GBT) and Support Vector Classification (SVC) witha Gaussian radial basis function kernel. To select the best
set of features, we experiment with different feature selection
approaches varying the size of the feature set. We evaluate
the quality of the feature selection through 10-fold-crossvalidation, training the models with the reduced set of features on the training set. At this point, we can proceedwith the parameters’ optimization for each model by using
the selected set of features. In both, feature selection andparameters selection, we choose an optimal set in order tomaximize the precision of the algorithm. The last step regards the selection of the best model. Again, through crossvalidation, we train each model with its best set of features
and the optimal parameters selecting the one that shows thehighest precision.

4.

RESULTSIn our experiments we compare four different models (LR,
CRF, GBT and SVC with a Gaussian radial basis function
kernel) to classify if a user will present flu-like symptoms ornot (i.e. fever, cough and cold) at a time thor . To train ourmodels, we used the machine learning library scikit-learn [3].
Due to the unbalanced nature of our dataset, we used wellknown metrics for assessing the accuracy of classification
systems: (i) Precision and (ii) AUCROC.

4.1

Symptoms classification

In Table 4 we present the classification results in terms ofprecision and AUCROC. We report the different performances for thist ∈ [−2, 0] and thor ∈ [0, 2]. The results
are obtained with 10-fold-cross-validation and using the best
setup for each different model.
Firstly, we observe that with thor = 0 (i.e, we consider alonger mobility history) we obtain better classification performance. This is a consequence to the fact that peoplechange their mobility habits in the days before the registration of some influenza-like symptoms, that is, they change
the mobility once they start to feel bad. For instance, if aperson is getting sick, s/he would likely go home after work
instead of doing other activities. Secondly, we can observethat as more days ahead we consider, less is the importanceof the history and more difficult it becomes to classify correctly the presence of symptoms by only looking at the mobility behaviours. This reveal an interesting aspect related
to the fact that there is a short period (e.g. few days) between feeling bad and reporting the symptoms. In sum,obtained results suggest that mobility behavior can be used
for our purpose, but only looking at a short period in the
future (e.g., thor = 2) and considering a limited historicalperiod. A long history of mobility data is not relevant.
In overall for all the built models, the following selected features emerged as most important in predicting correctly the

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73

SESSION 5HEALTHthor = 0thor = 1

thor = 2

LR
CRF
SVC
GBT
LR
CRF
SVC
GBT
LR
CRF
SVC
GBT

thist = 0
Precision AUCROC
65.2
52.2
45.8
52.6
58.8
60.3
54.5
60.5
54.1
54.6
54.0
57.0
67.8
59.5
56.5
60.5
61.1
59.9
63.6
60.4
65.6
59.9
62.7
65.4

thist = 1
Precision AUCROC
59.0
57.9
33.3
48.1
45.4
53.4
58.6
58.7
61.5
57.9
52.9
55.9
50.0
58.4
70.4
69.4
61.5
61.0
41.4
49.2
55.5
53.4
65.9
66.4

thist = 2
Precision AUCROC
54.1
55.6
56.0
56.5
81.2
60.2
68.2
68.3
55.1
56.0
50.0
53.6
62.5
55.1
63.4
63.7
61.5
61.0
48.9
54.6
62.5
61.9
64.7
66.9

Table 1: Precision and AUCROC of the classifiers with 10-fold-cross-validation and variations of thor and thist .
symptoms by only looking at the mobility behaviours of theuser. Our results provide evidence supporting our approachand represent a promising starting point for dealing with
influenza-like public health issues. The evolution of our proposed methodology could have a societal impact opening theway to customized mobile phone applications, which may detect suggest to the user specific actions in order to prevent
disease spreading and minimize the risk of contagion.

6.

Figure 1: Confusion matrix fot thor = 1.
presence of symptoms, namely: (i) the diversity of the visits(ii) the unique number of visited places, (iii) the number ofdifferent significant visited places, (iv) the number of moving
geo-location points and (v) the aggregated mobility features.
To complete our analysis, we also report in Fig. 1 the confusion matrix for the case thor = 1. Our model presents good
accuracy in classifying no symptoms while, mainly due tothe difficult nature of the problem, there is still margin of
improvements in the classification of symptoms.

5.

REFERENCES[1] L. Canzian and M. Musolesi. Trajectories of depression:
unobtrusive monitoring of depressive states by means ofsmartphone mobility traces analysis. In Proceedings ofthe 2015 ACM International Joint Conference on
Pervasive and Ubiquitous Computing, pages 1293–1304.
ACM, 2015.
[2] S. Centellegher, M. De Nadai, M. Caraviello,
C. Leonardi, M. Vescovi, Y. Ramadian, N. Oliver,
F. Pianesi, A. Pentland, F. Antonelli, et al. The mobile
territorial lab: a multilayered and dynamic view onparentsâĂŹ daily lives. EPJ Data Science, 5(1):1, 2016.
[3] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel,
B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer,
R. Weiss, V. Dubourg, J. Vanderplas, A. Passos,
D. Cournapeau, M. Brucher, M. Perrot, and
E. Duchesnay. Scikit-learn: Machine learning in
Python. Journal of Machine Learning Research,
12:2825–2830, 2011.

CONCLUSIONSIn this work we investigated how to use individuals’ mobility
behaviour for a novel and challenging and task: predicting
the future presence of flu-like symptoms such as fever, coughand cold. To this end, we used the mobility information collected by mobile phones and the daily self-reported flu-like
symptoms of 29 individuals in the time interval from February 20 to March 21 of 2013. Previous work has exploited theuse of mobility features to predict mental health and wellbeing dimensions such as positive and negative emotions,stress level, and depression symptoms. To the best of ourknowledge, this work represents the first study that utilizes
inference algorithms to predict the presence of influenza-like

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74

SESSION 5HEALTH

Impact of Human Mobility on Spread of Dengue in
Sri Lanka
Lasantha Fernando

Amal Shehan Perera

Sriganesh Lokanathan

LIRNEasia, Colombo
Sri Lanka
Email: lasantha@lirneasia.net

Dept. of CSE, University of Moratuwa
Sri Lanka
Email: shehan@cse.mrt.ac.lk

LIRNEasia, Colombo
Sri Lanka
Email: sriganesh@lirneasia.net

Azhar Ghouse

Hasitha Tissera

Rohan Samarajiva

Epidemiology Unit, Ministry of Health
Sri Lanka
Email: docazharghouse@gmail.com

Epidemiology Unit, Ministry of Health
Sri Lanka
Email: dr korelege@yahoo.co.uk

LIRNEasia, Colombo
Sri Lanka
Email: rohan@lirneasia.net

Abstract—Human mobility plays a significant role in spatiotemporal propagation of infectious diseases. But how much of
an impact does human mobility have on propagating a dengue
outbreak in a dengue endemic country such as Sri Lanka?
We show that a proxy value for human mobility, derived frommobile network big data, has a significant correlation withdengue incidence using past case data. Furthermore, we discuss
the applicability of this proxy measure of mobility to increase
accuracy of several prediction models based on machine learningtechniques. These improved models can be used to do spatiotemporal forecasting of dengue outbreaks which can help medical
officers and related governmental or non-governmental agencies
execute preemptive measures before the outbreak occurs in
actuality.

I. I NTRODUCTION
The significance of human mobility in propagation of infectious diseases has been established in multiple works done previously [1, 2]. Until recently, the datasets and methodologies
available for estimating human mobility have been limited.
However, there has been a significant increase in research done
to estimate human mobility using call detail records (CDR)
with applications in multiple, wide ranging domains [3–5].
The main focus of our work is in measuring the impact ofhuman mobility in propagating a vector borne tropical diseasesuch as dengue in a dengue endemic country. A recent study
from Pakistan [6] developed a model that validates the impact
of human mobility in introducing dengue to naive regions
as well as regions where dengue incidence is low. However
in regions where dengue is already prevalent throughout theyear, human mobility would not effect disease propagationin the same manner. The forecasting models developed forregions where the human population is immunologically naive
to dengue would not be applicable to a country like Sri Lanka,
where dengue is endemic to most regions of the country.
In the next section, we describe the proxy measure forhuman mobility developed by us, which we use to determine
the correlation between that value and dengue incidence. We
compare the correlation of this value with other values suchas temperature and rainfall (factors previously identified in

the literature as contributing factors to disease propagation[7]) to show that there is a significant correlation between
human mobility and dengue incidence. In section III, we
compare the accuracy of the proxy measure by incorporating
the mobility value to multiple prediction models to determine
whether mobility plays a significant role in spreading dengue
in a dengue endemic country such as Sri Lanka.
II. M OBILITY M ODEL
Most of the mobility models developed in related work
have been based on the number of trips between different
regions[6, 8]. Additionally, considering only overnight staysof subscribers in a region other than his or her home, anapproach taken for a study on malaria in Namibia [9], is notsuitable for modeling dengue propagation. The reason is thatthe primary vector of dengue, aedes aegypti, is known to be
most active during daytime [10]. Our focus was on developinga mobility measure that can be applied to a geographic region
so that it can be incorporated directly into machine learningmodels. In the context of disease forecasting, machine learning
methods can be described as working broadly on the principleof identifying a set of attributes that can describe dengue
incidence characteristics for a specific spatial region, andtraining a model to learn the weightage of those attributes that
contributes toward disease incidence. For such a forecastingmodel, we would need a mobility measure that corresponds toeach spatial and temporal unit of consideration.
In our project, the smallest spatial unit for which data isavailable is a Medical Officer of Health (MOH) division,
which is an administrative area defined by Sri Lanka’s Ministryof Health. Number of dengue cases reported from each MOH
division in a given week for the year of 2013 was used
to represent dengue incidence. Pseudonymized call detailrecords from multiple mobile operators in Sri Lanka for theyear of 2013 was used to derive the mobility measure. The
mobility model derived using the available mobile phone data
is described below.

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SESSION 5HEALTH

A. Probabilistic Mobility Model
The probabilistic model was developed by building upon
available literature on the subject where it is assumed thatthe number of calls taken or received by a subscriber in aparticular region is proportional to the amount of time spent inthat region [11]. Our model builds on the same assumption andobtains a normalized mobility value for each MOH division.
The mobility value for a subscriber is calculated by the numberof calls made by a subscriber who is not a resident of thatparticular MOH in a given week. This value is normalized by
dividing from the total number of calls taken by that subscriberin that week. This derived value per subscriber is aggregatedto get a mobility value for an MOH division.
If we consider M as a set of all MOH divisions, and S
as a set of all subscribers, our model can be defined as follows:
CDR(mi , sj , wk ) = No. of CDR in MOH division mi ,for subscriber sj during week wk where ∀mi ∈ M, ∀sj ∈ S
Mobility of subscriber sj at MOH mi can be definedasCDR(mi , sj , wk )(1)
mob(mi , sj ) = M
CDR(mi , sj , wk )iwhere ∀mi ∈ {M − Home(sj )}, ∀sj ∈ S

Mobility for MOH mi can be defined as
Nmob(sj )j(2)
mob(mi ) =N
where N - No. of subscribers travelled to mi for that week

TABLE IRMSE AND r2 VALUES OF DIFFERENT MACHINE LEARNING METHODS
Model
Neural Networks
Neural Networks
XGBoost [12]XGBoostSVR
SVR

Mobility Used
No
Yes
No
Yes
No
Yes

RMSE
10.923
9.867
10.023
9.548
7.445
7.297

r2
0.194
0.342
0.321
0.384
0.139
0.173

Several different machine learning methods were tried out
to come up with a dengue outbreak forecasting model withand without the derived mobility measure. The results fromour own preliminary analysis is also given in Table I. The
RMSE and the r2 values for each method improved with theintroduction of the mobility measure. Amongst the methods
that were tried, Support Vector Regression (SVR) [13] provided the greatest accuracy, reflecting recent literature [14]where the performance of SVR has been shown to be better
than Neural Networks. Therefore, SVR was selected as themethod to carry out further work on our predictive model. The
model was trained for 5 MOH divisions that were identified
beforehand and the resultant model was used to predict forthe sixth MOH division. After tuning the SVR model, wewere able to obtain an RMSE value of 6.463 without mobility
and 6.236 with the introduction of mobility. Similarly, the r2
values were 0.351 and 0.396 for SVR models without andwith mobility respectively. The related prediction graphs forMoratuwa MOH division are given in Fig.2 & 3.

III. R ESULTS & P REDICTION M ODELSThe distance correlation was used as a measure to determine
the dependence between dengue incidence and mobility. The
correlation graph for mobility and other input features is
depicted in Fig. 1. This graph shows that mobility has a significant correlation when compared to the other input parametersconsidered in our predictive models such as temperature,
rainfall and the no. of dengue cases in previous weeks.

Fig. 2. Predicted vs actual dengue incidence for Moratuwa MOH (without
mobility)

IV. O NGOING AND F UTURE W ORK

Fig. 1. Correlation of input parameters with dengue incidence

We are currently working on developing a trip based mobility model where the derived value can be aggregated to an
MOH division. This model can be compared with the mobility
model described above to determine which approach yields
the greatest accuracy and fit. It can also be used as anothervalidation of the observed improvement in predictive accuracy
due to the introduction of mobility. Since the mobility measure

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SESSION 5HEALTH

[3]

[4]

[5]
Fig. 3. Predicted vs actual dengue incidence for Moratuwa MOH (withmobility)[6]
is derived independently of the modeling of the disease itself,
this measure can be used in forecasting other infectious disease
outbreaks as well. Validating the feasibility of using thismeasure in forecasting other infectious diseases can be done
as a part of future work.
V. C ONCLUSION
We have proposed a new methodology to calculate humanmobility that provides a mobility measure for each administrative region. The advantage of this measure is it can bedirectly applied to any machine learning model without having
to calculate a separate measure for different machine learningtechniques. The results show that this measure is significantly
correlated with dengue incidence. Additionally, we were able
to obtain an improvement in prediction accuracy of all theconsidered models by introducing the mobility measure. Basedon these results, we can conclude that mobility does have asignificant impact in dengue propagation even in a dengue
endemic country such as Sri Lanka.
ACKNOWLEDGMENT
The authors would like to thank the Epidemiology Unit,
Ministry of Health, Sri Lanka for providing dengue incidence
data as well as expert knowledge on the disease dynamics andthe epidemiology of dengue. This research was funded througha grant from the International Development Research Centre(IDRC) of Canada and a grant from the Senate ResearchCommittee (SRC) of University of Moratuwa.

[7]

[8]

[9]

[10]

[11]

[12]

R EFERENCES[1][2]

Michele Tizzoni et al. “On the Use of Human Mobility
Proxies for Modeling Epidemics”. In: PLOS Computational Biology 10.7 (July 2014), pp. 1–15.
Dirk Brockmann. “Human Mobility and Spatial Disease
Dynamics”. In: Reviews of Nonlinear Dynamics andComplexity 2 (2010), pp. 1–24.

[13]

[14]

Enrique Frias-Martinez, Graham Williamson, andVanessa Frias-Martinez. “An Agent-Based Model of
Epidemic Spread using Human Mobility and Social
Network Information”. In: 3rd International Conference
on Social Computing (SocialCom’11) (2011), pp. 49–
56.
Sibren Isaacman et al. “Identifying important places inpeople’s lives from cellular network data”. In: Lecture
Notes in Computer Science (including subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics) 6696 LNCS.June (2011), pp. 133–151.
ISSN : 03029743.
R Samarajiva et al. “Big data to improve urban planning”. In: Economic and Political Weekly 50.22 (2015),
pp. 42–48. ISSN: 00129976 (ISSN).
Amy Wesolowski et al. “Impact of human mobilityon the emergence of dengue epidemics in Pakistan”.
In: Proceedings of the National Academy of Sciences
112.38 (2015), pp. 11887–11892.
Suchithra Naish et al. “Climate change and dengue: acritical and systematic review of quantitative modelling
approaches.” In: BMC infectious diseases 14.1 (2014),
p. 167. ISSN: 1471-2334.
Amy Wesolowski et al. “Quantifying the impact ofhuman mobility on malaria.” In: Science (New York,N.Y.) 338.6104 (Oct. 2012), pp. 267–70. ISSN: 10959203.
Nick W Ruktanonchai et al. “Identifying Malaria Transmission Foci for Elimination Using Human Mobility
Data.” In: PLoS computational biology 12.4 (2016),
e1004846. ISSN: 1553-7358.
Duane J Gubler and Gary G Clark. “Dengue/dengue
hemorrhagic fever: the emergence of a global healthproblem.” In: Emerging infectious diseases 1.2 (1995),
p. 55.
Flavio Finger et al. “Mobile phone data highlights therole of mass gatherings in the spreading of cholera
outbreaks”. In: Proceedings of the National Academyof Sciences 113.23 (2016), p. 201522305. ISSN: 00278424.
Tianqi Chen and Carlos Guestrin. “XGBoost: A Scalable Tree Boosting System”. In: Proceedings of the
22nd ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining. KDD ’16. San
Francisco, California, USA: ACM, 2016, pp. 785–794.
ISBN : 978-1-4503-4232-2.
Debasish Basak, Srimanta Pal, and Dipak Chandra Patranabis. “Support vector regression”. In: Neural Information Processing-Letters and Reviews 11.10 (2007),
pp. 203–224.
Yuhanis Yusof and Zuriani Mustaffa. “Dengue OutbreakPrediction : A Least Squares Support Vector Machines
Approach”. In: International Journal of Computer Theory and Engineering 3.4 (2011), pp. 489–493. ISSN:
17938201.

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SESSION 6DATA QUALITY
AND PRIVACY

NetMob 5-7 April 2017 – Vodafone Village – Milan

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Geographical veracity of indicators
derived from mobile phone data.
Maarten Vanhoof

Open Lab, Newcastle University, UK
Orange Labs, Paris, FR
M.vanhoof1@newcastle.ac.uk

Thomas Plötz

Open Lab, Newcastle University, UK
School of interactive computing,Georgia Institute of Technology, USA

Zbigniew Smoreda

Orange Labs, Paris, FR

ABSTRACTIn this contribution we summarize insights on the geographical veracity of using mobile phone data tocreate (statistical) indicators. We focus on problems that persist with spatial allocation, spatialdelineation and spatial aggregation of information obtained from mobile phone data. For each of thecases, we offer insights from our works on a French CDR dataset and propose both short and long termsolutions. As such, we aim at offering a list of challenges, and a roadmap for future work on the topic.

1. Introduction

Ever since the first analyses on mobile phone data, individual indicators have been constructed to describe
mobility patterns [1], presence patterns [2], aspects of social networks [3] or even personal characteristics[4]. Although originally used to explore the characteristics of large-scale datasets, several applications ofmobile phone indicators have been developed over time. And this to a degree that real-time monitoring ofsocio-economic landscapes based on mobile phone indicators seems now plausible [5], [6].
One logical application of mobile phone indicators is to complement national statistics. As they capture
behavior for large populations, the combination with census data (which typically offer contextual
information) opens up exciting research opportunities. [6], for example, investigate the relation between
deprivation of the neighborhood and the diversity of individual mobility by pairing national statistics withmobile phone data of 20 million users in France. Apart from the obvious confrontation with official
statistics, other applications integrate mobile phone indicators for a multitude of purposes. Mobility
indicators, for instance, are used to segment users in epidemiological model [7] or to express their‘vulnerability’ to wrongful home detection [8]. Indicators on calling behavior can be used to determinesocial influencers, to segment customers for marketing purposes [9], to annotate social networks [10], etc..
Here, our argument is that, despite many applications, important questions remain on how to evaluate theveracity of indicators produced from mobile phone data. Specifically, we focus on three different spatialaspects that influence uncertainty and error when creating or using mobile phone based indicators: spatial
allocation, spatial delineation and special aggregation.

2. Spatial allocation

Often, individual mobile phone users have to be allocated in space in order to enable further analysis ofindicators derived from their mobile phone use. Typically, such allocation happens by means of homedetection algorithms that incur the most plausible cell-tower to cover a user’s home location based onrather simple heuristics (e.g. maximum activities during nighttime). Until now, little work has been doneto assess and compare the performance of different heuristics as good validation datasets are hard to find(a.o. because of large population size, differences in cover grids with census, etc.). As a consequence, it isdifficult to evaluate the uncertainty for allocating users, and thus indicators, to geographical areas [8].
To address the spatial allocation problem, in [8] we investigated the performance of five simple heuristics
for home detection on a French CDR dataset. We applied each heuristic to almost 120 million mobility
traces derived from one month of mobile phone data and compared results between them. Additionally,
we collaborated with the French National Statistics Office (INSEE) to create a validation dataset
describing the French population at the spatial resolution on the cell-tower grid provided by the operator.
This allowed us to compare full population numbers with estimated population based on mobile phonedata and thus assess performance of the different algorithms both in time as in space (figure 1).

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(a)

(b)

(c)

Figure 1: (a) Cosinus Similarity Values (in °) for the comparison between the vectors of (i) the number of detected user homes by the given homedetection algorithm and (ii) the total population in the validation dataset based on census data. Vectors are made up of the totals of (i) and (ii) for
each cell-tower. A value of 0° denotes the highest possible similarity between both vectors, 90° indicates the lowest similarity in orientation
whereas 180° degrees refers to an opposite orientation. (b) and (c) Hotspots (red) and coldspots (blue) defined by the 90+% interval of the GetisOrd Gi* statistic for (a) the number of detected homes by the amount of activities algorithm in June and (c) the population numbers of thevalidation dataset based on census data. The map is a made up by the Voronoi tesselation based on cell-tower locations.

3. Spatial delineation
Uneven delineations of space are a second problem when pursuing analysis with mobile phone indicators.
Cell-tower cover areas, for example, typically have different boundaries compared to administrative
regions, resulting in translation and comparisons problems. Uneven spatial delineations also exist between
cell-tower cover areas. Cell-tower density in high population density areas, for instance, is typicallyhigher, resulting in smaller cover areas compared to lower population density areas. Logically, this isdirectly relevant for population density estimations [11], the creation of mobility indicators [12], orparameter estimation in statistical analysis (e.g. urban scaling laws [13]).
In [12] we address the spatial delineation problem by assessing its effect on the creation of one mobility
indicator from mobile phone data. As illustrated in figure 3 (a), we unveiled the calculation of mobility
entropy (as proposed in [1], [6]) to be dependent on the density of cell-towers and thus the spatialdelineation of cell-tower cover areas. To counter this, we propose a correction of the mobility entropy
indicator. Application to a French CDR dataset learns that our proposed Corrected Mobility Entropy isless dependent on cell-tower densities and exhibits a different spatial pattern. We find suburban areas oflarge cities to depict the highest diversity of movement, compared to city centers for the standard mobility
entropy (figure 3 (b) and (c)), as well as a clear decrease in mobility entropy with city size.

(b)

(a)

(c)

Figure 3: (a) Illustration of the effect of spatial delineation on the calculation of the temporally-uncorrelated entropy (H). The mobility entropy forthe same path (dotted line) of a user in three different density settings is calculated and shown to be different. Spatial patterns of mobility entropy
values (a) and corrected mobility entropy values (b). Values are calculated for all cell-towers in France as the average value of all users that have a‘home’ at these cell-towers.

4. Spatial aggregation
A third problem consists in the spatial aggregation of mobile phone indicators. Although related to thespatial delineation problem, with spatial aggregation we do not mean by which spatial delineation one
aggregates. Rather we wonder how the properties of indicators change when aggregating to another spatial
level. This is especially relevant for mobile phone based indicators as the choice of scale will imply

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SESSION 6DATA QUALITY AND PRIVACY

unintended selective filtering (e.g. highly active persons, communities), a change in representativeness of
population (e.g. unequal market shares for single operators between city and rural areas), a change inhuman behavior (e.g. short-distance vs. long-distance trips) and infrastructural context (e.g. transport
infrastructure); all of which are typically unaddressed in studies using mobile phone data.
Currently, there is little evidence that research is taking into account any (plausible) effects of spatial
aggregation. Even though, in our work, we find clear indications that different scales imply different
findings like, for example, in the relations between mobile phone indicators, home detection algorithms
and census information (table 1). It remains an open question as to what mechanisms are responsible.
Table 1: Correlation coefficients between different indicators at different spatial levels in France. Administrative level is theofficial IRIS level and municipality level is the commune in French official statistics. Cell-tower level corresponds to estimatedcover areas of Orange cell-towers in France. EDI is the European Deprivation Index, Mobility Entropy is as calculated in [12].
Distinct days home detection is also used in figure 1 and described in [8].
Correlation coefficient for

Cell-tower Level

Mobility Entropy vs. EDIDistinct Days Home Detection
vs. Population in Census data

0.62

Administrative Level

Municipality Level

Scale difference

-0.03

-0.43

-0.40

0.92

+0.30

5. Roadmap
Ultimately, we recognize that procedures to create mobile phone based indicators are being
institutionalized in, at least, two ways. On the one hand, there is published research and the developmentof open source software packages like, for example, the python toolbox bandicoot [14]. Such packages
allow researchers to easily calculate indicators but their recurrent use might eventually lead to illconsidered applications being legitimized by the toolbox instead of the specific nature of the researchcase. In this perspective, it’s a bit uncanny that current open-source packages offer little consideration to
the assessment of uncertainty or error and, as a consequence, to potentially wrongful deployment orinterpretation.
On the other hand, national statistic offices are putting considerable efforts to investigate the usability ofmobile phone indicators within national statistics. Here, extensive validation of mobile phone indicators isthe norm, given that, when ensured by the office, they will directly inform policy decisions. The problem,
however, is that the requirements, procedures and measures used to validate and publish official statistics
are not (yet) adapted to the nature of current big data sets, including mobile phone data. The assessment ofveracity of mobile phone data hence becomes a matter of re-invention of the national statistics services,which likely will take several years before being fully operational.
Within this context, we believe it is necessary for future work to investigate, or at least openly
communicate on, the different forms of uncertainties and errors that occur because of spatial allocation,
spatial delineation, and/or spatial aggregation. Table 2 offers a more specific roadmap for actions that canbe taken in the short and long term to prevent naïve application of mobile phone based indicators.
Table 2: Proposed short term actions and long term solutions to the spatial allocation, delineation and aggregation problem.
Problem

Short term actions

Long term solutions

Spatial allocation- Investigate errors that come with spatial allocation- Test heuristics for home detection on different databases- Design surveys to gather ground truth at individual level- Understand how characteristics of current mobile phone use(frequency, social context, etc.) influence spatial allocation- Standardize error assessment for spatial allocation

Spatial delineation- Assess the influence of spatial delineation on indicators- Develop standard practices that incorporate the effect of
spatial delineation on indicators- Reflect on possibilities to standardize spatial delineations
like cell-tower areas, administrative boundaries, etc.)- Develop techniques that allow translation between
different spatial delineations
Spatial aggregation- Develop techniques that define optimal spatial scale forstudying a specific process (be it theoretical or empirical)- Develop techniques that are capable of dealing withchanging nature of observations when spatially aggregating- Develop techniques that express sensitivity of data(interpretation) to spatial aggregation- Understand how the problem of spatial aggregation changes
in time due to changing mobile phone use and human behavior

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References[1]

C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, “Limits of Predictability in Human Mobility,” Science (80-.
)., vol. 327, no. 5968, pp. 1018–1021, Feb. 2010.

[2]

M. Janzen, M. Vanhoof, and K. W. Axhausen, “Estimating Long-Distance Travel Demand with Mobile
Phone Billing Data,” 16th Swiss Transp., 2016.

[3]

D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi, “Human mobility, social ties, and link
prediction,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery anddata mining, 2011, pp. 1100–1108.

[4]

Y.-A. de Montjoye, J. Quoidbach, F. Robic, and A. S. Pentland, “Predicting personality using novel mobile
phone-based metrics,” in International Conference on Social Computing, Behavioral-Cultural Modeling, andPrediction, 2013, pp. 48–55.

[5]

F. Giannotti, D. Pedreschi, A. Pentland, P. Lukowicz, D. Kossmann, J. Crowley, and D. Helbing, “Aplanetary nervous system for social mining and collective awareness,” Eur. Phys. J. Spec. Top., vol. 214, no.
1, pp. 49–75, 2012.

[6]

L. Pappalardo, M. Vanhoof, L. Gabrielli, Z. Smoreda, D. Pedreschi, and F. Giannotti, “An analyticalframework to nowcast well-being using mobile phone data,” Int. J. Data Sci. Anal., Jun. 2016.

[7]

A. Lima, V. Pejovic, L. Rossi, M. Musolesi, and M. Gonzalez, “Progmosis: Evaluating Risky Individual
Behavior During Epidemics Using Mobile Network Data,” arXiv Prepr. arXiv, vol. abs/1504.0, 2015.

[8]

M. Vanhoof, F. Reis, Z. Smoreda, and T. Plötz, “Investigating Performance and Spatial Uncertainty of Home
Detection Criteria for CDR data.”[9]P. Sundsøy, J. Bjelland, A. M. Iqbal, Y.-A. de Montjoye, and others, “Big Data-Driven Marketing: How
machine learning outperforms marketers’ gut-feeling,” in International Conference on Social Computing,
Behavioral-Cultural Modeling, and Prediction, 2014, pp. 367–374.

[10]

J. L. Toole, C. Herrera-Yaqüe, C. M. Schneider, and M. C. González, “Coupling human mobility and social
ties,” J. R. Soc. Interface, vol. 12, no. 105, 2015.

[11]

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,” Proc. Natl. Acad. Sci., vol. 111, no. 45, pp.
15888–15893, Nov. 2014.

[12]

M. Vanhoof, W. Schoors, A. Van Rompaey, T. Plötz, and Z. Smoreda, “Correcting Mobility Entropy forLarge-Scale Comparison of Individual Movement Patterns.”[13]

C. Cottineau, E. Hatna, E. Arcaute, and M. Batty, “Paradoxical interpretations of urban scaling laws,” arXiv
Prepr. arXiv1507.07878, 2015.

[14]

Y.-A. de Montjoye, L. Rocher, and A. S. Pentland, “bandicoot: a Python Toolbox for Mobile Phone
Metadata,” J. Mach. Learn. Res., vol. 17, no. 175, pp. 1–5, 2016.

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SESSION 6DATA QUALITY AND PRIVACY

Preserving Mobile Subscriber Privacy
in Open Datasets of Spatiotemporal Trajectories
Marco Gramaglia∗ , Marco Fiore† , Alberto Tarable† , Albert Banchs∗∗ IMDEA Networks Institute & Universidad Carlos III de Madrid

Avda. del Mar Mediterraneo, 22
28918 Leganes (Madrid), Spain
Email: name.surname@imdea.org

Abstract—Disclosure of large-scale datasets of individual trajectories collected by mobile network operations raises significant
privacy concerns, yet it offers an unprecedented outlook on thefine-grained activities of whole populations. Technical solutions to
the problem of privacy-preserving publishing of spatiotemporal
trajectories of mobile subscribers are key to the open circulationof such rich datasets. We introduce an original privacy criterion, named kτ, -anonymity, which tackles probabilistic attacks
against trajectory data. We also propose a proof-of-conceptalgorithm that is a first step towards practical kτ, -anonymity.

I. C ONTEXT
Subscriber trajectory datasets collected by network operators are logs of timestamped, georeferenced events associatedto the communication activities of individuals. The analysis ofthese datasets allows inferring fine-grained information about
the movements, habits and undertakings of vast user populations. This has many different applications, encompassing bothbusiness and research. For instance, they can be monetized
via added-value services such as transport analytics [1] orlocation-based marketing [2]. In addition, large-scale movement data has proven critical to studies in physics, sociology
or epidemiology [3].
All these use cases stem from the disclosure of trajectory
datasets to third parties. However, the open release of suchdata is still largely withhold, which hinders potential usages
and applications. A major barrier in this sense are privacy concerns: data circulation exposes it to re-identification attacks,and cognition of the movement patterns of de-anonymized
individuals may reveal sensitive information about them.
This calls for anonymization techniques. The common
practice operators adhere to is replacing personal identifiers(e.g., name, phone number, IMSI) with pseudo-identifiers (i.e.,
random or non-reversible hash values). Whether this is asufficient measure is often called into question, especiallyin relation to the possibility of tracking user movements.
What is sure is that pseudo-identifiers have been proven not
to protect against trajectory uniqueness, i.e., the fact that
subscribers have distinctive travel patterns that make themuniquely recognizable in very large populations [4]–[6].
II. P RIVACY IN SPATIOTEMPORAL TRAJECTORIESWe argue that a major threat to spatiotemporal trajectory data is represented by probabilistic attacks. The aimof probabilistic attacks is letting an adversary with partial† CNR-IEIITCorso Duca degli Abruzzi, 24
10129 Torino, Italy
Email: name.surname@ieiit.cnr.it

Fig. 1. Illustrative example of kτ, -anonymity of user i, with k=2.

information about an individual enlarge his knowledge on that
individual by accessing the target database. The uniquenessof mobile subscriber trajectories is a significant weakness thatprobabilistic attacks can exploit. Let us imagine a scenario
where an adversary knows a small set of spatiotemporal points
in the trajectory of a subscriber (because, e.g., he met thevictim there), and has access to a dataset of spatiotemporal
trajectories of (millions of) inhabitants of the city where thevictim lives such as, i.e., a call detail record dataset. The highuniqueness of spatiotemporal trajectory data implies a very
high risk that the attacker can use his knowledge to easily
pinpoint his victim in the dataset, and retrieve his/her complete
mobility. Clearly, revealing the complete movements enables
inference of sensitive information about the victim, suchas home/work locations, daily routines, visits to healthcare
structures or nightclubs.
The pertinent principle to counter probabilistic attacks on
spatiotemporal trajectory data is the so-called uninformative
principle. It ensures that the difference between the knowledgeof the adversary before and after accessing a database issmall [7]. In our context, this principle warrants that an
attacker who knows some subset of a subscriber’s movements
cannot extract from the dataset a substantially longer portionof that user’s trajectory.
To attain the uninformative principle, we introduce thek τ, -anonymity privacy criterion. k τ, -anonymity can be seen
as a variation of k m -anonymity, which establishes that eachindividual in a dataset must be indistinguishable from at least
k−1 other users in the same dataset, when limiting the attacker
knowledge to any set of m attributes [8]. k τ, -anonymity
tailors k m -anonymity to our scenario by defining:(i) The attacker knowledge τ . It can be any continued sequenceof spatiotemporal samples covering a time interval of length
at most τ : thus, the m parameter of k m -anonymity maps to

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SESSION 6(a) abi(b) dak(c) shn

τ

4h

1h

2h

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(e) dak

4hm2h(d) abi

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1h

τ

1h

30

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10

1h

m

τ

m

4h

30

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30

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m

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30

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10

4

Mean
Median25-75 %-ile

90

10

Mean
Median25-75 %-ile

510Space Granularity [Km]DATA QUALITY AND PRIVACY(f) shnFig. 2. Spatial (a-c) and temporal (d-f) granularity versus the adversary knowledge τ in the reference datasets. The spatial granularity is expressed as thesum of spans along the Cartesian axes. For instance, 1 km maps to, e.g., a square of side 500 m.

the (variable) set of samples contained in any time period τ .
During any such time period, every trajectory in the dataset
must be indistinguishable from at least other k −1 trajectories.
(ii) The leakage . It is the maximum additional knowledge
that the attacker is allowed to learn. It consists of the spatiotemporal samples of the target user’s trajectory contained in atime interval of duration at most , disjoint from the original
τ . In order to fulfill the uninformative principle, the leakage
 must be small.
The two requirements above imply alternating in time thek − 1 trajectories that provide anonymization. An intuitive
example of the rationale behind k τ, -anonymity is provided
in Fig. 1. There, the trajectory of a target user i is 2τ, anonymized using those of five other subscribers. The overlapping between the trajectories of a, b, c, d, e and that of i ispartial and varied. An adversary knowing a sub-trajectory ofi during any time interval of duration τ always finds at least
one other user with a movement pattern that is identical tothat of i during that interval, but different elsewhere. With
this knowledge, the adversary cannot tell apart i from theother subscriber, and thus cannot attribute full trajectories
to one user or the other. As this holds no matter where
the knowledge interval is shifted to, the attacker can neverretrieve the complete movement patterns of i: this achieves
the uninformative principle. Still, the adversary can increase
its knowledge in some cases. Let us consider the interval
τ indicated in the figure: the trajectories of i, d and e are
identical for some time after τ , which allows associating to i
the movements during : the opponent learns one additional
spatiotemporal sample of i.

