20157-10 April 2015
MIT MediaLab
http://netmob.org

Book of Abstracts :: Oral

Editors: Esteban Moro, Yves-Alexandre de Montjoye, Vincent Blondel, Alex ‘Sandy’ Pentland
March 26th version

Organized bySponsored by2015

Contents
Session 1 :: Mobility

3
1. Analyzing the Influence of Phone Context Data on the Performance of Human Mobility Predictors
Paul Baumann, Christian Koehler, Anind Dey and Silvia Santini . . . . . . . . . . . . . . . . . . . . . . . . . 4
2. Evaluating urban sensing applications using actively and passively-generated mobile phone location data
Fabio Pinelli, Giusy Di Lorenzo, and Francesco Calabrese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3. OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs
Vsevolod Salnikov, Renaud Lambiotte, Anastasios Noulas, and Cecilia Mascolo . . . . . . . . . . . . . . 9
4. Assessing the impact of ride sharing on congestion using mobile phone data
Lauren Alexander, Jameson Toole and Marta Gonzalez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Session 2 :: Cities (I)15
1. Energy Consumption Prediction using People Dynamics Derived from Cellular Network Data
Andrey Bogomolov, Bruno Lepri, Roberto Larcher, Fabrizio Antonelli, Fabio Pianesi and Alex Pentland 16
2. Predicting Crime Hotspots Using Aggregated and Anonymized Data on People Dynamics
Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Emmanuel Letouze, Nuria Oliver, Fabio Pianesi
and Alex Pentland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3. Estimating variability in health facility catchment population sizes using mobile phone call records
Elisabeth Zu Erbach-Schoenberg, Alessandro Sorichetta, Catherine Linard, Chris Lourenco, Victor
Alegana, Tom Bird and Andrew Tatem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4. Measuring de facto populations with Mobile Network Operator’s Call Detail Record data
Erki Saluveer, Rein Ahas, Siiri Silm and Margus Tiru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5. Investigating the relationships between spatial structures and urban characteristics
Marco De Nadai, Bruno Lepri, Roberto Larcher and Nicu Sebe. . . . . . . . . . . . . . . . . . . . . . . . . . 28

Session 3 :: Economies

30
1. Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment
Daniel Bjorkegren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2. Estimating Food Consumption and Poverty Indices with Mobile Phone Data
Adeline Decuyper, Alex Rutherford, Amit Wadhwa, Jean Martin Bauer, Gautier Krings, Thoralf Gutierrez, Vincent D. Blondel and Miguel A. Luengo-Oroz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3. Mobility and Productivity: Quantifying Urban Economic Activity using Cell Phone Data
Gabriel Kreindler and Yuhei Miyauchi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4. Cignifi Risk Solutions, An application of mobile phone data for Brazil microcredit approval
Adriano Massuia and Qiuyan Xu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5. Socioeconomic correlations in communication networks
Yannick Leo, Eric Fleury, Carlos Sarraute, J. Ignacio Alvarez-Hamelin and Márton Karsai . . . . . . . . . 42
6. Understanding the Role of Social Networks on Labor Market Outcomes Using a Large Dataset
from a Mobile Network
Filipa Reis and Pedro Ferreira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Session 4 :: Societies (I)47
1. Persistent daily patterns in mobile telephone communication
Talayeh Aledavood, Eduardo López, Sam Roberts, Felix Reed-Tsochas, Esteban Moro, Robin Dunbar
and Jari Saramäki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2. Inferring social ties from WiFi scan results
Piotr Sapiezynski, David Kofoed Wind, Arkadiusz Stopczynski, Radu Gatej and Sune Lehmann . . . . 50
3. On the complementary roles of face-to-face and mediated social interactions
Guy Zyskind, Bruno Lepri, Alex ’Sandy’ Pentland and Erez Shmueli . . . . . . . . . . . . . . . . . . . . . . 52

1
1

2015

4. Understanding User Attributes from Calling Behavior: Exploring Call Detail Records throughField Observations and Potential of Estimating User Attributes of Anonymized Call Records
Ayumi Arai, Apichon Witayangkurn, Hiroshi Kanasugi, Teerayut Horanont, Xiaowei Shao and Shibasaki
Ryosuke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5. The Evolution of Social Strategies across the LifespanNitesh Chawla, Jie Tang and Yuxiao Dong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
6. Temporal dynamics of intra and inter-city social networks
Alejandro Llorente, Manuel Cebrián, and Esteban Moro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Session 5 :: Societies (II)63
1. The Adoption of Network Goods: The Spread of Mobile Phones in Rwanda
Daniel Bjorkegren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2. The Strength of the Strongest Ties in Collaborative Problem Solving
Yves-Alexandre de Montjoye, Arkadiusz Stopczynski, Erez Shmueli, Alex Pentland and Sune Lehmann 67
3. nvestigating Social Influence Through Large-Scale Field Experimentation
Johannes Bjelland, Geoffrey Canright, Asif Iqbal, Rich S. Ling, Kenth Engø-Monsen, Taimur Qureshi,
Christoph Riedl, Pal Roe Sundsøy and David Lazer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4. Asymmetric Role of Social Influence in Smartphone Adoption in Large Mobile Networks
Qiwei Han, Pedro Ferreira and Joao Costeira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5. Using Mobile Phone Data to Predict the Spatial Spread of Cholera
Linus Bengtsson, Jean Gaudart, Xin Lu, Sandra Moore, Erik Wetter, Kankoe Sallah, Stanislas Rebaudete and Renaud Piarroux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6. Micro Dynamics of Social Interactions: Quantifying Human Life
Vedran Sekara, Arkadiusz Stopczynski and Sune Lehmann . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Session 6 :: Cities (II)79
1. Neighborhood and Network Segregation: Ethnic Homophily in a ?Silently Separate’ Society
Ott Toomet, Joshua Blumenstock, Rein Ahas and Erki Saluveer . . . . . . . . . . . . . . . . . . . . . . . . . 80
2. Untangling the effects of residential segregation on individual mobility
Suma Desu, Lauren Alexander and Marta Gonzalez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3. Spatial and Social Homophily at a Massive Religious Gathering
Ian Barnett, Tarun Khanna and Jukka-Pekka Onnela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4. Predicting gender from mobile phone metadata
Eaman Jahani, Pål Roe Sundsøy, Johannes Bjelland, Asif Iqbal, Alex Pentland and Yves-Alexandrede Montjoye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Session 7 :: Crowds (II)114
1. Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data
Adrian Dobra, Nathalie Williams and Nathan Eagle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
2. Estimating Attendance From Cellular Network Data
Marco Mamei and Massimo Colonna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
3. Seasonal Decomposition of Cell Phone Activity Series and Urban Dynamics
Blerim Cici, Athina Markopoulou, Minas Gjoka and Carter Butts . . . . . . . . . . . . . . . . . . . . . . . . . 121
4. Social Events in a Time-Varying Mobile Phone Graph
Carlos Sarraute, Jorge Brea, Javier Burroni, Klaus Wehmuth, Artur Ziviani and J. Ignacio AlvarezHamelin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5. Real-Time Social Event Analytics
Francesco Calabrese, Giusy Di Lorenzo, Gavin McArdle, Fabio Pinelli and Erik Van Lierde . . . . . . . . 126
6. Inferring spatio-temporal changes in urban areas from mobile phone data
Sofia Nikitaki and Maurizio Dusi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

2

2015

Session 1 :: Mobility

3

Paul Baumann, Silvia Santini

Christian Koehler, Anind Dey{paul.baumann, silvia.santini}@tu-dresden.de{ckoehler, anind}@cs.cmu.edu

Embedded Systems Lab, TU Dresden, Germany

1. INTRODUCTION

Table 1: List of phone-context features considered in this study.

Understanding and predicting human mobility has been alreadyfor a long time in the focus and interest of researchers and practitioners [4, 2, 5]. Over the years, the potential sources of information about human mobility, e.g., temporal data, calendar information, or social ties, have been grown dramatically.
In the context of this extended abstract, we focus on the phone
context data, e.g., number of recently used applications or timesince the last received or made phone call. By considering this new
source of data, we analyze its influence on the performance of 4
state-of-the-art and 3 baseline predictors for 3 human mobility prediction tasks. We capture the results of the potential influence byconsidering 3 well-known performance metrics. Our results highlight that our use of the phone context data does not lead to signification performance improvements. Statistical information from thedata set shows that users used 7 applications, and made/received 5
phone calls, on average per day. This highlights the fact that thephones have been used intensively by their owners.
Our contributions are two-fold: (1) we derive an extended listof 28 phone context features, and (2) we analyze their potential
influence on all combinations of the considered 3 prediction tasks,
7 predictors, and 3 performance metrics.

Labelc_time_call1
c_calllog_type
c_calllog_direction
c_sms_statusc_last_call_durationc_last_cal2
c_next_cal2
c_time_last_app
c_last_app
c_phone_charging
c_last_chargec_batteryc_ringc_profilec_last_action

In order to investigate the influence of phone context data on
the performance of several predictors for human mobility, we analyze a rich data set – Nokia Lausanne Data Collection Campaign(LDCC) [3] – that contains information from 141 users collected
over 18 months. We first derive a list of meaningful phone context
features based on the available information in the data set. After
that, we run feature selection to reduce the amount of derived features for all combinations of predictors, prediction tasks, and metrics. We evaluate the performance of the resulting combinations ofa predictor and a set of features on well-known metrics – accuracy,
F1 score, and Matthews Correlation Coefficient (MCC).

2.3

Features

3.

2

INFLUENCE OF PHONE CONTEXT DATAON PREDICTORS’ PERFORMANCEFor our analysis part, we adopt the Sequential Forward Floating
Selection (SFFS) algorithm to identify the best performing features
for all combinations of prediction tasks, metrics, and predictors. In
all these cases, we differentiate between using the entire feature set
F and using only a reduced feature set by excluding phone context
features (F\Fpc ). We next present our findings.

Prediction Tasks

In the context of this work, we consider 3 prediction tasks. The
Next-place (NP) prediction task only considers the next place vis1

Predictors

In this study, we consider 7 predictors that rely on different basic
techniques, have different weaknesses and strengths, and requiredifferent amounts of computational and memory resources. The 7
predictors include 4 well-known predictors – Support Vector Machine (SVM) [6, 1], k-Nearest Neighbor (k-NN) [6, 1], Classification and Regression Trees (CART) [6], and Perceptron [1] – as well
as 3 baseline predictors – Random predictor (R), Distribution-based
predictor (DB), and 0-R predictor (0-R).

The human mobility predictors that are used throughout this workneed adequate input data to be able to compute the required prediction. To this end, we derive a list of 51 features that contains 28
phone context features – indicated as set Fpc . Table 1 shows themalong with their corresponding description.

2.2

Description
Time since last call/sms made/received
Last calllog type
Last calllog direction
Last sms status
Last call duration
Information about last calendar entry
Information about next calendar entry
Time since last application used
Last used application
Current phone charging status
Time since last charge
Current phone battery status
Current ring profile
Current user profile
Time since last phone interaction

ited and treats as irrelevant when the user moves to the next locationand how long she stays in each place. Timing information can easily be included in a prediction task by considering equally spaced
time slots of length s and computing a new next place prediction foreach time slot. We refer to this task as the Next-slot place (NSP)
prediction task. Finally, the Next-slot transition (NST) prediction
task consists of estimating, at time slot k, whether or not there will
be a transition at time slot k + 1. A transition occurs when the user
moves between two places.

2. METHODOLOGY AND BACKGROUND

2.1

Carnegie Mellon University, USA

Session 1 :: Mobility

Analyzing the Influence of Phone Context Data on the
Performance of Human Mobility Predictors

Session 6 :: Cities (II)

2015

3.1

We consider calls and messages for both incoming and outgoing directions.
We consider date of creation, status, title, location, type, and confidence class.

Performance Results

The feature selection process returns an optimal feature subset
F 0 for each combination of user, predictor, prediction task, andmetric. Figure 1 shows the performance in terms of accuracy, F1
14

k−NN Perc. CART SVM R DB0−R
Predictors

100%
80%
60%
40%
20%
0%

No FS (F)No FS (F\F_pc)

FS (F)FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors

No FS (F)No FS (F\F_pc)

FS (F)FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors(g) Next-place – Accuracy

No FS (F\F_pc)

FS (F)FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors

100%
80%
60%
40%
20%
0%

100%
80%
60%
40%
20%
0%

No FS (F)No FS (F\F_pc)

FS (F)

100%
80%
60%
40%
20%
0%

FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors

No FS (F)No FS (F\F_pc)

FS (F)No FS (F\F_pc)

FS (F)FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors

100%
80%
60%
40%
20%
0%

No FS (F)No FS (F\F_pc)

FS (F)FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors(f) Next-place transition – MCC

FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors

No FS (F)(c) Next-slot place – MCC(e) Next-slot transition – F1 score

F1 score

Accuracy(d) Next-slot transition – Accuracy
100%
80%
60%
40%
20%
0%

No FS (F)(b) Next-slot place – F1 score

F1 score

Accuracy(a) Next-slot place – Accuracy

100%
80%
60%
40%
20%
0%

Session 1 :: Mobility

FS (F\F_pc)

Session 6 :: Cities (II)FS (F)MCC

No FS (F\F_pc)

MCC

No FS (F)

MCC

100%
80%
60%
40%
20%
0%

F1 score

Accuracy

2015

100%
80%
60%
40%
20%
0%

(h) Next-place – F1 score

No FS (F)No FS (F\F_pc)

FS (F)FS (F\F_pc)

k−NN Perc. CART SVM R DB0−R
Predictors(i) Next-place – MCC

Figure 1: Performance results for all 3 considered prediction tasks, 3 performance metrics, and 7 predictors.

4.

score, and MCC achieved by the predictors with or without thefeature selection (“FS” vs “No FS”). The markers indicate medianvalues and the whiskers indicate the 5th and 95th percentiles.
Figure 1a, Figure 1b, and Figure 1c show the median performance for the NSP prediction task. The plots for the metrics accuracy and F1 score reveal significant performance differences forthe extracted feature sets with phone context features (Fpc ) andthose without. It holds for the predictors k-NN and CART. In thesecases, the performance after applying feature selection is higher for
the feature set F \Fpc than for F. It is also the case for MCC, butwith a lower performance difference in the case k-NN is used. Furthermore, SVM shows similar performance for both feature sets (Fand F \Fpc ) after applying feature selection. We observe that on
the one hand SVM is able to leverage SFFS to extract meaningful subsets of features. On the other hand, SVM reveals dramatic
performance drops if no feature selection is applied. At the sametime, Perceptron does not outperform the distribution-based (DB)and the 0-R predictors, which are both just baseline predictors.
For the NST prediction task, we observer in Figure 1d, Figure 1e,and Figure 1f that all predictors achieve a high accuracy. It is not
surprising since the class of “No Transitions” dominates with almost 95%. Only Perceptron fails for a number of users to achievea high accuracy. In the case of the F1 score and MCC the predictors k-NN, CART, and SVM achieve similar performance after
applying feature selection to both sets – with and without the phone
context data. It is worth to note that for both metrics – F1 score and
MCC – the predictor CART tends to be much more robust in thecases with no feature selection by achieving at least twice as highperformance than the next best performing predictor.
Last but not least, Figure 1g, Figure 1h, and Figure 1i demonstrate results for the NP prediction task. For k-NN, CART, andSVM we make two observations. First, the feature selection leads
to performance improvements in terms of all 3 metrics. Second, theconsideration of the phone context data does not show any signification improvements in terms of the considered metrics.

CONCLUSIONS AND FUTURE WORK

We summarize our results and conclude that our use of the phone
context features does not lead to signification performance improvements. However, phone context data is in general a rich information source. Statistical information from the data set highlights thepresence of the potentially meaningful context data and the fact thatthe phones have been used intensively by their owners. Thus, we
believe that instead of capturing temporal phone information, e.g.,time since last action X, analysis on the correlation between theappearance of phone data events, e.g., received a phone call, and acorresponding mobility behavior may uncover additional potential
for prediction improvements. In the context of this work, we leave
the proof of this hypothesis as future work.

5.

ACKNOWLEDGEMENTS

This work has been partially supported by the Collaborative Research Center 1053 funded by the German Research Foundation.

6.

REFERENCES[1] C. Bishop. Pattern Recognition and Machine Learning(Information Science and Statistics). Springer-Verlag New
York, Inc., Secaucus, NJ, USA, 2006.
[2] J. Krumm and D. Rouhana. Placer: Semantic Place Labelsfrom Diary Data. In UbiComp’13, 2013.
[3] J. Laurila et al. The Mobile Data Challenge: Big Data for
Mobile Computing Research. In Pervasive’12, 2012.
[4] P. Baumann et al. The Influence of Temporal and Spatial
Features on the Performance of Next-place Prediction
Algorithms. In UbiComp’13, 2013.
[5] V. Srinivasan et al. Mobileminer: Mining Your Frequent
Patterns on Your Phone. In UbiComp’14, 2014.
[6] X. Wu et al. Top 10 Algorithms in Data Mining. Knowledge
and Information Systems, 14(1):1–37, 2008.

2

5

Fabio Pinelli, Giusy Di Lorenzo, Francesco Calabrese
IBM Research – Ireland
Email: {fabiopin, giusydil, fcalabre}@ie.ibm.com
Abstract—Mobile phone location data from telecom operatorsin the form of Call Detail Record (CDR) has been widely studied,especially to extract insights into urban dynamics [5]. Such
massive data can be useful to extract patterns of human mobility
at an incredible scale. This data sample the location of a mobile
device every time is actively interacting with the network, e.g. atcall time, while sending and receiving a SMS, or while connecting
to the Internet with Smartphones. The disadvantage of suchdata collection method is that the spatio-temporal sampling ofeach individual user trajectory over time might be very uneven,and perhaps biased to specific locations (e.g. home locations) ortimes (e.g. during the evenings). Moreover, some users mightinteract more or less with the network, resulting in more or lessmobility information extracted from them. This could result inunder-sampling the population, or more problematically, biasing
the extracted insights. In the past decade, there has been arising interest in using mobile phone location data to infer user
trajectories [1], [8], and so study human mobility and theirpatterns [7]. Different types of data have been used in thesestudies. CDR data were used in [7],[2],[6]. CDR information,enriched with records from Internet access was exploited in [4].
Data from idle phones were also used in [9] to estimate the roadtraffic. However, to the best of our knowledge, no work so far has
specifically compared the different types of datasets that a telecom
operator can collect. Moreover, no work so far has analysed thelimitations of using a specific dataset for a given urban sensing
application. We then ask the question whether insights extracted
from actively collected-mobile phone location data are a goodproxy for human mobility. To answer this question, we compare
such results, with results extracted by both actively and passively
sampled user location, which constitute a richer set of location
information.

We used a real dataset collected from a telecom operator
in Belgium, which had a system which allowed to collect
both CDR, records of Internet connections (which we call
IPDR), and passively generated data (which we call Signaling),
generated because of location updates, radio access network or
data sessions. We were able to decompose the dataset in threedifferent ones: only CDR, CDR + IPDR, and all data, sinceeach record event was tagged with the specific type of event
generating it.
The dataset contains anonymised mobile phone location
data from Mobistar, for users in the area around the cityof Mons, Belgium. The data include users connected to 150
distinct cell towers in the city area. For each cell tower, wewere given the coordinate and the azimuth of each cell sector.
Thus we were able to derive a voronoi tessellation of the space,following the approach presented in [3]. Some of the cells
cover the same area (i.e. 2G and 3G antennas installed on thesame tower), therefore we were able to identify 58 distinct
locations in the city.

Session 6 :: Cities (II)Evaluating urban sensing applications using actively
and passively-generated mobile phone location data

The available data covers one week in October 2014. We
use the available data to simulate 3 different scenarios:•

availability of only CDR information, in which we
only use the CALL and SMS data items, and are
representing cases in which only CDR information isprovided.

•availability of CDR and IPDR, in which we use theabove data, together with IPDR, to represent cases
where all Event-driven signaling information is provided.

•

availabilityofallsignaling
information(CRD+IPDR+Signaling), which will be our reference
for comparison.

Session 1 :: Mobility

2015

Figure 1 depicts an example of temporal sequence of events
for a user in the dataset. We also depict the trajectory that
we are able to detect, given the tree different scenarios. The
example clearly shows that for this user, the availability of all
information allows detecting 3 different visited locations, andan estimated stop time for locations 2 and 3. If no Signaling
information is available, only two visited places could be
detected, and the estimated stop time would also be reduced,
with lowest accuracy in the case of only CDR information
available.
At the general level, the advantages of using Networkdriven location data (in addition to event-driven) include:•

sampling more users (people who are not making
calls/SMS/Internet connections);•

having more samples of user locations, particularly attimes where users are not too active, e.g. at night);

Motivated by this example, in the following sections
we quantitatively and qualitative compare properties of thethree datasets. This is firstly done by extracting applicationindependent characteristics. Then, we selected frequently used
urban sensing applications designed for Telco data, and compared the accuracy of the extracted insights among the different
datasets, highlighting in which cases one dataset is preferable
compared to the others.
I.

A PPLICATION - INDEPENDENT COMPARISON

We compare the three datasets along different dimensions.
A first one is in terms of the set of users for which wehave data. We counted, for each user, the number of thethree different types of events (CDR, IPDR and Signaling).
Figure 2 shows a Venn diagram of the unique users by data6

(b) daylight hours

Fig. 3. Distribution of user by number of hours for which at least one event

Fig. 1.
Example of temporal sequence of events (top), and estimatedtrajectories using three different datasets (bottom). To simplify the reading, thelocations have been drawn in one dimension (as opposed to the two dimensions(latitude and longitude).

Session 1 :: Mobility(a) all hours

Session 6 :: Cities (II)2015then ask the question whether this decrease in the numberof events is concentrated in particular hours of the day, or isequally spread over time. Figure 3(a) show the distribution ofusers by number of distinct hours for which there is at least
one event. Curves CDR and CDR+IPDR look very close, toshow that the large amount of IPDR events are on average
concentrated in the same hours as the CDR events. Signaling
events are able to sample the user location over many more
hours. However, if we only consider daily hours (from 6 to 22),
as reported in Figure 3(b) we notice that difference decreases.
This let is hypothesise that actively generated location data area good sample of user location during daylight hours. We willverify this intuition in the next section.
II.

A PPLICATION - DEPENDENT COMPARISON

We have taken a frequently used example of urban sensing
application using mobile phone location data: the count estimation over time, such as the number of people being in acertain cell in a given time interval. This information is highly
relevant for many sectors, such as Retail, Property, Leisureand Media, since it allows to compare locations in terms of
expected crowd. Clearly, an accurate estimation of the timeseries of number of users by location is crucial to provide
trustable insights. We computed user count time series for eachlocation in the city, starting from the three different datasets.

Fig. 2.

Percentage of users per data type and relative intersections.

type. Only for about 11% of the users we can see all threedifferent types of events. This is due to that fact that not allusers have smartphones for which IPDR can be generated.
Moreover, some users are only observed very temporary in thedataset (users only traversing the city), and so only Signaling
information is available. Not all users generate the same
number of events. The number of events by user follows a longtail distribution. Clearly considering only CDR or CDR+IPDR
events, the average number of events per user is smaller. We

Cumulative count estimation by location is show in Figure4(a), ranked by increasing value of count (based on thereference dataset). We computed two measures of error. The
first measure is the root mean square error (RMSE) computedon the hourly estimates compared to the counts estimatedusing all data (CDR+IPDR+Signaling). The error ranges from0 to 1, and low values correspond to low error. Moreover,
we computed a measure of Normalised discounted cumulative
gain (nDCG) [10] used in recommender systems to measurerank quality. We have chosen this measure to evaluate whether
the estimated ranking of crowded locations is kept the same byusing CDR or CDR+IPDR only information. The error ranges
from 0 to 1, and high values correspond to low error. Thismeasure is different from the RMSE, since it does not take
into account the absolute estimate count for each location ortime, but just the relative order of such counts by location.
This measure is directly useful in application scenarios suchas choosing the most crowded place between a set of locations.
As Figure 4(a) shows, the error is higher if we only consider CDR information. Moreover error measured in terms ofnDCG is much lower, and there is no much difference in using

7Fig. 4.

(b) error by day of week

Density estimation(a) RMSE
Fig. 5.

(b) nDCG

Density estimation error by hour of day

CDR or CDR+IPDR data. This shows that, for the purpose ofcomparing user counts by locations, CDR information in on
average a good source of information. Figure 4(b) shows theaverage RMSE computed on the 7 distinct days. As it canbe seen, the error is higher over the weekend (last 2 days).
Moreover, Figure 5 shows the errors as function of the hourof day (averaged over all days). It is interesting to see thaterror is large over the night hours, and quite low during thedaylight hours. In conclusions, we can observer that using
CDR or CDR+IPDR as proxy for user count per location works
relatively well in application scenarios such as choosing themost crowded place between a set of locations, especially if
the focus is on daylight hours over weekdays.