D4D Challenges, and by the University of Minnesota. They
are denoted as abi, dak and shn, and describe the spatiotemporal trajectories of tens of thousands mobile subscribers
in the urban regions of Abidjan, Dakar and Shenzhen.
While our algorithms guarantee that k τ, -anonymity isachieved by construction, it is interesting to assess the level ofaccuracy retained in the anonymized data. Fig. 2 portrays themean, median and first/third quartiles of the sample granularity in the k τ, -anonymized citywide datasets abi, dak andshn. The plots show how results vary when the adversary
knowledge τ ranges from 10 minutes to 4 hours (except forshn, whose limited temporal span prevents us from testing
attacks with knowledge τ higher than one hour), and  = τ .
We remark how the k τ, -anonymized datasets retain significant levels of accuracy, with a median granularity in the orderof 1-3 km in space and below 45 minutes in time. These levelsof precision are largely sufficient for most analyses on mobile
subscriber activities, as discussed in, e.g., [10]. The temporal
granularity is negatively affected by an increasing adversary
knowledge τ , which is expected. Interestingly, however, thespatial granularity is only marginally impacted by τ : protecting
the data from a more knowledgeable attacker does not have asignificant cost in terms of spatial accuracy.
Overall, these results show that our algorithms attains k τ, anonymity of real-world datasets of mobile traffic, while
maintaining a remarkable level of accuracy in the data. Moreresults proved that an equivalent performance is achieved innationwide datasets, and that the data accuracy is better whenmost needed, i.e., at daytime, when the majority of relevant
human activities take place.

III. ACHIEVING k τ, - ANONYMITYR EFERENCES

We propose algorithms that are a first step towards acomplete solution to transform a dataset of spatiotemporal
trajectories so that it fulfils the uninformative principle. We
refer the reader to the extended version of this abstract [9]for further details. Our algorithms implement, through generalization and suppression, the k τ, -anonymity criterion for
all trajectories in the dataset, under the assumptions that theattacker can track his victim continuously during any amountof time τ , and learn all spatiotemporal samples in the victim’s
trajectory over that timespan.
We evaluate our anonymization algorithms with three realworld datasets of mobile subscriber trajectories extracted fromcall detail records (CDR), and released by Orange within their[1] Telefonica Smart Steps, http://dynamicinsights.telefonica.com/smart-steps/[2] Orange Flux Vision, http://www.orange-business.com/fr/produits/flux-vision[3] D. Naboulsi, M. Fiore, R. Stanica, S. Ribot, “Large-scale Mobile Traffic Analysis:a Survey,” IEEE Communications Surveys and Tutorials, 18(1), 2016.
[4] H. Zang, J. Bolot, “Anonymization of location data does not work: A large-scalemeasurement study,” ACM MobiCom, 2011.
[5] Y. de Montjoye, C.A. Hidalgo, M. Verleysen, V. Blondel, “Unique in the Crowd:
The privacy bounds of human mobility,” Nature Scientific Reports, 3(1376), 2013.
[6] M. Gramaglia, M. Fiore, “Hiding Mobile Traffic Fingerprints with GLOVE,” ACM
CoNEXT, 2015.
[7] A. Machanavajjhala, D. Kifer, J. Gehrke, M. Venkitasubramaniam, “l-diversity:
Privacy beyond k-anonymity,” ACM Transactions on Knowledge Discovery from
Data, 2007.
[8] M. Terrovitis, N. Mamoulis, P. Kalnis, “Privacy-preserving Anonymization of Setvalued Data,” VLDB, 2008.
[9] M. Gramaglia, M. Fiore, A. Tarable, A. Banchs, “Preserving Mobile Subscriber
Privacy in Open Datasets of Spatiotemporal Trajectories,” IEEE INFOCOM, 2017.
[10] M. Coscia, S. Rinzivillo, F. Giannotti, D. Pedreschi, “Optimal Spatial Resolution for
the Analysis of Human Mobility,” IEEE/ACM ASONAM, 2012.

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Detecting the leakage of personal confidential informationfrom mobile data
Wang Ti1

Ding Wei1

Liu Xinhai2

1 China Unicom Network Technology Research Institute, {wt, dingwei}@dimpt.com
2 the People's Bank of China, xinhai.liu@foxmail.com

Abstract:
So far, voice phishing is quite popular in China, leading to big loss for the public,
for example, in 2015, the damage caused by vishing in China was estimated to beabout USD 3 billion. Due to its complicate mechanism, fighting vishing attack is hardchallenge. Therefore, with mobile data analytics, we propose an approach to detect
the pattern of personal information leakage in telecom fraud. The vishing cases in the
40 cities of China are analyzed, which demonstrates that our approach can help
detect how privacy is leaked.

Key words: Mobile Data; Voice Phishing; Binary Classification; Information Entropy
1 Introduction
Vishing (Voice phishing) is a variant of phishing. The scammers use phone calls totrick people into divulging financial data or transferring money by masquerading as
trusted authorities (for instance, government agency, bank and so on). Moreover thevishing criminals is becoming more and more complicated. Once the scammers havegotten the victims’ personal confidential information, especially the certain
population groups such as the elderly are more vulnerable to vishing. These factors
have led to an increase in vishing attacks. In 2015, the damage due to vishing in china
was estimated to be about USD 3 billion. Even though there is an undergroundmarket of collecting and reselling the personal confidential information, theseinformation breach still can promote the planning of criminal acts.
Motivated by the challenge of vishing attack, we propose an approach to detect
the pattern of personal information leakage in telecom fraud from mobile data. To
evaluate the performance of our approach, we analyze the vishing cases in the 40
cities of China. The results demonstrate that our approach can help detect how
privacy is leaked.

2 Methodology
It was reported that[1], no matter whether there were specific targets, at thegeneral level the scammer’s calling with a phone number would be made for 189
times daily, and one success of telephone fraud costs 357 times calling at average.
Meanwhile, based on former research, the successful rate of vishing is about 0.28%[1]. Based on our statistical analysis as shown in Figure 1, the vishing call over mobile

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SESSION 6DATA QUALITY AND PRIVACY

phone has certain evident patterns, such as, high calling frequency(it often occurs
during the work day and work time), short talking duration (most less than 2
minutes) , and the called parties is likely hang up the call.

Figure1: The statistical profiles of typical vishing calls

Usually there are not specific targets in some fraud cases, but the situation haschanged as both Information Technology and the Internet scenario have made it easy
to collect personal confidential information through breaches. In vishing cases, if thephone numbers called in a divided segment are not in digit sequence, except theauspicious phone numbers, the users’ personal confidential information probably
would be leaked to a large degree. Hence we introduce the discrete rate of calledphone number as the index which indicates the disorder degree. We use theinformation entropy algorithm to evaluate it. Due to the low efficiency of the
traditional method called following window, we adopt an elaborate approach called
sliding window (see Fig.2), which calculate the index with different window size aswell as various calculation manner.

Figure 2: Example of calculating the disorder degree of a phone number by various methods

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SESSION 6DATA QUALITY AND PRIVACY

Since 2015, China Unicom, one of the telecom giants in China, has been carryingout the project to leverage the location technology of cell-sites and map the areas
into geo-grids in a proper scale with the functional characteristics. Up to now, themicro grid division of functional areas in 40 major cities has been completed. Thenon the basis of consumer’s behavior analysis, combined with the real nameregistration rule of mobile phone user, we can tell the approximate identity of aphone user as shown in Figure 3. The dots indicate the times and locations recorded by theclose antennas. In Subfigure A, Trace of an anonymous mobile phone user during a day isrecorded. Subfigure B focuses on the two main periods when the user’s working and sleeping.
While in subfigure C, more precise location information of the working area is available.

Figure 3: The demonstration of location of a phone user

Herein, we test our approach on a dataset spanning over a period of 6 months and40 cities in China, where the fraud callers and the called victims are in the sameprovinces. We use the information entropy algorithm to distinguish the clusters ofcalled number and extract the cluster characteristics (see fig4).

Figure 4: Clusters of called phone users divided by entropy and the characteristics

Typically, we use the available data to analyze the following 4 different scenarios:

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SESSION 6DATA QUALITY AND PRIVACY

Scenario I: The phone users in the special cluster of called parties are usuallystaying in the proximity area, such as the same school, the same governmentbuilding, or the same company. So it is possible to divulge the details from thecurrent organization.
Scenario II : If the Scenario I does not exist, one of the user’s family members hastaken part in some certain activities, for example, the children attended the same
extracurricular class, who were often in the same area at some certain time. Thenthe personal confidential information of the parent was leaked indirectly. (The social
relationship of the phone user can be mined through mobile data as well[2]).
Scenario III: if the fraudsters know the phone user’s bankcard number and IDs in
the vishing cases, and it was reported by the victims to police or the complaint
department of service operators, we can query whether they all have taken financial
account in the same bank from the credit center of the central bank, which will provethat their private information were leaked from the bank or some e-commerce
websites with payment function.
Scenario IV: Beside all above, if the significant correlation among the users ofmobile phone cannot be found through big data in the form of the first three
scenarios, perhaps it was due to the individual factors of the user’s own incaution.
According to the dichotomy principle, we propose three analysis models for the
first three scenarios. It means we pose the analysis in the first three scenarios as thebinary classification task in the corresponding models. Meanwhile, we take the realfacts reported by victims in the vishing cases as the checking dataset to train themodels, and demonstrate the feasibility of the method by evaluating its performancethrough ROC curve. The result shows the areas of the three models are all better
than the benchmark (see fig.5).

Figure 5: Areas of the classification models under the ROC curve (>0.5)

3 Conclusion

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SESSION 6DATA QUALITY AND PRIVACYThis research explores the framework to detect the pattern of personal
confidential information leakage. The approach we present in this paper has several
advantages: (i) it has good scalability and so it is suitable for detecting the breach of
the confidential information leakage in mobile phone fraud.（ii）it is helpful tosafeguard the personal confidential information because it can help the relatedparties for protection of personal privacy in the criminal acts, no matter malicious
intention or inadvertent errors. In some sense,

References[1] 360 company, Mobile guards Security Report in 2016：the Fraud with phonesituation and Characteristics Analysis. http://www.199it.com/archives/545187.html[2] H Zhang，R Dantu ,Predicting social ties in mobile phone networks. Intelligenceand Security Informatics (ISI), 2010 IEEE International Conference

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SESSION 6DATA QUALITY AND PRIVACY

Biases and errors in the temporal sampling of random movements
Riccardo Gallotti
Instituto de Fı́sica Interdisciplinar y Sistemas Complejos (IFISC),
CSIC-UIB, Campus UIB, ES-07122 Palma de Mallorca, Spain

Rémi Louf
Centre for Advanced Spatial Analysis (CASA), University College London, W1T 4TJ London, United Kingdom

Jean-Marc Luck
Institut de Physique Théorique, CEA, CNRS-URA 2306, F-91191, Gif-sur-Yvette, France.

Marc Barthelemy
Institut de Physique Théorique, CEA, CNRS-URA 2306, F-91191, Gif-sur-Yvette, France. and
CAMS (CNRS/EHESS) 190-198, avenue de France, 75244 Paris Cedex 13, France

The new sources of data, available thanks to Information and Communication Technologies, allow to track individual trajectories at an unprecedented scale [1, 2]. In
empirical studies, trajectories individuals [3, 4] are sampled in space and time. However, as it is the case for any
dataset, these new sources of information have limits andbiases [5, 6] that need to be assessed.
Here, we study trajectories alternating rests and movesof random durations [7–9]. Isolate and identifying theseintertwined static and dynamic behaviours is an important statistical challenge and a growing array of segmentation methods based on spatio-temporal characteristicsof the trajectories have been tailored for the specificdataset in question [2, 6]. These procedures are however limited from technological constraints that impose atemporal sampling of the trajectory, as one needs a time
∆ between sampled points significantly smaller than thecharacteristic duration of rests and moves in analysis toreconstruct the trajectory.
35

30

x (km)

25

20

15

10

5

0
0

4

8

12

θ (h)

16

20

24

FIG. 1: Examples of trajectory sampling. On a trajectory with exponentially distributed rest and move durations,
we show the case of constant sampling interval (red circles)
and the case of random sampling interval (blue crosses) withP (∆) ∝ ∆−1 (∆min = 5 min, ∆max = 12 h).

This issue is particularly evident in human mobility

data. Currently, the most common sources used are Call
Detail Records (CDR) of mobile phone data [11] andgeo-located social media accesses [12], where the flaws
described above are amplified by random and bursty nature of human communications [3, 13].
In this paper, we discuss the effect of periodical andbursty sampling on the measured properties of random
movements. We consider trajectories as an alternating
renewal process [14], a generalisation of Poisson processes
to arbitrary holding times and to two alternating kindsof events, moves and rests, whose durations t and τ areregarded as independent random variables. The samplingtime interval ∆ depends on the particular experiment andcan be either constant or randomly distributed.
We analytically solve the ideal case of constant sampling and short-tailed distributions of rest and move durations with the naive assumption that every observed
displacement is to be associated to a movement. We obtain explicitly the distribution P (∗ ) of sampled displacements and its first two moments, that also allow us toquantify difference between the real  = vt and sampled∗ displacement lengths. Moreover, we √are able to proˆ = 1.96 tτ maximizing
vide an optimal sampling time ∆the fraction of correctly sampled movements. We thenextend these results numerically, and show that sampling
human trajectories in more realistic settings is necessarily
worse. Finally, we use high-resolution (spatially and temporally) GPS trajectories [15] to verify our predictions onreal data. We find that for real cases, characterized bylong-tailed rest durations [4, 10], the fraction of correctly
sampled movements is dramatically reduced. We test our
results with high-resolution GPS trajectories of human,
where constant sampling allows to recover at best 18%
of movements, while even idealized methods cannot recover more than 16% of moves from sampling intervals
extracted from real communication data [16] .
These figures suggest that in the sampling of of human trajectories alternating rests and movements it is
not possible to successfully reconstruct the real moves
from the empirical sequence of displacement observed
only through the lens of mobile phone communications.

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SESSION 6DATA QUALITY AND PRIVACY

Further studies, taking advantage of the new analytical
tools we provide here to evaluate the quality of a sampledindividual trajectory, are certainly necessary to assess thebias induced by sampling on statistics aggregated at individual or collective level.

[1] Vespignani, A. Modelling dynamical processes in complex
socio-technical systems. Nat Phys 8, 32–39 (2012).
[2] Zheng, Y. Trajectory data mining: An overview. ACM
Transactions on Intelligent Systems and Technology 6,
29–41 (2015).
[3] González, M.C., Hidalgo, C.A. & Barabási, A.-L. Understanding individual human mobility patterns. Nature
453, 779–782 (2008).
[4] Song, C., Koren, T., Wang, P. & Barabási, A.-L. Modelling the scaling properties of human mobility. Nature
Phys 6, 818–823 (2010).
[5] Williams, N.E, Thomas, T.A., Dunbar, M., Eagle, N.,
Dobra, A. Measures of human mobility using mobile
phone records enhanced with GIS data. PLoS ONE
10(7), e0133630 (2015).
[6] Çolak, S., Alexander, L.P., Alvim, B.G., Mehndiratta,
S.R., González, M.C. Analyzing cell phone location datafor urban travel. Transp Res Record 2526, 126–135(2016).
[7] Turchin, P. Quantitative analysis of movement: measuring and modeling population redistribution in animals
and plants. (Sinauer Associates, Sunderland 1998).
[8] Brockmann, D., Hufnagel, L. & Geisel, T. The scalinglaws of human travel. Nature 439, 462–465 (2006).
[9] Gallotti, R., Bazzani, A., Rambaldi, S., Barthelemy, M.
A stochastic model of randomly accelerated walkers forhuman mobility. Nat Commun 7, 12600 (2016).
[10] Gallotti, R., Bazzani, A. & Rambaldi, S. Toward a statistical physics of human mobility. Int J Mod Phys C 23,
1250061 (2012).
[11] Blondel, V.D., Decuyper, A., Krings, G. A survey of results on mobile phone datasets analysis. EPJ Data Science 4, 10 (2015).
[12] Hawelka, B. et al. Geo-located Twitter as proxy for global
mobility patterns. Cartogr Geogr Inform 41(3), 260–271(2014).
[13] Bild, D.R. et al. Aggregate characterization of user behaviour in Twitter and analysis of the retweet graph.
ACM Transactions on Internet Technology 15(1), 4(2015).
[14] Godrèche, C., Luck, J.M. Statistics of the occupation
time of renewal processes. J Stat Phys 104, 489–524(2001).
[15] Zheng, Y., Xie, X., Ma, W.-Y. GeoLife: A Collaborative Social Networking Service among User, location andtrajectory. IEEE Data Engineering Bulletin 33(2), 32–40(2010).
[16] de Montjoye, Y.A., Smoreda, Z., Trinquart, R., Ziemlicki,
C., Blondel, V.D. D4D-Senegal: the second mobile phonedata for development challenge. arXiv:1407.4885 (2014).

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SESSION 6DATA QUALITY AND PRIVACY
1Application of trajectory based models forcontinuous behavioural user authentication throughanomaly detection
Piotr Kałużny, Piotr Jankowiak, Agata Filipowska, Witold Abramowicz1
1

Department of Information Systems, Poznan University of Economics and Business, Poland

Abstract—This paper describes how mobility patterns understood as trajectory based models can be applied for an anomaly
detection and authentication using telecommunication data. The
trajectory based mobility model utilizing stay-point extraction
suited for the sparse CDR data is used to describe mobility
patterns of a user. In this model, the observed activities are
assigned with anomaly scores in three distinctive areas including:geographical, sequential and temporal dimensions. Activities
with threat values exceeding the user confidence threshold are
identified as anomalies. The model is tested on the sample of
Poznan inhabitants. Evaluation of the model performance isbased on the similarity classes and the results are presented
within the paper.

I. I NTRODUCTION
In recent years, due to the ubiquity of cell phones and raisein their penetration rates, mobile phones became not only abasic tool for everyday use, but also a valuable source of
information about their users. The value of data generated bythose devices has risen significantly over the years [6].
Regardless of value of this data as perceived by a user,
mobile devices suffer from a lack of proper protection against
unwanted access to this data in case of a theft (of both the device or user identity). Proper authentication techniques and automatic systems are needed to ensure those devices are securefrom theft and an unintended use. The traditional methods havetheir drawbacks caused mostly by users’ negligence resulting
in 40% of the phones not secured by any means [7]. Due to thatfact and drawbacks of point-of-entry traditional approaches,currently existing methods are not enough [1]: sometimes lacking the required security, availability or usability. As a possible
answer to this problem a new family of methods is introduced,
named behavioural biometry. These methods address unique,non transferable, difficult to reproduce and hard to forget or
loose characteristics derived from user behaviour rather thanphysical traits. The methods can rely on various factors andfocus on different aspects of behaviour to find unique patternsof e.g. gait, signature or keystroke dynamics. Those patterns,
created while the device is running, can be used to secure it.
This allows the authentication process to work continuously
and transparently without the user interaction needed. The
use of those methods can provide an additional layer of
security on top of existing methods without diminishing theusability, e.g. PIN or password would be used only when the
behaviour analysis system is not sure about the user’s identity.

Within those methods there exists a subgroup, referred toas behavioural profiling, which: identifies people based upon
the way in which they interact with services of their mobile
device [13]. The user’s identity is determined based upon thecomparison of a sample of activities with his profile. If thesample matches the profile, the user will be granted with anaccess, otherwise he will be refused [17] or an additional proofof identity will have to be brought (e.g. PIN).
Studying behavioural patterns and especially the user mobility had proven to be quite successful in differentiating between
users and identifying deviations from a user profile. This ispossible due to the fact that cell phone traces closely resemble
user trajectories, regardless of the mobility data source being
CDR or phone-collected data. Also observations of the human
movement conducted by the researchers confirmed the stabilityof those patterns [9]. The predictability of mobility patterns
was proven to be high and stable given historical behaviourof a user [15], [18], [19] regardless of the daily distancetravelled [3]. The proxy of BTS (Base Transceiver Station)labelled geographical information, derived mostly from CDR,
was proven to be precise enough to study human mobility.
Its applications included identifying patterns on a large scaleconfirming correlation with e.g. population density [2] ortransport networks. Those traces also remained precise enoughto capture mobility patterns to allow for an individual user
analysis based on visited locations and travel models [4], [5],[14], also introducing methods for a better cell dwell time
prediction [16], [21]. The users’ profiles mostly utilized thesemi-structured patterns that can be observed when analysing
mobility in a weekly manner in hourly bins [8] even in more
frequently generated phone data [12]. Such models can be asource of features for behavioural profiling approach for the
anomaly detection model that utilizes multiple methods basedon: probability of visiting a location at a given time [22] orsequential characteristics [20] of movement. Those methods
gave highly satisfying results on frequent, phone generated
data [7].
II. DATASET AND APPROACH
Our dataset is the database of Orange covering about 4
million of anonymized users over six months (from February
to July) in the 2013. For each record we are given the following information: an anonymized user identification number, a

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SESSION 6DATA QUALITY AND PRIVACY2

BTS id which are grouped to obtain locations covering distinct
geographical areas, and a timestamp at the initial moment of
the phone activity.
The mobility model that we propose utilizes the user focused stay point extraction model mentioned above, with a
few assumptions:
Activities in the same location separated by less than anhour are considered to contain enough information toassume that a user has been in the location during theperiod.
1• Activities which have a stay time larger than 30 minutes
or are separated by more than 4 hours of inactivity2
are labeled as stays. Stay locations are the places whereusers engage in some activity (contrary to the ”pass-by”
locations).
• All this information is kept in a weekly calendar of a user
- a structure divided into hourly bins (timeframes).
•

The model outcome - profile is treated as a pattern, which
is a base for comparison of the activities loaded to detect
potential anomalies and frauds. As a result of that comparison,
threat scores (which are measures between 0 and 1) are
assigned to each activity in three dimensions. They define
how much each tested activity varies from a user pattern in thefollowing areas: geography - each activity is tested against theuser geographical profile where the geography of a locationis compared with a distance to the closest location from a
set of important locations3 compared to the user daily travel
range4 . Time - each activity is assigned a threat metric basedon the distance in (hourly) timeframes, when a user is present
at a given location and the usual time he is at the location
divided by 24 hours5 . Sequence - by using the trajectories built
upon the extracted stays, all of the passed-by BTS are used toconstruct a mobility trie (TrieRoute) that contains information
about frequencies of stations visited by a user when travelling
between point A and B on the learning data along with theorder. Each activity is assigned a sequence threat dependingon the probability of a given point appearing in a sequence.
After defining those measures we conducted an experimentchoosing the inhabitants of a Poznan area which is shown in
the Figure 1. Home locations were extracted from our modelas the longest stay between 7 p.m. and 7 a.m.6 .
The following approach was applied on the data: firstly, themobility model was built on 24 days of data from March 2013,
then 7 day verification period was used to generate a typicalthreat level for a user. This was used to test how consistent theusers are with their patterns, generating threats in three above
mentioned perspectives. 90th percentile of those threat values
was used to create user confidence interval for each of thetarget threats. Each tested activity having its threat level above
1 The difference between a departure and an arrival time from a location

based on actions.
2 To avoid very long movement sequences spanning over multiple days dueto the sparse activity data and the uncertainty period of this sparsity [16].
3 Places that a user visits that comprise at least 5% of the model extracted
stay time in any hourly bin during the period of comparison.
4 The approach is based on [10].
5 Which is a max. time distance at which threat equals one.
6 Described in [11].

Figure 1. Poznan area - visualization made in Javascript (Mapbox, D3.js,
jQuery) to showcase Voronoi cells of BTSs from the chosen area.

the threshold value of the confidence interval is classified as
an anomaly in this dimension. Next, based on those upper
thresholds for every user, test data from 14 days of April was
loaded to test how the model performed in the authentication
scenario, evaluating whether the threats generated by otherusers’ data can be used to differentiate between the profileduser and an impostor.
Due to the fact that for the evaluation of mobility authentication models, a random user case is not a valid use
case scenario7 , we introduce a class based verification modelthat utilizes similarity classes. This idea can be compared tosimulation of an uninformed and informed attacker case fromthe security domain (seen also in behavioural biometry cases
e.g. [12]). Therefore, the test data is divided into five classes,
listed considering the expected raising similarity to the baseuser and difficulty of the model adjustment: random user, user
living in the same town, user having home in the same BTS,
user having same home and work locations in respective BTS
and finally the same user - with the data loaded from thetesting period. Based on this comparison a model is run on
the user base, without any prior filtering besides two aspects:
all users from respective classes need to be found for the baseuser and each of the test class users needs to have at least 5
activities in the testing period.
III. R ESULTS
The experiment was run on a sample of 1000 users, forwhich we were able to find corresponding users in all of testclasses. We excluded users with unstable mobility patterns
or sparsity of the data within CDRs. This fraction accounted
for about 0.8% of the sample. The average level of threat
generated was highly dependent on the test class as it is shown
7 Model detecting anomalies for a random user appearing in a different partof country may achieve high accuracy but be practically unusable.

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DATA QUALITY AND PRIVACY

in the Figure 2. This proved that such division is useful inevaluating method accuracy.
Next, an anomaly/impostor scenario was prepared. Eachloaded set of consecutive activities (an activity window of 3 activities was used8 ) was classified as an anomaly or normal user
behavior. The anomaly was detected when the activities threatvalues exceeded the user confidence threshold in at least two
dimensions (as it was tested to have the best anomaly detection
accuracy). Additionally, an algorithm iteratively decreasing thethreat percentile to achieve best results was used to improve
the accuracy of the method. The average fractions of properly
detected anomalies over the sample were as follows:• 98,97% for a random test user class,• 91,1% for a test user living in the same town as a baseuser class,• 53,32% for a test user having the same home location asa base user class,• 31,84% for a test user having the same home and work
location as a base user class.

Figure 2. The distribution of average threat levels (considering three threatsmentioned in the model) for all of the users.

The false rejection rate, interpreted as a portion of situations
where valid user data from the test period was classified as
an anomaly (compared to a profile), averaged to 13,71% over
our sample. Presented results show that our model has a very
high accuracy when applying a testing methodology used in
the literature (random user) - about 99%. This showcases
the usability of the model performance even on a sparse
dataset like the event based CDR logs. However, we foundthat when comparing a user with another user having avery similar profile (living or working in similar areas), theaccuracy is much lower. This indicates that a potential theftof a mobile phone by a thief, who has a similar mobility
behaviour profile as the victim may be significantly harderto detect. The division in testing classes also introduces a
new methodology for evaluation of methods’ performance
8 Meaning an average threat value for three activities was used for classifying whether the data belongs to a user or an impostor.

in both the authentication scenario (e.g. phone theft) anduser pattern differentiation (distinguishing between patterns ofsimilar users like e.g. family members).
R EFERENCES[1] Beyondthepassword:The
futureofaccount
security.
https://www.telesign.com/wp-content/uploads/2016/06/
Telesign-Report-Beyond-the-Password-June-2016-1.pdf.
Accessed:
2016-09-10.
[2] R. Ahas, S. Silm, O. Järv, E. Saluveer, and M. Tiru. Using Mobile
Positioning Data to Model Locations Meaningful to Users of Mobile
Phones. Journal of Urban Technology, 17(1):3–27, 2010.
[3] J. P. Bagrow and Y.-R. Lin. Mesoscopic structure and social aspects ofhuman mobility. PloS one, 7(5):e37676, 2012.
[4] F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti. Estimating origindestination flows using mobile phone location data. IEEE Pervasive
Computing, 10(4):0036–44, 2011.
[5] S. Çolak, L. P. Alexander, B. G. Alvim, S. R. Mehndiratta, and
M. C. González. Analyzing cell phone location data for urban travel:
current methods, limitations, and opportunities. Transportation Research
Record: Journal of the Transportation Research Board, (2526):126–135,
2015.
[6] B. Fox, R. van den Dam, and R. Shockley. Analytics: Real-world useof big data in telecommunications. IBM Institute for Business Value,
2013.
[7] L. Fridman, S. Weber, R. Greenstadt, and M. Kam. Active authentication
on mobile devices via stylometry, application usage, web browsing, andgps location. 2015.
[8] B. Furletti, L. Gabrielli, C. Renso, and S. Rinzivillo. Analysis of GSM
calls data for understanding user mobility behavior. Proceedings - 2013
IEEE International Conference on Big Data, Big Data 2013, pages 550–
555, 2013.
[9] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding
individual human mobility patterns. Nature, 453(7196):779–782, 2008.
[10] S. Isaacman, R. Becker, R. Caceres, S. Kobourov, M. Martonosi,
J. Rowland, and A. Varshavsky. Ranges of human mobility in Los
Angeles and New York. 2011 IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops
2011, pages 88–93, 2011.
[11] P. Jankowiak and P. Kaluzny. Human mobility profiling based on
Call Detail Records analysis. Bachelor thesis, Poznan University of
Economics and Business, Poznan, 2015.
[12] H. G. Kayacik, M. Just, L. Baillie, D. Aspinall, and N. Micallef. Data
driven authentication: On the effectiveness of user behaviour modellingwith mobile device sensors. arXiv preprint arXiv:1410.7743, 2014.
[13] F. Li, N. Clarke, M. Papadaki, and P. Dowland. Active authentication
for mobile devices utilising behaviour profiling. International journalof information security, 13(3):229–244, 2014.
[14] F. Liu, D. Janssens, J. Cui, Y. Wang, G. Wets, and M. Cools. Building
a validation measure for activity-based transportation models based on
mobile phone data. Expert Systems with Applications, 41(14):6174–
6189, 2014.
[15] X. Lu, E. Wetter, N. Bharti, A. J. Tatem, and L. Bengtsson. Approaching
the limit of predictability in human mobility. Scientific reports, 3, 2013.
[16] M. Picornell, T. Ruiz, M. Lenormand, J. J. Ramasco, T. Dubernet,
and E. Frı́as-Martı́nez. Exploring the potential of phone call data tocharacterize the relationship between social network and travel behavior.
Transportation, 42(4):647–668, 2015.
[17] H. Saevanee, N. Clarke, S. Furnell, and V. Biscione. Continuous user
authentication using multi-modal biometrics. Computers & Security,
53:234–246, 2015.
[18] C. M. Schneider, V. Belik, T. Couronne, Z. Smoreda, and M. C.
Gonzalez. Unravelling Daily Human Mobility Motifs. Journal of The
Royal Society Interface, 10(84):20130246(1–8), 2013.
[19] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability
in human mobility. Science, 327(5968):1018–1021, 2010.
[20] G. Tandon and P. K. Chan. Tracking user mobility to detect suspicious
behavior. In SDM, pages 871–882. SIAM, 2009.
[21] P. Widhalm, Y. Yang, M. Ulm, S. Athavale, and M. C. González.
Discovering urban activity patterns in cell phone data. Transportation,
42(4):597–623, 2015.
[22] S. Yazji, P. Scheuermann, R. P. Dick, G. Trajcevski, and R. Jin. Efficient
location aware intrusion detection to protect mobile devices. Personaland Ubiquitous Computing, 18(1):143–162, 2014.

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SOCIAL NETWORKNetMob 5-7 April 2017 – Vodafone Village – Milan

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What comes first? Social strength or common friends?
Giovanna Miritello,1 Manuel Cebrián,2 and Esteban Moro3, ∗1

Telefónica Research, 28050 Madrid, Spain
Data61 Unit, CSIRO, Melbourne, Victoria, Australia
3
Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganés, Spain
2

A famous result in social network theory is the
weak ties hypothesis by Mark Granovetter bywhich the strength of a social ties correlates withthe social embeddedness of the tie. Strong ties
are surrounding by common friends, while weakties happen between communities.
Althoughthis hypothesis was confirmed in a number of
online and offline networks, little is know about
how this correlation is built and/or destroyedin time, that is, how common friends and tie
strength evolve together when a tie is createdor destroyed. By analyzing the mobile phone
communication network of about 20 million
people over a long period of time of 19 months,
we found that once that a tie is created is reaches
almost instantaneously its strength while its
embeddedness slowly growths even months after
tie formation. The opposite is find when a tie
is destroyed: tie strength last until the very
last minute although common friends started todisappear months before. Our results highlightthat the Granovetter weak ties hypothesis is a
dynamical process that happen at a very slow
time scale, showing the intricate evolutionary
dynamics of network interactions and structure. .
Understanding the very dynamics which regulates theprocess of why an individual decides to add or remove
a social tie is a very complex process which has beenof interest of many studies [1, 4–6]. However, since tie
creation/removal processes alter the structure of the network in which the individual participates, we may wonder whether changes in the topology of the local network
surrounding a tie can tell us something about the factthat a new connection is going to appear or has justbeen removed. In particular, what we want to answer
here is: to what extent individuals choice to create a newtie is related to the their social network before the newconnection has been created? Does the topology of their
network change after the establishment of a new tie? The
same questions can be applied when a connection is removed, instead than created.
To address this we first introduce a new methodology
to investigate temporal networks [7], an issue which is
convoluted with the way social networks are observed
and modeled, and which has been recently been pointed
out as a problem in the field of social networks [8].

∗ Corresponding author emoro@math.uc3m.es

By means of this methodology we are able to disentangle tie activation/deactivation from their bursty activity.
This method allows us to determine, for each tie in ourdataset, the set of their common neighbors that are active at any time instant. Moreover, for any given tie, we
can identify with high precision the time at which it has
been created or destroyed.
As mentioned before, we are interested in understanding whether changes in the local topology are related tothe creation/removal of a new/old social connection. For
this reason, for each of these two categories of ties, westudy the evolution of the topological overlap in a time
window which spans a period going from before to after the tie has been created or removed. Accordingly
to the weak tie hypothesis, ties between individuals who
have many common friends (large overlap), are stronger
than the ones between people which few common friends(small overlap), who instead act as bridges between different tight groups Since our analysis allows us to assesswhen a tie has been created or removed and, at the sametime, to analyze the instantaneous contact network, wetake advantage of this and try to investigate the dynamical Granovetter effect. Specifically, we also separate theties in different groups according to their strength (total
number of calls during the whole 19 months period) and
we analyze the temporal evolution of their neighboorhood overlap for each of these groups. For comparison,
we also show the average overlap between pairs of nodes
randomly chosen from the whole population, and the average overlap of ties that do not form or decay within theobservation window.
Our contribution shows a number of important results.
According to the weak ties hypothesis (see figure 1), we
observe that topological overlap is strictly related to theintensity or weight of a social relation, meaning thatthe stronger is the relationship between two persons, themore friends they have in common, in line with previous
results [1, 2]. Moreover, the overlap between two individuals who form (remove) a connection at some time
during their lifetime, is significantly higher than the one
observed between any random pair of individuals in thewhole population months before (after) the link has been
established (removed). This constitutes a more clear evidence of why the topological overlap is usually a very
good feature in the prediction of tie creation [3, 9, 10].
More interestingly, we find that the process that drives
two individuals to link together is highly dynamical andthat, locally, it entails the change of the underlying topology of the network. We observe, in fact, a large overlap
many days before the connection has been established,

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factor(limite)factor(limite)

000.10 factor(limite)
1
0
2
1
3
2
4
3
5
0.05
4

1
2
3

var

Overlap
var
var

0.10

0.05

4
5

factor(tam)5factor(tam)

0.2

0.2

0.1

0.00

0.00
−100

−50

2.0

day

0

50
50

−50

2.0

0.1

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factor(limite)50

day8 wij

100  wij < 200

2
4
3
5

var

Intensity
var

1.0

1.0

200  wij

4

0.5

0.5

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−50

0

t day tini

50

1
2
3
4
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factor(tam)5factor(tam)

0.2

0.2

0.1

0.0
−100

0

10  wij < 2020  wij < 100

0
2
1
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1.5

factor(limite)wij < 100

factor(limite)
1
1.5

100

−50

0.1

0

t

day

50

tf in

100

FIG. 1. Co-evolution of the topological overlap (Jaccard coefficient, top) and intensity of communications (number of calls perday, bottom) before and after the creation (left) and destruction of a link (right) for different intensity of the links. Dashed lines
in the top panel correspond to the average overlap of two randomly selected nodes in the newtork (bottom) and the averageoverlap for all the links in the network.

and it continues to increase over time after the tie forms.
A similar result is found when a tie is removed: the topological overlap between two individuals starts to decreasemuch before the connection is removed and it keeps decreasing in time after the breakage, although very slowly.
This allows us to reply to the following question: whatcomes first, tie strength or common neighbors? Our results indicate that the connection comes first, and only
after the correlation between tie strength and topologicaloverlap starts to form, suggesting indeed a sort of dynamical Granovetter effect that, to the best of our knowledge,
has not been investigated before.
These results indicate that the properties of the local
network between two individuals contain important in-formation about their relationship even if no interaction
between them is observed. They also have outstanding
interest from a sociological and anthropological point ofview since they shed more light on the way in which humans establish and remove social connections. We haveseen, in fact, that the Granovetter effect is not just a correlation observed in the aggregated contact network, but
a dynamical process that happens at a very slow timescale. The correlation between the number of common
friends between two individuals and their strength is infact observable within a time period significantly longer
than the lifetime of the social relationship and acts as a”fingerprint” of the social relation itself.