Session 6 :: Cities (II)(a) by antenna[2] N. Caceres, J. Wideberg, and F. Benitez. Deriving origin
destination data from a mobile phone network. Intelligent
Transport Systems, IET, 1(1):15 –26, 2007.
[3] R. Caceres, J. Rowland, C. Small, and S. Urbanek.
Exploring the use of urban greenspace through cellular
network activity. In Proc. of 2nd Workshop on Pervasive
Urban Applications (PURBA), 2012.
[4] F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti.
Estimating origin-destination flows using mobile phone
location data. Pervasive Computing, IEEE, 10(4):36 –44,
april 2011.
[5] F. Calabrese, L. Ferrari, and V. D. Blondel. Urban sensing
using mobile phone network data: A survey of research.
ACM Comput. Surv., 47(2):25:1–25:20, Nov. 2014.
[6] G. di lorenzo, M. L. Sbodio, F. Calabrese, M. Berlingerio,
R. Nair, and F. Pinelli. Allaboard: Visual explorationof cellphone mobility data to optimise public transport.
In Proceedings of the 19th International Conference onIntelligent User Interfaces, IUI ’14, pages 335–340, New
York, NY, USA, 2014. ACM.
[7] M. Gonzalez, C. Hidalgo, and A.-L. Barabasi. Understanding individual human mobility patterns. Nature,453(7196):779–782, 2008.
[8] S. Hoteit, S. Secci, S. Sobolevsky, G. Pujolle, andC. Ratti. Estimating real human trajectories throughmobile phone data. In Mobile Data Management (MDM),
2013 IEEE 14th International Conference on, volume 2,
pages 148–153, June 2013.
[9] A. Janecek, K. A. Hummel, D. Valerio, F. Ricciato, andH. Hlavacs. Cellular data meet vehicular traffic theory:
Location area updates and cell transitions for travel time
estimation. In Proceedings of the 2012 ACM Conference
on Ubiquitous Computing, UbiComp ’12, pages 361–370,
New York, NY, USA, 2012. ACM.
[10] K. Järvelin and J. Kekäläinen. Cumulated gain-based
evaluation of ir techniques. ACM Trans. Inf. Syst.,
20(4):422–446, Oct. 2002.

Session 1 :: Mobility

2015

While some of the reported results might depend on thespecific configuration of the monitoring system that the telecom operator used, the methodology we presented to evaluate
the limitation of each dataset is general. Moreover, it can beapplied by other telecom operators to measure the effectivenessof using each individual dataset for urban sensing applications.
In addition, we plan to extend the analysis to others, and more
complex, urban sensing applications, such as event detection,
trajectory pattern extraction and trajectory clustering.
ACKNOWLEDGMENT
The authors would like to thank Mobistar for providing
access to the anonymized data.
R EFERENCES[1] T. Bao, H. Cao, Q. Yang, E. Chen, and J. Tian. Mining
significant places from cell id trajectories: A geo-gridbased approach. In Mobile Data Management (MDM),
2012 IEEE 13th International Conference on, pages 288–
293, July 2012.

8

2015

1

2

naXys, University of Namur, Belgium
ComputerLab, University of Cambridge, UK

The rise of Uber as the global alternative taxi operator
has attracted a lot of interest recently. Aside from themedia headlines which discuss the new phenomenon, e.g.
on how it has disrupted the traditional transportationindustry, policy makers, economists, citizens and scientists have engaged in a discussion that is centred aroundthe means to integrate the new generation of the sharing
economy services in urban ecosystems. In this work, we
aim to shed new light on the discussion, by taking advantage of a publicly available longitudinal dataset that
describes the mobility of yellow taxis in New York City.
In addition to movement, this data contains informationon the fares paid by the taxi customers for each trip. As aresult we are given the opportunity to provide a first head
to head comparison between the iconic yellow taxi and its
modern competitor, Uber, in one of the world’s largest
metropolitan centres. We identify situations when Uber
X, the cheapest version of the Uber taxi service, tends tobe more expensive than yellow taxis for the same journey.
We also demonstrate how Uber’s economic model effectively takes advantage of well known patterns in human
movement. Finally, we take our analysis a step further
by proposing a new mobile application that comparestaxi prices in the city to facilitate traveller’s taxi choices,hoping to ultimately to lead to a reduction of commuter
costs. Our study provides a case on how big datasets that
become public can improve urban services for consumers
by offering the opportunity for transparency in economic
sectors that lack up to date regulations.
I.

TAXI PRICE COMPARISON EXPERIMENTThe New York City Taxi Dataset. The Freedomof Information Law in United States encourages publicauthorities to release their data where appropriate to thebenefit of the citizens. In 2014 the law was exploited
by Chris Whong to acquire and post on the web oneof the most comprehensive taxi mobility datasets available today. The dataset describes taxi journeys in New
York City during the full course of 2013, and informs
us not only on the origin and destination points of taxi
trips, noted in the related jargon as pick up and drop
off points respectively, but also on the financial costs incurred to the customer (trip fair) with unprecedented detail. This rather dense mobility dataset, containing hundreds of millions of trips is of gigabytes in size and can be
downloaded here http://chriswhong.com/open-data/
foil_nyc_taxi/. A sample of the traces generated by
the data can be seen is in Figure 1, where we have drawn

Session 1 :: Mobility

Vsevolod Salnikov,1 Renaud Lambiotte,1 Anastasios Noulas,2 and Cecilia Mascolo2

Session 6 :: Cities (II)OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce
Commuter Costs

FIG. 1: Marking the traces of new york city yellow taxis. For
every pick up and drop off point in a uniform sample of thedata we draw a black point.

a black point for every pick up and drop off point of ataxi journey.
Comparing Taxi Prices In August 2014, Uber
opened up an API with access to valuable information
about its services. The occasion allowed us to perform afirst head to head comparative analysis of prices between
Uber and Yellow taxis in New York City. To achieve this
we run the following experiment :• 1. For every trip in the New York City Yellow Taxi
dataset, record the geographic coordinates (latitude and longitude) of the pick up and drop off
points.
• 2. Retrieve the total fare paid by the customer forthe trip (including the tip).
• 3. Query Uber’s API and ask how much they would
charge for the same trip (same pick up and drop
off points), considering the cheapest version of theservice, Uber X.
• 4. Uber’s API returns a value range indicating theminimum and maximum price estimate. We takethe mean of the two values.
• 5. We then compare the prices from the two services.
As can be observed in Figure 2 where the distribution of prices for the two services is shown, despite thequalitative similarity of the two distribution, yellow taxi
appear on average (median) 1.4 US dollars cheaper than
Uber X. In Figure 3, we compare Uber and yellow taxis
from another perspective: for every observed yellow taxi

9

2015

2

0.35

0.20
0.15
Median Yellow Taxi Distance: 2.097

0.10
0.05
0.00
0

FIG. 2: Distribution of prices per journey for Uber X andYellow Taxis in New York City.

90
80

5

10
15
20
Distance in kilometers

25

30

Session 1 :: Mobility

Normed Frequency

0.25

Session 6 :: Cities (II)0.30

FIG. 4: Distribution of geographic distances between drop off
and pick up points for Yellow Taxi journeys.
Yellow Taxis VS Uber - Price Comparison

Equal Price Axis

70

Uber Price

60
50
40
30
20
10
0
0

10

20

30

40
50
Taxi Price

60

70

80

90

FIG. 3: Median Uber price for a given Yellow Taxi price.

price, we show the median Uber X price. Uber appears
more expensive for prices below 35 dollars and begins tobecome cheaper only after that threshold. As one would
expect, the cheaper journeys are those that are in principle of shorter range. As observed in a variety of empirical
data, human mobility tends to be characterised by a vast
majority of short trips [1, 2]. This observation therefore
suggests that Uber’s economical model exploits this trendof human mobility in order to maximise revenue. We also
confirm the skewed frequency distribution of movement
distances in the present context by visualising it in Figure 4, where we note a mean distance for a yellow taxi
trip in New York equal to 2.09.
The above experiment may involve a number of biases
which we refer to here. The NYC Yellow taxi data corresponded to year 2013 whereas Uber to 2014. Althoughnote that the prices for yellow taxis in the city had lastchanged in 2012 after 8 years [3]. So it should offer agood approximation of todays prices. Further, there wasno control for time of the day/week for the API query,an additional dimension which should be incorporated
when available. However, we argue that the process ofcomparing two different companies that provide the same
service in the same geographic area is of value to commuters. Just as consumer have open access to airfares
for a long time now allowing for transparency in a free,competitive, market we believe that similar approaches
could benefit commuters in modern cities.

FIG. 5: Geographic comparison between Uber and Yellow
Taxi prices. We paint an area black if Uber is cheaper by trip
majority and yellow otherwise.

II.

HELPING COMMUTERS

Our observations show that it might be financially advantageous on average for travellers to chose either Yellow Cabs or Uber depending on the duration of theirjourney. However the specific journey they are willingto take matters. In order to help users to take the rightdecision, we have developed a smartphone app, called
OpenStreetCab, designed as follows.
One limitation for the design of our service is that only
prices for trips with origins and destinations in the New
York City Taxi Dataset can in principle be retrieved. Inorder to evaluate the price of any trip, as needed for a
usable App, we have divided the NY region into a meshwith cells of size around 100m by 100m in order to index
trips in the database efficiently. For each user query,
we find a set of trips in our dataset with the origin inneighbouring cells of desired origin and, among them, wefind the trip whose destination is closest to the desired
one. This strategy has the advantage of being sufficiently
fast to perform online queries and expected to provide
reliable price estimates. For the same trip, Uber price isobtained through their API.
A real-time prototype has been designed and is currently launched on popular mobile platforms. Future
improvements include the possibility to change predic-

10

Session 6 :: Cities (II)tions depending on the time of the day, or on the expected traffic on the way, but also to suggest other typesof transportations, such as walking when the distance issufficiently short, or only part of the way, in situations
when a small change in the origin point can lead to a significant change in the price quote. In the meanwhile thecurrent version (Fig. 6) already provides a fully working solution, including geolocation services and address
retrieval. We are planning to launch the application on
the related stores very soon.

Session 1 :: Mobility

2015

3

FIG. 6: The proof of the concept[1] Dirk Brockmann, Lars Hufnagel, and Theo Geisel. The
scaling laws of human travel. Nature, 439(7075):462–465,
2006.
[2] Marta C Gonzalez, Cesar A Hidalgo, and Albert-Laszlo
Barabasi. Understanding individual human mobility pat-terns. Nature, 453(7196):779–782, 2008.
[3] Taxi Fares To Rise.
New York Times, 2012.
http://cityroom.blogs.nytimes.com/2012/07/12/
taxi-fares-in-new-york-to-rise-by-17/?_r=0.

11

Lauren Alexander1,∗ , Jameson Toole2 , Marta González1
1 Department of Civil Engineering and Environmental Engineering, MIT, 77 Massachusetts Ave., Cambridge, MA 02139, USA
2 Engineering Systems Division, MIT, 77 Massachusetts Ave., Cambridge, MA 02139, USA

January 30, 2015
I∗n recent years, smart-phone based technology has
enabled ride-sharing services like UberPool and LyftLine to match customers making similar trips in realtime [1, 2]. In this manner, ride-sharing services offer
customers the option to share a ride with other users
for a reduced rate and minimal increased travel time.
But what are the impacts of such services on city-widecongestion? With policy makers faced with decisions
about regulating the growing ride-share market, external impacts of such services are important considerations. This research explores the impact of ridesharing on Boston area congestion using mobile phonedata. We first extract average daily origin-destination(OD) trips from the mobile phone records and estimate
the proportions of these trips made in vehicles. Next,
we match spatially and temporally similar auto trips,
and assume a range of adoption rates to distill hourly
vehicles trips according to users’ willingness to sharerides. Finally, for each level of adoption, we evaluate
roadway level of service characteristics, including volume over capacity (VOC) and congested travel time,
to assess the network-wide impacts of ride-sharing.

1

Session 1 :: Mobility

Assessing the impact of ride sharing on congestion using mobile
phone data

Session 6 :: Cities (II)2015

trips in this study to estimate end-to-end† ride-sharing potential. Due to spatial and temporal limitations of our dataset, we are not able to explicitly estimate en-route ridesharing‡ .

2 DataFor this analysis, we use Call Detail Records (CDRs)
containing more than eight billion mobile phone records
for roughly two milion anonymized users in the Boston
metropolitan area over two months in the Spring of 2010.
Each record contains an anonymous user ID, longitude,
latitude, and timestamp at the instance of a phone callor other types of phone communication (such as sending
SMS, etc.). The coordinates of the records are estimatedby service providers based on a standard triangulation algorithm, with an accuracy of about 200 to 300 meters.
Because CDRs contain traces of a user at approximated locations only when his/her phone communicateswith a cell phone tower, they provide an inexact and incomplete picture of daily trip-making. Accordingly, muchresearch has focused on developing methods to extract
meaningful information about human mobility from mobile phone traces as well as understanding its limitations.
It has been demonstrated that CDR data can be used toinfer origin-destination (OD) trips using microsimulationand limited traffic count data [7]. At the level of the individual, daily trip chains/trajectories constructed frommobile phone data are consistent with household surveys [8, 10]. Further, road usage inferred from the CDR
data has been validated against GPS speed data [12] andhighway assignment results from a travel demand model[6]. Methodology to estimate aggregate average daily OD
trips by time of day and purpose has been validated in
Boston and Rio de Janeiro against national and local travel
surveys [3, 5].

SignificanceResearchers have found significant overlap in taxi trips
made in New York City, demonstrating the potential for
real-time vehicle pooling with minimal increase in travel
time [9]. Using mobile phone and social network data,
researchers evaluated demand for ride-sharing between
strangers, friends, and friends-of-friends in four cities in
Spain and the US [4]. Unlike both of these studies, thiswork focuses on the network-wide congestion impactsof ride-sharing rather than determining the proportion of
trips eligible for sharing given defined spatio-temporalconstraints. Furthermore, we use aggregate, average daily∗ corresponding author: lpalex@mit.edu† ride-sharing between users with similar origins and destinations‡ ride-sharing between users sharing portions of their paths between dissimilar origins and/or destinations, such that additional passengers canbe picked up en-route1

12

Methods

Next, we determine which trips have the potential to
be shared using spatial and temporal resolution of Tractand hour, respectively. In effect, we assume that trips
are uniformly distributed within each hour and Tract, andtherefore trips occuring within the same Tract-pair andhour can be matched with minimal additional travel time
incurred. Lastly, using Equation (5) we compute the number of hourly OD vehicles Ci jha for given adoption rate aand maximum carpool size s.

We estimate average daily OD trips from triangulated mobile phone records by applying the methods developedby our team in [3, 5] to the Boston CDR dataset. CDRs
are first converted into clustered locations at which users
engage in activities for an observed duration. These locations are inferred to be home, work, or other depending on observation frequency, day of week, and time ofday, and represent a user’s origins and destinations. Since
the arrival time and duration at these locations reflect theobserved (based on phone usage) rather than true arrival
time and duration of a user, we probabilistically infer departure time using survey data on trips in major US cities.
Trips are then constructed for each user between two consecutive observations in a day. These trips are multipliedby expansion factors based on the population of a user’s
home Census Tract and divided by the number of days on
which we observed the user, distilling average daily trips.
Aggregating individuals’ daily trips by Census Tract pairand hour of the day results in the trip matrices used in thisstudy.

(5) Ci jhas = Vi jh ∗ (1 − a + 1s )Session 1 :: Mobility

3

Session 6 :: Cities (II)2015

Finally, we can assess road usage and level of service
characteristics by assigning vehicles to a directed networkwith edges representing road segments and nodes representing intersections. Each edge has a free flow travel
time tFF and hourly capacity c, based on the road segment’s number of lanes and facility type. We use the
Bureau of Public Roads (BPR) volume-delay function tocapture the relationship between vehicle flow and travel
time under congested conditions and Iterative Traffic Assignment (ITA) to assign vehicles to the network minimizing total travel time.

Expanding on these methods, we now propose amethodology to estimate vehicle trips and road usage
under varying ride-share adoption rates. First, we estimate the proportion of incoming and outgoing person trips
made using automobiles from Equations (1) and (2) below. To infer travel mode, we use the 2006-2010 Census
Transportation Planning Products (CTPP) parts 1 and 2,
which provide residence- and workplace-based characteristics of Census Tracts [11]. Auto mode share of outgoing (incoming) trips is approximated by the ratio of auto
ai (a j ) to total ti (t j ) trips made by residents commuting
from Tract i (workers commuting to Tract j). By multiplying these auto shares by the average daily Tract-pairperson trips Ti j inferred from the CDR data and summingover distination Tracts j (origin Tracts i), we compute thenumber of outgoing Vi (incoming V j ) person trips madeby automobile.

4 Results

We performed traffic assignment for trips computed using
Equation (5) for the 5 pm hour (h = 17), a maximum carpool size of 4 (s = 4), and adoption rates ranging from 5
to 25% (a = {0.05, 0.10, 0.15, 0.20, 0.25}). Accordingly,
we are able to assess the network-wide impacts of traffic
conditions in the peak weekday hour depending on users’
willingness to share rides with 3 other passengers. Table
1 summarizes the percent change in vehicles, congested
travel time, and vehicle miles traveled (VMT). We findthat, regardless of the adoption rate, the change in vehicles and VMT are inversely proportional to roughly 75%of the adoption rate. The reduction in time spent in trafn an(2) V j = ∑ t jj ∗ Ti j
(1) Vi = ∑ atii ∗ Ti jfic, however, is much larger in magnitude than the correi=1
j=1
sponding adoption rate (258% to 224%). This decrease
The output from Equations (1) and (2) serve as in returns reflects the inherent relationship between roadmarginal totals and the average daily Tract-pair person segment volume and travel time, such that a reduction intrips Ti j serve as the seed matrix used to estimate OD per- the number of vehicles has the most impact on travel time
son auto trips Vi j with Iterative Proportional Fitting (IPF). in highly-congested conditions.
Finally, we estimate OD-specific mode shares vi j , which
Figure 1 (a) and (b) compares the distributions of road
we use to compute OD person auto trip matrices Vi jh forsegment volume and volume over capacity (VOC) for
each hour h using hourly OD person trips Ti jh , as shown
adoption rates of 0% and 25%. We see that ride-sharing
in Equations (3) and (4), respectively.
has the largest impact on the road segments that are theV(4) Vi jh = vi j ∗ Ti jh(3) vi j = Tii jj
most used and most over capacity.
213Vehicles
(%)
-3.7
-7.5
-11.2
-15.0
-18.8

Congested
TT (%)
-12.9
-25.6
-36.9
-47.0
-56.0

VMT
(%)
-3.8
-7.5
-11.2
-15.0
-18.7

pose and time of day inferred from mobile phonedata. Transportation Research C (2015).
[4] Cici, B., Markopoulou, A., Frias-Martinez, E., andLaoutaris, N. Assessing the potential of ride-sharing
using mobile and social data: a tale of four cities.
In Proceedings of the 2014 ACM International Joint
Conference on Pervasive and Ubiquitous Computing, ACM Press (2014), 201–211.

Table 1: Percent change in vehicles, congested travel time(TT), and vehicle miles traveled (VMT) compared withan adoption rate of 0% (i.e. 100% single occupancy vehicles). Results are for 5 pm hour (h = 17) a maximum
carpool size of 4 (s = 4).

[5] Colak, S., Alexander, L., Alvim, B. G., Mehndiretta,
S. R., and Gonzalez, M. Analyzing cell phone location data for urban travel: Current methods, limitations and opportunities. Transportation Research
Records (2015).

Session 1 :: Mobility

Adoptionrate (%)
5
10
15
20
25

Session 6 :: Cities (II)2015[6] Huntsinger, L. F., and Donnelly, R. Reconciliationof regional travel model and passive device tracking
data. In Proceedings of the 93rd Annual Meeting of
the Transportation Research Board (2014).
[7] Iqbal, M. S., Choudhury, C. F., Wang, P., andGonzález, M. C. Development of origin-destination
matrices using mobile phone call data. Transportation Research C 40 (2014), 63–74.

Figure 1: Distribution of (a) road segment volumes and(b) volume over capacity (VOC) ratio.

5[8] Jiang, S., Yang, Y., Fiore, G., Jr., J. F., Frazzoli,E., and González, M. A review of urban computing for mobile phone traces: Current methods, challenges and opportunities. In Proceedings of the ACM
SIGKDD International Workshop on Urban Computing (2013).

Conclusions

This research explores the extent to which ride-sharing
services impact network-wide congestion using mobile
phone records. To-date other research efforts have used [9] Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strorich data such as GPS taxi traces to explore potential ridegatz, S. H., and Ratti, C. Quantifying the benefits
sharing demand under explicitly-defined spatio-temporalof vehicle pooling with shareability networks. Proconstraints. In contrast, we estimate aggregate, average
ceedings of the National Academy of Sciences of thedaily ride-share demand using comparitively lower resoUnited States of America 111, 37 (2014), 13290–
lution CDR data. Despite the limitations, we are able to13294.
use CDR data to recreate reasonable travel demand forall vehicles, enabling us to evaluate the impact of ride- [10] Schneider, C., Belik, V., Couronné, T., Smoreda, Z.,sharing on the entire road network. In Boston, we demonand González, M. C. Unraveling daily human mostrate that ride-sharing has the potential to significantly
bility motifs. Journal of The Royal Society Interface
decrease time spent in traffic by all drivers, even for mod10 (2013).
erate levels of ride-sharing adoption.
[11] U.S. Department of Transportation Federal Highway Administration. CTPP 2006-2010 Census Tract
References
Flows. http://www.fhwa.dot.gov/planning/
census_issues/ctpp/data_products/[1] Lyft Line. https://www.lyft.com/line.
2006-2010_tract_flows/index.cfm, 2013.
[2] UberPool. http://blog.uber.com/uberpool.
[12] Wang, P., Hunter, T., Bayan, A. M., Schectner, K.,[3] Alexander, L., Jiang, S., Murga, M., and Gonzalez,
and González, M. C. Understanding road usage patM. C. Validation of origin-destination trips by purterns in urban areas. Scientific Reports 2 (2012).
3

14

2015

Session 2 :: Cities (I)

15

2015

1

MIT Media Lab, USA

2

FBK, Italy

Introduction

3

SKIL Telecom Italia, Italy

Energy eﬃciency is a key challenge for building sustainable societies. Due to growing populations, increasing
incomes and the industrialization of developing countries, the world primary energy consumption is expected
to increase annually by 1.6%. Hence, this scenario raises issues related to the increasing scarcity of natural
resources, the accelerating pollution of the environment, and the looming threat of global climate change.
Recently, several computational works have started in focusing on energy and sustainability problems. In
particular, they have targeted energy issues with two main objectives: (i) modeling and predicting energy
consumption behaviors [3] and (ii) inducing behavioral changes in energy consumption [1].
Our paper targets the energy consumption prediction task but adopting a novel approach. We use aggregatedand anonymized human behavioral data, derived from mobile network activity, in order to predict the energy
consumption of a given geographical area. More specifically, we deal with two diﬀerent tasks: (i) average dailyenergy consumption and (ii) peak daily energy consumption. The method we propose in our paper has several
advantages: (i) it is cheap in the sense that it uses existing data generated by telecom infrastructure. No
modification to mobile phones or telecom equipment is required, and (ii) it has good scalability and so it issuitable for large populations, which is desiderable for city planning and energy management.

2

Session 2 :: Cities (I)1

Session 1 :: Mobility

Andrey Bogomolov1 , Bruno Lepri2 , Roberto Larcher3 ,Fabrizio Antonelli3 , Fabio Pianesi2 and Alex Pentland1

Session 6 :: Cities (II)Energy Consumption Prediction using
People Dynamics Derived from Cellular Network Data

Methodology

We built our approach on a dataset spanning over a period of 2 months and a territory of 6000 square kilometersin the Northern Italy. The telecommunication and the energy consumption datasets have the same spatiotemporal aggregation. The temporal aggregation is ten minute intervals while the spatial one is obtained bypartitioning the territory using a regular square grid, called partitioning grid. Each square of the grid measuresapproximately 1 square kilometer.
The telecommunication dataset is obtained from the Call Detail Records (CDRs) generated by the cellular
network of a telecommunication operator oﬀering its services on the territory under analysis. For the generationof this dataset CDRs recording sent SMSs, received SMSs, issued calls, received calls, and events related with
Internet connections have been considered. The energy consumption dataset provides information about thestructure of the electrical grid and the electrical current flowing through the 180 primary distribution lines
serving the majority of the users living in the territory under analysis. Primary lines (medium voltage) aremanaged by a local company and bring energy from the national grid (high voltage) in order to distributeit among all the users. The dataset is composed by two sub-datasets: (i) the structure of the electrical grid,each line is described by providing the number of customer sites it serves in each square of the partitioning
grid (customer sites often provide energy to more than one customer and they can also serve structures like
condominiums, businesses and government organizations), and (ii) the line measurement grid that provides theinstantaneous current flowing through each primary line every ten minutes.
Looking at the amount of electric current passing a point in an electric circuit per unit of time for each powerline, we found that it has a number of cyclic characteristics and trends. We found predictable changes that
repeat over daily and weekly periods. Based on these regularities we separated all power lines into 3 clusteredareas: residential, touristic and city center/industrial area (see Fig. 1).
Then, we hypothesised that cellular communication patterns, which represent human dynamics in space andtime, could be a good proxy for energy consumption prediction. To this end, we computed, from the anonymized
and aggregated mobile network activity, a number of features characterizing diversity, regularity and generalmobile phone usage in each part of the territory spatially aggregated by square grid. The discovered regularities,
described above, were explicitly coded into the feature space by extracting of the number of hour in a day andthe number of a weekday for each data source being processed.