[1] Granovetter M (1973) The strength of weak ties. Am J
Sociol 78:13601380.
[2] Onnela J-P, Saramki J, 1176 Hyvnen J, Szab Z, Lazer
D, Kaski K, Kertsz J, Barabsi A-L (2007), Structure
and tie strengths in mobile communication networks.

Proc.Natl.Acad. SciUSA104:7332.
[3] Hidalgo C, Rodriguez-Sickert C (2008) The dynamics ofa mobile phone network. Phys A387:3017.
[4] Burt R. (2001b). Structural Holes Versus Network Closure as Social Capital. in Lin, N., Cook, K. and Burt, R.S.

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SESSION 7
SOCIAL NETWORKSocial Capital: Theory and Research. Sociology and Economics: Controversy and Integration series. New York:
Aldine de Gruyter.
[5] Kossinets G., and Watts D. J. (2006). Empirical analysisof an evolving social network. Science 311, 5757.
[6] Podonly J., and Baron J. (1997). Resources and Relationships: Social Networks and Mobility in the Workplace.
Americal Sociological Review 62, 673693.
[7] Miritello G, Lara R, Cebrián M, Moro E (2013) Limited
communication capacity unveils strategies for human interaction, arXiv:1304.1979.
[8] Krings, G. Karsai, M., Bernharsson, S., Blondel, V.D.,

Saramäki, J. Effects of time window size and placementon the structure of aggregated networks, EPJ Data Science, 2012, Volume 1, Number 1, 4.
[9] Liben-Nowell D., and Kleinberg J. (2007). The linkprediction problem for social networks. In CIKM 03: Proceedings of the twelfth international conference on Information and knowledge management, New York, NY,
USA, pp. 556-559.
[10] Raeder T., Lizardo O., Chawla N., and Hachen D. (2011).
Predictors of short-term decay of cell phone contacts in alarge scale communication network. Social Networks 33,
245-257.

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Kernel-based approaches to large social networks
Till Hoffmann and Nick Jones
Department of Mathematics, Imperial College London
Mobile phone datasets such as CDRs provide rich data about user behaviour, mobility and call
networks [4]. But the data are often difficult to interpret because of their size and complexity. Community detection algorithms attempt to shed light on the data by reducing their dimensionality[3], but usually give rise to a network of communities which presents us with the same difficulties–although at a smaller scale.
In contrast, we assume that the network can be explained by observable node attributes such as
physical location or demographic data (as opposed to latent attributes such as community membership [14] or a latent embedding [5, 7]). We develop a model that connects network data to third-party
information about node attributes. The model has a number of attractive properties: (1) it can be
fitted to aggregated communication data protecting the privacy of users, (2) it is generative andcan be used to synthesise (aggregated) network data that can be shared without privacy concerns,(3) fitting the model has relatively small computational cost, and (4) the model parameters areinterpretable. We fit the model to synthetic communication data in the London metropolitan area
to validate the inference algorithm.
I.

INTRODUCTION

Social network data have become abundant in thedigital age [4]. Seemingly private information such as
sexual orientation, party affiliation, and ethnicity canbe inferred from the behaviour of users of online social media [9]. How people use their mobile phones canyield information about their socioeconomic status [2].
Despite these successes, Golder and Macy [4] conclude
that such data do not “provide the demographic background information needed by social scientists.” By contrast, censuses and sample surveys provide detailed demographic information about respondents but are lacking
data about their associates.
Fortunately, demographic and network data can befused by leveraging the residential location of mobile
phone users: the address of a user can be highly informative of their demographic background. For example,
residents of areas with high property prices are likely to
be more affluent than areas with cheaper accommodation. In this work, we assume that the residential areaof mobile phone users is known and use aggregate communication volumes between different areas to constrain
the parameters of a kernel which predicts the interaction rate between users conditional on their demographic
background.

II.

NETWORK MODEL

where we have omitted the explicit dependence on thelength-p parameter vector θ to avoid clutter. Unfortunately, despite the abundance and sensitivity of call data,
they rarely provide individual-level demographic information [4]. Yet distributions for demographic attributes
conditional on geographic areas are widely available fromthird parties such as the census or large surveys [11].
We assume that each node i has an associated residential area gi ∈ {1, ..., K}. The aggregate call volume
between two areas a and b is thus

yij ,
mab =
i,j∈A,B:i<jwhere A = {i ∈ N : gi = a}. Because the Poisson distribution is infinitely divisible [8], the aggregate call volume
is also Poisson-distributed with aggregate rate parameter

Λab =
λij .
i,j∈A,B:i<j

The likelihood for the aggregated call volumes is thus
P (m|θ, g) = dz P (z|g)
P (mab |θ, z) .
(1)a<b

As the integral over node attributes is non-trivial, we
evaluate the expected aggregate call rateΛ̄ab = dz Λab P (z|g) ,(2)
and approximate the likelihood by

We assume that each node i ∈ {1, . . . , N } of the call
network is endowed with a q-dimensional coordinate vector zi which captures node attributes. The undirectedcall volumes yij between two nodes i and j are modelled
as conditionally-independent Poisson random variables
with rate
λij = λ (zi , zj , θ)P (m|θ, g) ≈ Λ̄mab exp −Λ̄ab
ab

a<b

mab !

.

(3)

The approximation neglects the (co)variances of the aggregate call rates. But because they are U-statistics, thevariances are known to be small for sufficiently large communities [10].

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have access to aggregated communication volumes, thealgorithm is able to recover the regression coefficients.
IV.

DISCUSSION

We have developed an inference algorithm to fit a generative network model to aggregated communication volumes. Using only aggregated data can alleviate some of

FIG. 1. Aggregated communication network amongst 32 local
authority groups in the London metropolitan area.

III.

SIMULATION AND INFERENCE

We use regional micro data from the 2011 census [11]in the United Kingdom to simulate a social network
amongst 283,155 people over the age of 18 in the London
metropolitan area with connectivity kernel

λij = exp

 p


k=1



θk fk (zi , zj ) ,where f : Rq × Rq → Rp is a function that maps coordinates to features used to predict the interaction rateof two nodes. In particular, the first feature is one irrespective of the coordinates and accounts for the linkdensity. The second and third feature are equal to one
if i and j identify with a different religion and ethnicity, respectively. The fourth feature is the natural logarithm of the Euclidean distance between i and j. We let
θ = (−9, −1, −1.4, −0.7) based on previous results fromsurvey data [6, 13]. The synthetic network has an average
degree of 27 and is shown in figure 1.
To evaluate the approximate likelihood in equation 3,
we approximate the expected aggregate rate in equation 2
using a subset of the micro data, i.e. Monte Carlo integration. We use a Metropolis-Hastings sampler to draw
samples from the posterior distribution. Kernel density
estimates of the marginal posterior density for the regression coefficients is shown in figure 2. Although we only

FIG. 2. Gaussian kernel density estimates of the marginal
posterior density for regression coefficients inferred from simulated data.

the privacy concerns associated with the sensitive natureof individual CDRs–even when they are anonymised [1].
Because our model is generative, the inferred rate kernel can be used to simulate networks with realistic attribute correlations. Synthetic networks can be sharedwithout fear of deanonymisation [12] and allow us to
study the effect of the connectivity kernel on network
structure and dynamics unfolding upon the networks.
In contrast to latent embeddings of community detection algorithms, the kernel-based approach proposed hereis easily interpretable. Each unit increase in the k th feature changes the interaction rate between the corresponding individuals by a factor exp θk .
Finally, the algorithm has a relatively low computational cost even for large networks because the numberof data points is of order K 2 rather than N 2 .
In future work, we would like to extend the feature map
f to a wider range of demographic attributes such as sex
and age. More generally, we believe that building tools
to derive meaningful information from aggregated data
is an important step to prompt mobile phone providers
and online social networks to make public data releases
and allow a wider audience of researchers to access theserich data sets.

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SOCIAL NETWORK[1] Lars Backstrom, Cynthia Dwork, and Jon Kleinberg.
Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography.
Commun. ACM, 54(12):133–141, 2011.
doi:
10.1145/2043174.2043199.
[2] Joshua Blumenstock, Gabriel Cadamuro, and Robert
On. Predicting poverty and wealth from mobile phone
metadata. Science, 350(6264):1073–1076, 2015. doi:
10.1126/science.aac4420.
[3] Santo Fortunato.
Community detection in graphs.
Physics Reports, 486(3–5):75–174, 2010.
doi:
10.1016/j.physrep.2009.11.002.
[4] Scott A. Golder and Michael W. Macy. Digital footprints:
Opportunities and challenges for online social research.
Annual Review of Sociology, 40(1):129–152, 2014. doi:
10.1146/annurev-soc-071913-043145.
[5] Mark S. Handcock, Adrian E. Raftery, and Jeremy M.
Tantrum. Model-based clustering for social networks.
Journal of the Royal Statistical Society A, 170(2):301–
354, 2007. doi:10.1111/j.1467-985X.2007.00471.x.
[6] John R. Hipp and Andrew J. Perrin. The simultaneous
effect of social distance and physical distance on the formation of neighborhood ties. City & Community, 8(1):
5–25, 2009. doi:10.1111/j.1540-6040.2009.01267.x.
[7] Peter D Hoff, Adrian E Raftery, and Mark S Handcock. Latent space approaches to social network analysis.

Journal of the American Statistical Association, 97(460):
1090–1098, 2002. doi:10.1198/016214502388618906.
[8] Norman L. Johnson, Samuel Kotz, and Adrienne W.
Kemp.
Univariate Discrete Distributions.
WileyInterscience, 1993.
[9] Michal Kosinski, David Stillwell, and Thore Graepel.
Private traits and attributes are predictablefrom digital records of human behavior.
Proceedings of the National Academy of Sciences, 2013. doi:
10.1073/pnas.1218772110.
[10] E.L. Lehmann.
Elements of Large-Sample Theory.
Springer, 2004.
[11] ONS. 2011 census microdata individual safeguarded sample (local authority): England and wales, 2015.
[12] JJ Pfeiffer, S Moreno, T La Fond, J Neville, and B Gallagher. Attributed graph models: Towards the sharing ofrelational network data. Technical report, Purdue University, 2014.
[13] Jeffrey A. Smith, Miller McPherson, and Lynn SmithLovin. Social distance in the united states: Sex, race, religion, age, and education homophily among confidants,
1985 to 2004. American Sociological Review, 79(3):432–
456, 2014. doi:10.1177/0003122414531776.
[14] Yuchung J. Wang and George Y. Wong. Stochastic blockmodels for directed graphs. Journal of the American Statistical Association, 82(397):8–19, 1987.

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SESSION 7
SOCIAL NETWORKFamiliar Strangers: the Collective Regularity in
Human Behaviors
Yan Leng

Dominiquo SantistevanAlex ’Sandy’ Pentland

MIT Media Lab
Cambridge, MA, USA
Email: yleng@mit.edu

Electrical Engineering & Computer Science
Cambridge, MA, USA
Email: niquo@mit.edu

MIT Media Lab
Cambridge, MA, USA
Email: pentland@mit.edu

Abstract—The social phenomenon of familiar strangers was
identified by Stanley Milgram in 1972 with a small-scale experiment. However, there has been limited research focusing on
uncovering the phenomenon at a societal scale and simultaneously
investigating the social relationships between familiar strangers.
With the help of the large-scale mobile phone records, we
empirically show the existence of the relationship in the countryof Andorra. Built upon the temporal and spatial distributions,
we investigate the mechanisms, especially collective temporal
regularity and spatial structure that trigger this phenomenon.
Moreover, we explore the relationship between social distanceson communication network and the number of encounters andshow that larger number of encounters indicates shorter social
distances in social network. The understanding of the physical encounter network could have important implications to understandthe phenomena such as epidemics spreading and information
diffusion.

We all have the experiences of encountering strangers
during regular activities in our daily lives whom we are able torecognize [1], [2]. This is the interesting social phenomenon”Familiar Stranger” of urban environment, first identified by
Stanley Milgram in 1972 by asking travelers to recognize”strangers” they met on a bus platform [1]. In 2013, Sun [3],for the first time, uncovered the encounter mechanisms of three
million public transit users in Singapore to capture the timeresolved, in-vehicle encounter patterns and familiar strangers
using transit smart-card data.
This hidden dynamic social network plays an unnoticeable
but significant role in information diffusion, behavior synchronization and epidemics spreading process. Christakis [4]and Montanari [5] shed light on the diffusion of information,
innovations and behaviors via social contagion driven by social
interactions. Dong used a Markov jump process to capture theco-evolution of friendship and visitation patterns in a student
dorms with monthly surveys and locations tracking throughmobile phones [6]. Besides, a series of studies has focusedon using large-scale or high-resolution data to empiricallyand computationally model the epidemics process. Danon
showed that large-scale interaction data are needed to verify
the assumptions of random transmission models and simple
network structures in order to better understand and prediction
the disease transmission [7]. Stopczynski utilized large-scale
behavioral data for modeling epidemic prediction by physical proximity network [8]. Isella and Salathe tracked highresolution proximity network to understand the transmission
paths of diseases [9], [10].
I order to capture the physical proximity network inurban environment, we use countrywide mobile phone logs
to explore the phenomenon and underlying mechanisms thattrigger familiar strangers at a societal scale covering many
aspects of social lives. This data simultaneously captures twolayers of network: physical proximity and social networks.
To our knowledge, we are the first to identify country-widefamiliar strangers utilizing both mobility and social networks.
We confirm the existence of the phenomenon of familiar
stranger in urban environment. At a macro scale, we foundthat the collective regularities - temporal regularity and spatial
structure - explain the phenomenon of familiar strangers.
We also investigate the relationship between physical cooccurrences and the proximity in social networks and showthat physical co-occurrences indicate shorter social distances
via communication network. The understanding of collective
regularity has implications for the prevention of epidemics andthe facilitation of information spreading.
DATA AND SETTINGS
To capture both the social network and mobility network,
we study the anonymized Call Detail Records of Andorra, aEuropean country, for July, 2016. This dataset includes thecaller, receiver, connected cell tower, start and end time of
the connection. The spatial and social network encoded inCall Detail Records enable us to identify familiar strangers.
We created a communication network where each user isa vertex and an edge exists if there exists direct contact
between two users. In the month of July, we are looking
at a total of 1,264,292 users. After filtering out users withmore than 100 connections that may be hotels or vendors, weonly consider the remaining 1,211,814 users. We identify thephysical encounters by observing if two users called or text
on the same tower within a one-hour window of each other.
In our study, we define familiar strangers as individual pairs
who physical co-locate at one cell tower within one-hour time
window, but there exists no direct links on the communication
network.
S PATIAL AND TEMPORAL PATTERNS OF ENCOUNTERS
To understand the temporal distribution of physical encounters, we extract the time when an encounter happen as we seein Fig. 2. There existed prominent spikes between working
hours (8 am - 11 am and 3 pm - 5 pm) on weekdays, andslight shifts for weekends (10 a.m. - 11 a.m. and 3 p.m. - 6p.m.). Interesting, we see a small peak at 11 p.m. on Saturdaynight, which captures the encounters of Saturday night life.

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Fig. 1.

Temporal distribution of physical encounters

We also analyze the spatial distribution of encounters
and the number of encounters happens for each encounter
pair. More specifically, each tower has different usage that isdependent on the popularity around that tower, but there isalso a distribution over how many encounters happen between
two users at a single tower. As shown in Fig. 2, there exists
some towers that are not quite as popular with encounters, yetthey have some of the highest number of encounters counts
between pairs of users.

strangers on the same hour of day within the next d days. In
addition, we analyze the time of the consecutive encounters.
The right panel of Fig. 3 can be categorized into two typesof encounters - weekday encounters and weekend encounters.
Specifically, people who encounter during weekdays are lesslikely to encounter each other during weekends and vice versa.
Besides, morning encounters are more likely to encounter
again during morning, explaining the 24 hour peak as shown in
the figure in the left panel. Both empirical exercises highlightthe collective temporal regularity in people’s daily routines
partly explains the repeated physical encounters.

Fig. 3. Temporal regularity of collective mobility behaviors. The left panel
shows the probability density function of inter-event time (P∆t between two
consecutive encounters. The right panel shows the encounter time of twoconsecutive co-locations

B. Spatial structure
We also investigate the spatial structure of repeated encounters, specifically the popularity of the two locations, distances
between the two locations and the Points of Interests.

Fig. 2. Map of cell towers, where the size of the node is proportional to total
number of encounters and the color is darker where a pair of users encountermore times on that tower.

C OLLECTIVE REGULARITIESHuman mobility follows a high degree of temporal andspatial regularity [11] and physical encounter do not happen
out of ”coincidence”. We believe that collective regularityof human behaviors triggers the physical co-occurrences. In
particular, we focus on the temporal regularities and spatial
structure of two consecutive encounters to uncover the underlying mechanisms that drive the encounter.
A. Temporal regularity
Human daily routines, such as commuting, follow certain
temporal patterns. To explore the repeated encounters at a
population scale, we created a encounter network based on
mobility behaviors across a week and measure the inter-event
time ∆t between consecutive encounters of each familiar
strange pair.
The left panel of Fig. 3 shows the distribution of inter-event
time between two encounters. We observe noticeable peaks
for every 24 hours and another lower peaks for 24 · d ± 6hour, where d is the dth day after the first encounter. This
indicates that people are very likely to encounter their familiar

1) Popularity of and distances between the consecutive
encounter locations: To gain insights on the encounter andre-encounter pairs, we use the general gravity model to understand the relationships between spatial distances, attractions(popularity) of the two locations. The gravity model utilizedin our study assumes the following functional form, as shownin (1). The fit of the model, and the relationships between
encounter and re-encounter flow and the above-mentioned
variables of interests are shown in Fig. 4.
Tij = CNiα Njβγ
Dij(1)

where Tij is the number of encounter and re-encounter
flow between two geographical location i and j. Dij is the
distance between two geographical areas and Ni and Nj are
the number of encounters of area i and j respectively. After
applying a logarithmic transformation and fit parameters witha linear regression, we found α = 0.38, β = 0.407, γ = 0.823.
FAMILIAR STRANGERS IN SOCIAL NETWORK
There has been research establishing the relationships between mobility behaviors and social ties. Crandall (2010) developed a framework to empirically and mathematically investigate the relationship between social ties and co-occurences[12]. Along the same line, Toole (2015) found that that thecomposition of a user’s ego network in terms of the type ofcontacts they keep is correlated with mobility behavior[13].
Apart from the observed relationship between mobility behavior and the probability of the formation of a tie or the

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T0.38
T0.407ij

Tij = 5.7

8

6

8

6

4

5

6

7

8

predicted OD pairs

9

5

10

8

8

OD pairs

10

OD pairs

have important implications for epidemics preventions andinformation spreading facilitation.

10

Dij0.823

OD pairs

actual OD pair

10

6

6

7

log(distance)

8

9

10

R EFERENCES
6

6

8

[1]

10

origin counts

Our study posits several interesting future studies. Firstof all, one natural future work is to investigate how the
relationship of familiar strangers grows into actual friendship,and how their behaviors intersect and synchronize with eachother. It would be even more interesting to understand thecausal relationships between physical co-occurrences and theformation of social ties. Another interesting direction is to integrate the familiar stranger relationships into the modeling andsimulation of epidemics spreading and information diffusion.

6

8

[2]

10

destination counts

Fig. 4. Gravity law fit of encounter network. (A) Encounter and re-encounter
flow obtained from data on the number of encounters the first and second
locations, as a function of the distance between the encounter and re-encounter
locations. (B-D) Encounter and re-encounter flow as a function of distance,encounter popularity of first and second locations.

strength of a ties, we are specifically interested in the social
distances between ’strangers’ who physically encounter one
another multiple due to similar routines or interests. As shownin Figure 5, there exists a negative relationship between social
distance and the number of encounters - the more time eachfamiliar stranger pair encounter one another, the closer they
are on the social networks.

[3]
[4]
[5]
[6]

[7]

3.0
2.8

[8]
2.9

encounter_times

2.6

[9]

social_dist

2.8

2.4

2.7

[10]
2.2

2.6

2.5

5.0

7.5

encounter_times

10.0

2

4

6

social_dist

8

[11]

Fig. 5. Relationship between number of encounters and social distances insocial networks of familiar stranger pairs[12]

D ISCUSSIONS[13]

In this study, we use the large-scale Call Detail Records ofa European country Andorra to create the physical encounter
network and phone communication network. We show theexistence of the familiar stranger phenomenon in urban environment. By analyzing the temporal and spatial characteristicsof the encounters, we uncover the underlying mechanisms,
especially collective temporal regularity and spatial structurethat trigger the phenomenon. In the end, we explore therelationship between social distances along social networkand number of encounter in mobility network. We show thatlarger number of encounters indicates nearer social distances.
The understanding of the physical encounter network could

S. Milgram, J. E. Sabini, and M. E. Silver, The individual in a socialworld: Essays and experiments . Mcgraw-Hill Book Company, 1992.
E. Paulos and E. Goodman, “The familiar stranger: anxiety, comfort,
and play in public places,” in Proceedings of the SIGCHI conferenceon Human factors in computing systems, pp. 223–230, ACM, 2004.
L. Sun, K. W. Axhausen, D.-H. Lee, and X. Huang, “Understanding
metropolitan patterns of daily encounters,” Proceedings of the National
Academy of Sciences, vol. 110, no. 34, pp. 13774–13779, 2013.
N. A. Christakis and J. H. Fowler, “Social contagion theory: examining
dynamic social networks and human behavior,” Statistics in medicine,vol. 32, no. 4, pp. 556–577, 2013.
A. Montanari and A. Saberi, “The spread of innovations in social
networks,” Proceedings of the National Academy of Sciences, vol. 107,
no. 47, pp. 20196–20201, 2010.
W. Dong, B. Lepri, and A. S. Pentland, “Modeling the co-evolutionof behaviors and social relationships using mobile phone data,” inProceedings of the 10th International Conference on Mobile andUbiquitous Multimedia, pp. 134–143, ACM, 2011.
L. Danon, T. A. House, J. M. Read, and M. J. Keeling, “Social encounter
networks: collective properties and disease transmission,” Journal of theRoyal Society Interface, p. rsif20120357, 2012.
A. Stopczynski, A. S. Pentland, and S. Lehmann, “Physical proximity and spreading in dynamic social networks,” arXiv preprint
arXiv:1509.06530, 2015.
L. Isella, J. Stehlé, A. Barrat, C. Cattuto, J.-F. Pinton, and W. Van den
Broeck, “What’s in a crowd? analysis of face-to-face behavioral networks,” Journal of theoretical biology, vol. 271, no. 1, pp. 166–180,
2011.
M. Salathé, M. Kazandjieva, J. W. Lee, P. Levis, M. W. Feldman, andJ. H. Jones, “A high-resolution human contact network for infectious
disease transmission,” Proceedings of the National Academy of Sciences, vol. 107, no. 51, pp. 22020–22025, 2010.
M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779–
782, 2008.
D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher,and J. Kleinberg, “Inferring social ties from geographic coincidences,”
Proceedings of the National Academy of Sciences, vol. 107, no. 52,
pp. 22436–22441, 2010.
J. L. Toole, C. Herrera-Yaqüe, C. M. Schneider, and M. C. González,“Coupling human mobility and social ties,” Journal of The Royal SocietyInterface, vol. 12, no. 105, p. 20141128, 2015.

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Impact of university admission on student’s
egocentric network
Sami Jouaber∗ , Yannick Leo∗ , Carlos Sarraute† , Eric Fleury∗ and Márton Karsai∗∗ Univ Lyon, ENS de Lyon, Inria, CNRS, UCB Lyon 1, LIP UMR 5668, IXXI, F-69342, Lyon, France† Grandata Labs, 550 15th Street, San Francisco, CA, USA

sami.jouaber@ens-lyon.fr, yannick.leo@ens-lyon.fr, charles@grandata.com,
eric.fleury@inria.fr, marton.karsai@ens-lyon.fr
I NTRODUCTION
Social relations are well-known to bring significant individual and collective benefits, however their maintenance requires efforts, which are limited by certain capacities. Examples are time [1], [2] or space [3] but
constraints were also found on the cognitive level. As argued by the social brain hypothesis [4], there is aquantitative relationship between primate’s brain capacities and the social group size they can effectively handle
and maintain. Due to these reasons, individuals need to develop social strategies to maintain their immediatesocial network. These limitations and strategies not only determine the size of one’s egocentric network but playalso an important role in the maintenance dynamics and prioritizing of social ties. It has been recently shownthat while the overall distribution of efforts that an ego commits to his/her friends is invariant in time [5], theturnover speed of creation and dissolution of social ties disclose individual social strategies on the long run [6].
These observations were possible due to the recent availability of large datasets collecting digital records ofinteractions of millions of individuals using mobile phone devices. Mobile phones became personal items of
our everyday life and they allow to automatically capture temporal data of human interactions on the populationlevel.
In this work we used mobile call communication data recorded in a developing country to study how somechanges in the social environment affect the structure and the dynamics of one’s egocentric networks. Moreprecisely, we study the impact of university admission on the composition and evolution of the egocentric
networks of 1675 freshmen students. The data was obtained via the combination of two anonymized datasets,
one which records a sequence of anonymized call detailed records (CDR) of customers of a single telco operator
over 2 years; and an anonymised bank dataset which was recorded over an inclusive period of 6 months andprovided demographic informations (age, gender, postal code) and certain details about the evolution of thefinancial situation of students. This combined dataset gives us the opportunity to follow the egocentric network
evolution of freshmen students, with observations starting 6 months before their university admission, andcontinuing over the following 18 months.
S OCIAL SIGNATURE : PERSISTENCY AND NETWORK CORRELATIONS
The first part of our study is based on the measurement of social signature, which was introduced recently by
Saramäki and others [5]. Informally, social signature measures the ranked distribution of communication efforts(total number or duration of calls) by an ego toward his/her acquaintances over a period of time. In their original
study, observing 24 students over 18 months, Saramäki et.al. found that although the acquaintances may change
in one’s egocentric network, the social signature remains rather invariant over time but highly specific to anindividual. Using our considerably larger dataset first we confirm these findings by providing three important
insights:• The communication effort of people is not uniformly distributed (see Fig. 1a), as people focus their efforts
on a small number of ties that may relate them to family or close friends. The shape of the social signaturefunction is not well-balanced and reveal an unequal spread of efforts.
• Although acquaintances in the egocentric network of an individual are constantly changing, the overall
shape of the social signature function stays rather invariant over time (as shown for three randomly selectedstudents in Fig. 1a).
• By measuring the Jensen-Shannon Divergence (JSD)between the social signatures of the same ego
observed in consecutive periods, its variance appears to be constant over time (see red bars in Fig. 1b),

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(a)

(b)

(c)

Fig. 1. Measures of social signature. In (a), social signatures of three random selected students over 5 months (15 curves) are shown
together with their average (see inset). (b) JSD δ measured between the social signature of different students (in blue) and between pairsof consecutive 3-month periods of the same student (in red). (c) Cumulative distribution of δ JSD of social signatures measured between
connected pairs (in blue) and not connected pairs (in grey) of students.

and remains relatively smaller than the same measure between the average social signatures of randomly
selected pairs of users (see blue bars in Fig. 1b).
Beyond these results we establish two new findings:• The social signature of egos are more similar to the signature of their friends than to others. Fig. 1c shows
the cumulative distribution of the JSD of social signatures of connected pairs of people (in blue) andunconnected pairs (in grey). It reveals that people who are connected through the communication network
appear with a smaller difference as compared to unconnected others.
• People exhibiting similar social signatures can be clustered in groups, where similarities inside the groupare significantly higher as compared to people from other groups (results not show here).

(a)

(b)

(c)

number of ties
number of ties

activated ties

activated ties

deactivated ties

deactivated ties

STUDENTSRANDOM

1stsemester

2ndsemesterFig. 2. Formation and decay of communication ties. (a) The average of the actual number of ties (in blue), the number of created (ingreen) and dissolved ties (in red) for 1675 students as a function of time (in month) (b) The same plot as (a) for a random sample ofpeople measuring the actual number of ties (in grey), and the number of created (in green) and dissolved ties (in red). (c) The logarithm of
the fraction of two average turnover functions measured for students and a random sample of people as a function of time. If this function
is positive, there are larger turnover in the student egocentric network during two consecutive months as compared to random people.

T EMPORAL FORMATIONS AND DECAYS OF COMMUNICATION TIESIn the second part of our study we address the turnover of social ties in one’s egocentric network, i.e. how
new ties replace old relations as a function of time. To observe precisely evolution of egocentric networks wefollow the methodology of Miritello et.al. [6], which allows us to carry out a continuous temporal analysis of
decays and creations of ties by considering two reference periods and one experiment period.

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SESSION 7
SOCIAL NETWORKOur observations confirm earlier results [6] that although the turnover of acquaintances is relatively high in
one’s egocentric networks, the actual number of active ties is rather constant as a function of time. This has
been observed for a large control group of randomly selected people, who are not students, as shown in Fig. 2b.
On the contrary, we found that students behave somewhat differently as during the year that they enter theuniversity, the number of their active ties is increasing (from 15 to 18) suggesting social benefits and new social
strategies developed during this period (see Fig. 2a). Moreover, the turnover of their egocentric network follows
different patterns as other people. Here we measured the turnover as the fraction of newly created and deletedsocial ties of an ego during a given period. In Fig. 2c we show the logarithm of the ratio of average turnoverfunctions measured for the set of students and in the control group. As we see most of new relations are createdby students just after the entrance of the first and the second academic semester revealing that students choose
different strategies to socialize during these periods to receive collective benefits.
R EFERENCES[1] G. Miritello, E. Moro, R. Lara, R. Martı́nez-López, J. Belchamber, S. G. Roberts, and R. I. Dunbar. Time as a limited resource:
Communication strategy in mobile phone networks. Social Networks, 35(1):89–95, 2013.
[2] S. G. Roberts and R. I. Dunbar. The costs of family and friends: an 18-month longitudinal study of relationship maintenance and decay.
Evolution and Human Behavior, 32(3):186–197, 2011.
[3] G. A. Miller. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychologicalreview, 63(2):81, 1956.
[4] R. Dunbar. The social brain hypothesis. brain, 9(10):178–190, 1998.
[5] J. Saramäki, E. A. Leicht, E. López, S. G. Roberts, F. Reed-Tsochas, and R. I. Dunbar. Persistence of social signatures in humancommunication. Proceedings of the National Academy of Sciences, 111(3):942–947, 2014.
[6] G. Miritello, R. Lara, M. Cebrian, and E. Moro. Limited communication capacity unveils strategies for human interaction. Scientificreports, 3, 2013.

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NetMob 5-7 April 2017 – Vodafone Village – Milan

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More reliable and more accurate traffic models using mobile phone data

Klaas Friso, DAT.Mobility, Deventer, the Netherlands, kfriso@dat.nl
Jasper Keij, Mezuro, Weesp, the Netherlands, jasper.keij@mezuro.com

Abstract
Mobile phone data are a rich source to infer all kinds of mobility related information. Mezuro and DAT.Mobility arecollaborating in developing innovative data-analysis techniques of transforming mobile phone data in information thatcan be used for traffic models. In this paper it is explained how the national origin-destination matrix (OD-matrix) is
derived, based on the Call Detail Records (CDR) from Vodafone (one of the three mobile phone providers in the
Netherlands), facilitating between 30 and 40% of the Dutch mobile phone usage. This OD-matrix is currently used in the
update of the traffic model of the Rotterdam region. No other data source is known that gives travel information at anational scale at this high level. The developments of improving the quality of the OD-information are still going on. In
this paper the next planned steps in our development will be highlighted.

Introduction
The aim of a traffic model is to provide insight into current and future traffic and transport flows as
good as possible. Questions that can be answered with a traffic model are for example: How muchtraffic is driving on the road? At which location and at what time? Where are the locations ofcongestion? What is the effect of a change in the infrastructure or in traffic policy? What measures
are needed to regulate the additional traffic of an event?
An important source for current traffic modelling in the Netherlands are the results of the national
household travel surveys, called OViN (Onderzoek Verplaatsingen in Nederland) [1] conducted byCentral Bureau of Statistics in the Netherlands. By conducting surveys (40,000 trip days annually) the
movement behaviour of the Dutch population is described by place of origin and destination, time oftravelling, means of transport used and the purpose of travelling. The results of this yearly survey
will then be scaled to represent the entire Dutch population. Although this research is the gold
standard in terms of available data sources, the sample size is very small and as a result it allows
hardly spatial detail. On average OViN-data record the traffic movements of slightly more than 100
people on a particular day over the Netherlands. Therefore, it is not possible to make representative
statements for specific regions (for example regions with a low number of inhabitants). Also theavailable information from OViN is not up-to-date (about 2 years old). Notice that information of
trips made by trucks are not included in OViN and information of these trips is hard to gather by
surveying.

Deriving trips from mobile phone data
The availability of this relatively new big data source is potentially an enormous enrichment for
traffic models. Mezuro made it possible to define trips in the Netherlands based on CDR-data (Call
Detail Records) of Vodafone users by developing several algorithms in the past few years. In this way
it is possible to monitor the trip movements of more than 3.5 million persons daily. By using thisdata in traffic modelling the reliability and accuracy will increase because the analyses are not any
longer based on a survey of 40,000 trips a year, but more than 1.25 billion trips per year.

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MOBILITYAt this moment it is not possible to replace all current data sources on which the traffic models are
based by the mobile sensing data source of Mezuro. For example, there is no answer yet about the
purposes of the travellers and all used modes. Furthermore, it is not possible yet to detect shortdistance trips. In order to answer such questions, the data will need to be linked with external
databases.

Mezuro (specialists in processing big data) and DAT.Mobility (specialists in traffic consultancy/data)
are collaborating in developing innovative data-analysis techniques of transforming mobile phonedata in information that can be used for traffic models. A national origin-destination matrix (ODmatrix) for the Netherlands is available on a regular base. Therefore the Netherlands is divided in
1,261 traffic analysis zones where each zone represents a city or a town. The larger cities are split
into separate city districts. The most detailed time granularity of the OD-matrix is an hour. In trafficmodelling commonly the average working day and especially the peak periods are interesting forwhich the total mobility is needed. Because the mobile phone data gives information about 1/3 ofthe total amount of trips a scaling procedure is developed [2]. The scaling procedure depends on thenumber of inhabitants and penetration of mobile devices (per age group and area), the Vodafone
market share per area and the change that a person will make a trip (for each day of the week). It is
important to take into account the information per age group because there is anunderrepresentation of mobile phone usage (in relation to trips made) amongst people younger
than 12 years and older than 60 years.
600%% trips with above distance(26 km is 100%)

OViN

Mezuro

500%
400%
300%
200%
100%
0%
0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Trip distance (km)Figure 1: comparison between OViN and Mezuro-data of the relative number of trips per travel
distance.