1

16

10

20

30

40

50

time

Session 2 :: Cities (I)Session 6 :: Cities (II)

0

70
60
40
0

10

20

30

frequency

50

60
40
0

10

20

30

frequency

50

60
50
40
30
0

10

20

frequency

City Center/Industrial

70

Residential Area

70

Touristic Area

Session 1 :: Mobility

2015
The prediction tasks were solved for the next 7 days interval for each electric line ID and were designed
as non-linear multiple regressions. We used the Random Forest algorithm proposed by Breiman [2]. The
consistency of this algorithm has been proven: the algorithm adapts to sparsity in the sense that the rate ofconvergence depends only on the number of strong features and not on the number of noisy or less relevantones.
To solve the problem of computational complexity of the huge amount of data samples (>600 millionsof CDRs) we moved from time domain of communication patterns to the frequency domain, applying fast
Fourier transform algorithm for each group of daily time series. Then, we computed second-order featuresby mathematical functions, such as mean, median, sum, variance, skewness, kurtosis, entropy, characterising
the distributions and it’s properties. Also we found that only a small set of harmonics in Fourier domain
explains the response variable variance for each type of first-order feature space time series, which reduces
the computational complexity by a number of orders. Finally, the computed second-order feature space foreach spatial had a reduced number of dimensions (>3000), but still represented the temporal and spatial
characteristics (i.e. diversity and regularity) of communication patterns.
A feature selection step was performed before the model building. The feature selection was done on a
reduced sample of the training data, which was one week long. The metric used was the decrease in Gini
index, which is the impurity measure of a decision tree node. This method is proven to be superior thancorrelation-based measures or information gain criteria. The feature selection step reduced the feature space to
32 dimensions for each of the two models without losing much accuracy.

0

10

20

30

40

50

0

10

20

time

30

40

50

time

Figure 1: Spectral Characteristics of Typical Response Variables

3

Results and Discussion

Prediction metrics for daily average energy consumption for the next 7 days are 2.43 times better than thebaseline, which is the training set mean value (MSE = 325.2679 compared to the baseline MSE = 790.6041).
Prediction metrics for daily peak energy consumption model for the next 7 days prediction interval are 59.93
times better than the baseline, which is the training set maximum value (MSE = 601.7531 vs baseline MSE =36062.7851).
Our results prove that human dynamics, extracted from aggregated and anonymized mobile phone data,
are good proxies for modelling energy consumption. The introduced models technically do not account forseasonality on a yearly scale due to the 2-months limitation of the data sample. However, this limitation could
easily be solved by training the model on more data.
Our results have several practical implications. Our approach could help to optimize the economy of energy
producers/distributors value chain, also acting as an eﬃcient tool for meeting peak electrical energy demand.
Hence, could help to reduce total primary energy consumption and ecological footprint including climate change
still meeting the people’s energy needs.

References[1] H. Allcott and S. Mullainathan. Behavioral science and energy policy. Science, 327:1204–1205, 2010.
[2] Leo Breiman. Random forests-random features. Technical report, Technical Report 567, Department of
Statistics, UC Berkeley, 1999. 31, 1999.
[3] Z.J. Kolter and J. Ferreira. A large-scale study on predicting and contextualizing building energy usage. In
Proceedings of the Conference on Artificial Intelligence (AAAI), Special Track on Computational Sustainability and AI, 2011.

17
2

2015

MIT Media Lab, USA{abogomol,pentland}@mit.edu

1

2

3

FBK, Italy{lepri,staiano,pianesi}@fbk.eu

Introduction

Telefonica Research, Spain
nuriao@tid.es

4

University of California,
USA
eletouze@datapopalliance.org

Session 2 :: Cities (I)1

Session 1 :: Mobility

Andrey Bogomolov1 , Bruno Lepri2 , Jacopo Staiano2 ,Emmanuel Letouze4 , Nuria Oliver3 , Fabio Pianesi2 and Alex Pentland1

Session 6 :: Cities (II)Predicting Crime Hotspots
Using Aggregated and Anonymized Data on People Dynamics

Crime is a well-known social problem aﬀecting the quality of life and the economic development of asociety. Urbanists and architects have investigated the relationships between people dynamics, urban
environment and crime. Urban activist Jane Jacobs emphasized natural surveillance as a key deterrent
for crime: as people are moving around an area, they will be eyes on the street able to observe what
is going on around them. Hence, Jacobs suggests that high diversity among the population and a highnumber of visitors contribute to the safety of a given area and lead to less crime [2]. Conversely, Newman
proposed an alternative theory arguing that a high mix of people creates the anonymity needed forcrime [3]. Thus, according to Newman, low population diversity, low visitors ratio and a high ratio of
residents are the features contributing to an areas safety. Several studies have tried to shed light onto
these conflicting theories.
Criminologists have also started to investigate in detail significant concentrations of crime at microlevels of geography, regardless of the specific unit of analysis. Research has shown that in what are
generally seen as good parts of town there are often streets with strong crime concentrations, and inwhat are often defined as bad neighborhoods, there are locations relatively free of crime.
In this paper, we propose a novel place-centric and data-driven approach for crime prediction: specifically we investigate the predictive power of people dynamics – derived from a combination of mobile
network activity and demographic information – to determine whether a specific geographic area is likely
to become a scene of the crime.

2

Methodology and Results

In our model, we used 1 month of aggregated and anonymized mobile data combined with demographics
information to predict whether a particular neighborhood in London will be a crime hotspot or not in
the following month, i.e. whether it will have more or less crimes than the median number of crimes
per neighborhood in London’s metropolitan area. Our model (based on random forests) achieves 68.37%
accuracy when using only mobile + demographics information, and 69.54% accuracy when adding census
data (borough profiles data). For more detail, see [1].
A spatial visualisation of our results is reported on a map of the London metropolitan area in Figure
2 and compared with a similar visualisation of the ground truth labels in Figure 1. In the maps, green
represents low crime (class 0, number of crimes < median number of crimes) and red high crime (class 1,
number of crimes > median).

3

Discussion

Our results show that human behavioral and demographic data (at a daily and monthly scale) significantly
improve the prediction accuracy when compared to using rich statistical data about a boroughs population(households census, demographics, ethnicity, employment, etc.). The borough profiles data provides afairly detailed view of the living conditions of a particular area in a city, yet this data is expensive andtime-consuming to collect. Hence, this type of data is typically updated with low frequency (e.g. every

18
1

2015

51.7

51.7

51.6

Crime Level 51.6

Crime Level

0

0
1
51.5

0.5

1.0

1.0

1.5

1.5

51.4

51.4

51.3

51.3

−0.50

−0.25

0.00

Session 6 :: Cities (II)

0.5

0.25

−0.50

lon

−0.25

0.00

0.25

lon

Figure 1: Ground Truth

Figure 2: Predicted Crime Hotspots

Session 2 :: Cities (I)RadiusSession 1 :: Mobility

Radius

51.5

lat

lat

1

few years) making diﬃcult the observation of potential changes. Human behavioral data derived frommobile network activity combined with demographics provides significantly finer temporal and spatial
resolution.
The features with highest predictive power in our combined model (mobile+borough profiles variables)
are derived from the mobile data and not from the oﬃcial statistics coming from borough profiles.
Moreover, higher-level features extracted over a sequence of days from variables encoding the daily
dynamics have more predictive power than features extracted on a monthly basis. This finding points
out at the importance of capturing the temporal dynamics of a geographical area in order to predict its
levels of crime.
Interestingly, features derived from the percentage of people in a certain cell who are at home bothat a daily and monthly basis seem to be of extreme importance. In fact, 11 of the top 20 features are
related to the at home variable. Newman approach of defensible space postulates the relevance of a highnumber of residents in an area to reduce crime. The predictive power of home variables seems to confirm
their relevance. However, we found positive associations between the home variables and crime. Hence,
our findings do not support Newman thesis, suggesting that an increased ratio of residents is linked toless crime and higher urban safety. It is also interesting to note the role played by the unpredictabilityof the variables, captured by Shannon entropy features. The entropy-based features in fact seem usefulfor predicting the crime level of places (8 features out of the top 20 are entropy-based features). In ourstudy, the Shannon entropy captures the predictable structure of a place in terms of the types of people
that are in that area over the course of a day. A place with high entropy would have a lot of variety inthe types of people visiting it on a daily basis, whereas a place with low entropy would be characterised
by regular patterns over time. In this case, the daily unpredictability in patterns related to diﬀerent age
groups, diﬀerent use (home vs work) and diﬀerent genders seems to be a good predictor for the crimelevel in a given area. Our findings support Jacobs theory of natural surveillance [2] that high diversityof functions in a area and high diversity of people (gender-diversity and age-diversity) act as eyes on thestreet decreasing the number of crimes.
We believe that this work helps deepen our understanding of the relationship between human dynamics, urban characteristics and crime.

References[1] A. Bogomolov, B. Lepri, J. Staiano, N. Oliver, F. Pianesi, and A. Pentland. Once upon a crime:
Towards crime prediction from demographics and mobile data. In Proc. of ICMI, pages 427–434.
ACM, 2014.
[2] J. Jacobs. The death and the life of great american cities. Random House Inc., 1961.
[3] P. Newman. Defensible space: Crime prevention through urban design. Macmillian Pub Co, 1972.

19
2

2015

3

1

University of Southampton, 2 Université Libre de Bruxelles

Clinton Health Access Initiative, 4 Fogarty International Center, 5 Flowminder foundation

Introduction

2008, Tatem et al., 2009, Wesolowski et al., 2012],
we used the location of the towers through which
an individual’s communications were routed to
calculate the daily locations for the anonymous
subscribers and subsequently estimated the number of daily users for each mobile phone tower.

Many health indicators are measured through passive surveillance systems reporting at the levelof health facilities. To convert these measures
to population–level metrics, estimates of facility
catchment population sizes are needed. A rangeof recent studies have highlighted the dynamic
nature of human populations through quantitative analyses, particularly in low income settings[Bengtsson et al., 2011, Bharti et al., 2011, Lu
et al., 2013]. However, populations are frequently
assumed to be static over the study period due toa lack of time–resolved data. Recent analyses haveshown the potential of using anonymised mobile
phone call data records (CDRs) to produce accurate and dynamic population distribution maps
[Deville et al., 2014]. Here we describe how CDRs
can be used to estimate changes in population distribution in a low income setting and how measures relevant for public health applications canbe calculated using this data. Using the example
of health facility catchment areas in Namibia, wedemonstrate that the population size of health facility catchments varies throughout time and that
CDRs can be used to improve catchment area population size estimates.

2

Session 2 :: Cities (I)1

Session 1 :: Mobility

zu Erbach-Schoenberg, E.1 , Sorichetta, A.1 , Linard, C.2 , Lourenco, C.1,3 ,Alegana, V.1 , Bird, T.J.1 , Tatem, A.J.1,4,5Session 6 :: Cities (II)Estimating variability in health facility catchment population sizes
using mobile phone call records

The user density over the coverage area of each
tower (determined by its Voronoi polygon) was
spatially extrapolated to census units, to obtain
density values at the level of census units. Following the method described in Deville et al. [2014],
we used a linear regression on training data tomodel the relationship between census unit mobile phone user density and observed population
density according to the most recent census, conducted in August–September 2011.
Using the obtained parameters, we predicted
population densities for each census unit for eachmonth within the period covered by the data, adjusting the estimates to match the total predicted
population count to the census counts. We thenused a dataset detailing health facility catchment
areas, created based on travel time [Alegana et al.,2012], to estimate the size and change in size ofthe population in each catchment area (see Figure 1 for catchment areas overlayed on a static
population distribution estimate provided by theWorldpop project).