When compared to OViN a similar distribution of the number of trips on longer distances is seen, butdiverging trip counts for trips with a travel distance below 10 kilometre (see Figure 1). We find there
are about as many trips in both datasets over 10 kilometre, though below 10 kilometre OViN
appears to record far more trips. Hence, we choose to only take trips over 10 kilometre into accountas we have an underrepresentation below 10 kilometre. This is a bias that is hard to correct forprovided it depends on the physical cell tower infrastructure. In areas with large reaching cell towers
one might never be able to detect short distance trips, whereas in cities with smaller cells one might.
An assumption which explains the underrepresentation of short distance trips is that people are
more tended to leave their device at home, for example during a short walk with the dog or a trip tothe bakery. So at this stage it is just possible to use trip information above 10 kilometre in traffic

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modelling, but this is already very useful. The expectation is that in the near future this limit can belowered to 5 kilometre because of technical improvements. In the meantime we will work on
algorithms for an estimation procedure concerning the number of trips between 5 and 10 kilometre,because our goal is to make the national OD-matrix as complete as possible for all kinds of mobility
applications.
Privacy
The privacy of the raw mobile phone data is assured by a rigorous protocol. Firstly the identifying
information (phone number) is one way hashed. The one way hashing information is changed every
month. In this way the movements of a single device can only be detected for one month. Secondly,
all data on device level is stored in a ‘black box’ at the telecom provider. The data on device level
cannot be seen and only algorithms tested beforehand can transform the raw data into movements.
At last all results are aggregated. The export function to create results allows only 16 or more
devices as output. OD-pairs having less than 16 devices are not within the dataset. In this way it is
impossible to relate information to a single device of an individual.
National OD-matrix
The raw data is processed into basic location information removing data which is subsequently
translated into OD-information based on the time ordered stay sequence. A trip between an origin
and destination is defined when a mobile device is longer than 30 minutes at a certain location. A
deviant location in between will result in an extra destination, which means two separate trips. In
Figure 2 the OD-data of mobile phone data for a single day is presented showing a plausible spatial
distribution. The figure shows that most trips are made in the Randstad area (Amsterdam, The
Hague, Rotterdam and Utrecht).

Figure 2. Spatial distribution of trips from mobile phones (data of one single day)Derivation of trips by train and non-train
For a trip that is defined from the mobile phone data the mode by which this trip is made is notknown directly from the data itself. For traffic modelling and also other applications it is very

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interesting to determine the mode of a trip. At this stage we are able to distinguish between trips
made by train and otherwise (non-train). For trips above 10 kilometres it means that the non-train
trips are mostly made by car or truck. This is about 85%, the other 15% represent trips made by bus,
tram, metro, scooters or cycling [1]. Figure 3 shows the share of trips made by train from the mobile
phone data per origin zone per day together with the railroads and train stations. One of the next
steps in our planned developments is therefore to split the non-train OD-matrix further into car andtruck (and other).

Figure 3: share of trips made by train (per origin zone)

Pilot studies
In 2015 we performed a successful pilot study in the Rotterdam region with enriching their synthetica priori OD-matrix with the OD-matrix of mobile phone data [3]. Results show a better match of theassignment results of this enriched matrix with the counts indicating a better quality of the matrix. It
also has been shown that specific (strong or weak (historic)) OD-relations which are known to bedifficult to model become more plausible by the enriching procedure. In theory it is possible in trafficmodelling to put special attention in known specific OD-relations. However, there exist unknown
OD-relations that will not be modelled in a proper way. By making use of the measured ODinformation from mobile phone data these relations will also improve in the model. In the update of
the model of the metropole region Rotterdam-The Hague, which is currently in progress, the ODmatrix of mobile phone data will be actually applied in the matrix estimation for the base year.

In 2016 a pilot study has been performed with the National Model System of the Ministry ofTransport in the Netherlands. In this pilot also an enrichment of the a priori model was performed.
From both pilots we have learned that the average trip length as is measured in the mobile phonedata is higher than of the synthetic models. Because the number of trips per zone was kept constantin the enrichment procedure this leads automatically to increased flows after assigning the OD-

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matrix on the model network. In the Rotterdam pilot the choice was made to apply a correction
factor on the number of trips per distance class with the result that the original trips length
distribution was obtained again in the resulting OD-matrix. This was not part of the pilot with the
National Model System.

It can be concluded that the enrichment procedure works well if the trip length distribution of thesynthetic a priori model corresponds with the distribution of the mobile phone data, which is basedon real measurements. It is possible, by means of usage of corrections factor to adjust for this in themodel system. But, in fact, it means that the parameters of the a priori model should bereconsidered.

Goals for the near future
As mentioned before Mezuro and DAT.Mobility continue on the developments with regard to
obtaining high quality OD-matrices out of mobile phone data which can be used already for a lot ofapplications with respect to transport analysis and as input for traffic models. At this stage we areable to deliver an OD-matrix at national level in the Netherlands for any given time period that iswanted for trips more than 10 kilometres. We are able to divide this matrix for the trips made bytrain and non-train. Our first goal is to work on algorithms to divide the non-train matrix further onin modes car, truck and other (bus, tram, metro, scooter and bike). Also we want to improve theusability of the data further on to work on methods that will obviate the underrepresentation ofshort distance trips (below 10 kilometre).

References[1] OViN, Onderzoek Verplaatsingen in Nederland, www.cbs.nl/nl-

NL/menu/informatie/deelnemersenquetes/personenhuishoudens/ovin/doel/default.htm[2] J. Meppelink, J. van Langen, A. Siebes, M. Spruit, Know Your Bias: Scaling Mobile Phone Data to
Measure Traffic Intensities, 2016
[3] L.J.J. Wismans, K. Friso, J. Rijsdijk, S.W. de Graaf, J. Keij, Improving a-priori demand estimatestransport models using mobile phone data. Rotterdam region case. Mobile Tartu Conference, June
2016.

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Understanding Drivers of Short Term Mobility

Sveta Milusheva​1​*, Elisabeth zu Erbach-Schoenberg​2,3​*, Linus Bengtsson​2,4​, Erik Wetter​2,5​, and Andy
Tatem​2,3*Correspondence: svetoslava_milusheva@brown.edu, ​elisabeth.zu.erbach@flowminder.org
1 ​Brown University, Providence, Rhode Island US 2​ Flowminder Foundation, Roslagsgatan 17, 113 55
Stockholm, Sweden. 3​​ WorldPop, Geography and Environment, University of Southampton, University Road,
Southampton SO17 1BJ, UK. 4​​ Dept. of Public Health Sciences, Karolinska Institute, Sweden. 5​​ Stockholm
School of Economics, Stockholm, Sweden

I. Introduction
Populations are highly mobile, both in terms of long term movements of individuals relocating their placeof residence as well as shorter term mobility such as commuting, work travel and recreational trips. Shortterm internal population movements have been shown to have an impact on spread of diseases [Oster
2012, Balcan et al 2009, Huang et al 2013, Wesolowski, Metcalf et al 2015, Wesolowski, Qureshi et al2015], and increases in short term mobility will likely increase pollution and congestion and havepotential implications for economic activity. Yet, the drivers of short term mobility have not been studied
due to the lack of available data on fine spatial and temporal scales.
Call detail records (CDRs) provide a valuable data source for measuring population movements. By
providing geo-location and timestamps for calls and text messages, they allow researchers to observechanges in location of individuals within a country and are commonly used to estimate short term
mobility on the scale of whole countries. Long term movements are often measured as part of a census,
available for many, but not all, countries. Previous studies have shown that short term movements
measured using mobile phone data correlate with the long term migration variables available in census
data. [Wesolowski et al 2013, Ruktanonchai et al 2016]. In this paper, we extend previous work in two
ways. First, we include a time component to study how the relationship between long and short term
movements changes over time. Second, we try to understand the drivers of this relationship at theindividual level and the motivation behind an individual’s choice to make a short or long term movebased on previous movement patterns and the location of their social network. Working with call detailrecord data from Namibia and Senegal, we study the link between migration and short term movement
and investigate how well the relationship found in one country translates to another country.

II. Data
For Senegal, we use CDR data provided by Sonatel and Orange in the context of the Data for
Development Challenge in order to measure short term movement. The data consist of call and text data
for Senegal between January 2013 and December 2013 for all of Sonatel’s 9.5 million SIM cards,
representing a large portion of the 14.2 million Senegalese population. In addition to timestamped
locations for each communication, the data contains a hashed identifier for the second party involved in
the call or text. For Namibia, CDR data was provided by MTC with the purpose of estimating internal
mobility and its impact on malaria transmission. The data spans a period of 3.5 years, from October 2010
to May 2014 and includes all subscribers to MTC, corresponding to 72 billion entries for a user base of
4.5 million unique users.
Migration data for Senegal comes from the Agence Nationale de la Statistique et de la Demographie(ANSD) from a 10 percent sample of the 2013 census data. Migration data for Namibia was published by

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the Namibia Statistics Agency [Namibia Statistics Agency 2015] using data collected in the context of the2011 census.

III. Empirical Analysis
We study the relationship between long term migration and short term movements for both countries andthen move on to explore two important drivers of short term movement--long term migration and social
networks. For long term migration, we utilize the data for Namibia, which contains a longer time span of
3.5 years and use the Senegal data in order to explore the relationship between social networks and shortterm movements.
Long Term Migration as a Driver of Short Term Movement
We first examine the aggregate relationship between long term migration and short term movement for a
full year and then incorporate the timing throughout the year into the analysis. Specifically, we can study
how the correlation between short term and long term movement changes from day to day. Preliminary
results for Senegal show positive and negative spikes in the coefficients throughout the year areassociated with important holidays when people travel to visit family and friends. Usually we see anegative coefficient prior to the holiday, as individuals conduct short term moves in the opposite direction
from where they migrated. After the holiday there are large positive coefficients, signaling individuals
going back from their home location to their new place of residence. There is a consistent correlation
between short term movement and long term migration patterns. Locations that have high levels of longterm migration, also have high levels of short term movement.
We also explore the relationship between short and long term moves at an individual level, using the
Namibia CDR data. We can explore the short term movement patterns of those individuals for which weobserve a long term migration move in the CDR data using a fixed effects empirical strategy. This
analysis helps us measure, for a district pair, whether long term migration between the two districts isdriven by short term moves prior to the long term migration (the theory of someone gaining experience
with a location prior to migration) or whether the relationship is the other way round, with short termmoves following long term migration (individuals traveling to visit family and friends). And if both arepresent, this analysis can help us investigate how the magnitude of the effect varies.
Social Networks as a Driver of Short Term Movement
A large part of the relationship between short and long term movement can be explained by social
networks. When individuals migrate, they often maintain social ties to individuals at the location oforigin through calls and texts, and when possible will visit in person. Even for individuals that do not
migrate, the locations visited during short term trips might be locations where they already haveestablished contacts. The Senegalese CDR data is used to create a contact network of the locations where
an individual has contacts that he or she has communicated with via mobile phone. We also have alocational network of the places the individual has physically visited, derived from an individual’s
location trajectory.
To study the relationship between the social network and short term movement, we regress the number of
total short term moves on person contacts between two districts in Senegal. The relationship is verystrong, with an R​2 of over .8. When analyzing the relationship between daily contacts and daily moves,

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there is also a very high correlation and a weekly pattern emerges, with Sundays having the highest
correlation. Conducting an analysis at the individual level as well, we determine the group of social
contacts for each user. We use a fixed effects strategy and a logit model to estimate the effect of having acontact in a district on the probability of visiting that district. We also study the strength of the network
and whether having a higher number of contacts increases the number of visits to that district.

IV. Conclusion
This paper aims to gain a better understanding of the drivers of short term movement. Using unique data
for two different countries, we can exploit the longevity of the Namibia data and the detailed network
structure of the Senegal data to study two important factors affecting short term movement. In addition,
this is one of the first papers to combine mobile phone analyses from two different country contexts
within sub-Saharan Africa. While it is necessary to study the factors of movement separately due to thedifferent strengths of the two datasets, we are able to conduct similar analyses using census data for bothcountry settings. As more and more work is conducted using mobile phone data in particular contexts, itwill become important to examine how much of the analysis and results can be extrapolated to othercountry settings. This paper is a first step in doing this type of analysis, and it allows us to also consider
how the results from the separate empirical analyses on the drivers of movement could translate to theother context and more broadly to other countries within sub-Saharan Africa.

References
Balcan, Duygu et al. (2009). “Multiscale mobility networks and the spatial spreading of infectious
diseases”. In: Proceedings of the National Academy of Sciences 106.51 (2009), pp. 21484–21489.
Huang, Zhuojie, Andrew J Tatem, et al. (2013). “Global malaria connectivity through air travel”. In:
Malar J 12.1 (2013), p. 269.
Namibia Statistics Agency (2015). “Namibia 2011 Census Migration Report”. Namibia Statistics Agency(2015).
Oster, Emily (2012). “Routes of Infection: Exports and HIV Incidence in Sub-Saharan Africa”. In:
Journal of the European Economic Association 10.5 (2012), pp. 1025–1058.
Ruktanonchai, Nick W., et al. « Census-derived migration data as a tool for informing malaria elimination
policy.” Malaria journal 15.1 (2016): 1.
Wesolowski, Amy, Caroline O. Buckee, Deepa K. Pindolia, Nathan Eagle, David L. Smith, Andres J.
Garcia, and Andrew J. Tatem (2013). "The use of census migration data to approximate human movement
patterns across temporal scales." PloS one 8, no. 1 (2013): e52971.
Wesolowski, Amy, CJE Metcalf, et al. (2015). “Quantifying seasonal population fluxes driving rubellatransmission dynamics using mobile phone data”. In: Proceedings of the National Academy of Sciences
112.35 (2015), pp. 11114–11119.
Wesolowski, Amy, Taimur Qureshi, et al. (2015). “Impact of human mobility on the emergence of
dengue epidemics in Pakistan”. In: Proceedings of the National Academy of Sciences 112.38 (2015), pp.
11887–11892.

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Short-Term Traffic Prediction Using Visitor Location
Registry Data
Sabra Ossen1 , Kulakshi Fernando2 , Rakshitha Godahewa3 , Fathima Dilhasha4 ,Amal Shehan Perera5 , Malaka Walpola6
Department of Computer Science and Engineering
University of Moratuwa
Moratuwa, Sri Lanka
Abstract
Congestion due to road traffic is a major issue in urban areas. Currently, there are methods to predict
short-term traffic using data based on Global Positioning System (GPS). This research aims at finding a
cost-effective solution to congestion using Visitor Location Registry (VLR) data gathered through mobile
cell towers. Prediction models were built using mobile network big data and results were validated using
Closed Circuit Television (CCTV) records. Most suitable model for the given road segment is identfied
by comparing multiple traffic prediction models. After extensive research on individual and ensemble
models, we propose the majority-voting ensemble approach of Neural Network, Bayesian Combined
Neural Network, and Kernel Regression method.
Keywords: Visitor Location Registry (VLR) Data; Traffic Prediction; Ensemble Models; Closed Circuit
Television (CCTV) Data;
1. Introduction

Use of vehicles is the main transportation mechanism used worldwide in this century. Vehicles
have made human life easier by reducing the travel
times significantly compared to other transportation methods. However, the excessive usage of vehicles has resulted in many unexpected outcomes,
but mainly traffic congestion.
At present, there are intelligent transportation
systems which predict traffic with the help of roadsensors. These types of methods are very expensive because of higher costs due to equipment,
manpower and maintenance requirements. And
with the increase of size and complexity of urban
traffic networks the cost of road sensors can beseen to increase [5].
Visitor Location Registry (VLR) is a database ina mobile communications network and it has location details of mobile subscribers within the service area of a particular Mobile Switching Centre. A record in a VLR database has 4 attributes.
Namely, International Mobile Subscriber Identity(IMSI) value of the user, Cell Global Identity (CGI)of the cell tower handling the mobile activity, date
and time. A user is uniquely identified using theencrypted IMSI value.
Due to the availability of data, the possibility ofusing VLR data for traffic prediction has been analyzed in the paper by Singha and Kalita [7]. Alternative methods to build intelligent transportation
systems using VLR data are analyzed in this re-search paper. With this motivation, we have proposed a traffic prediction model using VLR data
based on machine learning as a low-cost solution.
2. Background

Traffic is identified as a dynamic, nonlinear process. In the literature, comparison on many parametric and nonparametric methods to modelling
dynamic processes have been made and it can beseen that nonparametric methods perform betterfor dynamic processes [8]. But in general Neural
Networks are known to handle such stochastic processes, similar to traffic, with better accuracy [1].
Literature highlights [2] that considering only asingle link in the road network is not sufficient, andbased on communication between the models an
approach to model the overall network of junctions
should be done. Similarly, it has been identified
that individual neural networks exhibit good performance only on certain time periods. Therefore only
a few of the individual methods are used for network wide prediction while most of the approaches
suggest ensemble methods [9].
3. Methodology and Results

Traffic exhibits regular patterns over time due tothe repetitive nature of human mobility [6]. Thus,
we have introduced algorithms to identify patterns
such as stationary users, pedestrians, and driving
users.
A unique data record can be identified from a

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distinct combination of IMSI, CGI, date and time of the prediction models built below is validated usvalues. Distinct data records were identified using ing CCTV records where three traffic congestion
Algorithm 1.
levels ”high”, ”medium”, ”low” were defined.
In the research conducted by Lee, Kim, Kim andCho [3] comparison on models to predict shortAlgorithm 1 Duplicate Data Removal Algorithm
term traffic has been made. Due to the dynamic
1: Categorize: Categorize data set based on CGI
nature of traffic, they have concluded that neuraland IMSI values.
networks are more accurate when predicting the
2: Sort by Date: For each subset categorized
traffic. Based on the above, most of the models weabove , sort the records based on the date.
have used include neural networks.
3: Sort by Time: For each subset sorted above,
First is the Bayesian Combined Neural Network
sort the records by time. For each record con(BCNN)
which is a combination of two neural netsidered in sorting if the time already exists disworks
using
the Bayesian approach. Zheng andcard that record.
Lee have identified that existing traffic prediction
models predict well only in a particular time peIf a user is stationary, he will take numbers of
riod and they have proposed the above model [10].
calls over a long time window within a day. We
Based on the above we used the Back Propagation
used this concept to identify the stationary users in
Neural Network and Radial Basis Function Neural
Algorithm 2.
Networks. We have considered two roads of theroundabout in the junction in Sri Lanka as two segAlgorithm 2 Stationary User Removal Algorithm
ments that form the complete path of analysis.
1: Get users with high frequency: Identify the
The user count for the current time interval ofsubset of records from the dataset containing the current road segment is calculated, based on
IMSI values whose frequency is greater than previous and current time intervals of the previous
five.
road segment. For the testing data set the BCNN
2: Get stationary users: For each record in the predicted values are calculated and then validated
subset, if the time gap between the first and from a back propagation neural network using thelast call is equal or greater than two hours, then normalized user count and the peak or non-peakthat record is related to a stationary user.
quality of the time interval.
3: Remove stationary users: Identify and disNext, we used two multi-layer feed forward neucard the IMSI values of stationary user records. ral networks with five hidden layers each. We divided the total time segment for ten minute time peIn Algorithm 3 as the mechanism of filtering riods. For each of these periods, we found the norPedestrians, we used the number of unique CGI malized user count of that time period for a given
values that a particular person has gone through. road. An additional field to identify whether the time
It can be considered that the number of repetitions period is peak or non-peak was also used.
For the first neural network, the peak/non-peakof CGIs is high in a slow movement.
bit for the current time period and normalized user
count of the selected road was given as input. The
Algorithm 3 Pedestrian Removal Algorithm
user count for the next ten minutes of the road
1: Get users with high frequency: Identify the segment was considered as the output. Then, forsubset of records from dataset containing IMSI the second neural network, we input the peak/nonvalues whose frequency is greater than four.
peak bit for the current time period and the output
2: Calculate CGI’s traversed per user: Calcu- we got from the first neural network (user count forlate the unique number of CGIs for each user the next ten minutes). Finally, the second neural(each IMSI value) of the subset.
network produced the traffic level for the next ten
3: Get walking users: For each record in the minutes for the selected road segment as the outsubset, if the time gap between the first and put.
last call of a particular user is greater than orMoving away from Neural Networks we next anequal to two hours and during this time period alyze the usage of regression models to forecast
he has passed only less than three CGIs, then traffic [8]. Since nonparametric models do not asclassify those users as walking users.
sume an underlying distribution for data we test our
4: Remove walking users: Identify and discard
data set with different nonparametric methods such
the IMSI values of walking users.
as K-nearest neighbor regression, logistic regression, support vector machine regression and kerWith these filtering mechanisms, we were able to nel regression.
identify the users who were related to traffic. Each
Kernel regression tries to fit the local data

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points into a distribution by using a kernel basis function for a specific kernel bandwidth (aselected window of data points). The kernel
bandwidth can be a fixed bandwidth, an adaptive
nearest-neighbor based bandwidth or a generalized nearest-neighbor based bandwidth which is
specified using the method of Racine and Li [4].
Picking the simplest kernel which gives the best
results decides the accuracy and applicability ofkernel regression. Kernel regression method gave
better accuracy when an Epanechnikov kernel,
with bandwidth Type fixed and Kernel order of four,
is used.
Finally, we used the majority-voting ensemble
approach and combined the three prediction models described above.
CCTV data was used to validate the proposedmodels. For all days considered, we manually observed the amount of vehicles and assigned a traffic level (low, medium or high) for each road segment for each of the ten minute time periods. Inmodel validation, we checked the predicted trafficlevel from a particular model against the actual traffic level which we identified through CCTV data.
We measured the accuracy of the predictions ofeach model as follows.
iedRecords×100
Accuracy% = N o:of CorrectlyClassif
T otalN umberof Records

Table 1 summarizes the accuracy levels for eachof the prediction models described above.

Model

Accuracy Pct

NN Combination with
User Count Prediction
Bayesian Combined
Neural Network

80%

Kernel Regression
Ensemble Model(Majority Voting
Approach)

78%

73%

85%

Table 1: Results of Different Prediction Models for Gnartha
Pradeepa Mawatha SouthEast Road

4. Conclusions

ter applying custom filtering mechanisms. Basedon the accuracy given by each prediction model
we concluded that the majority voting ensemble
approach of Bayesian Combined Neural Network,
Neural Networks, and Kernel Regression is themost suitable approach. In the future, lightweight
prediction models should be built to address theneed of low computing power when predicting thetraffic.
References[1] K. Kumar, M. Parida, and V. Katiyar. Shortterm traffic flow prediction for a non urban highway using artificial neural network.
Procedia-Social and Behavioral Sciences,104:755–764, 2013.
[2] C. Ledoux. An urban traffic flow model integrating neural networks.
Transportation
Research Part C: Emerging Technologies,
5(5):287–300, 1997.
[3] S. Lee, D. Kim, J. Kim, and B. Cho. Comparison of models for predicting short-term travel
speeds. In 5th World Congress on Intelligent
Transp. Systems (CD-ROM), 1998.
[4] Q. Li and J. Racine. Predictor relevance andextramarital affairs. Journal of Applied Econometrics, 19(4):533–535, 2004.
[5] L. E. Y. Mimbela and L. A. Klein. Summaryof vehicle detection and surveillance technologies used in intelligent transportation systems. 2000.
[6] K. Puntumapon and W. Pattara-Atikom. Classification of cellular phone mobility using naive
bayes model. In Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, pages
3021–3025. IEEE, 2008.
[7] M. R. Singha and B. Kalita. Using mobile
phone network for urban traffic management.
International Journal of Computer Applications, 65(2), 2013.
[8] B. L. Smith, B. M. Williams, and R. K. Oswald.
Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 10(4):303–321, 2002.

Increasing road traffic is one of the major prob[9] H. van Lint, A. Valkenberg, and A. van Binslems in the current world. Short-term traffic prebergen. Advanced traffic monitoring (atmo)
diction can be considered as a necessary solution
for sustainable traffic management. In Tranfor the above congestion problem. Since existing
sitions Towards Sustainable Mobility, pages
methods of traffic prediction are very expensive, in
267–295. Springer, 2011.
this paper we investigated the approach of using
VLR data as a cost-effective solution. With the raw [10] W. Zheng, D.-H. Lee, and Q. Shi. Short-term
VLR data obtained from telecom service providers,
freeway traffic flow prediction: Bayesian comwe were able to build the prediction models afbined neural network approach. Journal oftransportation engineering, 132(2):114–121,
2006.

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Understanding Multiday Activity Patterns Based on
Mobile Internet Usage Behaviour
Yihong Wang, Gonçalo Correia,
Bart van Arem
Department of Transport &Planning, Delft University ofTechnology{Y.Wang-14, G.Correia,
B.vanArem}@tudelft.nl

H.J.P. (Harry) Timmermans

Section of Urban Systems & Realestate, Department of the Built
Environment, Eindhoven Universityof Technology

erik.deromph@tno.nl

h.j.p.timmermans@tue.nl

I. INTRODUCTION
In traditional travel demand models, socio-demographic andsocio-economic information is used to segment population and thus
better explain the heterogeneity of travel behavior. However, suchinformation is generally absent in anonymous mobile phone data due
to privacy reasons. This gap should be bridged if we aim to replace
traditional survey data with mobile phone data for travel demandmodelling.
A possibly effective way is to infer one’s personal attributes fromcalling behavior [1]. This approach has two drawbacks:
Today, mobile phones are hardly used for calls, making it
more irrelevant to study calling behavior, and people spend
more time on services provided by mobile internet such as
mobile apps [2]. Thus, mobile internet usage behavior seems
to have a better potential to reflect individuals’ traits [3].

Supplementary surveys need to be conducted to collect groundtruth data (i.e., the personal attributes of a small subset ofmobile phone users) for training the inference model.
However, such surveys are difficult to implement.

Therefore, the question we want to answer in this research is: is it
possible to use persons’ mobile internet usage behavior to better
understand their travel behavior without knowing or estimating theirpersonal attributes? Thanks to the data provided by the Shanghai
Unicom WO+ Open Data Application Contest1, we are allowed, in
this study, to explore the possibility of finding latent factors behindmobile internet usage behavior and use them to explain heterogeneous
travel behavior. Specifically, in this research, we are concerned with
explaining persons’ activity patterns across several days.
A by-product of this approach is the opportunity to find therelationship between travel and telecommunication usage in themobile internet era. This issue has been extensively debated between
two sometimes opposing views: there are those who think that this is a
substitute relationship and those who think that it is a complementary
relationship [4], [5]. However, the context of these debates is mostly
outdated, and few studies have empirically examined this relationshipconsidering mobile internet services as an emerging usage of phones.
In summary, the significance of this study is two-fold: (1) it
proposes a possibility of overcoming an existing limitation of using
mobile phone data to model travel demand; and (2) it addresses thetimely question about the relationship between travel andtelecommunication usage by providing empirical evidence.

1

Erik de Romph

Sustainable Urban Mobility &Safety, TNO

https://www.kesci.com/woplus/ (retrieved date: January 5th, 2017)

II. METHODOLOGY
A. Analysis of mobile internet usage behaviour
In this study, we consider an individual’s mobile internet usage
behavior in terms of the frequencies of using different types of mobileinternet services (e.g., news and e-shopping) through mobile apps or
websites over a period of several days. This information is provided in
two main indicators: (1) the frequency of using all mobile internet
services, which reflects an individual’s usage intensity; and (2) the
frequency proportion of using each type of mobile internet service,reflecting the different lifestyles and interests. The latter one contains
a number of dimensions, corresponding to the types of mobile internet
services, some of which are likely to be highly correlated with eachother. Therefore, an exploratory factor analysis is applied to find thelatent factors underlying those dimensions. Both the intensity and thelatent factors underlying the frequency proportions are regarded as theproxy variables for one’s personal characteristics. This assumption is
supported by evidence obtained in existing research [3], [6].

B. Analysis of multiday activity programs
We use the term “activity program” to refer to daily choices ofseveral activity types, their frequency, sequence, start time andduration [7]. Existing studies show that one’s activity programs arestrongly dependent on one’s personal attributes [8], [9]. In our study,
we especially focus on activity types and durations. For eachindividual, we calculate the share of time used in each type of activity
in each day. Using the longitudinal mobile phone data, we can identify
the time use patterns of all sampled users over several days, which canthen be clustered to distinguish the heterogeneity of the users.
There are mainly three challenges in this step of the methodology.
Firstly, stay points need to be distinguished from pass-by points
among all mobile phone traces. Extensive literature has discussed thisproblem as well as its case-by-case solutions, and we mainly follow
the approach of Alexander et al. [10], [11]. Secondly, missing traces
would cause a deviation on the revealed activity programs; hence, inthis study, we only sample the users leaving rather complete traces
over the study period to prevent such errors. Thirdly, it is difficult to
identify activity types in mobile phone data. One possible solution is
to infer the specific types based on the ground truth (e.g., thecharacteristics of a certain activity type revealed in survey data [12]).
Another solution is to only distinguish between different activity types
without knowing which specific types they are [8], [13]. In our case,by applying the arbitrary thresholds (i.e., the least duration and thelowest frequencies of visiting a place over a certain period) to thespatial-temporal traces, we detect one’s home and workplacelocations, and we assume that the traces of staying at the detected
home locations should be classified into the home activity. The same
applies to classifying the work activity, and the remaining traces arelabeled as the non-work activities.

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C. Classifying the patterns of multiday activity programs
The first step of the methodology aims at calculating for eachindividual the mobile internet usage intensity as well as the latent
factors reflecting the different lifestyles and interests based on themobile internet usage behavior. The second step aims at labelling eachindividual’s multiday activity pattern. In the final step, a multinomial
logit model is estimated to explain the influence of the latent factors
and the usage intensity on different multiday activity patterns.

III. DATA
Having removed the noise in the data, we have a sample of373,577 mobile users. Each anonymous user ID corresponds to not
only a list of the page view counts for different types of mobile apps
and websites, but also a list of spatial-temporal records within the cityof Shanghai hour by hour from 27th of December, 2015 to 6th ofJanuary, 2016, in terms of longitude, latitude and timestamp.
According to the mobile provider, the coordinates of the records are
estimated with an accuracy of about 300 meters.
The data preprocessing steps are explained as follows. Firstly, we
merged the page view counts of mobile apps and websites for thesame type, thus producing a total of 14 types of mobile internet
services: finance (excluding stock), stock, e-shopping, news, housing,tourism, sports, car, social network, entertainment, online education,
job seeking, game and food. Secondly, we calculated the total pageview counts for all mobile internet services for each user. For each of
those who had ever used any mobile internet service, we constructeda vector containing the page view counts for the 14 types of mobileinternet services. To calculate a fair indicator of the frequency
proportion, the counts of each dimension were normalized over allusers, and then the counts of each user were normalized over all
dimensions. Thirdly, we extracted the stay points and labeled each ofthem with the activity type.

IV. RESULTS
Based on the eigenvalue criterion, there are 4 latent factors that
explain the mobile internet usage. The results of factor analysis using
a varimax rotation are shown in Table 1. Factor loads > 0.30 are
tagged with asterisks. Factor 1 seems to reflect the lifestyle of men
with things such as stock and sports. Most of them prefer not to spend
time on e-shopping and games. Factor 2 can best describe thelifestyle of young professionals who like travelling, social
networking and online learning. Also, it appears to show that theycare about finance and probably manage their money using online
platforms. Factor 3 seems to represent the taste of more senior people
who like car-related hobbies and reading news. Factor 4 is probably
representing the relatively young feminine taste since the loads on eshopping, online education and game are high, while the loads on
finance and stock are negative.
We divided the mobile users into two groups: the detected
commuters and the detected non-commuters, and we clustered theirmultiday activity patterns using the k-means clustering algorithm.
The number of clusters can be determined by using the DB-index,
which can indicate the compactness of a clustering solution [14]. The
indices for both groups are shown in Fig. 1. Considering the trade-off
between the DB-index and the interpretability (not too few and not
too many clusters), we decided to have 4 clusters within each group.
The average patterns of the clusters are presented in Fig. 2 and Fig. 3.
Before interpreting the results, one should note that the first day
during the study period is Sunday, and the sixth, seventh and eighthdays are the public holiday for celebrating the western new year.
Within the commuter group, people belonging to cluster 1 and cluster
3 seem to be those who seldom performed out-of-home activities
except going to workplaces during the weekdays. The only difference
between the two clusters is that the people from cluster 3 tend to have
longer working times. People from cluster 2 were mostly active urban

travelers who performed non-work activities from time to time. It is
worth pointing out that the figure shows the average patterns of acluster of people. Thus it does not necessarily mean that they
performed non-work activities every day. The same applies to cluster
4: the people in this cluster did not necessarily work on every day of
the weekend and the holiday. However, one who worked on any nonworking day is more likely to be assigned to this cluster. Within the
non-commuter group, people belonging to cluster 1 were different
from the rest because they stayed at home for most of the time.
People from cluster 2 generally spent even more time on out-of-homeactivities than the ones from cluster 3. Cluster 4 represents the noncommuters who usually stayed at home but hung out on the weekend
and/or the holiday.
TABLE I.

FACTOR LOADS

Factor 1

Factor 2

Factor 3

Factor 4

Finance (excl. stock)

0.58*

0.45*

0.01

-0.16

Stock

0.77*

0.01

-0.03

-0.18

E-shopping

-0.09

0.14

-0.07

0.40*

News

0.00

0.08

0.51*

-0.06

Housing

0.09

0.15

0.27

0.19

Tourism

0.02

0.41*

0.13

0.10

Sports

0.30*

0.01

0.14

0.10

Car

0.09

0.15

0.35*

0.05

Social network

0.04

0.48*

0.13

0.09

Entertainment

0.08

0.19

0.27

0.19

Online education

0.06

0.34*

0.25

0.40*

Job seeking

0.03

0.11

0.06

0.09

Game

-0.10

0.05

0.18

0.22

Food

0.01

0.10

0.03

0.05

Fig. 1. DB indices for commuter group and non-commuter group

Fig. 2. Average activity patterns of 4 clusters for the commuters

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V. CONCLUSION

Fig. 3. Average activity patterns of 4 clusters for the non-commuters

For each group of users who had used any of the mobile internet
service at least once during the study period, we estimated amultinomial logit model to classify their multiday activity patterns.
The results are presented in Table 2. Firstly, it can be observed thatthe usage intensity is significantly related to the multiday activity
patterns. People who used mobile internet services more intensely
seem to be more engaged in non-work activities, and the noncommuters who preferred to always stay at home had remarkably lessmobile internet usage. Secondly, the latent factors reflecting different
lifestyles and interests are significantly related to some patterns. A
higher value of factor 1 is more associated with the less interest in
non-work activities. Comparing cluster 1 to cluster 3 within thecommuter group, we find that people who worked longer are more
likely to be younger, given their relatively higher values of factor 2
and factor 4. It is also interesting to see that the ones who likehanging out on non-working days are more likely to be characterized
by a higher value of factor 4 representing relatively more young
feminine taste. These results are mostly consistent with the priorknowledge about the relationship between one’s activity patterns andthe lifestyles represented by the corresponding factors.
TABLE II.

RESULTS OF MULTINOMIAL LOGIT MODEL
Cluster 3

Cluster 4

Param.

t-valueParam.

t-valueParam.

t-value

Intercept-1.231-18.9*-0.811-14.0*-1.067-17.3*

Intensity

0.109

4.7*

0.093

4.5*

0.083

3.8*

Factor 1-0.104

-5.4*

0.006

0.36

-0.205-10.4*

Factor 2-0.144

-6.0*

0.149

7.3*

-0.338-13.8*

Factor 3

0.122

5.0*

-0.087

-4.0*

0.071

2.94*

Factor 4

0.073

2.6*

0.194

7.9*

-0.028-1.05

Non-commuter group
Cluster 2

Cluster 3

ACKNOWLEDGMENT
We would like to express our gratitude to the Shanghai Unicom
WO+ Open Data Application Contest for making the data available
for this research. Thanks go also to the TRAIL research school and theDutch Organization for Scientific Research (NWO) for sponsoring thefirst author for his PhD study.

REFERENCES
[1]

[2]

[3]

[4]

Commuter group
Cluster 2

Our approach was able to capture the latent factors behind themobile internet usage behavior and cluster multiday activity patternsof users based on their spatial-temporal mobile phone traces.
Moreover, we used a multinomial logit model to find the association
between mobile internet usage behavior and multiday activity
patterns. Two results stand out. Firstly, a complementary relationshipwas generally found between travel and mobile internet usage in ourcase because the usage intensity was observed to be significantly
related to the preference for out-of-home activities. Secondly, thelatent factors reflecting different lifestyles can explain someheterogeneity of activity patterns.

[5]
[6]

[7]
[8]
[9]

Cluster 4

Param.

t-valueParam.

t-valueParam.

t-value

Intercept-3.540-43.9*-1.676-38.3*-3.499-39.1*

Intensity

0.370

13.1*

0.201

12.8*

0.298

9.4*

Factor 1-0.167-6.0*-0.031-2.2*-0.064-2.1*

Factor 2-0.346-10.2*-0.106-5.8*-0.023-0.7

Factor 3

0.291

9.3*

0.191

10.8*

0.042

1.2

Factor 4

0.006

0.17

-0.015

-0.76

0.549

13.7*

[10]
[11]

[12]
[13]
[14]

A. Arai, A. Witayangkurn, H. Kanasugi, T. Horanont, X. Shao, and R.
Shibasaki, “Understanding User Attributes from Calling Behavior,” inProceedings of the 12th International Conference on Advances in
Mobile Computing and Multimedia - MoMM ’14, 2014, pp. 95–104.
S. Richmond, “Smartphones hardly used for calls,” 2012. [Online].
Available: http://www.telegraph.co.uk/technology/mobilephones/9365085/Smartphones-hardly-used-for-calls.html. [Accessed:
03-Jan-2017].
S. Seneviratne, A. Seneviratne, P. Mohapatra, and A. Mahanti,“Predicting user traits from a snapshot of apps installed on asmartphone,” ACM SIGMOBILE Mob. Comput. Commun. Rev., vol. 18,
no. 2, pp. 1–8, Jun. 2014.
P. L. Mokhtarian, “Telecommunications and Travel. The Case forComplementarity,” Journal of Industry Ecology, vol. 6, no. 2. Wiley
Online Library, pp. 43–57, 2003.
I. Salomon, “Technological change and social forecasting: the case of
telecommuting as a travel substitute,” Transp. Res. Part C Emerg.
Technol., vol. 6, no. 1, pp. 17–45, 1998.
G. Chittaranjan, J. Blom, and D. Gatica-Perez, “Who’s Who with BigFive: Analyzing and Classifying Personality Traits with Smartphones,”
in 2011 15th Annual International Symposium on Wearable Computers,
2011, pp. 29–36.
T. A. Arentze and H. J. . Timmermans, “A learning-basedtransportation oriented simulation system,” Transp. Res. Part B
Methodol., vol. 38, no. 7, pp. 613–633, 2004.
G. Goulet Langlois, H. N. Koutsopoulos, and J. Zhao, “Inferring
patterns in the multi-week activity sequences of public transport users,”
Transp. Res. Part C Emerg. Technol., vol. 64, pp. 1–16, 2016.
K. Müller and K. Axhausen, “Using Survey Calibration and Statistical
Matching to Reweight and Distribute Activity Schedules,” Transp. Res.
Rec. J. Transp. Res. Board, vol. 2429, pp. 157–167, Dec. 2014.
L. Alexander, S. Jiang, M. Murga, and M. C. González, “Origin –destination trips by purpose and time of day inferred from mobile
phone data,” Transp. Res. Part C, vol. 58, pp. 240–250, 2015.
Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, “Mining interesting
locations and travel sequences from GPS trajectories,” Proceedings ofthe 18th international conference on World wide web - WWW ’09, no.
49. ACM, p. 791, 2009.
F. Liu, D. Janssens, G. Wets, and M. Cools, “Annotating mobile phone
location data with activity purposes using machine learningalgorithms,” Expert Syst. Appl., vol. 40, no. 8, pp. 3299–3311, 2013.
A. Pozdnukhov, “Demand Forecasting and Activity-based Mobility
Modeling from Cell Phone Data,” UCCONNECT Final Reports, 2016.
D. L. Davies and D. W. Bouldin, “A Cluster Separation Measure,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, no. 2, pp. 224–
227, Apr. 1979.

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The Effect of Pokémon Go on The Pulse of the City
Eduardo Graells-Garrido*, Leo Ferres, and Loreto Bravo
Data Science Institute, Faculty of Engineering, Universidad delDesarrollo; Telefónica R&D, Santiago, Chile.

1 Introduction
Pokémon Go is a location-based augmented reality
game that became an instant world-wide hit when it
launched on August 3rd, 2016. At one point, peopleof all ages seemed to be caught in a frenzy of walking out of their way to find the next pocket monster.
Due to the game’s emphasis on the exploration of thephysical world around the player, the general perception was that the game could go as far as making
whole populations change their mobility patterns.
This would imply a breakthrough, since changing
mobility behavior at this scale is notoriously difficult
to do, with well-known exceptions like catastrophes,
some sports events, and the such. In this work wetest the hypothesis whether the game had indeed asignificant impact on the pulse of a city.
Our analysis relies on a set of mobile communications records (CDRs) from Telefónica Movistar, thelargest telecommunications company in Chile, witha market share of 33% as of 2016. CDRs originally
include logs of phone calls and SMS, while ours also
include data-type network events (e. g., Web browsing, application usage, etc.), aggregated by contextdependent amount of downloaded information [1].
Using these data we follow a natural experiment approach whereby we evaluate floating populations at
two specific intervals of time: the seven days before
and the seven days after the launch of Pokémon Go.
We geographically circumscribe our data to Santiago,
the capital of Chile and its most populated city. We
select a specific number of devices to ensure that weanalyze floating population patterns of active usersthat live in the city.
Our results show that Pokémon Go increased thenumber of people on the streets, after accounting forcovariates like land use [4], availability of points ofinterest and daily patterns. However, this increment* egraells@udd.cl.

Figure 1: Telefónica cell tower stations and Pokémonpoints of interests in Santiago. Backgroundtile data was provided by ©OpenStreetMap
contributors and ©CartoDB.
is not present during the entire day. There are twostrong effects: one at lunch time and other at night.
We discuss the relationship between mobility andthese findings, as well as the implications for the city.

2 Datasets
We study an anonymized CDR dataset of Internet
network events from Telefónica Chile. The dataset
contains records from the seven days prior to thelaunch of Pokémon Go (from July 27th to August2rd) and the seven days after (from August 4th to

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Figure 2: Amount of connected devices to the network for all days in the dataset. Note that the distributions
are normalized by dividing the actual number of connections by the global maximum value. Thisis done to avoid publishing sensitive business information and for anonymization purposes.
August 10th). Since we focus on Santiago, we only
analyze the records from the 1,464 cell towers within
the urban area (see Figure 1). An assumption is that
network events are triggered when devices are on thestreet. For instance, when comparing two differentdays at a specific time, an increase in the number ofconnected devices would mean that there are more
people on the street. We do not analyze the records
from the entire customer population in Santiago. We
apply the following filtering procedure: first, we filter out those records that do not fall within the limitsof the ODS explained above and also those with atimestamp outside the range between 6:00am and11:59pm. In addition, to be considered, mobile devices must have been active every day under study.
This is needed because a device that does not show
regular events may belong to a tourist or someone
who is not from the city. Finally, only devices that
downloaded more than 2.5 MiB and less than 500
MiB per day are included, as that indicates either
inactivity or an unusual activity for a human (i. e.,
the device could be running an automated process).
After these filters, the dataset comprises records from142,988 devices. In other words, every measure istaken to ensure that events are triggered by humans.
Additionally, we use two external datasets: a travel
survey from Santiago, held in the year 2012, and acrowdsourced list of Pokéstops, i. e., points of interests
where players could check-in and obtains items. As
Figure 1 shows, these POIs are scattered around thecity, having even more coverage than cell towers.

ing three categories of days: before, during, and after
the launch of Pokémon Go. One can see that, although the curves tend to have similar shapes, after
the launch of the game the number of connected devices is often greater. The patterns are stable acrossmost days, with the means for connections generally higher when Pokémon Go was available. Thismeans, intuitively, that there were more people connected to the network, presumably playing the game.
Two rather surprising effects are found in Mondays
between 10am and 12pm when Pokémon Go wasavailable, and Tuesdays at about 12pm when it was
not yet available. In the first case, we hypothesizethat since it was the first Monday after the launch of
the game, people were trying it out. In the second
case, there does not seem to be any explanation forthe sudden drop of connections before the launch of
the game. This might be due to general network outages. The curve for when Pokémon Go was available
in the same time period behaves as expected.
Then, we aggregate the device counts from eachsnapshot at the traffic analysis zone level, as definedin the travel survey. We do so by assigning tower
events to the zones that contain the respective towers.
These aggregated counts define a set of observations
that we evaluate in a regression model, taking into
account covariates that allows us to isolate and quantify the Pokémon Go effect. Thus, for every minutewithin 6am and midnight, we perform a Negative
Binomial Regression using the following model:

3 Approach

log E[X(t)] = log a + β0 + β1 PoGo + β2 DayOfWeek+ β3 LandUse + β4 PokéPoints,

Our first step is to smooth the number of connected
devices at each cell tower, according to several 1minute snapshots of the tower network. A snapshot is the status of the cell phone network in agiven time-window [6]. Figure 2 shows the citylevel aggregated number of connected devices, hav-

where E[X(t)] is the expected value of the numberof active devices within a zone at time t. The PoGo
factor represents the availability of the game; DayOfWeek and LandUse account for the fluctuations in
population on different days according to land use;
PokéPoints represents the number of Pokémon points

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Figure 3: Trip start time from the Santiago travel
survey. Each rectangle shows when the
Pokémon Go factor is significant.
of interest within a given zone; and we specify thesurface area a of each zone as exposure in the model.
The NB regression allows the following interpretation: the β coefficient assigned to a factor represents
the difference of the logarithm of expected counts ina zone at time t, if all other factors were held equal.
The exponential of this coefficient is defined as Incidence Rate Ratio, IRRβ (t) = eβ(t) . Then, we build
a time-series of IRRβ (t) values for each factor. By
inspecting these time-series we determine when, in
terms of time-windows within a day, there is a significant effect of each factor.

their homes after having dinner.
These results show the potential of location-based
augmented reality games to improve city life. For instance, the presence of pedestrians on the street at all
times of the day is one of the four conditions needed
for lively and safe cities, as proposed by the urbanist
Jane Jacobs [5]. However, it is not clear how to motivate people to walk on the streets – just because there
is a new park it does not mean that people will use it,
specially on cities with biased perceptions of safety
within the public space [2]. Pokémon Go showed that
it is possible to motivate people to go out.
Even though the usage of mobile datasets to study
urban theories is not new [3], such analysis is expost, and thus, it does not help understand causality. A more granular approach would be to perform natural experiments like ours. To the extent
of our knowledge, this is the first natural experiment
performed using mobile records of data-type. Our
method makes possible to measure the effect not onlyof long-term interventions, but also short-term ones,
opening a path to quantify how much specific actions
help to improve quality of life in the city.

References[1] Francesco Calabrese, Laura Ferrari, and Vincent
D Blondel. “Urban sensing using mobile phone
network data: a survey of research”. In: ACM
Figure 3 shows the time-windows within a day where
Computing Surveys (CSUR) 47.2 (2015), p. 25.
the Pokémon Go factor is significant. There are two
windows with prominent lengths: from 11:58 to 12:46 [2] Lucia Dammert and Mary Fran T Malone. “Fearof crime or fear of life? Public insecurities in(with max IRR = 1.138), and from 21:24 to 22:12
Chile”. In: Bulletin of Latin American Research 22.1
(with max IRR = 1.096). The other time-windows(2003),
pp. 79–101.
exhibit shorter durations as well as smaller IRRs. Thismeans that, having all other factors held equal, the [3] Marco De Nadai, Jacopo Staiano, Roberto
availability of the game increased the amount of peoLarcher, Nicu Sebe, Daniele Quercia, and Bruno
ple connected to mobile towers in the city by 13.8% at
Lepri. “The death and life of great Italian cities: alunch time and 9.6% at night. The figure also displays
mobile phone data perspective”. In: Proceedings
the trip start time distribution from the travel survey.
of the 25th International Conference on World Wide
One can see that before 6pm, which marks the end of
Web. 2016, pp. 413–423.
laboral hours, the Pokémon Go effect tends to occur [4] Eduardo Graells-Garrido, Oscar Peredo, andat moments where people starts to or is commuting,José Garcı́a. “Sensing urban patterns with aneither because they are going to work or because theytenna mappings: the case of Santiago, Chile”. In:
are taking a break. Instead of modifying their rouSensors 16.7 (2016), p. 1098.
tines to play, people took advantage of commuting
time and breaks during the day to play. As such, play- [5] Jane Jacobs. The death and life of great American
cities. Vintage, 1961.
ers tend to be nearby their work/study places, which
are concentrated on downtown. Conversely, during [6] Diala Naboulsi, Razvan Stanica, and Marco
Fiore. “Classifying call profiles in large-scale moweekends, where daily routines are not as strong andbile traffic datasets”. In: IEEE INFOCOM 2014there is more flexibility, the relation with commuting
IEEE Conference on Computer Communications.
is not present: the saturday night effect shown inIEEE. 2014, pp. 1806–1814.
Figure 2 is not present on the trip distribution. Thisimplies that people played the game in places near

4 Results and Discussion

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Traffic Analysis of Railways using Call Detail Records
Hiroki Ishizuka

KDDI Research, Inc.
2-1-15 Ohara, Fujimino
Saitama 356-8502, Japanhk-ishizuka@kddiresearch.jp

Nao Kobayashi

KDDI Research, Inc.
2-1-15 Ohara, Fujimino
Saitama 356-8502, Japan

no-kobayashi@kddiresearch.jp

Chihiro Ono

KDDI Research, Inc.
2-1-15 Ohara, Fujimino
Saitama 356-8502, Japan

ci-ono@kddi-research.jp

Mori Kurokawa

KDDI Research, Inc.
2-1-15 Ohara, Fujimino
Saitama 356-8502, Japan

mo-kurokawa@kddiresearch.jp

Takahiro Hara

Osaka University
1-5 Yamadaoka, Suita Osaka
565-0871, Japan

hara@ist.osaka-u.ac.jp

ABSTRACT

General Terms

Recently, increasing car traﬃc congestion especially at commuting time has degraded the eﬃciency of a transportation
infrastructure. To overcome this situation, railway services
that can carry a massive amount of people at once have been
expected to play an important role. In order to reduce carcongestion by improving public transports, we should optimize complex urban railway networks, and as this ﬁrst step,
we need to understand behaviors of railway commuters inpractical period. For this aim, understanding human behaviors using call detail records (CDRs) that automaticallyrecorded the location of the user and without the user’s
intentional operation have attracted attention. Althoughlarge-scale data are accumulated from CDRs through everyday use of mobile phones, the spatial and temporal resolution of CDRs is lower than that of existing positioning techs.
To cope with this problem, conventional works have focusedon the cellular handoﬀ patterns as a substitute for the sparse
location of CDRs. To handle the complex urban railway networks, the works does not work enough, because they haveto learn the patterns for each route manually. To solve thisissue, we propose the system that self-learns cellular handoﬀ patterns for all urban rail lines using a huge amount ofanonymous sparse location of CDRs. In this paper, We evaluate the three kinds of self-learning methods which include
the voronoi diagram based approach, the mahalanobis distance based approach and the static range based approach
using CDRs from a millions of subscribers. As the result,the accuracy of classifying the rail lines from the individual
cellular handoﬀ pattern achieved 81% against accurate 7600
GPS trajectories. In addition,the correlation about the trafﬁc ﬂow of urban railway networks between our output andthe latest census data was 0.768. As the contribution of thispaper, our proposed system indicates the possibility of understanding the traﬃc ﬂow of the urban railway networks
more often using only CDRs as substitute for the census.

Algorithms, Design, Experimentation

Categories and Subject Descriptors
I.5.2 [Pattern Recognition]: Design Methodology—classifier design and evaluation

Keywords
GIS, CDRs, Transportation Estimation, Localization, Cellular Handoﬀ Patterns, Self-learning

1. INTRODUCTION
Increasing traﬃc congestion at commute times has significantly degraded the eﬃciency of the transportation infrastructure and increased travel time, air pollution, and fuelconsumption. As a result, urban areas are faced with thenecessity of improving public transport to reduce car use.
Urban areas (New York, London, Paris, Beijing, and Tokyo)
have multimodal public transport systems consisting of railways and buses or light rail. In particular, railway services
can carry massive amount of people at once are expected tobe eﬀective during commute times[16].
Tokyo is well known as one of the railway-oriented cities
where the huge traﬃc demand generated from the megacity
is well supported by a sophisticated urban railway system[7].
The results of the 2008 Person Trip Survey show that rail’s
modal share was 30%. Approximately 8 million commuting workers and students use railways on a daily basis in
the Tokyo metropolitan area. Even now, railway networks
surrounding Tokyo have grown from year to year[12]. In particular, the 2020 Summer Olympic and Paralympic Gamesin Tokyo have boosted massive investment in new railway
infrastructures in Tokyo. To adjust and rebuild the hugecomplex railway networks, understanding the behavior ofrailway commuters in practical period is necessary.
From another point of view, the Tokyo metropolitan area
experienced a shaker in the upper 5 during the Great East
Japan Earthquake. As a result, almost all railways temporarily stopped their service. Since the earthquake occurred on a weekday afternoon, a large number of commuting workers and students were in their oﬃces and schools inTokyo. The stoppage of railways made it impossible for suchpeople to return home[20][17]. During the Great East Japan
Earthquake, there were 3.5 million as commuters strandedin the Tokyo metropolitan area. For the temporary evacuation of such stranded commuters, the government should

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estimate a number of potential candidates for the stranded
commuters by analyzing railway commuters in daily life.
In order to roughly understand the behavior of railway
commuters, we can use the results of a metropolitan transportation census provided by Japan’s Ministry of Land, Infrastructure, Transport, and Tourism’s (MLIT) and local
governments. However, the census has been conducted every ﬁve years. , e.g., the latest result was summarized in2010. Because the environment of a transport infrastructurechanges every year, understanding the behavior of railway
commuters in detail is diﬃcult using such old data. As another approach to ﬁnd the behavior of railway commuters,
we may use ticket sales from railway operators, however,railway operators have not provided such data to the public
because of privacy issues.
On the other hand, mobile phones have become a key device for pervasive computing with users carrying them atalmost all times. The ubiquity of these platforms has transformed mobile phones into one of the main surveyors of human behavior. However, conventional work using sensors ona mobile phone might not be suitable for large-scale, sustainable data collection in the analysis of human behavior. For
example, participants (users) must install to analyze user
activities. Moreover, the application wastes energy from themobile phone battery with the unintended sensing operations. Though participatory sensing is one of the attractive
solutions, collaborative users have been limited.
In order to cope with the problem, understanding human behaviors using call detail records (CDRs) that automatically recorded the location of the user and without
the user’s intentional operation have attracted attention. In
fact, every time a subscriber makes or receives a phone call,an short message service(SMS), an multimedia messaging
service(MMS), or data communication regarding the interaction and the location of the user (in the location of thebase station used for the communication) is logged as CDR
for billing purposes. CDRs have automatically and routinely been recorded without the user’s intentional operation. Even if the user don’t operate the mobile phone intentionally, some social networking applications on the mobile
phone periodically are sending keep alive messages[4][1] inseveral minutes. Although large-scale data are accumulated
from CDRs through everyday use of mobile phones, the spatial and temporal resolution of CDRs is lower than that ofexisting positioning technologies.
To overcome the weak points of CDRs, some existing
works have focused on the cellular handoﬀ patterns as a
substitute for the sparse location of CDRs. Characteristicsof the handoﬀ pattern when people get on the same rail line
are likely to be similar. Thus, the handoﬀ pattern of basestations can be useful data to analyze railway commuters.
To manage a large numbers of rail lines, however, the theconventional works are not suitable at the point of scalability, because the works have to learn the handoﬀ patterns foreach route manually. Moreover, the works don’t adapt tobe rebuilt the correct cellular handoﬀ pattern including themodiﬁcation when a new rail line or a new base station has
been laid after once building the pattern.
To solve the issue, we propose the system that enable tomaintain a large number of the cellular handoﬀ patterns
automatically. Our proposed system self-learns all cellularhandoﬀ patterns in urban complex railway networks using ahuge amount of anonymous sparse location data of CDRs. In

this paper, We evaluate the three kinds of self-learning methods which include the voronoi diagram based approach, themahalanobis distance based approach and the static range
based approach using CDRs from a millions of subscribers.
As the result, the accuracy of classifying the rail lines fromthe individual cellular handoﬀ pattern achieved 81% against
accurate 7600 GPS trajectories. In addition,the correlation
about the traﬃc ﬂow of urban railway networks between our
output and the latest census data was 0.768. As the contribution of this paper, our proposed system indicates thepossibility of understanding the traﬃc ﬂow of the urban railway networks more often using only CDRs as substitute forthe census.
The system design of the proposed system is presented in
Section 3. We describe the data cleaning process, the selflearning process and the classifying process in Section 4, 5
and 6, respectively. In Section 7, we represent the datasetsthat is both of CDRs and GPS data as the ground-truth
data for our experiments. Then, we validate the accuracy of
our system and the correlation with the latest census data
in Section 8. Finally, we conclude our work in Section 9.

2. RELATED WORKS
Every mobile phone leaves location trajectories as CDRs
while interacting with its infrastructure. Massive numbersof CDRs from mobile phone users have been used in a variety of urban-related applications, including understanding
mobility patterns and models[6, 10, 9, 11] the use of urban
spaces [21], travel demand during speciﬁc events [5], social
network structure [19], and geographical distribution of mobile communications [13]. Thus, CDRs are now recognized
as one of main data sources indicating user location trajectories.
Many conventional studies have tried to estimate the transportation mode as human movement behavior. As mentioned by Stenneth et al. [24], trying to classify public transportation from accelerometer data has limitations with regard to accuracy. Given that result and the easy availabilityof GPS on mobile phones, researchers can use location and
GIS information. Hemminki et al. [8] discussed an approach
using only accelerometers for distinguishing diﬀerent modalities while testing the system across a few cities. In contrast
to other work using accelerometers only, they obtained impressive results of around 80% recall and precision since they
focused on periods of vehicle acceleration and deceleration.
However, they need to keep sensing accelerometers while
detecting the mode of transit without considering energy
consumption. In addition, the method required the installation of a special sensing application on the user’s mobile
phone. Rahul et al.[22] used several sensors on a smartphone
and GIS information. Their contributions were that they reduced the learning time of sensor data using General Transit Feed Speciﬁcation (GTFS), which described the schedule
for public transit. The accuracy of the proposed system wasaround 85%. However, they could not deploy the unit inother countries, including Japan, since opened GTFS was
not so prevalent thourghout world.
In a parallel eﬀort to our approach，Thiagarajan et al.[25]proposed CTrack, a system for trajectory mapping usingbase station ﬁngerprints. CTrack was able to match thestream of new GSM ﬁngerprints to road segments with a
median accuracy of 75%. CTrack could also use informationfrom an accelerometer and a compass to improve accuracy.

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Becker et al. [3, 2] showed the practical capability of deriving
the trajectories using handoﬀ patterns of CDRs. The paper
validated the cellular handoﬀ patterns as relatively stable
across diﬀerent routes, speeds, directions, phone models, andweather conditions. Zhou et al. [28] used an accelerometer,
audio, and the base station sequences to identify whether theuser was on public transit or a car, which mostly worked
for bus detection. The approach was power eﬃcient as it
used the base station sequences rather than GPS, however,training was required for all routes that the person took
making deployment harder in new places or for new routes.
In contrast with conventional work, our approach has not
required speciﬁc data gathering before creating cellular handoﬀ patterns. We propose a self-learning algorithm of a cellular handoﬀ pattern for a rail line using massive numbersof CDRs and the geo shape of the rail line.

3.

SYSTEM OVERVIEW

Our proposed system enables the discovery of railway commuters throughout three steps. In the ﬁrst step called theData Cleaning process, we extract the moving data sequencesfrom CDRs, because our system focuses on the commuters ofrailways. As the second step called the Self-learning process,we starts creating a database of self-learnt cellular handoﬀ patterns for each rail line using the proposed three algorithms. At the ﬁnal step called the Classifying process,we identify whether the sequences of the user’s CDR wererecorded when the user rode the speciﬁc line. the databaseof self-learnt cellular handoﬀ patterns is used for the identiﬁcation method of speciﬁc rail line that the user rode. Figure
1 shows the whole system design. We summarize the briefrole of each process in the following.
1) Data cleaning Process
Sequences of CDRs when a user is moving are only needed,since the aim of the proposed system is ﬁnding railway
commuters. Thus, in this process, all CDRs are distinguished between moving and staying. We use only the
CDRs of moving status in the following process.
2) Self-learning ProcessThis process generates a training dataset using CDR sequences of moving status and the shape of the rail line.
Using the training data set, a cellular handoﬀ pattern for
the rail line is built automatically into the proposed system. In this paper, we create cellular handoﬀ patterns
for 110 rail lines.
3) Classifying Process
In this process, the rail line is discovered by collating
all cellular handoﬀ patterns. Similarities among the patterns of the user’s CDRs and the patterns of all rail lines
in a database are calculated using the Smith–Waterman
algorithm.

4.

DATA CLEANING PROCESS

Before starting the main processes, we should extract sequences of CDRs when a user is moving as the data cleaning process. Connection timestamps, base station IDs, andwireless connection information are recorded in the CDR.
Therefore extracting user locations and associated timestamps from the CDR is viable. Our proposed method estimates user positions using CDRs when the user is driving

Figure 1: The overall architecture of our proposedsystemor on a train, and as a ﬁrst step, it is necessary to estimate
the status of the user, namely, whether the user is currently
moving or staying in one position.
Although multiple algorithms[14][27][26] for estimating user
movement status and location and time information havebeen proposed, we propose a method in this paper that isbetter tailored for the use of CDRs. Our proposal consists
the following three steps. First, we perform a temporal analysis in which, we record the timestamp and denote it as tc foreach record in the CDR. We then extract a set of locations
whose associated timestamps fall within a predeﬁned time
window beginning from the speciﬁed time point. We denotethe beginning time of this time window as t0 . Then in thespatial analysis, we calculate the distances between each location in the extracted location set and the location of thecurrently being processed record. We denote the movement
status at tc as stc and we set stc as staying if all the calculated distances fall below a predeﬁned distance threshold,otherwise we set stc as moving. Both the start time t0 andthe span of the time window are variable and depend on
the estimated movement status from the precedent record.
Speciﬁcally, if the estimated movement status is staying, we
do not modify t0 but we extend the span of the time windowby tc − t0 . If the estimated movement status is moving, we
set t0 to tc and recover the span of the time window to its
default value.
However, the ratio of moving and staying depends on thepredeﬁned distance threshold, which in turn can aﬀect the
precision and recall of our estimations. Speciﬁcally, setting
a lower threshold can easily cause a rise in moving estimations which lowers estimation precision; on the other hand,a higher threshold causes a decreasing trend of moving estimations, which makes precision look better. Therefore, wedeﬁne two distinct distance thresholds (D1, D2 : D1 > D2)
for movement status estimation, and the ﬁnal decision isbased on the aggregated judgment from these two thresholds.
Speciﬁcally, we extend our notation of stc to sdtci to denote the movement status as determined by threshold di .
As for the aggregated judgment, we compare pairs of sdtc1
and sdtc2 for each tc , if they disagree with each other or areboth staying, we set stc to staying. Otherwise we set stc tomoving. Furthermore, if stc is moving, we set stc ’s neigh-

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bors stc +/−1 to moving if sdtc2+/−1 is moving.
As is illustrated in ﬁgure 2, after the aggregated judgment,
it is possible to estimate the ground-truth movement status
more accurately.

method is described in the next.

5.1 Creating Training Dataset
In order to learn the sequence of cellular handoﬀs for a
speciﬁc rail line, we need a set of training CDR data thatwas recorded when the user rode on the line. To elect thetraining CDR data, our method processes a map-matchingalgorithm with the limitation of speed. The four conditions
for electing training data are shown in the following. The
ﬁrst condition is that a data set for the moving state keeps
recording at least THLDcnt point consecutive times, sincesuch data are needed for learning successive cellular handoﬀ
patterns. Second, the average speed of movement using a vehicle should be over THLDavg speed . As the third condition,
we set the total distance of the movement(THLDtotal dist ) tolearn the length of a sequence of cellular handoﬀs. For thelast condition, we set threshold THLDdist lineQ for dist lineQi ,since we would like to elect only pi points located along line
Q. However, location data for CDRs are not accurate because they record the location using the result of triangulating by signals from just a few of cellular towers. A few
dist lineQi are over THLDdist lineQ . Therefore, we set thewhich is located withinratio(THLDrate linematch ) of pi ,THLDdist lineQ to all points of P .
∑1) THLDcnt point < ni=0 pi2) THLDavg speed <3) THLDtotal dist <∑n−1

speedi
n−1

i=0

∑n−1

i=0 disti∑n>THLDdist lineQ
i=0 pi |dist lineQ∑n i
i=0 pi4) THLDrate linematch <When the location data set α meets all conditions against
the line β, α use as a training data for the line β. This
process repeatedly executes against all location data sets
regarded as moving to all 110 rail lines. After the process,all routes have suﬃcient training data to learn the sequenceof cellular handoﬀs for each rail line.

Figure 2: The process flow of the data cleaning

5.

SELF-LEARNING PROCESSWe use a set(α) of location data(p1 , p2 , ..., pn ) ∈ Pα froma sequence of CDRs which were regarded as moving in
the data cleaning process. Each pi has three tuples like
(loc, time, tower id). A distance and a velocity between pi
and pi+1 are represented as disti and speedi , respectively.
We also use a set of geographical points(q1 , q2 , ..., qn ) ∈ Qβof a rail line(β). lineQ that is the shape of the rail line isa line that connected Q. A distance between pi and lineQ
is shown as dist lineQi . Figure3 shows an election methodof training data for a public transportation. A detail of the

Figure 3: The example of creating the training
dataset for rail line β

5.2 Building Cellular handoff patterns
We describe how to build a cellular handoﬀ pattern using
training datasets. The system assigns mobile base stations
for each point composing a geographical polyline of a railline using training datasets as mentioned in the previous
section. In this paper, a cellular handoﬀ pattern is deﬁned
as a geographical sequence of identiﬁed base stations representative of base stations assigned such points. We propose
three kinds of assignment algorithms for the mobile base stations of each point. Assignment algorithms are expressed indetail in the following.

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5.2.1

Voronoi Diagram Based Assignment

A p of a CDR has three tuples like (loc, time, twoer id).
A centroidCi of the base station γ using locs of all pj including the tower id of the base station γ as its twoer id is
calculated. Thus, the centroids of base stations covering all
CDRs in the training dataset are calculated. We conduct
Voronoi-based space partitioning using centroids for eachbase station. When a point composing a geographical polyline of a rail line is contained in a Voronoi region of a basestation, the identiﬁcation of the base station is learned as apiece of the cellular handoﬀ pattern for the point. Note thatthe point does not have an assigned base station when any
centroids from the base stations are not able to be foundwithin 1000 m of it. An example of the Voronoi Diagram
Based Assignment is shown in Figure 4(upper). Some conventional works[28] also elect this method.

5.2.2 Mahalanobis Distance Based Assignment
First, the method calculates the variance-covariance matrix using all centroid C calculated at creating the Voronoi
region and locs of all pj including the tower id of the basestation γ as its tower id. The second step creates an ellipse
region of equal probability using GDRs within the limit of
the speciﬁc conﬁdence interval(80%) in a Gaussian distribution of two variants derived by the variance-covariancematrix. When a point composing a geographical polyline ofa rail line is contained in the ellipse region of a base station,
the identiﬁcation of the base station is learned as a pieceof a cellular handoﬀ pattern for the point. Though some
ellipse regions overlap at the point, the method identiﬁes
the base station that has the nearest centroid to the point.
In the same way as the method of the Voronoi diagram assignment, the point regards as it does not have an assigned
base station when any centroids of base stations are not able
to be found within 1000m around. Additionally, we set the
maximum number of base stations for a point as three. An
example result of Mahalanobis Distance Based Assignmentis shown in Figure 4(middle).

5.2.3

Static Range Based Assignment

In the static range based assignment, we calculate theoccupancies of base station IDs within the speciﬁed range(500 m in this paper) of the composition point of a geographical polyline for a speciﬁc transport. The base station
id of the highest occupancy in a composition point adds tothe base station alignment of the speciﬁc transport as thedominative base station. The following equation shows as
extracting the base station IDs of the highest occupancy atcomposition point qi .
tower idqi = argmax(∑

tower idqi |dist(pi , qi ) < r∑)pi

As the ﬁrst composition point in Figure 4(bottom), basestation A is the dominative base station. For the second
composition point, base station B is the dominative basestation. Thus, we determine the cellular handoﬀ alignment
to choose the dominative base stations for each composition point. The ﬁgure 4(bottom) shows the example of thecellular handoﬀ pattern in this method.

6.

CLASSIFYING PROCESS

Figure 4: The cellular handoﬀ patterns created byproposed three methods

By the classifying of a rail line that a user rode on, oursystem calculates the similarity between the transition series of base stations from the user’s CDRs and learnt basestation transition series of a rail line. By computing thesimilarity, the CDRs are associated with the nearest pointof a rail line. The rail line with the highest similarity scoreis considered the rail line used by the rider. The calculationof the similarity is used a variant of the Smith-Waterman
algorithm[23]. Smith-Waterman is a dynamic programmingalgorithm for local sequence alignment that has been widelyused in bioscience (e.g., to determine similar regions between
two nucleotide or protein sequences). In the scoring schemeof the Smith-Waterman algorithm, a positive value is assigned for a match and a negative or a lower value is assigned for mismatch. Once the scoring has started, it stops
when a score is negative. Since, we want to compare theglobal sequences at both the base station sequence of theuser’s CDRs and a learnt base station sequence, the penalty
is set as 0 when the compared two base stations are a mismatch. U (u1 , u2 , ..., um ) and L(l1 , l2 , ..., ln ) are represented
as a base station sequence of a user’s CDRs and a learnt

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base station sequence of a rail line, respectively. We formulate our scoring method in the following.

H(i, j)s(a, b)

=

=



0






H(i − 1, j − 1) + s(ui , lj )max
H(i − 1, j) H(i, j − 1){1 ≤ i ≤ m, 0 ≤ j ≤ n0, if b ∈ a
1, otherwise

Using the above scoring matrix, the total score of the similarity between U and L is calculated in the following equation.
maxH(i, j)min(m, n)
0 ≤ e(U , L) ≤ 1e(U , L) =Since we want to calculate a top-k sequence matching
against the user’s CDRs, we use modiﬁed Smith-Waterman
algorithm by [28]. The similarity between sequences is deﬁned as the ratio of similar local sequences to all sequencesof a user’s CDRs.

7.

DATA COLLECTION

For the sake of evaluations, we collect some kinds of datafrom subscribers of mobile communication services as provided from KDDI. Before using such data, we completely
allow opt-in and obtain permission. In this section, we describe the details of the dataset for training and testing of
our system.

7.1

Anonymous CDRs

We collected a large number of CDRs to create training
data. For large-scale CDRs, our company as a mobile operator obtains clear opt-in permission for the use of CDRs
from each individual. We want to learn the cellular handoﬀ patterns for commuting rail lines from dormitory towns
surrounding Tokyo to central Tokyo. Then, the area of datacollection is the Tokyo metropolitan region with a horizontal width and vertical length of 230 km and 160 km, respectively. The number of users for the experiment is about
300,000. The period of data collection is two months (October and November 2014) and one month (April 2015). Each
CDR has global location data that is calculated using thesignal strengths of neighboring base stations using a simple
triangulation algorithm. The average error of the locationis around from 300 m to 500 m. All results of our proposedsystem are presented as aggregates. That is, no individual
anonymous identiﬁer was singled out for this study. By observing and reporting only on the aggregates, we protect theprivacy of individuals.
On the other hand, we also need the geographical shapesof the rail lines to build cellular handoﬀ patterns. We acquired all shape ﬁles of Japanese rail lines from the website[18] of the national land information division of the National Spatial Planning and Regional Policy Bureau in Japan.
From the data on the rail lines, we selected 110 rail lines
within the area matched to both the latest census and ourdata collection.

7.2 The Ground–truth Location Dataset
For evaluation of our classiﬁcation results, we need theground–truth location dataset against the location of CDRs.
We invited data collection volunteers from our subscribers
using an Android phone to allow us to use their CDRs andGPS data at the same time. The period of data collection is
one month in April 2015. Acquiring location data from theGPS uses a Google Android application that can gather thedata periodically. The area of data collection is the Tokyo
metropolitan area as deﬁned in the previous section. The
total amount of data and the unique number of users are 10
million and 12175, respectively.
Moreover, we extract GPS data for riding on a railway tocreate accurate comparative data. The extraction method
is similar to creating the training data set explained in thesection on the self-learning process. Over 7600 cases of GPS
location sequences moving along a rail line were discoveredby the extracting process. To evaluate the accuracy of theextracted GPS location sequences, we carefully checked therandomly selected results visually, respectively. After the
check process, we believed the datasets were suﬃciently accurate for the comparative data as extracted GPS location
sequences.