Data and Methods

Mobile phone call data records covering a 3.5 year
period from October 2010 to April 2014 were provided by the leading mobile phone service provider
in Namibia, MTC (Mobile Telecommunications
Limited). Following methods outlined in previous
studies [Bengtsson et al., 2011, Gonzalez et al.,

To demonstrate the importance of population
mobility in the context of disease surveillance, wethen applied our monthly population estimatesto assess population variability in health-facility
catchment areas.
120Figure 3:

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)2015

Change of catchment area population
counts, showing the difference of the monthly prediction to the estimated based on static census population
counts. Catchment area populations for facilities in the
North (shown in red) are substantially larger than thestatic estimate during the holiday period. The opposite can be observed for health facilities in and around
Windhoek (blue), showing a marked decrease in catchment population sizes for the same period of time.

Figure 1: Catchment areas overlayed on static population estimate layer (Worldpop project http://www.
worldpop.org.uk).

3

Results

The population distribution is dynamic with aparticularly marked change around the Christmas
holiday period. Figure 2 shows the difference inpopulation numbers for each census unit between
November 2011 and December 2011 as percent
change. We can clearly see a marked decrease in
the population number around the capital, Windhoek. At the same time population sizes increase
substantially in the North of the country. The
opposite dynamic can be observed in the period
following the holidays (not shown).
Figure 2: Percent difference in population numbers

In Figure 3 we show the relative difference of estimated catchment area populations to the population number estimated from the census. We
show the difference for health facilities from theNorth (shown in red) and the Windhoek area
(blue). The peaks of relative increase in population numbers in the North around December correspond to the lows for the Windhoek region.

between November 2011 and December 2011. Red corresponds to a strong increase in population numbers
in December, whereas blue corresponds to a decrease.
The most substantial change in population distribution at Christmas time is the migration from the capital, Windhoek, to the North and North-East regionsof the country.

2

21

2015
Conclusionof towers is dynamic due to continuing growth ofthe mobile phone network.

We show that even in a sparsely populated country such as Namibia with large distances between
population centres, populations are highly dynamic across the year. As a result, catchment
populations sizes of health facilities can change
substantially through the year, with dynamic estimates differing by up to 15000 people (or up to
a 30% difference) from a static estimate. CDRs
can be used to assess the seasonality of population density and improve estimates by generating
dynamic estimates.

L. Bengtsson, X. Lu, A. Thorson, R. Garfield, and
J. Von Schreeb. Improved response to disasters
and outbreaks by tracking population movementswith mobile phone network data: a post-earthquake
geospatial study in Haiti. PLoS medicine, 8(8):e1001083, 2011.

Implications

N. Bharti, A.J. Tatem, M.J. Ferrari, R.F. Grais,
A. Djibo, and B.T. Grenfell. Explaining seasonal
fluctuations of measles in Niger using nighttime
lights imagery. Science, 334(6061):1424–1427, 2011.

The results have big implications on health metrics that rely on estimates of catchment denominators, with likely substantial under- and overestimations of population prevalences of healthoutcomes present. Moreover, the period of largest
changes in population distribution coincides withthe beginning of the malaria transmission season.
Therefore, neglecting to take into account seasonality is likely to bias passive surveillance measures. Furthermore, we have seen that the observed changes in population density can be rapid
at some times of the year, with substantial differences within the space of a single month. This
has implications for the design of targeted intervention studies, intervention planning and the difficulties of reliably quantifying effects.

6

Session 2 :: Cities (I)

V.A. Alegana, J.A. Wright, U. Pentrina, A.M. Noor,
R.W. Snow, and P.M. Atkinson. Spatial modellingof healthcare utilisation for treatment of fever in
Namibia. International Journal of Health Geography, 11(6):10–1186, 2012.

Session 6 :: Cities (II)5

References

Session 1 :: Mobility

4

P. Deville, C. Linard, S. Martin, M. Gilbert, F.R.
Stevens, A.E. Gaughan, V.D. Blondel, and A.J.
Tatem. Dynamic population mapping using mobile
phone data. Proceedings of the National Academy of
Sciences, 111(45):15888–15893, 2014.
M.C. Gonzalez, C.A. Hidalgo, and A. Barabasi. Understanding individual human mobility patterns. Nature, 453(7196):779–782, 2008.
X. Lu, E. Wetter, N. Bharti, A.J. Tatem, and
L. Bengtsson. Approaching the limit of predictability in human mobility. Scientific reports, 3(2923),
2013.
A.J. Tatem, Y. Qiu, D.L. Smith, O. Sabot, A.S. Ali,
and B. Moonen. The use of mobile phone data forthe estimation of the travel patterns and imported
Plasmodium falciparum rates among Zanzibar residents. Malar J, 8:287, 2009.

Perspectives

Compared to the method presented in Devilleet al. [2014] there are some additional technicaldetails to consider due to the different setting.
While in France mobile phone coverage can beassumed to be complete, some areas in Namibia
have no coverage. While these are often sparsely
populated, using the same method as applied to
the France data might lead to estimation errors as
a result of substantially overestimating the coverage area of certain towers. Additionally, due tothe growing mobile phone market in Namibia andthe long period covered by our data set, the set

A. Wesolowski, N. Eagle, A.J. Tatem, D.L. Smith,
A.M. Noor, R.W. Snow, and C.O. Buckee. Quantifying the impact of human mobility on malaria.
Science, 338(6104):267–270, 2012.

3

22

2015
Measuring de facto populations with Mobile Network Operator’s Call Detail Record
data
Erki Saluveer1,2, Rein Ahas1, Siiri Silm1, Margus Tiru1,2

2

Positium LBS, spin-off company of Univeristy of Tartu, margus.tiru@positium.ee,

http://positium.com/

The aim of this presentation is to introduce a methodology for measuring de facto populations based on

Session 2 :: Cities (I)

http://mobilitylab.ut.ee/eng/

Session 1 :: Mobility

Department of Geography, University of Tartu, Estonia, rein.ahas@ut.ee,

Session 6 :: Cities (II)1

mobile positioning databases. In the mobile and rapidly changing world, demographic processes must also
be monitored more precisely than before. Censuses are usually conducted with a 10-year cycle andpopulation register data is usually published as at the 1st of January, but today, it is necessary to know thenumber of residents located in an area at every moment of time. The consumers of such information include
urban planners, transport managers, marketing companies, managers of emergency situations, and manyothers.
De jure populations are determined on the basis of an official fact or registration (census, register). De facto
populations are formed by the people located in a certain area at a certain moment of time. (1) “Population groups
or segments of de facto population consist six categories: (a) visitor population; (b) homeless population; (c)seasonal population, which we subdivide into (d) the amenity seeking population and (e) migrant workers and theirfamilies; (f), the portion of the Daytime population that consists of residents from elsewhere; and (g) the De Jure
population that is “present.” (1). For accurate measurement of de facto populations, various registers and sourcesof data are usually combined (2); they can also be measured within a limited unit of space and at a specific unit oftime through observation or survey. They are more difficult to measure in large areas (3,4).
When mapping the distribution of population with the help of secondary data, it is necessary to know the factors
and migration processes influencing the population. Distribution of population is most directly influenced by thegeography of the building stock and the hierarchy of the population system. De jure populations can be directly
connected to the locations of residential buildings. In the case of de facto populations, there are stronger
connections with the locations of places of work, educational institutions, services, and various public agencies aswell. The population system and the geographical distribution of the population are, for example, also influenced
by the hierarchy of the population system and the development cycle of cities. Of population processes, the shortterm dynamics of the population, its circadian, weekly, and seasonal cycle, are very important in mapping de facto
populations (5,6). Migration processes that are tightly related to demography, economy, and international relations
are also an important factor (7,8).
In this presentation, we are using passive mobile positioning, i.e. Mobile Network Operator (MNO) Call Detail
Record CDR) and Data Detail Record (DDR) data, to measure de facto population. Mobile data has been used for

23

2015
measuring population in many study projects (9, 10). Only the consent of the mobile operator and ensuring theprocedures required for privacy protection are needed. Eurostat has also been most interested in this source of data
(11).
In this presentation, we introduce a methodology, which produces de facto population statistics from CDR files.
The CDR data include: the anonymous ID connected to the phone number (follows the same phone throughaccuracy of a second) (12). The CDR data are distributed unevenly in time and space, but they are collected for along period of time. Thus, several algorithms for temporal and spatial interpolation of the data are needed. In our
research, we have developed important data processing algorithms in the following stages:a)Temporal interpolation of the data – harmonisation of the temporal distribution of location points.

b) Spatial interpolation – harmonisation of the special distribution of location points.
c)

Session 2 :: Cities (I)location Cell ID (network cell location based on the location of the antenna); time of the call activity (with theSession 1 :: Mobility

Session 6 :: Cities (II)history); call activity (any active use of the phone network, calls and text messages in and out, data services)

Finding anchor points – determining regularly visited and meaningful places.

d) Determining population segments – segmentation of population groups of different statuses on the basisof characteristics.
We determine the following units of population segments:a)Permanent residentsb) Temporary residentsc)Domestic visitorsd) Foreign visitorse)Transit visitors

In the analysis, we used algorithms to calculate the segments of de facto residents for all Estonian local
municipalities and larger towns for the years 2012-2014. The calculated population segment figures are comparedto the 2011 Census data and the later data will be compared to the number of people on the Census day (1.01.2012).
The results show that the number of permanent residents correlates best with the number of residents in the Census,
the relationship is very strong (the correlation coefficient is 0.998). There is also a very strong relationship between
the number of residents in the Census and the number of temporary residents and the number of visitors (Fig 1).
There is a medium strength relationship between the number of residents in the Census and the number of foreign
visitors and the transit segment.

24Session 2 :: Cities (I)Figure 1. Average correlations of the whole period between population segments and the number of residents in

Session 1 :: Mobility

Session 6 :: Cities (II)2015

the Census.

On the Census day (1.01.2012), there was a very strong relationship between all segments and the number ofresidents, the correlation coefficient was higher than 0.9 in the case of all segments. The number of permanent
residents correlates best with the number of residents on the census day (correlation coefficient 0.998).

Figure 2. Variation of the correlations between population segments and the number of residents in the Census by
days.

25

2015
Comparison of the figures calculated by using algorithms to the Census data also shows that clear regularities havedeveloped between population segments and certain days of the week. Above all, weekend days and working days
are statistically different. In the case of certain segments, there are also great differences between working days
(such as Friday). According to the theory of temporary migrations, it is a rhythmic process, a decrease in one placemeans an increase in another (5,6). The segment of foreign visitors and transit also behaves differently. There are

migrations and earlier practice. There are also statistically significant differences in the locations and distributionof population segments in December; here, the influences of events are significant in addition to the holidays.