8. EVALUATION
To validate our proposed system, we conducted the two
evaluations. First, we evaluated the accuracy of each selflearnt handoﬀ pattern by comparing with our result and theground-truth dataset. Second, we also investigated the correlation between the number of train commuters as a resultof our system and the number of train commuters from thelatest census data.

8.1 Rail Line Classification
In this paper, we proposed the three methods to create
cellular handoﬀ patterns. Then, we evaluated the accuracyof the rail line classiﬁcation using the patterns which built
by the three methods. If the ratio that two names of the line
calculated from both the GPS sequence and our proposed
method matched was higher, we consider that our handoﬀ
pattern is accurate enough. Note that the GPS and the
CDRs were collected from the same user at the same time
period. Table 1 shows the total accuracy for three methods.
The number of trains represents the number of CDR sequences wherein a user rode on the rail line when the method
was used. The number of TP (True-Positive) means the total number of accurate results. The results of the accuracy
for each method indicated about from 80% to 81%. In addition, the result of using the Mahalanobis method was moreaccurate than using the other two methods.
Next, we compared the precision and recall of the results
classifying rail lines among three methods. The precision ofthis paper is deﬁned as the ratio of the number of results
classiﬁed as a rail line δ using our proposed system to theactual number of GPS sequences of a user riding a rail line
δ. And, FP (False-Positive) means the classiﬁed number as
the rail line when the GPS sequence was not classiﬁed as therail line. The precision and the recall were calculated using
the following equations.

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Table 1: Num of TPs and accuracy of three methods
Voronoi Mahalanobis Static Range
Train users
461
810
315
True-Positive
369
653
258
Accuracy
80.0%
80.6%
81.9%

Figure 6: The mapping result of 110 rail lines selected for our experiment

Figure 5: The cumulative frequency distributions ofboth the precision and the recall

P recision=

Recall=TP(T P + F P )TP(T P + F N )Figure 5 shows the cumulative frequency distributions ofboth the precision and the recall. As the results, though
there is no large diﬀerence among results from each threemethod, the precision values between 0.8 and 1.0 of the Mahalanobis method is better than the others. The precision
values of the Voronoi and the Static Range method tend toindicate the higher ratio when a value is around 1.0. In conclusion, although the Mahalanobis method indicates better
results than the others on the average, both the Voronoi and
the Static Range methods have better classiﬁcation results
for the speciﬁc rail lines.
From the view of the results of the recall, the cumulativevalue of the Mahalanobis method is small when the valuesof recall are small. Furthermore, the cumulative value islarge when the values of the recall are large. Thus, theMahalanobis method is more accurate than the others. The
Voronoi and the Static Range methods indicate a similartendency for the result of the precision.

8.2

Correlation with the Latest Census Data

In the previous session, we mentioned that the accuracyof our classifying engine have been practical and feasible
to extrac a line name that a individual user rode on. We
also evaluate a macro performance by comparing a wholeuser’s results of our system with the latest census. As theground truth dataset, we utilize the survey of the number ofmetropolitan railway user of the 11th Metropolitan Travel
Survey[15] that was conducted by Ministry of Land, Infrastructure, Transport and Tourism in 2010. 110 rail lines forthis evaluation is elected as rail lines that are overlapped thedataset of the survey and the dataset of the geographicalshapes of the rail lines mentioned at section 7.1. Notes that
we excluded subways and super-express trains from evaluation dataset. The ﬁgure 6 shows a mapping result of 110
rail lines for this evaluation.
We create the mahalanobis distance based cellular handoﬀ
patterns for 110 lines using three hundred thousand user’s
CDRs during one month(Oct, 2014). The input data forclassifying are also the same numbers of user’s CDRs creating the pattern at Nov, 2014. The travel survey conductedfrom 16th to 18th Nov 2010. Therefore, we also use input
data of same month to compare same condition. From thetwo points of view of the number of railway commuters at
various time zones and the number of railway commuters at
various rail lines, we compare results of our system with thelatest census data.

8.2.1 Hourly Railway commuters
As the results of our system, we shows the average hourly
number of railway commuters on weekdays in Figure 7. The
ﬁgure is shown that our system can analyze a transitionof the number of hourly train commuters on all rail lines.
For verifying suitability of the number of hourly train commuters, we compare the results and the the latest census
data. We set a comparing time as a core active time forpeople from am 6 to pm 11. Before the comparison, we normalize both our results and the latest census data, since thetotal number of user in two dataset is diﬀerence. the resultof comparing our system results and the latest census datais shown in Figure 8. There are two peaks at am 8 and pm
6 in both results of the latest census data and our output.
And trend of two transitions seem to be similar. The resultof the latest census data is higher at the peaks. In contrast,
the result of our system is higher in time between peaks.

8.2.2 Railway commuters on various rail lines
We analyze the correlation between our output and the

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Figure 7: The average of an hourly number of railway commuters on weekdays in 110 rail lines

Figure 8: The comparing result of an hourly numberof the railway commuters between our output andthe latest census data

latest census data to compare the number of railway commuters for each rail line. The correlation is shown in Figure
9. In the ﬁgure 9, the x-axis, the y-axis means the numberof railway commuters of the latest census data, the number of railway commuters of our output, respectively. The
correlation coeﬃcient is 0.768. Thus, there is the meaningful correlation between our output and the latest census
data. Next, we analyze diﬀerences between our output andthe latest census data to verify characteristics of our system. Since the absolute number of two data set is totallydiﬀerent, we rank each rail line according to the number ofrailway commuters. Then, the absolute value by diﬀerencing
between ranks is calculated and sorted in descending order.
We classify according to diﬀerence of the absolute value between our output and the latest census data under threegroups(diﬀ large, diﬀ middle, diﬀ small ). In other words,
diﬀ large, diﬀ middle and diﬀ small are expressed rail linesof which a diﬀerence between our output and the latest census data is large, middle, and small, respectively. Figure
10 shows the sum and the average of the number of railway
commuters in the latest census data including each group.
As shown in the ﬁgure 10, we discovered that the diﬀerence
between our output and the census data is large, when thenumber of railway commuters in the census data is small.
Finally, though the result is reliable in a rail line that the
number of users is large, a gap between our output and actual situation tend to be large in case of a rail line that the
number of users is small.

To solve the issue, we propose the system that enable tomaintain a large number of the cellular handoﬀ patterns
automatically. Our proposed system has process in threestep which consist of the data cleaning, the self-learning,
and the classifying to identify railway commuters. In the
self-learning process, we propose the three kinds of selflearning methods which include the voronoi diagram based
approach, the mahalanobis distance based approach and thestatic range based approach using a huge amount of anonymous sparse location of CDRs. As the result of evaluation,
the accuracy of classifying the rail lines from the individual
cellular handoﬀ pattern achieved 81% against accurate 7600
GPS trajectories that regard as the ground truth dataset.
We also evaluate the correlation about the traﬃc ﬂow ofurban railway networks between our output and the latest
census data. the result of the correlation was 0.768. Figure
11 shows the mappting result of traﬃc ﬂow from both our
output and the latest census data. For visualizing the traﬃc
ﬂow as the color gradation, we normalized at the diﬀerence
between the maximum value and the minimum value of thetraﬃc ﬂow. The two ﬁgure show the same trend at the pointof the traﬃc ﬂow. In conclusion, our proposed system indicates the possibility of understanding the traﬃc ﬂow of
the urban railway networks more often using only CDRs as
substitute for the census. By using our system, we can measure the traﬃc ﬂow of urban railway networks whenever you
need.

9.

This work was supported by Research and Developmentof Technologies for the Utilization of Real-time Information
on Geospatial Platforms of Ministry of Internal Aﬀairs andCommunications of Japan.

CONCLUSIONSThe user’s location trajectories with large-scale and longterm from CDRs have been attractive data source to understand human behavior. In particular, the cellular handoﬀ
pattern have been paid attention as the route or rail line
classifying method that a user rode. To manage a large
numbers of rail lines, however, the the conventional works
are not suitable at the point of scalability, because the works
have to learn the handoﬀ patterns for each route manually.

10. ACKNOWLEDGMENTS

11. REFERENCES[1] A. Aucinas, N. Vallina-Rodriguez, Y. Grunenberger,
V. Erramilli, K. Papagiannaki, J. Crowcroft, and
D. Wetherall. Staying online while mobile: The hidden

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Figure 9: The correlation between our output andthe latest census data

Figure 10: The sum and the average of a numberof the railway commuters in the latest census data
which be included in the defined three groups

costs. In Proceedings of the Ninth ACM Conference on
Emerging Networking Experiments and Technologies,
CoNEXT ’13, pages 315–320, New York, NY, USA,
2013. ACM.
[2] R. Becker, R. Cáceres, K. Hanson, S. Isaacman, J. M.
Loh, M. Martonosi, J. Rowland, S. Urbanek,
A. Varshavsky, and C. Volinsky. Human mobility
characterization from cellular network data. Commun.
ACM, 56(1):74–82, Jan. 2013.
[3] R. A. Becker, R. Caceres, K. Hanson, J. M. Loh,
S. Urbanek, A. Varshavsky, and C. Volinsky. Routeclassiﬁcation using cellular handoﬀ patterns. In
Proceedings of the 13th International Conference on
Ubiquitous Computing, UbiComp ’11, pages 123–132,
New York, NY, USA, 2011. ACM.
[4] M. Böhmer, B. Hecht, J. Schöning, A. Krüger, and
G. Bauer. Falling asleep with angry birds, facebook
and kindle: A large scale study on mobile application
usage. In Proceedings of the 13th International
Conference on Human Computer Interaction with

Mobile Devices and Services, MobileHCI ’11, pages
47–56, New York, NY, USA, 2011. ACM.
[5] F. Calabrese, F. C. Pereira, G. Di Lorenzo, L. Liu,and C. Ratti. The geography of taste: Analyzingcell-phone mobility and social events. In Proceedingsof the 8th International Conference on Pervasive
Computing, Pervasive’10, pages 22–37, Berlin,
Heidelberg, 2010. Springer-Verlag.
[6] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi.
Understanding individual human mobility patterns.
Nature, 453(7196):779–782, June 2008.
[7] Y. Hayashi, X. Mai, and H. Kato. The role of railtransport for sustainable urban transport. In
W. Rothengatter, Y. Hayashi, and W. Schade, editors,
Transport Moving to Climate Intelligence,
Transportation Research, Economics and Policy, pages
161–174. Springer New York, 2011.
[8] S. Hemminki, P. Nurmi, and S. Tarkoma.
Accelerometer-based transportation mode detection
on smartphones. In Proceedings of the 11th ACM
Conference on Embedded Networked Sensor Systems,
SenSys ’13, pages 13:1–13:14, New York, NY, USA,
2013. ACM.
[9] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov,
M. Martonosi, J. Rowland, and A. Varshavsky.
Identifying important places in people’s lives from
cellular network data. In Proceedings of the 9thInternational Conference on Pervasive Computing,
Pervasive’11, pages 133–151, Berlin, Heidelberg, 2011.
Springer-Verlag.
[10] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov,
J. Rowland, and A. Varshavsky. A tale of two cities.
In Proceedings of the Eleventh Workshop on Mobile
Computing Systems &#38; Applications, HotMobile’10, pages 19–24, New York, NY, USA, 2010. ACM.
[11] S. Isaacman, R. Becker, R. Cáceres, M. Martonosi,
J. Rowland, A. Varshavsky, and W. Willinger. Human
mobility modeling at metropolitan scales. In
Proceedings of the 10th International Conference on
Mobile Systems, Applications, and Services, MobiSys’12, pages 239–252, New York, NY, USA, 2012. ACM.
[12] H. Kato. Urban rail development in tokyo from 2000
to 2010. International Transport Forum Discussion
Paper 2014-05, Paris, 2014.
[13] R. Lambiotte, V. D. Blondel, C. de Kerchove,
E. Huens, C. Prieur, Z. Smoreda, and P. Van Dooren.
Geographical dispersal of mobile communication
networks. Physica A Statistical Mechanics and its
Applications, 387:5317–5325, Sept. 2008.
[14] J. Liu, O. Wolfson, and H. Yin. Extracting semantic
location from outdoor positioning systems. In MDM,
page 73. IEEE Computer Society, 2006.
[15] Ministry of Land, Infrastructure, Transport andTourism in Japan. 11th Metropolitan Transportation
Census. http://www.mlit.go.jp/sogoseisaku/
transport/sosei_transport_tk_000007.html, 2012.
[16] K. Nakamura and Y. Hayashi. Strategies andinstruments for low-carbon urban transport: Aninternational review on trends and eﬀects. Transport
Policy, 29(C):264–274, 2013.
[17] H. Nakanishi, K. Matsuo, and J. Black.
Transportation planning methodologies for

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

137

SESSION 8
MOBILITY(a) The result of the latest census data(b) The result of the output of the proposed system

Figure 11: The mapping results of the traﬃc ﬂow from both the latest census data and our output

post-disaster recovery in regional communities: theeast japan earthquake and tsunami 2011. Journal ofTransport Geography, 31(Complete):181–191, 2013.
[18] National Spatial Planning and Regional Policy and
Bureau in Japan. National Land Numerical
Information Railway Data. http://nlftp.mlit.go.
jp/ksj-e/gml/datalist/KsjTmplt-N02.html, 2012.
[19] J.-P. Onnela, J. SaramÃd’ki, J. HyvÃűnen,
G. SzabÃş, D. Lazer, K. Kaski, J. KertÃl’sz, and A.-L.
Structure and tie strengths in mobile
BarabÃasi.

communication networks. Proceedings of the National
Academy of Sciences, 104(18):7332–7336, 2007.
[20] T. Osaragi. Modeling a spatiotemporal distribution of
stranded people returning home on foot in theaftermath of a large-scale earthquake. Natural
Hazards, 68(3):1385–1398, 2013.
[21] J. Reades, F. Calabrese, A. Sevtsuk, and C. Ratti.
Cellular census: Explorations in urban data collection.
IEEE Pervasive Computing, 6(3):30–38, 2007.
[22] R. C. Shah, C.-y. Wan, H. Lu, and L. Nachman.
Classifying the mode of transportation on mobile
phones using gis information. In Proceedings of the2014 ACM International Joint Conference on
Pervasive and Ubiquitous Computing, UbiComp ’14,
pages 225–229, New York, NY, USA, 2014. ACM.
[23] T. F. Smith and M. S. Waterman. Identification ofcommon molecular subsequences. Journal of Molecular
Biology, 147(1):195–197, March 1981.
[24] L. Stenneth, O. Wolfson, P. S. Yu, and B. Xu.
Transportation mode detection using mobile phones
and gis information. In Proceedings of the 19th ACM
SIGSPATIAL International Conference on Advancesin Geographic Information Systems, GIS ’11, pages
54–63, New York, NY, USA, 2011. ACM.
[25] A. Thiagarajan, L. Ravindranath, H. Balakrishnan,
S. Madden, and L. Girod. Accurate, low-energy
trajectory mapping for mobile devices. In Proceedingsof the 8th USENIX Conference on Networked Systems

Design and Implementation, NSDI’11, pages 267–280,
Berkeley, CA, USA, 2011. USENIX Association.
[26] Y. Zheng, Q. Li, Y. Chen, X. Xie, and W.-Y. Ma.
Understanding mobility based on gps data. In
Proceedings of the 10th International Conference on
Ubiquitous Computing, UbiComp ’08, pages 312–321,
New York, NY, USA, 2008. ACM.
[27] C. Zhou, D. Frankowski, P. Ludford, S. Shekhar, and
L. Terveen. Discovering personally meaningful places:
An interactive clustering approach. ACM Trans. Inf.
Syst., 25(3), July 2007.
[28] P. Zhou, Y. Zheng, and M. Li. How long to wait?:
Predicting bus arrival time with mobile phone based
participatory sensing. In Proceedings of the 10th
International Conference on Mobile Systems,
Applications, and Services, MobiSys ’12, pages
379–392, New York, NY, USA, 2012. ACM.

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A Framework for Evacuation Hotspot Detection after Large
Scale Disasters using Mobile Phone Location Data
Takahiro Yabe, Yoshihide Sekimoto

Kota Tsubouchi

University of Tokyo

Yahoo Japan Corporation

yabe0505@iis.u-tokyo.ac.jp
Keywords

Disaster Management, Human Mobility, Urban Computing,Evacuation Hotspot Detection, Mobile Phone Location Data

1. INTRODUCTION

Large scale disasters such as Magnitude 7 earthquakes, tsunamis,
and mega-typhoons damage urban infrastructure and cause severesocial disorder. As a result of mass evacuation activities of thevictims, it is often extremely difficult to grasp the locations of all
the evacuation hotspots in the confusion after the disasters. This is
made even complex especially because some people evacuate tolocations that are not officially designated as evacuation shelters,such as parking areas of large shopping malls and local parks.
Consequently, many evacuation hotspots couldn’t be provided withfood and supplies efficiently [2].
Therefore, there is an urgent need for an efficient framework for
estimating evacuation hotspots right after a natural disaster. The
framework needs to require less time and less work load for thedecision makers compared to the conventional on-foot field searchfor evacuation hotspots.
Recently, GPS and call detail records (CDR) of mobile phones arebeing used for human mobility analysis [3-6], and are applied tovarious fields of study such as traffic management [7-9], urban
planning [10], and pandemic simulations [11]. Some studies haveanalyzed the irregular human mobility after natural disasters suchas Hurricane Sandy, Great East Japan Earthquake, and Haiti
Earthquake [12-14], but none have proposed a framework for areal-time evacuation hotspot estimation.
In this paper, we propose a framework for estimating evacuation
hotspots using location data from mobile phones that works
efficiently in disasters of any scale. To demonstrate the accuracy
and efficiency of the framework, we used Yahoo! Japan’s GPS
dataset of over 20,000 IDs for detecting and analyzing theevacuation hotspots after the Kumamoto earthquake. Through thedemonstration on the Kumamoto earthquake, we show that ourframework accurately detects evacuation hotspots, and also that thisprocess can be completed at a significantly high speed and requires
low effort compared to the conventional on-foot investigations.
Our key contributions of this paper are as follows:
We propose a framework to detect evacuation hotspots
following large scale natural disasters by using mobile phone
location data.
We validate the accuracy and efficiency of our framework by
estimating the evacuation hotspots after the Kumamoto
earthquake using actual GPS data provided by Yahoo Japan.

ktsubouc@yahoo-corp.co.jp
1 million individuals (sample rate around 1% from all over Japan)
have been collected. As shown in Table 1, for the experiment, we
use a total of 22,124 users’ 418,119 total GPS logs from a periodof January 1st to May 16th of 2016 which were located in Kumamotoarea. The data is dense to the extent that plotting all the GPS points
taken on a single day, as shown in Figure 1, can draw the entire
road network of the Kumamoto area.

3. PROPOSED EVACUATION HOTSPOTDETECTION FRAMEWORKOur proposed framework calculates the congestion anomaly values(𝐾𝐾) of each grid cell by comparing the nighttime congestion (𝑀𝑀)after large disasters in each cell with the usual average (𝜇𝜇) andstandard deviation (𝜎𝜎) of nighttime population in that cell, by thefollowing equation.
𝐾𝐾 =𝑀𝑀 − 𝜇𝜇⁄𝜎𝜎(3)

The framework is consisted of the manual parameter input process
and the automated calculation process. The manual parameter input
could be completed momentarily, since shapefiles are available
online, and the parameters are easy to input. The automatedcalculation process is consisted of 4 parts; 1) location datacollection, 2) aggregation and smoothing the collected GPS data, 3)
calculating the anomaly value of each grid cell, and 4) visualization
Table 1. Number of unique IDs, number of logs of Yahoo Japan’s
GPS data taken from users’ mobile phones
Period ofdata

Average daily
number of IDs in
Kumamoto area

Average daily total GPS
logs in Kumamoto area

2016/01/01~
2016/05/16

22,124(1.2% sample rate)

418,119(avg. 19 logs/user/day)

Kumamoto City Center

2. Mobile Phone GPS Dataset

The Yahoo Japan Disaster App collects the GPS data of mobile
phones of individuals who have agreed to provide their location
data to Yahoo! Japan when installing the app. Each GPS record
contains an anonymized user ID, longitude, latitude, and timestamp.
The GPS data are collected every day when the individuals movearound, while the phone is turned on. In total, GPS data of aroundFigure 1. Collected GPS data points on April 1st 2016 of
Kumamoto area. We can clearly observe the congested city
center areas and also the road networks connecting theclusters, by plotting the dense GPS data of mobile phones.

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Figure 2. Map of Kumamoto area; before, 2 days after, and 1 month after the earthquake. The grid cells with anomaly values K>3,
2<K<3, and 1<K<2 are colored red, orange, and yellow, respectively. We can observe the increase in irregularly congested gridcells right after the earthquake, and its decrease as time passes and start to get back to normal from the shock.
of estimated evacuation hotspots. This framework is performed
iteratively after the occurrence of the disaster, to update the output
locations of evacuation hotspots. Our framework is efficiently
designed so that it could be quickly processed, and also has little
burden for the framework users since most of the parts areautomated. Users need to input the grid cell size for outputting theanomaly maps. The grid cell sizes can be set as either 100m, 250m,
500, or 1000m. This parameter needs to be selected according to
the wideness of the disaster affected area. For detailed explanationon our method, please read Yabe et al. [1].

4. CASE STUDY: Kumamoto Earthquake

To demonstrate and to test our framework, we estimate theevacuation hotspots with real time GPS dataset after the Kumamoto
earthquake. Furthermore, we verify the efficiency of our system by
calculating the time taken for estimation.

4.1 Visualization of Evacuation Hotspots

Figure 2 shows the map of Kumamoto with grid cells with 𝐾𝐾 𝐾 𝐾
colored in red, 2 < 𝐾𝐾 𝐾 𝐾 colored in orange, and 1 < 𝐾𝐾 𝐾𝐾 inyellow. We can observe very few grid cells with 𝐾𝐾 𝐾 𝐾 on April 1st
before the earthquake, meaning that the majority of the grid cells
have a population within usual range. However on the 18th, after thelarge earthquake, there is a significant increase in the number ofgrid cells with high anomaly values, especially near the city centerand the southern part where many people evacuated. These gridcells indicate the “evacuation hotspots”, where people evacuated ata significant rate compared to the usual population in that grid cell.
It is also interesting to observe 𝐾𝐾 𝐾 𝐾 grid cells located on roads
near the coastline. The high anomaly values in these grid cells inferthat many people stayed in their cars away from their houses toavoid being injured by building collapses. However, after a monthfrom the earthquake, we can see a decrease in congested areas in
Kumamoto area. This implies that many evacuees returned home
(if their house was not completely damaged) or moved away toother areas of Japan for shelter, and many outsiders who weregathering in Kumamoto to volunteer work went back to their homes.
Using these anomaly maps, we can detect locations that are
significantly congested with people in real time, which can beutilized for rescue and supply distribution strategies. Using ourframework, these maps will be easily obtained by the decision
makers, by just entering three parameters.

4.2 Features of Evacuation Hotspots

In this subsection, we validate our estimation of evacuation
hotspots by checking the facilities located in each grid cell using amap. Figure 3 shows a map of central Kumamoto area and the gridcells with 𝐾𝐾 𝐾 𝐾 colored in red. The types of features located in

Figure 3. Map of central Kumamoto with K>3 grid cells coloredin red. Close investigation on these grid cells revealed that mostof these grid cells contain features that have a large capacity forevacuees such as schools, city halls, and convention centers. The
blue circle shows Grand Messe Kumamoto (GMK).
each of the grid cells are written beside the grid cell. Many of theestimated hotspots contain facilities that have the capacity tocontain large evacuation population, such as schools, city halls,
convention centers and parks. From these results, we can conclude
that our framework can accurately estimate the evacuation hotspots
after the earthquake, and that our framework provides a convenientinterface that shows the decision makers what features arecongested with evacuees. We also have to note that out of the 1100
hotspots, around 100 were not designated as evacuation shelters
before by the prefectural or the municipal government. This is an
issue that made the detection of evacuation shelters difficult for thedecision makers after the earthquake. As a result, people who
evacuated to these non-designated facilities could not be satisfied
with much supplies compared to people who evacuated todesignated shelters.

4.3 Population Transition of Hotspots

To analyze the evacuation activities with more detail, we focus onan evacuation hotspot where 𝐾𝐾 𝐾 𝐾 anomaly was detected, and plot
the transition of the daily population in that selected hotspot. We
focus on an area that includes “Grand Messe Kumamoto (GMK)”(circled in blue in Figure 3), a convention center in Kumamoto area,
and analyze its daily transition of population near that facility.
According to newspaper articles [15], many evacuees gathered in
the parking area of GMK right after the earthquake, despite the fact

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6. CONCLUSION

Figure 4. Transition of population density near “Grand Messe
Kumamoto”, a large shopping mall which was not designatedas an evacuation place. We can observe high congestion of K>3
after the earthquake, on April 15th ~ 21st.
that this facility was not designated as an evacuation location. The
broad black line in Figure 4 shows the daily transition of the nighttime population in GMK from March 20th to 14th May 2016. The
dotted gray line shows the average population μ of usual days near
GMK, and the gray, orange, and red lines indicate the 𝐾𝐾 𝐾 𝐾, 𝐾𝐾 𝐾
2, 𝐾𝐾 𝐾 𝐾 lines for GMK, respectively. We can observe a rapid
increase after the earthquakes on April 16th, and a significant
anomaly in population density for more than a week over 𝐾𝐾 𝐾 𝐾.
We can also spot an instantaneous 𝐾𝐾 𝐾 𝐾 on Saturday, March 26th.
There was actually a large music festival held on this day at GMK,
which is an example of an anomaly on a “usual” day. After April
18th, we can observe a quick decrease of population in GMK, and a
gradual return to a usual level of population. By the beginning ofMay, the population in GMK has transferred back to the normal
state. The evacuation hotspot in GMK was diminished after just aweek after the earthquakes.
The increase of population on April 16th coincides with theinformation on the newspaper article [15]. We can conclude thatthe population in GMK, was accurately estimated from GPS dataset,and that the function as an evacuation shelter was finished after
only one week from the earthquake.

5. DISCUSSION

Our framework for estimating evacuation hotspots using location
data from mobile phones provides useful information to decision
makers and shelter managers quicker and with less effort thanconventional methods. Providing useful and accurate information
can contribute to making efficient supply distribution and rescue
operation plans after disasters.
By calculating the anomaly value of each grid cell in Kumamoto
after the earthquake, we were able to estimate the distribution ofirregularly congested grid cells, despite the sample rate (around1%). We were able to quantify the significant increase in numberof grid cells with high anomaly values right after the occurrence ofthe earthquake, and statistically show the irregularity of thesituation compared to usual days. In addition, the gradual decreaseof grid cells with anomaly values higher than 2, showed the settling
down of irregularity after a few weeks from the occurrence of theearthquakes. Through the analysis and visualization of the anomaly
with our framework, we were able to observe the impact ofearthquakes on the people’s evacuation activities.
Secondly, by checking the features located in each grid cell thatwere estimated to be extremely congested, we verified the accuracyof our estimation. We were able to detect locations whereadministrative organizations had not designated as evacuation
shelters, such as Grand Messe Kumamoto.

We proposed a framework for estimating evacuation hotspots by
calculating each grid cell’s anomaly value after large disasters,
using location data from mobile phones. To the best of ourknowledge, this framework is the first to focus on estimating
evacuation hotspots, and to actually demonstrate the frameworkusing real GPS dataset. Our framework can function quicker andwith less effort compared to conventional methods that involve onfoot searches for evacuation centers where people are gathering.
To validate our method, we analyzed the population density
anomaly after the Kumamoto earthquake, and observed the sharp
increase of high anomaly value grid cells in Kumamoto area causedby the evacuation activities of the victims. We then verified ourestimation by observing the features included in each anomaly cell,and also newspaper articles that reported the population transition
in one of the evacuation hotspots.
Through the validation in the case study of Kumamoto and theefficiency test, we have confirmed the high accuracy andpracticality of our evacuation hotspot framework using location
data from mobile phones.

7. REFERENCES
1.

2.
3.
4.
5.
6.
7.

8.
9.
10.
11.
12.
13.
14.
15.

Yabe, T., Tsubouchi, K., Sudo, A., Sekimoto, Y., A Framework for
Evacuation Hotspot Detection after Large Scale Disasters using
Location Data from Smartphones: Case Study of Kumamoto
Earthquake, Proc. 22nd ACM SIGSPATIAL Int’l Conference on
Advances in Geographic Information Systems. ACM (2016).
West Japan Newspaper, Disparity between evacuation centers, April
20th (2016). (in Japanese)
Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. Understanding
individual human mobility patterns. Nature, 453(7196), (2008).
Sevtsuk, A., & Ratti, C. Does urban mobility have a daily routine?
Learning from the aggregate data of mobile networks. Journal of
Urban Technology, 17(1), 41-60. (2010).
Ashbrook, D., & Starner, T. Using GPS to learn significant locations
and predict movement across multiple users. Personal andUbiquitous Computing, 7, 5, 275-286. (2003).
Calabrese, F., Di Lorenzo, G., Liu, L., & Ratti, C. Estimating origindestination flows using mobile phone location data. IEEE Pervasive
Computing, 10(4), (2011).
Demissie, M. G., de Almeida Correia, G. H., & Bento, C. Intelligent
road traffic status detection system through cellular networks
handover information, Transportation research part C: emerging
technologies, 32, 76-88. (2013).
Wang, P., Hunter, T., Bayen, A. M., Schechtner, K. & González, M.
C. Understanding road usage patterns in urban areas. Sci. Rep. 2,1001 (2012).
Yang, Y., Gerstle, D., Widhalm, P., Bauer, D. & González, M. The
potential of low-frequency data for the monitoring and control of bus
performance. Transport. Res. Rec. J. Transport. Res. (2013).
Pentland, A. Society's nervous system: Building effective
government, energy, and public health systems. IEEE Computer 45,
31–38 (2012).
Colizza, V., Barrat, A., Barthelemy, M., Valleron, A. J., & Vespignani,
A.. Modeling the worldwide spread of pandemic influenza: baseline
case and containment interventions. PLoS Med, 4(1) (2007).
Lu, X., Bengtsson, L., & Holme, P. Predictability of population
displacement after the 2010 Haiti earthquake. Proc. National
Academy of Sciences, 109(29), 11576-11581. (2012).
Wang, Q., & Taylor, J. E. Quantifying human mobility perturbation
and resilience in Hurricane Sandy. PLoS one, 9(11), (2014).
Song, X., Zhang, Q., Sekimoto, Y., Horanont, T., Ueyama, S., &Shibasaki, R. Modeling and probabilistic reasoning of population
evacuation during large-scale disaster. Proc. ACM SIGKDD (2013).
Kumamoto Daily Newspaper, Evacuation center at Grand Messe
Kumamoto, April 24th (2016). (in Japanese)

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Mapping poverty using mobile phone and satellite data
Jessica E Steele1,2, Pål Roe Sundsøy3, Carla Pezzulo1, Victor Alegana1, Tomas J Bird1, Joshua Blumenstock4,
Johannes Bjelland3, Yves-Alexandre de Montjoye5, Kenth Engø-Monsen3, Asif M Iqbal6, Khandakar N
Hadiuzzaman6, Xin Lu2,7, Erik Wetter2,8, Linus Bengtsson2,7, Andrew J Tatem1,2,10
1 Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK 2 Flowminder Foundation,
Roslagsgatan 17 SE-11355, Stockholm, Sweden 3 Telenor Group Research, Snarøyveien 30, 1331 Fornebu, Norway 4 University of California,
Berkeley, School of Information, USA 5 Imperial College London, Data Science Institute 6 Grameenphone, Dhaka, Bangladesh 7 Karolinska
Institutet, Solnavägen 1, 171 77 Solna, Sweden 8 Stockholm School of Economics, Kungstensgatan 32, 113 57 Stockholm, Sweden 10 Fogerty
International Center, National Institutes of Health, 16 Center Drive, Bethesda, MD 20892, USA

1

Introduction

Eradicating poverty in all its forms remains a major challenge and the first target of the Sustainable
Development Goals (SDGs). To eradicate poverty, it is crucial that information is available on whereaffected people live. The geographic distribution of poverty in low and middle-income countries (LMICs) isoften uncertain. Small area estimation forms the standard approach to produce poverty maps, but thesetechniques rely heavily census data, which in most LMICs are unavailable or out-of-date. Recently, there
are promising signs that novel sources of high-resolution data can provide an accurate and up-to-date
indication of living conditions. Recent analyses have shown the potential using of features derived fromsatellite and Geographic Information System data and mobile operator call detail records (CDRs). Here weuse overlapping sources of satellite data, CDRs, and traditional survey-based data in Bangladesh to provide
the first systematic evaluation of the extent to which different sources of input data can accurately
estimate three different measures of poverty. We additionally compare our results with previous poverty
estimates for Bangladesh at coarser and finer resolutions.
2

Data and Methods

Mobile phone CDR features were generated from 4 months of mobile phone metadata collected between
November 2013 and March 2014. Grameenphone subscribers consented to the use of their data for the
analysis. CDR features range from metrics such as basic phone usage, top-up patterns, and social network
to metrics of user mobility and handset usage. They include various parameters of the corresponding
distributions such as weekly or monthly median, mean and variance.
We further identified, assembled, and processed twenty-five raster and vector datasets into a set ofsatellite covariates for the whole of Bangladesh at a 1-km spatial resolution. These data were obtained
from existing sources and produced ad hoc for this study to include environmental and physical metrics
likely to be associated with human welfare such as vegetation indices, nighttime lights, climatic conditions,and distance to roads or major urban areas.
We employed a model selection stage using non-spatial generalised linear models, implemented via the R
glmulti package to build every possible non-redundant model for every combination of covariates. Models
were built on a randomly selected 80% of the data to guard against overfitting. Models were chosen using
Akaike’s information criterion (AIC), which ranks models based on goodness of fit and complexity, while
penalizing deviance. This process was completed for national, urban, and rural strata, and using satellite
data only, CDR data only, and combined satellite + CDR datasets (27 resulting models). Then, using themodels selected by the previous step, we employed hierarchical Bayesian geostatistical models (BGMs) topredict the three poverty metrics at unsampled locations across the population.
All BGMs were implemented using integrated nested Laplace approximations (INLA), and the models fit
using R-INLA, with the Besag model for spatial effects specified inside the function. The geostatisticalmodels defined for the poverty data were applied to produce poverty predictions as a posterior

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distribution with complete modelled uncertainty around estimates. The posterior mean and standard
deviation were then used to generate prediction maps with associated uncertainty. Model performance
was based on out-of-sample validation statistics calculated on a 20% test subset of data.
3

Results

We find models employing a combination of CDR and satellite data generally provide an advantage overmodels based on either data source alone. The fine spatial granularity of the resultant poverty estimates
can be seen in figure 1, which shows the predicted distribution of poverty for the DHS wealth index. We
also find that explicitly modelling the spatial covariance in the data was critically important. This resulted in
improved predictions, lower error, and better measures of fit based on cross-validation and the devianceinformation criteria.
Separating estimation by urban andrural regions highlights theimportance of different data in
different contexts. Nighttime lights,
transport time to the closest urban
settlement, and elevation were
important nationally and in ruralmodels; climate variables were also
important in rural areas. Distance toroads and waterways weresignificant in urban and rural strata.
Top-up features derived fromrecharge amounts and tower
averages were significant in every
model, affirming their importance inpoverty work. People predicted to
be poorer top up their phones more
frequently in small amounts. Percent
nocturnal calls, and count andduration of SMS traffic weresignificant nationally. Mobility and
Figure 1. National level prediction maps for mean wealth index. The map was
generated using CDRs + satellite data in Bayesian geostatistical models. Red
social network features wereindicates poorer areas.
important in all three strata. In
urban areas, SMS traffic wasimportant, whereas multimedia messaging and video attributes were key in rural areas. The resulting
predictions line up well with existing SAE estimates for Bangladesh, and with high resolution maps of slumareas in Dhaka.
4

Discussion

Models built with CDR data and models built with satellite data perform comparably in their ability to map
poverty indicators, and integrating data from mobile phones and satellites provided improvement inpredictive power and lower error. In urban areas, the CDR data were able to produce accurate, highresolution estimates not possible using RS data alone. As such, CDRs potentially allow for estimation of
wealth at much finer granularity – including the neighbourhood or even the household or individual – thanthe current generation of RS technologies. CDRs also provide significant advantages in temporal
granularity: CDRs update in real-time versus RS data, which update far less frequently. Although in thisstudy we have not utilised dynamic validation data, it is a clear future application for CDRs in real-time tobetter comprehend the dynamic nature of poverty.

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5

References*Pozzi, F., Robinson, T. & Nelson, A. Accessibility Mapping and Rural Poverty in the Horn of Africa. (Food andAgriculture Organization of the United Nations, 2009).
Rogers, D., Emwanu, T. & Robinson, T. Poverty Mapping in Uganda: An Analysis Using Remotely Sensed andOther Environmental Data. (Food and Agriculture Organization of the United Nations, 2006).
Tatem, A. J., Gething, P. W., Pezzulo, C., Weiss, D. & Bhatt, S. Development of High-Resolution Gridded
Poverty Surfaces. (2014).
Blumenstock, J., Cadamuro, G. & On, R. Predicting poverty and wealth from mobile phone metadata.
Science 350, 1073–1076 (2015).
Noor, A. M., Alegana, V. A., Gething, P. W., Tatem, A. J. & Snow, R. W. Using remotely sensed night-timelight as a proxy for poverty in Africa. Popul. Health Metr. 6, 5 (2008).
Ghosh, T., Anderson, S. J., Elvidge, C. D. & Sutton, P. C. Using Nighttime Satellite Imagery as a Proxy
Measure of Human Well-Being. Sustainability 5, 4988–5019 (2013).
Watmough, G. R., Atkinson, P. M., Saikia, A. & Hutton, C. W. Understanding the Evidence Base for Poverty–
Environment Relationships using Remotely Sensed Satellite Data: An Example from Assam, India. World
Dev. 78, 188–203 (2016).
Blangiardo, M., Cameletti, M., Baio, G. & Rue, H. Spatial and spatio-temporal models with R-INLA. Spat.
Spatio-Temporal Epidemiol. 4, 33–49 (2013).
Blangiardo, M. & Cameletti, M. Spatial and Spatio-temporal Bayesian Models with R - INLA. (John Wiley &Sons, 2015).
Gruebner, O. et al. Mapping the Slums of Dhaka from 2006 to 2010, Mapping the Slums of Dhaka from2006 to 2010. Dataset Pap. Sci. Dataset Pap. Sci. 2014, 2014, e172182 (2014).
glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models | Calcagno |Journal of Statistical Software. Available at: http://www.jstatsoft.org/article/view/v034i12. (Accessed: 21st
January 2016)
Rue, H., Martino, S. & Chopin, N. Approximate Bayesian Inference for Latent Gaussian Models by Using
Integrated Nested Laplace Approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 319–392 (2009).

*Please see full details and references from this study published in the Journal of the Royal SocietyInterface

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Estimating population behaviour to describe
activity-based land-use in emerging economies using
mobile phone event series
Gregor Engelmann

James Goulding

N-Lab, Horizon CDT
University of Nottingham

N-Lab
University of Nottingham

Gavin Smith

David Golightly

N-Lab
University of Nottingham

Understanding and monitoring the makeup of a city is integral to successful urban planning. Traditional ways of describing land-use involve the use of surveys, which are costly
and quickly outdated. Unparalleled mass urbanisation makethem particularly unsuited in emerging economies, where
mobile phones are now nearly ubiquitous. This paper explores the potential of utilising automatically generated Call
Detail Records (CDR) to understand population behaviour
and by extension provide a summary of activity-based landuse. Using principal component analysis, non-negative matrix factorisation and unsupervised clustering, the paper
shows the feasibility of describing activity-based land-use
from CDR data in an exemplar East African city.

1.

INTRODUCTION

The understanding and monitoring of activity-based landuse is intrinsic to successful urban planning. Land-use has
traditionally been understood in terms of: land characteristics; ownership characteristics; and activity undertaken on
the land itself [1]. Traditional techniques to monitor landuse have focused on manual surveys and more recently analyses of satellite imagery. These approaches are time consuming, expensive, infrequent, and quickly outdated. The
rapidly increasing ubiquity of mobile phones, its role as aproxy for human movement, and the vast amounts of data
generated by such devices provide much potential to supplement or even replace these traditional methods. This is ofparticular relevance in East Africa, where data gaps due tothe expense and logistics required to employ sensor technologies exist. Closing such gaps is key in generating information to guide effective infrastructure investments and urban
planning. In this paper we introduce a framework for identifying activity-based land-use via call detail record (CDR)
data. The approach applies and compares principal component analysis (PCA) and non-negative matrix factorisation(NMF) to factorise underlying usage trends, prior to clustering regions into land-use classes via k-means clustering.
Due to its dynamic nature, the use of CDR data to characterise actual behaviour rather than aggregate descriptors,
is particular appropriate in emerging economies where mass
urbanisation is resulting in rapidly changing environments.
A number of previous studies have utilized aggregate CDR
data to analyse urban environments. One of the first was
Reades et al [4], that found a correlation between calling

Human Factors Research
Group
University of Nottingham
activity and land use patterns in Rome. Subsequent studies
have used either:- Unsupervised [5, 6, 2], clustering according to aggregate
statistics related to network behaviour such as average number of connected mobile phone subscribers, or call volumes;- Semi-structured [3], seeding clustering algorithms with asmall set of known points of interest to calibrate results or;- Supervised [7], using ground truth or existing land use datafor calibration techniques to identify land use classes at thebase transceiver station (BTS) levelapproaches. Accurate and fine grained land-use data necessary for the use of semi-structured and supervised approaches is extremely hard to find in East Africa. As weare restricted by the lack of accurate zoning for validation
we focus improving unsupervised learning approaches instead. Our work goes further than previous unsupervised
approaches, however, in that we: 1. first convert raw CDR
event series to time series at different levels of temporal granularity to perform sparsity analysis to identify BTS outlierswith anomalous behaviour that would skew the observed behaviour patterns; and 2. we then also apply and compare
principal component analysis (PCA) with non-negative matrix factorisation (NMF) to extract latent features within
population behaviour, reflecting daily patterns of life. Using factorisation techniques allows us not only to generate a vocabulary to describe underlying behaviours (suchas nightlife, commuting, or industrial patterns), but thenalso allows us to go on to produce interpretable clusters inthis compressed behavioural space. The resulting land-use
archetypes can be understood and characterised in termsof how much they express each of these building block behaviours.

2.

METHODOLOGY

The data used as part of this study covers a total of 433.6
million network events covering calls and SMS for 415k mobile phone subscribers taking place across the Dar es Salaamregion of Tanzania over a period of 122 days in the autumn of20141 . A raw CDR record is automatically created for each1

Due to both individual and commercial privacy, theanonymised data used as part of this study is not publicly
available, and was provided to us through a partnership witha major private sector network operator in Tanzania

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Figure 1: Six trends extracted via PCA (left) and NMF (right) from the CDR data. Each describes a different
underlying population behaviour, which form the building blocks for our activity based land-use clustering approach.
With the exception of component 5, the NMF components are far easier and more intuitive to interpret.

network event and includes a range of attributes including:timestamp; an anonymised mobile phone subscriber ID; callduration; and a Base Transceiver Station (BTS) identifier.
Our approach for estimating land-use patterns took thismass data set and followed a multi-step process.
First an event series of network events was extracted foreach BTS. These event series were then themselves split intohour, day and week periods, and binned to produce timeseries of hourly event counts. The resulting time series instances were used to identify BTS with high levels of sparsity
indicating potential issues with the operational status of a
BTS over the study period. Any BTS showing activity forless than 55% of the bins was removed as noise from subsequent analysis. We then used the time series generated
from the week periods for further analysis. This data set
contained approximately 9,500 weekly time series instances,each reflecting 168 hourly intervals (24 × 7). Some BTS
showed a uniform distribution of activity during day time,
while others showed a higher network activity pattern either
at evening times only, or at both morning and evening times.

Figure 2: Observed behaviour clusters over an average week in Dar Es Salaam, Tanzania. Each cluster
corresponds to a different land-use class

The total amount of network events differed quite significantly across the study area, making direct comparison andclustering difficult. In order to compensate for differing network event counts, feature scaling was applied to each timeseries for standardisation. Standardising by z-scores was notpossible, as NMF does not allow for the input matrix to contain any negative values. We applied both PCA and NMF
to these time series to identify latent features occurring inweekly usage behaviour. This factorisation process reduced
both noise and reduced our feature space from 168 to 7 dimensions, eliminating outliers and addressing the curse ofdimensionality. Moreover, manual analysis of the resulting
latent features allowed us to interpret subsequent cluster
archetypes. We found that due to allowance of negative
loadings, the factors resulting from PCA are far harder tointerpret (see Figure 1).
Following construction of our latent feature space, k-meansclustering was applied in order to identify k different landuse areas based on our interpretable activity trends.

3.

RESULTSThe factors resulting from our analysis reflecting underlying activity patterns are illustrated in Figure 2. These areinformative of population behaviours, with common weekly
trends being revealed. Component 2, for example, reveals
general underpinning network activity patterns (and is very
similar to the average weekly time series for all towers) witha gradual increase from 7am until 10am, plateauing out before an early evening spike in network events. Component
3, however, reflects a predominant residential activity pattern, with population leaving an area in the middle of theday, and returning after work. In contrast, Component 5
reflects a workplace behaviour, with high daytime activity,
zero nighttime events, and a significant weekend drop. Any
particular area may be composed of a combination of landuses (for example, half residential and half industrial), andso may express each of these building block behaviours to adifferent amount. These components provide us with a vocabulary through which we can discuss those combinations,
without need for imagery or demographic data.
With these building blocks in hand and each time series
projected into the lower dimensional space they represent,
k-means clustering technique was applied. The choice of kremains an arbitrary one dependent on the task in hand. As
such the number of clusters n was varied from 2 to 15 clus-

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The map in Figure 3 shows a plot of the spatial distribution of these clusters for the centre region of Dar es Salaam.
In order to protect commercial interests of the network operator that provided us with the data, we interpolate BTS
catchment areas into a 500m x 500m grid-cell representation.

4.

CONCLUSION

In this study we have shown how factorisation techniques
and k-means clustering might be used to identify interpretable
activity-based land-use clusters from CDR data. While notalways feasible, as data may have been pre-aggregated, hourly
aggregates seem to be the best temporal scale for performing sparsity analysis for outlier detection. We also foundthat NMF is far superior to PCA in describing underlying
behaviour trends due to the absence of negative loadings.

5.

Figure 3: Spatial distribution of activity-based landuse areas in the central Dar Es Salaam region. Purple is ‘Affluent-Commercial’, Green is ‘Slum’, Yellow is‘Formal-Night-Active’, Red is ‘Industrial’ and Blue is‘Residential-Commuting’ters. The most interpretable results were obtained at n = 5.
The centroids for each of the resulting clusters are illustrated
in Figure 22 . Annotation of each class was performed by examining the extent it expressed each underlying behaviourbuilding block in combination with external knowledge:
Cluster 1 - Affluent-Commercial: consistent activity
throughout the day (zones that bring people in due totourism, job opportunities and amenities).
Cluster 2 - Slum: characteristic of a poor demographic
with lower daytime activity, low morning activity andsignificant peak in the early evening (perhaps due tolack of mobility).
Cluster 3 - Residential-Commuting: this profile expresses a far higher expression of component 3 (the residential activity pattern) than other behaviours, suggesting a commuting pattern.

REFERENCES[1] J. R. Anderson, E. E. Hardy, J. T. Roach, and R. E.
Witmer. A land use and land cover classification system
for use with remote sensor data. Technical Report 964,
USGS, 1976.
[2] D. M. R. S. Kaushalya Madhawa,
Sriganesh Lokanathan. Using mobile network big data
for land use classification. Technical report, LIRNEasia,
July 2015.
[3] T. Pei, S. Sobolevsky, C. Ratti, and S. L. S. C. Zhou. A
new insight into land use classification based on
aggregated mobile phone data. International Journal of
Geographical Information Science, 28:1988–2007, 2014.
[4] J. Reades, F. Calabrese, and C. Ratti. Eigenplaces:
Analysing cities using the space-time structure of themobile phone network. Environment and Planning B:
Planning and Design, 36:824–836, 2009.
[5] V. Soto and E. Frias-Martinez. Automated land use
identification using cell-phone records. In HotPlanet ’11
Proceedings of the 3rd ACM International Workshop on
MobiArch, pages 17–22. ACM, 2011.
[6] V. Soto and E. Frias-Martinez. Robust land use
identification using cell-phone records. In Proceedings ofthe 1st Workshop in Pervasive Urban Applications, inconjunction with 9th Int Conf Pervasive Computing,
2011.
[7] J. L. Toole, M. Ulm, M. C. Gonzalez, and D. Bauer.
Inferring land use from mobile phone activity. In
Proceedings of the ACM SIGKDD International
Workshop on Urban Computing - URBComp. ACM,
2012.

Cluster 4 - Industrial: high expression of component
5 (non-residential). Some commuting, but a highly
significant lack of mobility activity in the mornings,
evenings and weekend.
Cluster 5 - Formal-Night-Active: average activity overthe course of the day, but with significant spikes in theevening and night.
2
A lower number of clusters mixed different behaviours,
while a larger number of clusters produced some with insufficiently distinct behaviours.

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Targeted disease containment strategies based on
spatial network analysis
Stefania Rubrichi a, Mirco Musolesi b, and Zbigniew Smoreda aa – Sociology and Economics of Networks and Services dept., Orange Labs, Parisb - Department of Geography, University College London

Abstract

Epidemic outbreaks are an important healthcare challenge, especially in developing countries
where they represent one of the major causes of mortality. Approaches that can rapidly target
subpopulations for surveillance and control are critical for enhancing containment processes
during epidemics. The identification of optimal candidates for targeting preventive andcontainment measures entails both theoretical and computational issues. Moreover, spatial
heterogeneity of risk distribution introduces new challenges in the way we describe epidemic
dynamics. In particular, in the case of infectious diseases where transmission takes places
because of individuals’ proximity, an effective study of epidemic spreading requires to take
into account both the social and the spatial dimensions of the problem, explicitly including
people’s mobility.
Using a real-world dataset from Ivory Coast, this work presents a method for interventions
based on the socio-geographical heterogeneity of disease transmission dynamics extracted
from cellular data. More specifically, exploiting structural properties of a spatial network
derived from mobile phone Call Detail Records (CDRs), we investigate different target
strategies based on the identification of the highest risk geographical areas or the highest risk
individuals as the best candidates for isolation in order to slow down an epidemic.
The approaches presented provide in this communication further evidence that mobile
phone data can effectively be exploited to model disease spreading at different geographic
levels. In particular, we show that combining meta-population models with explicit spatial
properties of mobility networks from CDR data is a promising approach for developing
containment strategies for supporting decision-making during country-level pandemics.
*
The empirical evaluation of this work is based on mobile phone and epidemiological data.
We analyzed an anonymized set of mobile phone data, Call Details Records (CDR) collectedby Orange Cote d’Ivoire. It consists of billing information of about 8 million mobile phoneusers (i.e., 35% of the country population), collected between February and October 2014 in
Ivory Coast, for a total of about 4.5 billion records.
Concerning the epidemiological data, to model realistic disease spread dynamics, weconsidering a scenario based on values of the parameters estimated from the Ebola
outbreak in Sierra Leone in 2014.
In order to describe the countrywide-scale infectious disease spread, where individuals
change location over the time, we use a meta-population model. This framework has
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traditionally provided an attractive approach to epidemics modeling. It permits to include amore realistic contact structure, and to reflect the spatial separation of the sub-populations,in which case the contact rate might vary with spatial separation.
To model the process of disease transmission we consider the SEIR epidemiological model.
Figure 1 shows a schematic representation of the model.

Figure 1. Schematic illustration of a meta-population epidemiological model with SEIR dynamics. On
the left side, the subpopulations network consists of patches connected among them according toindividuals’ moving. The individuals in the patches are either susceptible (blue filled circles), infected(red filled circles), exposed (green filled circles) or recovered (pink filled circles). In each patch, thedisease transmission and recovery occur according to the SEIR compartmental model. Then
individuals move among patches according to specific mobility pattern (i.e., mij, mji).

Further, we investigate the chance of success when developing control strategies coverage,
at both spatial and individual basis. For this purpose, we propose a method for identifying thehigher-risk sub-populations as best candidate for containment strategies. In particular, we
tried to estimate a measure of “spreading power” associated with a geographic area as wellas with a single person, based on spatial and behavioral indicators, respectively, inferred frommobile phone data.
First, we consider the spatial targeting. We approached this problem as the identification of
the influential spreader nodes within a complex spatial network. We consider, among theothers, the accessibility, which have been shown able to provide valuable insights into therelationship between structure and spreading dynamics (Viana et al. 2012). We approachedaccessibility issue by considering a recently proposed measure for valuing the spatial
interactions, namely the “place rank” (El-Geneidy & Levinson 2011).
As curbing the spread of a disease in an entire geographical region might be restrictive andsomewhat unlikely, to improve the targeting process, we consider the spreading power ofsingle person based on their mobility profiles. We investigate the effect of specific spatial
behavioral indexes, linked to users’ mobility, on the identification of individuals at highest
risk: radius of gyration, entropy of visited sub-prefectures, and percentage of time spent at
home.
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We focused on the case of person-to-person transmitted diseases, where social andenvironmental factors (e.g., crowded setting) are primary determinants of transmission, andtherefore must be considered when implementing control strategies. However, although
central in describing the course of epidemics, these factors bring an intrinsic spatial
variation, whose incorporation in epidemiological models remains a key theoreticalchallenge.
We have therefore considered the problem of the spatial localization of interactions andtried to capture and characterize the social-geographical heterogeneity of transmission. Thistask was carried out using two techniques which a focus on the influence of the structuralfeatures of the network on the epidemic process. In the first approach, we took into
consideration geographic diversity, seeking at identifying geographic areas with the higher
opportunity of contact (i.e., where the majority of exchange is likely to originate). By taking afundamentally novel method in defining space accessibility and attractiveness, we measured
the “spreading power” of the nodes in a spatial network to be isolated.
In the second approach, using a spatial-range-based mobility measure, we quantified the“spatial behavior” of single individuals as a measure of spreading risk of the nodes. Based on
this, we selected a subpopulation that is expected to become infected and simultaneously
infectious earlier and with higher probability than average population because of his/hermobility profile.
The results show the importance and relevant effects of the spatial dimensions on the
spreading of infectious disease. While space influence has frequently reported anecdotallyand there have been relatively little systematic investigations, our work tries to bridge thisgap through a twofold scheme: first, it seeks to quantify and capture the associated complex
diversity, then it utilizes such a diversity as predictive correlates, to enhance our ability tounderstanding the dynamics of the epidemiological process and defining effective targeted
control interventions.
Moreover, the proposed method for identifying the most influential elements is, in our
knowledge, fundamentally different than that taken by most typically used measures. On the
one hand, using a flow-based measure, we sought to incorporate the dynamic of the
spreading that unfolds along links into the measure itself. On the other hand, removing thedirect dependence of its definition on the location of cellular towers, we could rely on amobility measure which prevent from bias due to variations in tower density.
References:
Viana M P, Batista J L, Costa L da F, “Effective number of accessed nodes in complex networks.” Phys.
Rev. E: Stat Nonlin Soft Matter Phys., 2012.
El-Geneidy A & Levinson D, “Place rank: Valuing spatial interactions.” Netw. Spat. Econ., 11(4), 643–
659, 2011

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What does mobile metadata measure?
Insights from a pilot study during a sudden emergency
Carolina Mattsson

Drew Margolin

Stefan Wojcik

David Lazermattsson.c@husky.neu.edu dm658@cornell.edu stefan.wojcik@colorado.edu d.lazer@neu.edu
Northeastern University
Cornell University
Northeastern University Northeastern University
Harvard Kennedy School

1. Introduction
Mobile data is a frontier in understanding society, and has helped create the disciplineof computational social science.1 Behavioral data from mobile phones have been used to
study the societal reaction to emergencies2,3 and many other phenomena. A smallergroup of studies have sought to tie this big data revolution back to the heart of thesocial sciences.4,5 Continuing to do this is crucial, since big data approaches struggle
when separated from subject matter expertise.6 There is a pressing need for calibrationof our most powerful tools.
In a pilot study following the Boston Marathon bombing7 we collected mobile records,
asked traditional social network survey questions, and elicited bombing-relevant
information through a smartphone app. Using this data we first attempt to classify ties
into social categories based on mobile metadata, uncovering issues that illustrate theneed for contextualized measurement. Tragedy provides us with an unwelcome but
profound source of context. By measuring social ties when they are at their mostpoignant, we probe the meaning behind mobile metadata measurement. We also point
out likely biases introduced by mobile metadata in studying emergency communication.
2. Data Collection
The authors created a smartphone appthat accessed the call and text logs on
the respondent’s phone andadministered a survey based on thoselogs. The survey first asked respondents
about demographics, phone use, family
composition, and details of theirsituation at the time of the bombings.
The app then introduced into the survey
up to eight mobile contacts with whom
the respondent communicated after thebombing – although with a timing error of one hour. Respondents were asked eachcontact’s gender and location at the time of the bombing, their relationship to that
contact, and whether they exchanged help or information on the day of the bombing.
Lastly, the app collected aggregated behavioral data over the 30 days before from thephone itself. If given additional permission, the app kept the full call and text logs.
Data was collected after the Boston Marathon bombing from 152 voluntary respondents.
87 of the respondents agreed to donate their fully detailed call and text logs. To
motivate participation by those most affected by the bombing, the researchers made a
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$3 donation per respondent to The One Fund Boston, the official charity for the victims.
The smartphone app was available for download on Android phones for several months.
These efforts created a rich and focused dataset, with 48 respondents and 97 top mobile
contacts known to have been within a mile of the bombings. 29 and 56 of these,
respectively, are covered by fully detailed log data
3. Results
Using the 30 days of aggregated communication history available for most respondents
we explored the feasibility of classifying the two most common relationship categories:
mother and friend. We find that somevariables do fairly well – ex. dyads withno texts and high call volumes are
overwhelmingly mothers, and viceversa. But basic classifiers only reach an
out-of-sample AUC of 84, which
remains below the base rate of friends(85.5%, N=531). As a classification task,social ties are a formidable challenge.
But is this primarily a technical issue?
Wherever the highlighted partition isdrawn, most of the misclassifications
LDA classifier with apparent error 0.115
would be between mothers and those
Correctly classified in black, incorrect in red.
friends who text and talk especially much
Note neater sorting along the axes.
– ‘best friends’ perhaps. What constitutes
friendship, and to a lesser extent maternal ties, is a social construct that may not map
1:1 onto measurable actions and behaviors that we care to study with mobile metadata.4
Our app collected several measurable actions and behaviors in the context of a disaster.
We use classification trees where the splits are based on statistical significance tocompare the predictive power of mobile metadata to traditional relationship categories.
We limit our sample to the 216 dyads where the closest person was within a mile of thebombing, and where we have both mobile history and day-of mobile records.
Providing help: Our survey asked whether you gave or received help from specificcontacts, thus allowing for a dyad measure of providing help. We find that traditional
relationship categories do not significantly distinguish between dyads where help wasprovided and not. Interestingly, the fraction of communication days is a predictor.
Crisis communication: From the mobile records for the day of we determine whether the
dyad communicated in the first 30 minutes after the bombing. We find that the mostsignificant predictor is based on mobile history: log number of calls in the past 30 days.
The second predictor is based on relationship category: parent-child ties. These are
powerful predictors – both dyads who had 7 or more calls in the past month, and dyads
between parents and children who had fewer calls were an order of magnitude more
likely to share a call in the immediate aftermath of the bombing than other surveyed
ties. Both ‘mothers’ and ‘best friends’ are likely to communicate early in this case.
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We find that mobile metadata are useful in
predicting mobile communication and help
provision in a sudden emergency. They appear
to pick up on social categories – ‘strong’ andproximate ties – that become poignant in thiscontext. However, metadata does not pick up
on all such ties, missing parent-child ties that
do not call routinely.
Understanding what is and what is not
captured by mobile metadata in contexts wecare about is a promising avenue for further
research. Our sample is small and comes fromone very specific source – the Boston
Marathon bombing - so specific results must
be taken with a large grain of salt. Larger
studies employing this context-richmethodology across different scenarios havethe potential to determine more generally
what mobile metadata is measuring.
4. Additional findings

Did the dyad communicate via mobile in
the first 30 minutes after the bombing?

Classification tree with statistically significant
splits. Bars show the fraction of dyads who did
communicate early in each group.

The methodology presented here is also useful for illuminating biases introduced whenusing mobile metadata to study particular social processes. For example, in our pilot
study we find that many, but not all, of those most affected used their mobile phones in
the immediate aftermath of the bombing. Those who do use their phones appear to beoversampled from those away from their family and friends at the time of the bombing.
5. Conclusions
Methodologies such as those used here can provide much-needed context for evaluating
the power of mobile metadata as a measuring tool for social ties. Results of a pilot study
show cause for cautious optimism – despite feasibility issues classifying social ties,
mobile metadata does pick up on real relationships that are called upon in real
emergency situations. The pilot study also uncovers several sources of systematic biasthat future purely ‘big data’ studies must contend with.
1 Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., … Van Alstyne, M. (2009). SOCIAL
2 Bagrow, J. P., Wang, D., & Barabasi, A.-L. (2011). Collective response of human populations to large-scale

emergencies. PloS One, 6(3), e17680.

3 Sundsoy, P. R., Bjelland, J., Canright, G., Engo-Monsen, K., & Ling, R. (2012). The Activation of Core Social

Networks in the Wake of the 22 July Oslo Bombing (pp. 586–590). IEEE.

4 Eagle, N., Pentland, A. S., & Lazer, D. (2009). Inferring friendship network structure by using mobile phonedata. Proceedings of the National Academy of Sciences, 106(36), 15274–15278.

5 Boase, J., & Ling, R. (2013). Measuring Mobile Phone Use: Self-Report Versus Log Data. Journal ofComputer-Mediated Communication, 18(4), 508–519.

6 Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: traps in big data

analysis. Science, 343(6176), 1203–1205.

7 Lazer D ,Kennedy R, &Margolin D. (2013). Communication in the aftermath of the Boston Marathon bombing.

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TELCO APPLICATIONSNetMob 5-7 April 2017 – Vodafone Village – Milan

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Predict Cellular Network Traffic with Markov Logic
Marco Lippi, Marco Mamei, Franco Zambonellli
Dipartimento di Scienze e Metodi dell’Ingegneria
University of Modena and Reggio Emilia, Italy{marco.lippi,marco.mamei,franco.zambonelli}@unimore.it

ABSTRACT
We propose a Markov logic approach to cellular network
traffic forecasting, that exploits spatial relationships between
close cells in the network grid. Experimental results show
the advantages of using such information.

1.

INTRODUCTION

In this work we present a novel approach based on
Markov Logic [2] to predict cellular network traffic acrossthe city. As this traffic is a widely used proxy for people
presence, our approach can be applied to predict crowd
distribution. We focus on aggregated CDR data (i.e.,
data measuring the number number of CDRs generated
from a region, without any reference to the IDs of thepeople being involved). This kind of data presents a
number of ready-to-market applications as privacy concerns are much reduced (in contrast with CDRs withanonymized IDs). In fact, all major telecommunicationcompanies already have services for the analysis andcommercial exploitation of this data. While there areseveral works analyzing properties of aggregated CDR
data and predicting user movements from individual
CDRs [3], the task of predicting future density of CDRs
from aggregated data is relatively under-explored.

2.

METHODOLOGY

We focus the analysis on aggregated CDR data thatcount SMSs, calls and Internet traffic over specific areas of the city and at time intervals. Specifically, thegeographic area under analysis is tessellated with anirregularly shaped grid, similar to a Voronoi tessellation. Thus, the more cell network antennas present,
the denser the grid (see Figure 1a).
The resulting dataset (illustrated in Table 1b) is a setof counters estimating, for each cell of the grid and 15minutes-interval, the number of SMSs, calls and Internet traffic. Counters can also be fractional to take into
account CDR interactions originating in a cell and ending in another one. The original data comprises about
12 million records like the ones depicted. For each celland CDR type, the typical plot resembles the one in
Figure 1c (top) where it is possible to observe daily andweekly patterns in city dynamics.
To better highlight variability in our data, for eachcell, we computed the mean (µt ) of the time series inthat 15 minutes interval (i.e., the mean among all daysat that time) and obtain a “standardized” score by computing x̂t = (xt − µt )/µt . The resulting time seriesshows the deviation from the mean of that cell at thattime, in mean units (e.g., x̂t = 1 means that there is
twice – 100% more – as many people than usual), see
Figure 1c (bottom). Finally, we discretized x̂t into a setof classes. Working with discrete values notably simplify computations, without compromising the actual
significance and interpretability of the results.
To predict cellular network traffic, we apply Markov
logic, a statistical relational learning method, to perform collective classification on a grid of cells. While
traditional machine learning classifiers typically treat
the examples as independent and identically distributed,
statistical reltional learning approaches are capable oftaking into account relations and inter-dependencies between the examples to be classified, so that a joint classification spanning multiple examples can be performed.
In particular, we aim to exploit the spatial relationships
between cells, as the nature of CDR data is inherently
relational along this dimension: at time t, the traffic
at two cells c1 and c2 spatially close in the network will
typically be strongly inter-related. Inspired by the workin [1] for road traffic flow forecasting, we modeled ourdomain with a set of logic predicates that describe thedynamics of CDR traffic data during time and acrossdifferent cells. Supposing to discretize the amount oftraffic in C classes, predicates Class0(cell,time), . . . ,ClassC(cell,time) can be used to indicate the fact
that, at a certain cell and at a given time, the traffic
quantity falls in one of such classes. A simple predicate
Neighbors(cell,cell) indicates that two cells are spatially close in the grid. Time is modeled with predicate
Next(time,time). Additional information about the
day of the week and the part of the day can be alsoeasily modeled with logic predicates. Given a domain
described in terms of logic facts, a Markov Logic Network (MLN) consists in a set of weighted rules that

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TELCO APPLICATIONSb)Timestamp
1/3/2015 13:00
1/3/2015 13:00
2/3/2015 13:15...
30/4/2015 20:45

Cell
3943
3943
3943
...
3943

Type
SMS InCall In
Network...
Call Out

Count
4.34
2.34
9.34
...
3.45

c)
1000

cdr

a)

500

0
mar 30

apr 06apr 13time

apr 20

apr 27apr 20

apr 27

4

z

2

0

−2
mar 30

apr 06apr 13time

Figure 1: a) Irregular grid tessellating the area b) Example of aggregated CDR data c) Example ofdata in a grid cell: (top) Original behaviour extracted from mobile phone data. (bottom) “standardized” score showing the deviation from the mean of that cell at that time, in mean units.
model relationships among the existing predicates. For
example, a simple predictor that forecasts, for cell c andtime t, the same traffic class observed at cell c at timet − 1, is obtained with the rules:
Class0(c,t1) ∧ Next(t1,t2) => Class0(c,t2)...
ClassC(c,t1) ∧ Next(t1,t2) => ClassC(c,t2)

Clearly, such rules are not always true, but they aretrue with a certain probability. Given a collection ofobservations of past events, the Markov logic framework
allows to learn the weights of such rules directly from
data. The higher the weight, the higher is a probability
that the rule will be true. Once the weights of the MLN
have been learned, one can use the model to computethe truth value of some query predicates. In the caseof this work, the aim is to forecast the dynamics of
CDR traffic in the future. Given the current state of
the CDR traffic network, by performing inference over
the MLN it is possible to retrieve the cell configurationthat maximizes the probability of the rules in the model.
In order to exploit spatial relationships, rules like thefollowing one can be used:
ClassC(c1,t1) ∧ Next(t1,t2) ∧ Neighbors(c1,c2)=> ¬ Class0(c2,t2)

Such rule means that, if there is a high traffic (class C)
in a certain cell c1 at time t1, then at the next time
step t2 it is unlikely that a neighbor cell c2 will havevery low traffic (class 0). Complex relationships anddependencies can by modeled with such rules.

3.

EXPERIMENTS

We focused the analysis on the province of Milan and
we used aggregated CDR data collected over a periodof two months: from March 1, 2015 to April 30, 2015
and including calls and SMS sent and received and network traffic. In our experiments we sum together all
SMS and calls sent/received, while we do not consider
network traffic (as it is expressed in a different format
in our data). For each cell, at a given time t this sum is
our xt . The area under analysis is tessellated in 1,419

cells. Cell areas can range from 0.04 Km2 in the city
center to 40 Km2 in the suburbs. Temporal resolution
is 15 minutes. For each cell, we compute the standardized score x̂t = (xt − µt )/µt and we discretized thosevalues in 5 classes associated to intervals: [−∞, 0.25],[0, 25, 0.50], [0.50, 0.75], [0.75, 1], [1, ∞] (i.e., the class[0.75, 1] contains those values having from 75% to 100%
more traffic than the mean at that time). Overall, 79%of data fall in the first class, 11% in the second one, 4%both in the third and fifth one, 2% in the fourth one.
Following the work in [3], we try to establish upperbounds for the predictability of aggregate (discretized)
CDR behavior across cells. We compute different entropy measures for each cell: (i) The random entropy
srand = log2 N = 2.3 (N is the number of values exhibited by the cell – all cells have 5 values). (ii) The unN
correlated entropy sunc = − j=1 pj ∗ log2 pj . (iii) The time (Markov) correlated entropy st = − x̂t x̂t−1p(x̂t , x̂t−1 )log2 p(x̂
 t |x̂t−1 ). (iv) The spatial correlated
entropy ss = − x̂t p(x̂t , x̂1t ...x̂kt )log2 p(x̂t |x̂1t ...x̂kt ). wherex̂1t ...x̂kt are values in neighbor cells. (v) Finally,  thespatio-temporal correlated entropy sst = − x̂t x̂t−1p(x̂t , x̂t−1 , x̂1t ...x̂kt )log2 p(x̂t |x̂t−1 , x̂1t ...x̂kt ).
Naturally, for each cell, we will have srand ≥ sunc ≥st , ss ≥ sst . We then compute the predictability Π associated to each entropy according to Fano’s inequality [3].
This is an upper bound for any algorithm predicting x̂t .
Πrand = 20% for all cells. Results for other predictabilities are in Figure 2a. For example, from these results,
we can infer that the upper bound for a classifier using
only temporal information (1 step – 15 minutes back) is
about 85% (median value). Despite these encouraging
predictability results, it is worth noting that the class
distribution is highly skewed (e.g., 79% of all the x̂t are
in one class), and thus a simple majority classifier would
get very good results in terms of accuracy, according toΠunc . Therefore, more careful analysis is needed.
We run experiments to test the Markov Logic predictor focusing on a subset of 23 cells1 , and we employed1

We chose the cells whose id contains the prefix 3943.

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b)

0.80

0.90

a)

Πunc

Πt

Πs

Predictor
RandomMajority
RW
MLN

Accuracy
65.7
80.3
74.3
77.4

Avg F1
19.8
16.1
38.8
40.6

c)
0
1
2
3
4

0
48,015
4,257
951
470
1,163

1
3,738
1,894
418
134
163

2
931
381
568
164
194

3
437
110
103
111
208

4
976
122
145
191
1,546

Πst

Figure 2: a) Predictability according to Fano’s inequality associated with uncorrelated, time, spatialand spatiotemporal correlated entropies. b) Comparison of the classifiers employed in the experimental study. We report accuracy and average F1 over the five classes. c) Confusion matrix of the
MLN predictor. Rows: true labels, columns: predictions.
the first half of the data (March) for training our system, while the remaining part (April) was used as test
set. For Markov logic, we used the Alchemy software,2
training our model for 1,000 epochs with the voted perceptron algorithm. All the other software parameters
were left to their default values. We compared four different predictors (see Table 2b). As a first baseline, wemeasured the performance of a classifier that randomly
predicts one of the four classes, by drawing from a probability distribution that knows the true proportions between the classes (called Random in Table 2b). As asecond baseline, we employ a classifier that simply always predicts the most frequent class (named Majority
in Table 2b), that is class 0 in our case, corresponding
to low traffic. As a third predictor, we use a Random
Walk (RW in Table 2b) that produces as a forecast attime t the same class that was observed at time t−1 (foreach cell independently). Finally, we employ an MLN
exploiting spatial relationships between the cells. The
task is to predict the status of the grid 15 minutes aheadin the future. We report both the accuracy and average
F1 3 over the five classes. As already introduced when
discussing Πunc predictability, the accuracy is clearly
dominated by the most frequent class, that is present
80.3% of the times in the test set (which is, in fact, theaccuracy of the Majority predictor), while the average
F1 gives the same importance to each of the five classes,
and it is thus more significant in this setting. For example, the Majority predictor achieves the best accuracy, but actually it is a completely useless system, as
it never predicts something different from the low-leveltraffic. It is interesting to see that the RW predictor already achieves a significant improvement over Random,
thus proving to be a very strong competitor. This behavior suggests that CDR traffic has a dynamic whichchanges smoothly through time, and 15 minutes aheadin the future is a short horizon to observe big changes in
the network configuration. Nevertheless, the MLN approach achieves better results than RW, both in termsof accuracy, and of average F1 . Table 2c reports theconfusion matrix for the MLN model: rows/columns
23

http://alchemy.cs.washington.edu
The F1 is the harmonic mean between precision and recall.