The most complicated issue with the dataset has been distinguishing visits and transit, because it is not possible toregister the exact amount of time spent in an area on the basis of CRD and DDR – only the locations of phones areregistered. Since the number of phones does not characterise the presence of people with an absolute accuracy, we
have used various correction coefficients created on the basis of reference data to generalise the dataset to theSession 2 :: Cities (I)Session 6 :: Cities (II)August as a holiday and tourism period most distinct. These results relate well with the theory of seasonal

Session 1 :: Mobility

also certain regularities in the variation of monthly population figures, with the summer months of June, July, andgeneral population, and are using databases of censuses, accommodation statistics, road counters, ferry
connections, etc.
References(1)Swanson, D.A., Tayman, J. 2011. On Estimating a De Facto Population and Its Components, Review of
Economics & Finance, 1: 17-31.
(2)Sutton, P., Roberts, C., Elvidge, D., & Baugh, K. (2001). Census from heaven: An estimate of the global
human population using night-time satellite imagery. International Journal of Remote Sensing, 22, 3061–3076.
(3)UN 1991. Handbook of Vital Statistics Systems and Methods, Volume 1: Legal, Organisational and TechnicalAspects, United Nations Studies in Methods, Glossary, Series F, No. 35, United Nations, New York.
(4)Smith, S. K.,1989. “Toward a Methodology for Estimating Temporary Residents”, Journal of the American
Statistical Association, 84: 430-436.
(5)Bell, M. and Brown, D. 2006: Who are the visitors? Characteristics of temporary movers in Australia.
Population, Space and Place 12(2), 77–146.
(6)Silm,S., Ahas, R., 2010. 'The seasonal variability of population in Estonian municipalities, Environment andPlanning A, 42(10) 2527-2546.
(7)Zelinsky, W ., 1971. The hypothesis of the mobility transition. The Geographical
Review LX1, 219-249.
(8)Bhaduri, B. (2007). Population distribution during the day. In S. Shekhar & H. Xiong (Eds.), Encyclopedia of
GIS, Springer, December 2007 (print edition ISBN 978-0-387-30858-6).
(9)Reades, J. and Calabrese, F. and Sevtsuk, A. and Ratti, C. (2007), ‘Cellular Census: Explorations in Urban
Data Collection’, IEEE, Pervasive Computing, Vol. 6, No. 3, pp. 30-38.
(10)Devillea, P., Linard, C., Maryine, S., Gilbert, M., Stevens, F.R., Gaughanf, A.E., Blondel, V., Tatem, A.J.
2014. Dynamic population mapping using mobile phone data, PNAS, 111(45): 15888–15893.
(11)Positium LBS 2014. Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics,
Consolidated Report Eurostat Contract No 30501.2012.001-2012.452, 31p.
epp.eurostat.ec.europa.eu/portal/page/portal/tourism/documents/MP_Consolidated%20report.pdf

26

2015
(12)Ahas, R. Aasa, A., Roose, A., Mark, Ü., Silm, S. 2008. Evaluating passive mobile positioning data for
tourism surveys: An Estonian case study. Tourism Management 29(3): 469–486.
http://www.sciencedirect.com/science/article/pii/S0261517707001355

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)(13) Hugo, G.J. 2006 Temporary migration and the labour market in Australia. Australian Geographer 37, 211–31.
(14) Kang, CG, Liu, Y., Ma, XJ., Wu, L., 2012. Towards Estimating Urban Population Distributions from
Mobile Call Data, Journal of Urban Technology 19(4): 3-21.

27

2015

Investigating the relationships between spatial structures andurban characteristics
Marco De Nadai1 , Bruno Lepri2 , Roberto Larcher3 and Nicu Sebe1

Nowadays, the majority of people live in cities and the urban areas density is generally increasing.
Several works show that the size of cities plays a fundamental role in a systematic acceleration of socialand economic life [1]. These gains are applied to diﬀerent quantities including economic output, wages,
patents and epidemics and the average increase in these urban quantities, Y in relation to the population
size N , is usually described by a super-linear scale-invariant law. Recently, theoretical works suggestthat the origin of this super-linear scaling stems from social interactions [4]. Hence, understanding city’s
dynamics and spatial dynamics is of paramount relevance. Morphological urban analysis have been widely
studied in literature. However, this was almost limited to static reports coming from census or remotesensing data.
The recent availability of new large-scale data sets, such as those automatically collected by mobile phone
networks, open new possibilities of studying city dynamics.
In this paper, we analyzed the communication networks of 12 Italian cities with more than 150’000
inhabitants in order to investigate the impact of cities’ spatial structures and dynamics on diﬀerent
socioeconomic outcomes such as economic growth, innovation, contagious disease rates and crime.

2

Session 2 :: Cities (I)Introduction

Session 1 :: Mobility

1

University of Trento
Fondazione Bruno Kessler
3
Telecom Italia - SKIL

Session 6 :: Cities (II)1

2

Methodology and Results

Our analysis is based on hourly aggregated and anonymous mobile traces provided by an Italian telecommunications operator, which concern 14 Italian cities urban areas during five months (from February
2014 to June 2014).
Given the irregular spatial distribution of the antennas per each technology (GSM, UMTS, etc.), thecity area is spatially aggregated in a regular square grid with size α in order to simply compare the city’sareas and the cities among each others. The value α has to correctly describe the city’s neighbourhoods:
a very small value can create cells in which the same quarter is described by more than one cell, while
a too big value can hide interesting information and diﬀerences between quarters. In order to overcome
the arbitrariness limit derived by the α choice, α was set to the mean distance between the nearest
neighbourhoods’ centroids of Milan (0.6km). This means that a square cell of 0.6km2 can correctly
describe a quarter. The mobile traﬃc was re-calculated using the square grid, giving the access to the
local activity density ρ(i, t) at time t and place (square) i.
Firstly, we investigated the average people compactness in the city. This is an important urban metrics
which helps to understand how citizens move during the day. For this reason the average distance between
phone users in the city Dv is √computed thanks to the Venables distance [2]. This distance was weighted
with the squared city’s area A in order to compare diﬀerent cities. From fig. 1 it is possible to seethat Milan has a peak around 9, were people are still probably at home or in the commuting system,
then it collapses during the day, displaying a spatial concentration of people, probably because theywork and/or tourism is more active. Contrarily, Naples shows an almost constant distance, probably
meaning that workplaces and residential places are less separated among each other than in Milan, orthat Naples is more equally spatial distributed. From the distance Dv it is also possible to study the
dilatation coeﬃcient which describes an entire city by:µ=maxt (Dv (t))
mint (Dv (t)). In figure fig. 1 it is possible to compare the diﬀerent cities. This describes how the city is spatiallyorganized, showing for example if workplaces, schools and leisure places are concentrated in the same
area.

28

2015

0.48
0.46

√
Dv / A

0.44

MilanoTorinoNapoli

0.42
0.40

0.34

0

5

10

15

20

25

Hour

Figure 1: (a). Time evolution of the average distance between phone users in the various cities. (b).
Spatial distance between hotspots. (c). Power law fit between the average number of hotspots and cities’
population.
In order to look the morphological distribution of the city, it is very useful to look at its hotspots. The
simplest way to define the hotspots is by choosing a threshold δ and considering hotspots all the points
with a density ρ(i, t) ≥ δ. This threshold can be chosen by experience or by observing the data, leading to
an obvious arbitrariness in the results and to a unique threshold among time t. On the contrary, we usedthe Loubar threshold ρ(i, t)Lou , defined by [2], from the tangent of point p(1, 1) of the Lorenz Curve ofthe density ρ, which expresses the spatial inequality of a city at time t. The interesting features exploredby applying this method are here presented.
Louf and Barthelemy [3] showed that in U.S activity centres, as determined from employment data,
Na scales sub-linearly (with β ≈ 0.64) in relation to the population size. More recently, Louail et al. [2]demonstrated that there is a sub-linear relation, with a similar exponent β. between activity (mobile)
hotspots and population for Spanish cities. Here, we firstly try to verify the aforementioned sub-linearity
relation for Italian cites, then we investigate the associations between activity (mobile) hotspots andsocial quantities such as criminality, employment.
Interestingly, the power law fit between population and number of hotspots confirms the results obtained with Spanish cities in [2]. Furthemore, we carried out correlational analyses to investigate whether
associations between the average number of hotspots and social quantities like GDP, employment, unemployment, patents intensity and criminality exist. For these analysis we employed the non-parametric
Spearman’s Rho method with a level of significance p < .05 (see table 1 reporting Spearman correlationcoeﬃcients and the power law fit’s exponent β).
Variable
Population
GDP
Employment
Unemployment

Spearman correlation [−1, 1]
0.96
0.60
0.83
0.56

Session 2 :: Cities (I)0.36

Session 1 :: Mobility

Session 6 :: Cities (II)

0.38

β
0.54
0.68
0.61
0.40

Table 1: significant Spearman correlation coeﬃcients and power law exponent (β) between various social
quantities and the average number of hotspots

References[1] Luı́s MA Bettencourt, José Lobo, Dirk Helbing, Christian Kühnert, and Geoﬀrey B West. Growth,
innovation, scaling, and the pace of life in cities. PNAS, 104(17):7301–7306, 2007.
[2] Thomas Louail, Maxime Lenormand, Oliva Garcı́a Cantú, Miguel Picornell, Ricardo Herranz, Enrique
Frias-Martinez, José J Ramasco, and Marc Barthelemy. From mobile phone data to the spatial
structure of cities. Scientific Reports, 2014.
[3] Rémi Louf and Marc Barthelemy. Modeling the polycentric transition of cities. Physical review letters,
111(19):198702, 2013.
[4] Wei Pan, Manuel Cebrian, Ghoshal Gourab, Krumme Coco, and Alex Pentland. Urban characteristics
attributable to density-driven tie formation. Nature Communications, 4(1961), 2013.

29

2015

Session 3 :: Economies

30

Session 6 :: Cities (II)Many households in developing countries lack formal financial histories, making it difficult for banks
to allocate capital, and for potential borrowers to
obtain loans. However, many unbanked households
have mobile phones, and even prepaid phones generate rich transaction histories. This project showsthat behavioral signatures in mobile phone data predict default with accuracy near that of traditional
credit scoring methods that rely on financial histories. The method is demonstrated using call records
matched to loan outcomes for a sample of borrowers
in Haiti.
1. Introduction

Many studies have found that small firms in developing countries have access to opportunities with
2. Datahigh returns that, puzzlingly, remain untapped (De Mel
et al., 2008; McKenzie and Woodruff, 2008; Banerjee
My organizational partner is EFL (Entrepreneurial
and Duflo, 2014). One reason these opportunities Finance Lab), which works on alternative credit scormay remain untapped is if potential lenders have dif- ing methods in developing and emerging markets withficulty identifying the investments that will be prof- an emphasis on the unbanked.1 EFL obtained linked
itable.
phone and completed loan data for a sample of borDeveloping country banks that would lend to small rowers in Haiti. First, anonymous records were obfirms face several particular challenges. In developed tained for a sample of borrowers who took out a small
countries, banks have access to robust information on loan from a local bank. These records include baborrower reputation through credit bureaus, which sic demographics such as age and gender, the terms
aggregate information on an individual’s historical of the loan provided, and whether the loan was demangement of credit, but many developing country faulted on (defined by 90 days of nonpayment).2 Many
households do not interact with formal institutions of these borrowers also have mobile phone accounts
that generate these forms of data. As a result, lenders with a large operator in the country. These borrowers
have very little formal information on potential bor- were matched to their prepaid phone accounts using
rowers. This is particularly problematic, as banks encrypted (anonymous) individual identifiers. Mowho would lend to small or informal businesses may bile phone transaction records (CDR) were obtained
have little recourse if a borrower were to default. for these matched accounts, including metadata for
Even when banks can rely on institutions like police each call, SMS, top up, and data access, for the year
and courts, it is costly to follow up on small loans.
of 2012.
Traditional microfinance has presented one soluBecause we aim to predict default based on thetion to the repayment problem, but it is not clear that information available at the time a loan was granted,
the investments currently selected by microfinance only mobile phone transactions that precede the loan
have generated growth for small businesses (see for date are included. We focus on the bank’s small loan
example Karlan and Zinman, 2011; Banerjee et al., product. The data includes 3,131 loans granted be2014).
tween January and September 2012, of which 12.8%
This paper introduces a new method to identify ended in default. Borrowers have a median loan size
profitable investments, using information on poten- of $184 and term of 5 months (Haiti’s per capita GDP
tial borrowers that is already being collected by mo- was $775.52 in 2012).3
bile phone networks.

Session 3 :: Economies

DANIEL BJÖRKEGREN∗

Session 1 :: Mobility

Although unbanked households lack the formal
records needed for traditional credit scores, many
have maintained a rich history of interaction with aformal institution over an extended period of time—theirmobile phone activity, recorded by their operator.
Even with prepaid plans, operator records yield richinformation about individual behavior and social networks. If indicators derived from this data are predictive of creditworthiness, they can help banks identify
profitable opportunities. There are many straightforward indicators of behavior that are plausibly related
to loan repayment. For example, a responsible borrower may keep their phone topped up to a minimum
threshold so they have credit in case of emergency,
whereas one prone to default may allow it to run out
and depend on others to call them.
This paper demonstrates that indicators of behavior derived from mobile phone transaction records
predict loan repayment with accuracy near that of
traditional credit scores.

BEHAVIOR REVEALED IN MOBILE PHONE
USAGE PREDICTS LOAN REPAYMENT

Session 2 :: Cities (I)20151
From their website, website, “EFL Global develops credit
scoring models for un-banked and thin-file consumers and
MSMEs, using many types of alternative data such as psychometrics, mobile phones, social media, GIS, and traditional
demographic and financial data. We work with lenders acrossLatin America, Africa and Asia.” http://www.eflglobal.com
2
Loans that ended in default were oversampled to aid in
estimation.
3
All results reported in US dollars.