High

Low

Figure 3: CDR traffic at time t (left) and t + 1(right). Traffic at cells A and B increases, following the trend in the neighboring cells.

represent the true/predicted values, respectively (position i, j in the matrix indicates the number of examples
of class i that are predicted to belong to class j).
Figure 3 shows a case study in which spatial relationships help to improve the accuracy of the predictions.
The traffic classes for the cells in the network are represented for some timestamp t (left), and for the subsequent timestamp t + 1 (right). In this scenario, thetraffic in cells A and B increases (from green to yellow),which is a case where a Random Walk predictor would
fail. The MLN model, on the other hand, correctly forecasts the traffic classes for cells A and B by exploiting
spatial relationships, as most of the neighbors at time tbelong to a high (yellow, orange or red) traffic class.

Acknowledgment
Source of the Dataset: TIM Big Data Challenge 2015,
www.telecomitalia.com/bigdatachallenge

4.

REFERENCES[1] M. Lippi, M. Bertini, and P. Frasconi. Collective
traffic forecasting. In ECML/PKDD Proceedings,
Barcelona, Spain, 2010.
[2] M. Richardson and P. Domingos. Markov logic
networks. Machine Learning, 62(1):107–136, 2006.
[3] C. Song, Z. Qu, N. Blumm, and A. Barabási.
Limits of predictability in human mobility. Science,
327(5968), 2010.

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Towards a data science model for device upgrade
Roberto Caporicci, Elia Polo, Dario Patane, Francesco Calabrese
Vodafone Research
Email: {Roberto.Caporicci, Elia.Polo, Dario.Patane, Francesco.Calabrese}@vodafone.com
I.

I NTRODUCTION

Over the last decade, especially due to the introductionof Smartphones, the flexibility of their operating systems
and Social Networks, the basic phone user data requirements
are increasing to an exponential rate requiring faster, more
responsive and efficient network to rely on. Following, thetelecommunication industry is designing ever evolving infrastructures with continuous improvements and mobile providers
are adapting to the changing interest of their customer base.
It is of principal interest in the whole mobile communication industry to guarantee an extraordinary user experience byinvesting and give strategic importance to the latest wirelesstechnology standards. Mobile users, in order to adopt a new
major broadband update (commonly labeled generations) needa specific data plan from the provider and a compatible
phone device which has the necessary baseband capabilities,
leading to, in most cases, a purchase of a new mobile device
from the customer. Improving the estimation of the upgrade
rate on which consumers are spontaneously switching to a
new wireless generation is of fundamental importance fortelecommunication providers. Therefore knowing which are
the crucial underlying factors to motivate a customer to switch
to a more recent device is of increasing relevance.
Among the most pertinent aspects for device switchers
we can certainly recognise demographics. Younger people areoften more suitable to upgrade devices and are affected in alarger portion by recent trends. Country, province and wealth
may represent crucial indicator for capacity and willingness tospend on handsets. Communication habits are also playing animportant role in this decision making, thus amount and trendsof calls data traffic might be an early indicator of future data
demanding customers
Additionally, wear status, manufacturer, hardware and software characteristics of the current phone might be determinantelements to motivate a user to upgrade to a more modern one
and, going beyond, the possibility to acquire a device fromthe same manufacturer. Classical examples are brands suchApple and Samsung which rely on a great customer loyalty
for expensive phone tiers.
The main objective of this work is to understand the characteristics of users currently making use of previous wirelessarchitectures (such as 2G and 3G) and identify groups of themwith a particular tendency to switch to a new phone with 4G
connectivity.
Previous work has partially addressed the problem byfocusing on the operating change switch [4, 1]. In [2], amodel for phone replacement based on user traffic data (asa proxy for user’s economic condition) was developed. Suchmodel however was not used for actual prediction. A more

Fig. 1.

Proposed data science flow for device and recommender models.

comprehensive approach was proposed in [3], which however
highlights the importance of identifying a larger set of features
for the prediction task.
We dispose of data on million customers regarding demographics: age, gender, location, on traffic habits: trends andaverage values of data traffic, inbound and outbound calls
and SMS, on data plan: monthly data and calls allowance,
provider costs and information on the current device identified
by the TAC (Type Allocation Code). We could enrich device
information with characteristics such as hardware performance,
software version, wireless connection capacity, price, release
date and popularity. This set of features extends what was proposed in previous work, by combining both device information,
device usage, and customer demographics and billing info.
The idea is to introduce a flexible and scalable solution
to identify with a certain likelihood which users in a specific

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Fig. 2.

Important features for the Device change model.

customer base are more inclined to switch to a latest wirelesstechnology device in the immediate future. We thought tocreate two Machine Learning models in order to provide anestimation of the device switch probability (Device Change
Model) and, additionally, which would be the most relevant
device family of interest for each user (Device Recommender
Model). Figure 1 showcases the proposed data science flow
for the device change models.
II.

D EVICE CHANGE MODEL

The Device Change Model has been shaped as a binaryclassification problem by considering attributes of a certain
month (some of which have been built analyzing trends ofprevious months). The positive cases are determined by casesof device switch from a 2G/3G wireless device to a 4G one
in the subsequent N months, where N is a forecasting window
parameter. Such parameter can be tuned based on the specificbusiness application of the model. By construction, the natureof the label is very unbalanced causing a series of problematics
that have to be addressed properly.
The model has been implemented as a pipeline of data
cleaning, preprocessing and standard attributes selection and it
is making use of a Random Forest algorithm for classification
to determine the probability of belonging to each binary classwith a great capacity to scale to very large customer bases. The
most important selected attributes are related to traffic behavior
and current device characteristics: data usage and trend, voice
calls and SMS, device price, display size and device releaseage have the higher roles followed by customer age, see Figure2.
For what regards the Random Forest parameter search andevaluation we made use of a cross-validation technique andusing as scoring metric the area under the ROC (Receiver
operating characteristics) curve (AUC) because of its ability todeal with unbalanced class problems obtaining an AUC valueof 0.80 and looking at the 10% top scored population an uplift
value of 3.5.
III.

D EVICE RECOMMENDATION MODEL

In order to introduce the Device Recommendation Modela prior step has to be described: Device Clustering. Given

Fig. 3. A graphical representation of a subset of devices clustered basedon the defined features. The circle size is related to the number of customers
owning such device.

the very large number of different manufacturers and device
models, we decided to cluster together very similar phones
from hardware, software and capabilities prospective. Clustering attributes are including: Wireless Technology, release date,
CPU/RAM, operating system, popularity, price and camerafeatures. The clustering method used was K-Means with a
few number of clusters in order to give also a commercial
meaning to them (e.g. Feature Phone, Entry-level Smartphone,Mid-level smartphone, High, etc.). A graphical representationof devices clustered based on the defined features is shown in
Figure 3.
Differently to the Device Change Model the Device Recommender Model has been implemented by several binaryclassification problems (one for each cluster) using as positive
label the event of switching to a device belonging to such cluster and on input the same attributes of the Device Change withthe addition of the cluster identifier of the current device. The
considered population has been filtered to a subset for whicha phone switch has been observed, reducing substantially thedata points for training and evaluation. The reason for thischoice is to train the model only with relevant information
just about positive change cases. The most important features
for the device recommender models are shown in Figure 2.
Equivalently to the Device Change Model the evaluationand parameter tuning of each binary model have been determined by cross-validation and an AUC value which scoredwith a weighted average on cluster popularity around 0.80.
As an additional evaluation step the Device Change Model
has been trained with the most recent data and we checked themodel quality on Network data by observing the real devices
switch at the present time by looking to the complete customer
base who switched device from 2G/3G to 4G and putting
into relation to the positive event the likelihood previously

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Fig. 4.

Important features for the Device recommendation model.

computed by the model.
By looking at the top 13% of scored population by themodel a lift of 2.73 has been observed looking to a future
time window of one month. This initial result shows that asupervised learning approach to build a forecasting model topredict the population willing to adopt a new wireless technology device might be really effective if we consider together
demographics, phone traffic and current device attributes.
The proposed data science models have applicability indifferent scenarios. From one side, they can be used to performtargeted marketing campaigns toward customers with highpropensity for device change. Moreover, they could be used
to identify the right level of device orders in different storesbased on the predicted device preferences and replacement
rates. These aspects will be investigated in our future work.
R EFERENCES[1] S. Böhm, F. Adam, and W. C. Farrell. Impact of themobile operating system on smartphone buying decisions:
A conjoint-based empirical analysis. In International Conference on Mobile Web and Information Systems, pages
198–210. Springer, 2015.
[2] J. Liu, Z. Lei, L. Chen, and Y. Zhou. Understanding
how users change their mobile phones by massive data
analysis. In 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, volume 1,
pages 232–237, Aug 2015.
[3] Q. Ma, S. Zhang, W. Zhou, S. Yu, and C. Wang. When
will you have a new mobile phone? an empirical answer
from big data. IEEE Access, 4:10147–10157, 2016.
[4] Y. Wang, H. Zang, P. Devineni, M. Faloutsos, K. Janakiraman, and S. Motahari. Which phone will you get next:
Observing trends and predicting the choice. In 2014
IEEE Network Operations and Management Symposium(NOMS), pages 1–7, May 2014.
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SESSION 10
TELCO APPLICATIONSOn Added Value Of Feature Engineering for Churn
Prediction
Sandra Mitrović

Bart Baesens

Wilfried Lemahieu

Jochen De Weerdt

KU Leuven, Belgium
KU Leuven, Belgium
KU Leuven, Belgium
KU Leuven, Belgium
sandra.mitrovic@kuleuven.be bart.baesens@kuleuven.be wilfried.lemahieu@kuleuven.be jochen.deweerdt@kuleuven.be

I. I NTRODUCTION
Churn prediction (CP) in telecommunications has been a
topic of active research for more than a decade, with more
and more studies experimenting with different explanatory
variables (features), especially recently, as (social) network
analytics becomes more popular and call detail records (CDRs)
are being used to construct customer networks and extract
network features [1], [2], [3], [4]. Reported results showthat network and traditional features combined perform better
than traditional (also called local or individual) ones alone.
Additionally, some studies combine traditional, network andhistorical information and claim such approach to be evenmore beneficial [2]. Obviously, adding more features leads tobetter performance, but distinguishing between feature types
is important for various reasons. First, some studies claim that
network features alone do not express enough predictive power[1], while others claim the opposite [3], [4]. Second, sinceprevious benchmarking studies have shown a rather flat maximum effect in terms of performance of analytical techniques
for CP [5], the best way to improve the performance of a
CP model is to creatively think about new feature types as
showcased in this research. Third, due to differences in thecomputational burden needed to operationalize and calculate
each of the different types of features (e.g. network features
are a lot more resource intensive to obtain than local ones),setting off the performance difference provides strategic input
to firms taking their first steps in CP by indicating whatfeature types to invest in. To the best of our knowledge,
previous studies have not performed as thorough analysis
on the importance of each particular feature type. Hence, inthis work, we introduce a more fine-grained categorizationof features and perform an experiment of stacking different
feature types (adding one more at every step) with the aim
to investigate the influence that each particular feature typehas on the resulting performance and retained feature set. Our
results show that, in general, certain types of network features(indirect ones) and trend features do not improve performancein the setting at hand. Additionally, local and network features
are of different importance for prepaid and postpaid datasets.
II. DATASETS
We use one prepaid and one postpaid dataset, both consisting of CDRs and monthly snapshots. CDRs contain only calling information, while snapshots mostly contain information

about customer handset characteristics, the date of the firstusage and tariff plan information, scarce demographic data
(zip code for both datasets and the date of birth for postpaid
dataset). Additionally, for prepaid customers, we also haveinformation about last recharge and monthly amount spent on
voice and SMS services. For postpaid customers, we have an
indicator whether the customer has a fixed-time contract andif so, the number of days left till its expiration.
III. M ETHODOLOGYOur fine-grained classification consists of ten different feature types, whose classification can be seen in Table I, withthe hierarchy represented in Table II (abbreviations can beused to match the two tables; labels ’engineered’(’E’)/’nonengineered’(’NE’) serve to distinguish features requiring/notrequiring additional computational effort). As depicted inTable II, the first division is made on observational (themost recent month with customer base for which we makeprediction) and historical (the previous month) features, due
to the fact that historical data is not always available. Next,
as in many related studies, we distinguish between local andnetwork features. Network features can be further categorized
into direct and indirect, an idea from the fraud detection
domain [6]. Finally, as the same features are calculated fortwo consecutive months, a non-trend and trend classification
imposes as a rational follow-up. Definitions provided in TableI, can be applied to any type of dataset (even beyond the
Telco domain). On the contrary, some examples, especially if
related to L-E/L-NE classification, are dataset dependent (e.g.
age, can be considered as L-NE/L-E when provided in thedataset/calculated from the date of birth).
For the prepaid dataset, we construct the ten above mentioned feature types, ending up with 112 features in total. For
the postpaid dataset, we omit the HLT-E feature type, since
our dataset has no continuous local non-engineered features.
The total number of features for postpaid is 94. Hence, we
perform ten (for prepaid) and nine (for postpaid) iterations,where on each step we extend the feature space for one morefeature type set, in the same order as presented in Table II.
Due to the fact that our datasets do not contain customers’
churn date, we define churn as usage inactivity in consecutive
calendar month (similar to approaches in [1], [4]). Additionally, we opt for predicting churners in month M +2 (withM denoting the observational month) as we aim at detecting

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SESSION 10
TELCO APPLICATIONSTABLE IF EATURE TYPE DEFINITION
Abbrev.
L-NE

Feature Type Name
Local
NonEngineered

L-E

Local Engineered

ND-E

Network Direct Engineered

NI-E

Network
IndirectEngineered
Historical
Local
Non-Trend
NonEngineered
Historical
Local
Trend Engineered

HLNT-NE
HLT-E
HNNTD-E
HNNTI-E
HNTD-EHNTI-E

Historical Network
Non-trend
DirectEngineered
Historical Network
Indirect Engineered
Historical Network
Trend Direct Engineered
Historical Network
Trend Indirect Engineered

Definition
Customer based features which do not require any further preprocessing (modeldependent pre-processing is not considered)
Customer based features for which somekind of model-independent preprocessing isnecessary
Features calculated from the customer egonetwork (1st -level neighbourhood) acrossdifferent dimensions/granularities
Network features which cannot be calculated from customer ego-network only
The same type of features as L-NE, exceptthat they refer to the one month before theobserved month (month M −1)
Trend features calculated based on local
non-engineered (L-NE) and historical local
non-engineered (HLNT-NE) variables
The same type of features as ND-E, exceptthat they refer to the one month before theobserved month (month M −1)
Historical (one month before i.e. monthM −1) versions of the NI-E variables
Trend features calculated based on direct
network features corresponding to month M(ND-E) and month M −1 (HNNTD-E)
Trend features calculated based on indirect
network current (NI-E) and indirect network
historical (HNI-E) features

Examplegender; handset characteristics(e.g. POLYPHONIC); numberof reloads
number of days being a customer derived from customer
activation datenumber of calls/outgoingcalls/outgoing calls toward
home network in month M
Page Rank score; 2nd degreeof a node in month M
handset characteristics; number of reloads in month M −1
recharge amount trend between months M and M −1
number of calls/outgoingcalls/outgoing calls toward
home network in month M −1
Page Rank score; 2nd degreeof a node in month M −1
trend in number of incoming
calls between months M and
M −1trend in 2nd degree of a node
between months M and M −1

TABLE II
H IERARCHY OF FEATURE TYPES
Hierarchy
Observational

Local
Network
Local

Historical

Network

Non-Engineered
Engineered
DirectIndirectNon-Trend
Trend
Non-Trend
DirectIndirect
Trend
DirectIndirectAbbrev.
L-NE
L-E
ND-E
NI-E
HLNT-NE
HLT-E
HNNTD-E
HNNTI-E
HNTD-EHNTI-E

churn in a timely way by focusing on early warning indicators(we address this as the “one-month gap” approach), instead ofjust predicting churners in the next month (M +1), in whichcase our models would also detect those churners who alreadymade up their mind and are essentially lost causes not worth
targeting with a churn prevention campaign.
We convey our experiments using logistic regression (LR)with stepwise feature selection. Our motivation for choosing
LR as the churn classifier is twofold. First, previous benchmarking studies have empirically confirmed its performance[7]. Furthermore, since LR is a white-box classifier, it also
facilitates interpretation which is key to the developmentof churn prevention campaigns. For the stepwise selection
method, we start from the whole set of features and alternately
exclude and include one feature at a time. The stopping
criterion is based on the AUC score and a predefined thresholdt(=1%): we attempt excluding/including features as long as
the AUC score either improves or does not get relatively lower
than 1% both from the initial AUC score (with completefeature set) and the AUC score of the previous iteration. This
procedure enforces exploration of various feature combinations and obtaining the least number of features while stillretaining an AUC score closed to the initial one.
IV. R ESULTS
The distribution of the retained features across different
iterations can be seen in Fig. 1 and Fig. 2, for prepaid andpostpaid datasets, respectively. It is important to emphasizethat each iteration involves all preceding feature types as well,e.g. the iteration denoted with NI-E involves all features belonging to the L-NE, L-E and ND-E feature types as well. Eachfeature is uniquely represented by a symbol, determined byits colour and shape. Each colour corresponds to a particularfeature type and has the same meaning both for prepaid andpostpaid (e.g. blue for ND-E and red for HNNTD-E). Different
shapes correspond to different features (and these, unlike
colours, have different meaning for prepaid and postpaid).
The presence of the same shape in different colour indicatesthat the same feature was present (and retained) in different
iterations (typically both observational and historical), e.g. for
prepaid, the recency of the outgoing calls toward the customersof the home operator (denoted with left triangle) is retained
both in the observational (for NI-E and HLT-E) and historicalversion (NHTI-E).
It can be observed that for both datasets, only features offive feature types are always retained after feature selection:
L-NE, L-E, ND-E, HLNT-NE, HNNTD-E.
In case of prepaid, network features (both observational:
HLNT-NE and historical: HNNTD-E) appear to have the mostimportant role: once introduced, some of the features of thistype will always remain in the selected set of features. We canalso observe a limited effect of the local feature types (both LNE and L-E), which fade away with inclusion of other feature
types and eventually disappear when historical features are
introduced. Local historical features have even more restrictedinfluence since they disappear as soon as historical networkfeatures are introduced, even though one of them (the date offirst monthly reload), reappears in the last iteration.
In case of postpaid, L-NE features are much more dominating and at least one of them is retained throughout all theiterations. Although this might look surprising at the first sight,
the most frequent L-NE feature retained in our case is thenumber of days remaining till fixed-time contract expiration(denoted by a ‘+’ sign), which is actually consistent withreality: unlike prepaid customers which have no restrictions
and can freely behave based on their interactions influence,
the behaviour of postpaid customers is heavily dependent andsteered by their individual contracts as well. We can also
observe that for postpaid, historical features (both HLNT-NE
and HNNTD-E) are always retained, once introduced. The
effect of ND-E features is still present, although they disappear
when historical network features are added.
Obtained results indicate a clear difference between postpaid
and prepaid datasets, which is not surprising.
Due to differences in AUC performances for different
iterations (see Fig. 3), we aim at verifying if these differences
are significant. For this, we follow the approach from [8] andapply bootstraping (resampling 1000 times). Next, we apply
a non-parametric Friedman test. The resulting p-value of theFriedman’s test for the postpaid dataset is 0, 5469 (>0, 05),
which is not significant. The obtained p-value for the prepaid
dataset is 0, 0002 (<0, 05), which indicates that the differences
between feature sets in this case are significant. To discover ex-

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Fig. 4. The results of post-hoc Nemenyi test for prepaid dataset
Fig. 1. Distribution of retained features across iterations for prepaid dataset

level of 95%.
V. C ONCLUSION AND FUTURE WORK

Fig. 2. Distribution of retained features across iterations for postpaid dataset

In this work, we simulate the usual process of adding
different feature types (but on a more fine-grained level) anddetect which feature types’ feature(s) remain among the mostimportant ones in the final model. Obtained results show that
local and direct network features both in present and historical version dominate in the collection of retained features,
although local features are more dominant for postpaid, while
network ones are more significant for prepaid. Indirect networkand trend features seem not to be of much importance.
We are aware that the one-month gap approach lead tolower AUC than usually reported. For future work, we plan
to experiment with different ways of stacking features (forexample, from local to historical and then to network) to check
whether it would lead to the same (type of) retained features.
R EFERENCES

Fig. 3. AUC changes over different iterations for prepaid and postpaid datasets

actly which pairs of feature sets differ, we apply the Nemenyi
post-hoc test. Fig. 4 shows that there are significant differences
between several feature sets for the prepaid dataset. It is worth
noting that feature set ND-E is significantly different from allother feature sets except NHTD-E and NHTI-E at significance[1] P. D. Kusuma, D. Radosavljevik, F. W. Takes, and P. van der Putten,“Combining customer attribute and social network mining for prepaid
mobile churn prediction,” in Proc. the 23rd Annual Belgian Dutch
Conference on Machine Learning (BENELEARN), 2013, pp. 50–58.
[2] X. Zhang, J. Zhu, S. Xu, and Y. Wan, “Predicting customer churn throughinterpersonal influence,” Knowledge-Based Systems, vol. 28, pp. 97–104,
2012.
[3] K. Kim, C.-H. Jun, and J. Lee, “Improved churn prediction in telecommunication industry by analyzing a large network,” Expert Systems with
Applications, vol. 41, no. 15, pp. 6575–6584, 2014.
[4] A. Backiel, B. Baesens, and G. Claeskens, “Mining telecommunication
networks to enhance customer lifetime predictions,” in International
Conference on Artificial Intelligence and Soft Computing. Springer,
2014, pp. 15–26.
[5] W. Verbeke, K. Dejaeger, D. Martens, and B. Baesens, “Customer churnprediction: does technique matter?” in Proceedings of the Joint Statistical
Meeting, JSM2010, Vancouver, Canada.
[6] B. Baesens, V. V. Vlasselaer, and W. Verbeke, “Social network analysis
for fraud detection,” Fraud Analytics Using Descriptive, Predictive, andSocial Network Techniques: A Guide to Data Science for Fraud Detection,
pp. 207–278, 2015.
[7] B. Baesens, T. Van Gestel, S. Viaene, M. Stepanova, J. Suykens, andJ. Vanthienen, “Benchmarking state-of-the-art classification algorithms for
credit scoring,” Journal of the operational research society, vol. 54, no. 6,
pp. 627–635, 2003.
[8] G. Loterman, M. Debruyne, K. V. Branden, T. Van Gestel, and C. Mues,“A proposed framework for backtesting loss given default models,” The
Journal of Risk Model Validation, vol. 8, no. 1, p. 69, 2014.

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Layers of social acquaintance fortelecommunication churn prediction
Davide Cellai
Idiro Analytics, Clarendon House, 39 Clarendon Street,
Dublin 2, Ireland
davide.cellai@gmail.com
James P. Gleeson
MACSI, Department of Mathematics & Statistics,
University of Limerick, Co. Limerick, Ireland
January 15, 2017
Telecommunication churn is the phenomenon where subscribers of a phone
operator leave the service. Predicting churn is a very important topic in thetelecommunication industry, especially in mature markets where the numberof subscriptions exceeds, sometimes quite substantially, the country population[2].
In this work, we propose a new predictive model of telecommunication churnbased on social network approaches, called m-exposure model [1]. We use a data
set of anonymized mobile phone calls from an operator with a large segment of
the considered market. The records report all outgoing and incoming calls, text
and data of about 1.4 million subscribers, and the data set spans over 12 weeks.
Our model is based on two ideas: a multilayer description of different social
acquaintances to identify the layer where social influence is largest, and a social
threshold model of churn.
First, we give a score to each edge in the network that differentiates ties
based on the time of the day communication occur. In this way, we identify
distinct layers of communications in the network and show the existence of alayer (called mixed layer ) that carries most of the socially relevant information.
Then, we use this information to develop a threshold model based on the network
proximity of churning events. We also introduce a parameter that encapsulates
the delay between the observation data and the prediction time interval.
We apply the model to port out churn (churners that move to another operator) and calculate the performance of the model in terms of churn probability
and lift. The churn probability increases remarkably on the mixed layer, meaning that our method correctly individuates links that are more likely to lie on apredictive churn path (Fig. 1).

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0.1

tot churn rateallpeak
oﬀ-peak
mixed

prob churn

0.08

0.06

0.04

0.02

0

0

1

2

3

4

5

m_out

Figure 1: Churn probability as a function of the number of recent churningneighbours in a voice-based network. The graph aggregation time interval is 8
weeks. The plots obtained by restricting contacts to one of the three layers arealso reported. It is easy to see that the subscribers exposed to churn on themixed layer have a higher churn probability than on the other layers.
We also define lift as the ratio between the fraction of correctly predicted
churners and the fraction of churners predicted with a random method:L=cm,
pch nm(1)

where pch is the total fraction of churners in the considered time interval and cm
is the number of churners in the considered subset of subscribers (of size nm ).
We show that our m-exposure model performs significantly better on themixed layer, increasing the lift from 2.34 (as in the aggregated network) to 3.57,
without reducing too much the set of captured churners. We also discuss theeffect of other network-related features such as the exposure to traffic to otheroperators and temporal clustering of subscribers’ actions.
We compare the model with EDIX, another social network-based model ofchurn, and find similar performance (L = 3.2 for m-exposure and L = 3.5 for
EDIX), but, remarkably, a small overlap in the predicted churners, meaningthat the m-exposure model is able to capture high risk subscribers that were
not captured by other social network models.
Finally, we argue that this mechanism to identify social relationships in a
mobile phone network can be also applied to expiry churn, with a model basedon the inactivity time of a subscriber’s nearest neighbors, and other types ofusers’ behaviours.

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References[1] D. Cellai and J. P. Gleeson. Layers of social acquaintance for telecommunication churn prediction. (to appear), 2017.
[2] Derek Doran and Veena Mendiratta. Propagation models and analysis formobile phone data analytics. In Dariusz Król, Damien Fay, and Bogdan
Gabryś, editors, Propagation Phenomena in Real World Networks, volume 85 of Intelligent Systems Reference Library, pages 257–292. Springer
International Publishing, 2015.

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SESSION 10
TELCO APPLICATIONSLEARNING ABOUT NEW TECHNOLOGIES:EVIDENCE FROM PHONE PLANS IN
RWANDADANIEL BJÖRKEGREN∗
BROWN UNIVERSITY

Although the spread of new technologies is vitalfor economic development, it is difficult to study withtraditional sources of data. The mobile phone represents a new technology which automatically records
every potential learning experience, and nearly every remote interaction with peers who could share
their own learning experiences. In 2006, a Rwandan mobile phone operator introduced a new planthat represented substantial savings for over 85% of
subscribers; however, it took years to diffuse. This
project uses operator data to investigate how individuals learned about this new plan.
I exploit several features of the data. First, subscribers learn about call charges by requesting theirbalances between transactions; I observe these balance inquiries and thus the generation of information.
Second, I exploit the fine timing of the data to isolate actions that follow the generation of information.
Finally, I use data on the network of subscribers to
identify peer effects. Balance-conscious peers helped
diffuse information about the plan, and it appears
they communicated rich information about the relative prices.
Full version of paper: http://dan.bjorkegren.com
1. Introduction
Learning about new technologies is vital for economic growth. However, it is difficult to study: seldom is it possible to gather data rich enough to both
describe behavior and differentiate between channelsof learning. The mobile phone is an economicallyimportant technology that has seen widespread adoption. It has the relatively unique feature that operators must maintain a log of all actions taken on the
network in order to provide service. These records
provide a realtime window into how individual subscribers learn to use the network. This project usesrich data from a mobile phone network to track how
subscribers learn about a new cheaper phone plan, todetermine how profitable technologies diffuse throughsociety.
This setting has three features beneficial for studying learning over networks.
∗E-mail:danbjork@brown.edu,
Web:http://dan.bjorkegren.com
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.

First, mobile phone networks provide a rich, passively collected source of data on both actions and social networks. The data represents nearly the entire
network of mobile phone subscribers. The temporal
resolution of the data makes it possible to use microlevel event studies for identification: it is possible todetermine whether actions preceded or followed theprovision of information.
Second, subscribers are making economically significant decisions. Telephony represents 5% of expenditure in subscribing households.1 Plan choiceitself is not a trivial decision: on average, subscribers
saved almost half of their spending on domestic callsby switching to the newly offered plan; they could
have saved approximately 10% more by switchingearlier. The operator introduced the plan as part ofa sequence of billing changes that made the network
more attractive to poor consumers. Although the introduction of the plan also affects operator revenues,
this paper focuses on consumers, for which the newplan is analogous to a new technology with large butheterogeneous benefits.
Third, the context is information-poor: the mobile phone network in this country represents the vast
majority of remote communication apart from radio.
This feature makes it easier to distinguish between
confounding sources of information relative to settings with more complex communication networks.
2. A New Plan
Almost all subscribers have prepaid plans, witha balance that is depleted with use. This balancecan be refilled by purchasing an airtime scratch cardfrom any of the operator’s agents, who are locatedthroughout the country. The party that initiates acall pays for the call; receiving a call is free.
Initially, the operator offered one prepaid plan(“PerMin”), which was billed by the first minute andthen every 30 seconds thereafter. As the network
expanded to reach poorer consumers, the operator
made a sequence of changes in billing policy that reduced prices. The most notable of these was the introduction of a plan billed by the second (“PerSec”).
The pricing of the new plan was set so that a 45 second call cost the same under either plan; shorter calls
were cheaper under PerSec. The top panel of Figure
1 compares pricing between the two plans.
Most subscribers make very short calls (the median call length is 22 seconds), and as a result PerSec
was cheaper for most subscribers. The bottom panelof Figure 1 shows the distribution of call durations.
Over all domestic transactions, PerSec was cheaperfor 97% of subscribers. Even the charges incurred
while subscribed to PerMin would have been cheaper
under PerSec for 85% of subscribers. The savings associated with switching were substantial: switchers
1According to the government’s household survey, EICV2010-2011.

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Figure 3. Switch Patterns: Median Charge Ratio by Current Plan

Figure 1. Price schedule and density of observed calls by plan type,1.2005-1.2008

Charge Ratio Median by Number of Switches
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Ratio of Counterfactual Charges: PerMin/PerSec$0.80

PerSec

0

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A separate graph is drawn for subscribers of different initialplans and total number of switches. Subscribers change
behavior upon switching, but PerSec is still optimal for the
median.

Figure 2. Subscribers by planand date

3. Analysis

Subscribers settle on the optimal plan, which
for most is PerSec. Plans can be switched multiple times by entering a short code, at the user’s
convenience. We might expect subscribers to switch
between plans multiple times for two reasons: subscribers may switch to learn about which plan is bestfor them, or to reduce costs on calls of a certain type(e.g., one might switch to PerMin before making along call). A not insignificant number of subscribers
switch back and forth between plans; those who domostly settle on PerSec. Subscribers adjust their behavior in tandem with switching: they make shorter
calls when subscribed to PerSec and longer calls whensubscribed to PerMin.
Figure 3 shows the median charge ratio by thecurrent plan for subscribers with different sequencesof plans. Subscribers make calls that are cheaper under PerSec when subscribed to PerSec (higher ratio),and calls that are cheaper under PerMin when subscribed to PerMin (lower ratio). However, the moststriking feature of the graph is that all of the medianratios lie above 1, denoted by the red line: the median subscriber would be better off simply sticking
with PerSec rather than engaging in complex switching behavior. In fact nearly the entire distributionof subscribers would be better off sticking to PerSec
than switching back and forth.

Agents matter. Roughly half of switches occur immediately following a top up, suggesting the operators’ agents are involved in the switching process,either by providing information or by lowering theswitching cost.

Peers matter. To analyze the peer effects, I estimate the hazard rate of switching with regressions.
I find that an increase in the fraction of my social
network neighbors who have switched from 0% to

800,000

600,000

Subscribers

Plan
PerSec400,000

PerMinGonga

200,000

0
2005

2006

Date

2007

2008

saved on the order of half of their domestic calling
expenditure.
The process of switching was simple and did notincur a fee: to switch plans, a subscriber would enter a short code into the phone, not dissimilar fromthe codes entered when topping up airtime. Thus inorder to switch, a subscriber must know both that adifferent plan is available and the short code to enter
into the phone.
Despite the large savings associated with switching to per second billing, it took years for the plan to
diffuse through society, as shown by the evolution ofsubscriber counts in Figure 2.

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100% is associated with between 38-67 percentage
point increase in my hazard rate, including controls.
Subscribers who would have saved more by switching based on past usage are more likely to switch. I
am more likely to switch when I have more neighbors
who have switched; however, this could be due tolearning from peers or due to confounding homophilyor correlated shocks.
We can control for the most obvious form of homophily: I am likely to be clustered with others whomake similar types of calls, and thus would obtain
similar benefits from switching. Indeed I find I am
more likely to switch when my neighbors would havesaved more by switching (based on usage while they
are subscribed to PerMin). However, when I include
the direct peer effect as well as a control for neighbors’ savings, the peer effect barely moves.

Figure 4. Subscribers immediately adjust their calling to takeadvantage of the different prices
under PerSec
Duration Before and After Switch (Calls Sent)
35

Duration (median)

30

PerMin
PerSec

25

20

−100

−50

0

50

Transactions relative to switch

100

Figure 5. Subscribers check theirbalance frequently as they areswitching plans
AtLeastOneBalanceInquiry Before and After Switch (Calls Sent)

0.9

AtLeastOneBalanceInquiry (mean)

Peers provide information. Information about plansis generated either by the operator (and shared throughagents or marketing material), or through direct experience on the network. Since accounts are prepaid,
subscribers receive no monthly bill; instead, the precision of the information an individual gets from experience on the network depends on how much feedback he requests from the network. If an individual
submits a balance inquiry after every transaction, he
can learn from every transaction; if an individual seldom requests balance information, he will have muchless precise feedback about the costs of his usage. The
model would thus predict that the more precise information that an individual’s neighbors generate, thebetter his decision. This also suggests a placebo test:when the old plan is sufficiently well understood, only
the precision of neighbors who have switched to thenew plan should matter.
I build on the previous regressions by controllingfor how much information is generated by neighbors
who have switched, the ratio of balance inquiries tocalls. This information control absorbs some of thevariation that had been explained by the fractionof neighbors switched, suggesting that at least partof the peer effect could operate through transmission of information learned by experience. But if I
add the corresponding control for neighbors who havenot switched (who cannot generate any information
about the new plan from their experience) I find it
to be near zero.
Switchers reoptimize immediately for the newpricing schedule. The pattern of calls sent adjusts
immediately following a switch. Figure 4 shows thatthe median call duration drops immediately following a switch, consistent with subscribers reoptimizing behavior for the new plan (since shorter calls arecheaper). This suggests that subscribers are aware of
the pricing policy under PerSec at the point of switching. It suggests that the information being shared is
not simply that the new plan is cheaper, but a richer
description of the billing difference.

Plan.Current

0.8

Plan.Current
PerMin
PerSec

0.7

0.6

−100

−50

0

Transactions relative to switch

50

100

A further check is the pattern in balance inquiries.
If subscribers are learning about prices under the newplan, we would expect to see a jump in balance inquiries immediately following a switch. In Figure 5,
looking at the fraction of calls that were preceded byat least one balance inquiry, do we see a jump, but
the increase starts before the switch.
For more details, find the full paper at http://dan.bjorkegren.com

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