∗
Brown University. E-mail: dan@bjorkegren.com, Web:http://dan.bjorkegren.com
Revision January 12, 2015. Preliminary and incomplete.
Thanks to Entrepreneurial Finance Lab for data, and Nathan
Eagle, Seema Jayachandran, and Jeff Berens for helpful discussions. This work was supported by the Stanford Institutefor Economic Policy Research through the Shultz Fellowshipin Economic Policy.

31
1

2015

2

4
The measures in this paper correspond with a counterfactual of being more selective in extending loans.

Figure 1. Model Performance:
Receiver Operating Characteristic
Curve
1.00

0.75

0.50

0.25

0.50

False Positive Rate
Best benchmark without branch

0.75

1.00

Best model using phone data without branch

Naive

Session 3 :: Economies

0.00

Session 2 :: Cities (I)0.00

Session 1 :: Mobility

0.25

Session 6 :: Cities (II)The goal is to predict the likelihood of default using behavioral features derived from mobile phone
usage. The model is estimated using data on loansthat have already been completed; these estimates
are used to predict whether a loan would end in default based on the information available at the time
the loan was granted. Because this sample of individuals did obtain loans, risk is reported among those
who were allocated loans based on the bank’s scoring method at the time; default risk among unbanked
populations may differ, and can be explored in follow
up work.4
The loan data provides an indicator for whether aparticular borrower defaulted on their loan (90 days
past due). From the phone data we derive 6,841 features with variation. Features include measures of usage: intensity and distribution over space and time,
top up and depletion patterns, mobility, the patternof handset use, and strength and diversity of social
network connections. The performance of these features will be compared against a benchmark using thecharacteristics that the bank recorded at the time of
the loan: gender, age, loan size, and the loan term indays.
A first question is how individual features correlate with default. Benchmark features have very low
correlation with default (magnitudes between 0.004
and 0.06), which is reasonable: the bank should already be incorporating the features it observes into
the decision of whether to extend credit. Several categories of features from the phone data have highly
significant correlations with magnitudes between 0.12
and 0.18, including variation and periodicity of usage,
and several measures of mobility.
While individual features are significantly correlated with default, the best prediction method willtake into account multiple features at the same time.
Because of the large number of potential predictors,
including all of them in a simple method like ordinary least squares (OLS) would lead to overfitting.
Instead, two types of models are estimated, including a specification of OLS chosen by a model selection
procedure (stepwise search using the Bayesian Information Criterion) and random forests. To best measure how the method will perform out of sample, allestimates are computed using cross validation with
5 folds. The data is randomly divided into 5 folds,
and the outcomes for each fold are predicted using amodel estimated on the omitted folds.
The main result we report is the area under thereceiver operating characteristic curve (AUC), whichshould range from 0.5 to 1.0. The receiver operating characteristic curve (ROC) plots the true positive
rate of a classifier against the false positive rate. If

True Positive Rate

3. Method and Results

the ROC of a classifier has nonconvexities, it is possible to reweight it to obtain the convex hull of theoriginal ROC; thus I also consider the area under theconvex hull of the receiver operating characteristic
curve (AUC-H). Figure 1 presents the convex hullsof the best benchmark model and the best model using indicators derived from phone data.
The benchmark models using only demographic
and loan data have moderate AUCs ranging from0.531-0.536. Adding features derived from phone data
improves AUCs to the range of 0.660-0.682, and the
AUC-H of the best model is 0.690 (stepwise OLS).
This performance is near a sample of published results of AUC estimates from other studies that use
traditional credit scoring methods with financial data
in more developed settings. Baesens et al. (2003) reports top AUCs ranging 0.668-0.758 for different UK
samples and 0.776-0.791 using for samples from the
Benelux countries. Calabrese and Osmetti (2013) reports a top AUC of 0.723 for small and medium enterprises in Italy.
To account for the possibility of unforeseen shocks,
ideally the model would be tested not only on out ofsample individuals, but also on out of sample time
periods. Since the data spans only a short time thisis left for future work. Results so far suggest that theperformance of the model without financial data approaches the performance obtained with traditional
credit scoring methods in more developed settings.
Performance is likely to improve with additional tuning and data (this paper uses an average of 2.5 monthsof phone data per borrower).

32

2015
3

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

Baesens, B., T. Van Gestel, S. Viaene,
M. Stepanova, J. Suykens, and J. Vanthienen (2003): “Benchmarking state-of-the-art
classification algorithms for credit scoring,” Journal of the Operational Research Society, 54, 627–
635.
Banerjee, A., E. Duflo, C. Kinnan, and
R. Glennerster (2014): “The Miracle of Microfinance? Evidence from a Randomized Experiment,” .
Banerjee, A. V. and E. Duflo (2014): “Do Firms
Want to Borrow More? Testing Credit Constraints
Using a Directed Lending Program,” The Review
of Economic Studies, 81, 572–607.
Calabrese, R. and S. A. Osmetti (2013): “Modelling small and medium enterprise loan defaults
as rare events: the generalized extreme value regression model,” Journal of Applied Statistics, 40,
1172–1188.
De Mel, S., D. McKenzie, and C. Woodruff(2008): “Returns to Capital in Microenterprises:
Evidence from a Field Experiment,” The Quarterly
Journal of Economics, 123, 1329–1372.
Karlan, D. and J. Zinman (2011): “Microcredit in Theory and Practice: Using Randomized
Credit Scoring for Impact Evaluation,” Science,
332, 1278–1284.
McKenzie, D. and C. Woodruff (2008): “Experimental Evidence on Returns to Capital and Accessto Finance in Mexico,” The World Bank Economic
Review, 22, 457–482.

Session 6 :: Cities (II)References

33

2015

1

Estimating Food Consumption and Poverty Indiceswith Mobile Phone Data

Session 2 :: Cities (I)Session 1 :: Mobility

challenge, this time on data from Senegal, will be presented
in April 2015 [8].
Given the importance of mobile devices to people in developing economies for accessing information and economic
opportunities, phone usage data represents a clear barometer ofa user’s socio-economic conditions in the absence or difficultyof collecting official statistics [2], [9], [10]. Many studies havealready addressed the question of the relationship between themobile phone usage and socio-economic levels [11], [10], [12],[9], [13].
The objective of this research is to assess the suitability of
metrics derived from mobile phone data, specifically CDRs
and airtime credit purchases, as proxies for food security andpoverty indicators in a low-income country context. We use
a country of central Africa as a case study, and compare ata fine-grained spatial scale features measured from mobile
phone data collected in 2012 with the results of a detailed foodsecurity survey conducted at the same time. The World Food
Programme regularly assesses the situation of nutrition, theirreports with updated information are available online [14].
While food access has greatly improved between 2009 and2012, there is still approximately one household in five thatis food insecure in these regions. One of the recommended
steps for the way forward to tackle malnutrition in Africa
is to obtain better and more frequent information on foodsecurity. Therefore, a real-time proxy of relevant trends onfood security indicators would be of great interest for regions
where household surveys can be time and resource consuming
if they are possible at all. In order to complement the information obtained from food security indicators, we also assess
the possibility of using the same mobile phone data to map
poverty levels, using the results of a survey on non-monetary
poverty as a ground truth comparison.

Session 6 :: Cities (II)Recent studies have shown the value of mobile phonedata to tackle problems related to economic development
and humanitarian action. In this research, we assess thesuitability of indicators derived from mobile phone data asa proxy for food security indicators. We compare the measures extracted from call detail records and airtime credit
purchases to the results of a nationwide household survey
conducted at the same time. Results show high correlations(> .8) between mobile phone data derived indicators andseveral relevant food security variables such as expenditure
on food or vegetable consumption. This correspondence
suggests that, in the future, proxies derived from mobile
phone data could be used to provide valuable up-to-date
operational information on food security throughout low
and middle income countries.
In recent years the explosion in the use of digital services has led to what has come to be called the “Big Data
Revolution”. This revolution has fundamentally changed how
companies are able to understand their users through analysisof data produced passively. However, more recently, as lowcost mobile handsets and internet usage have proliferated indeveloping countries, reaching 90% coverage in the developingworld in 2014 [1], the potential of these signals to transform
development and humanitarian action has emerged.
Big Data has the potential to guide policy makers by providing an alternative to traditional data sources such as costly andtime-consuming manual surveys [2]. For planning purposes,
access to reliable and up-to-date statistical data is essential
to humanitarian organizations when deciding where and whenhelp is most needed. Many rich data sources exist with thepromise of providing early warning and real-time monitoringof vulnerable populations including remote sensing, social
media, remittances and anonymized mobile phone records.
In particular, many recent studies have shown how valuablemobile phone data in the form of Call Detail Records (CDRs)
can be used to guide agile development policy and humanitarian action [3], [4], [5]. The Orange D4D challenge [3],using Côte d’Ivoire as a case study, led to a large number ofinnovative uses of CDR data ranging from mobility modelingfor transport optimization [4] to epidemic modeling [5] andnetwork analysis of social communities [6]. The challenge wassuch a success that other challenges of big data exploration
have followed [7], [8], and the results of a second development

We analyze the correlations between each pair of mobile
phone metric and survey variable aggregated over the different
administrative divisions of the country. We find very highcorrelations (> 0.7) between several food security indicatorsand measures computed from airtime purchases. Generally, thesum of expenses and the maximum top-up value are the mobile
variables that correlate best with several of the food indicators,
see table I for a few selected correlation coefficients.

Session 3 :: Economies

Adeline Decuyper∗ , Alex Rutherford† , Amit Wadhwa§ , Jean Martin Bauer§ , Gautier Krings∗‡ , Thoralf Gutierrez‡ ,Vincent D. Blondel∗ , Miguel A. Luengo-Oroz†∗ ICTEAM institute, Université catholique de Louvain, Belgium† United Nations Global Pulse‡ Real Impact Analytics, Brussels, Belgium§ Vulnerability Analysis and Mapping, World Food Programme

34

2015
TABLE IS ELECTED VARIABLES FROM HOUSEHOLD SURVEYS WITH HIGH

CORRELATION WITH THE SUM OF EXPENSES IN MOBILE CONSUMPTION .

survey variable
Share food from production
Share food from purchases
Food consumption score
Monthly food expenditure
Education expenditure
Communication expenditure
Total monthly expenditure
Per capita income
Multidimensional poverty index

cor. sum
-0.64
0.66
0.62
0.82
0.72
0.69
0.6
0.63
-0.75

95% conf. int.
[−0.71, −0.55][0.58, 0.73][0.54, 0.7]
[0.77, 0.86][0.65, 0.78][0.62, 0.75][0.51, 0.68][0.54, 0.7][−0.8, −0.69]

2consumption uncorrelated to top up values are cultivated for
personal consumption. Therefore this study is compatible witha new hypothesis: expenditure in mobile phone top up is
proportional to the expenditure in food in the markets.
When analyzing the relation between mobile phonevariables and poverty indices, we have been able to createa proxy indicator for multidimensional poverty index at thesector level (> .8 correlation). Interestingly, we have foundthat the most important variable to shadow poverty levels is
the top up information.

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)For selected variables, we also test linear and quadratic
regression models, using more than one mobile phone derived
metric. The highest correlation value of all the models tested
in this study corresponds to a quadratic model with the sum of
expenses and the average amount of top-up as mobile phone
derived indicators that shadows the survey variable measuring
the amount of food expenses (0.89), see figure 1.

This study is a first step towards demonstrating the utilityof mobile phone data, as a novel new data source for foodsecurity monitoring. All in all, this analysis shows that simple
statistics on top-up data could very accurately provide valuable
information about the evolution of food security at a finegrained spatial level. In particular, we have begun to outline
which aspects of ground truth food security information arereflected in CDR and mobile phone top-up information. This
research illustrates a feasible mechanism whereby mobile data
could be shared outside a mobile company at a lower resolution than the individual level, yet still providing information
to guide policy.

Session 3 :: Economies

The correlation shown here is the correlation between each indicator, andthe sum of expenses on mobile phone airtime credit, along with their 95%
confidence intervals. All p-values are below 10−15 .

The full article of this research is available online [15].
R EFERENCES

Fig. 1. Quadratic combination of CDR variables against expenses onfood. Correlation coefficient: 0.89.

Looking in detail at the relations between the consumptionof each food item (survey question “how many times have you
eaten [item] in the last 7 days?”) and mobile phone expenditure(measured by the average of the sum of airtime expenses peruser over 6 months), we found different correlation values
ranging from no relation to very high correlation between
certain food items and airtime expenses. In particular the
consumption of vitamin rich vegetables, rice, bread, sugar
and fresh meat have a very high correlation (> 0.7) with theairtime purchases. On the other side of the spectrum, broadly
cultivated items like cassava and beans have no relation
with the expenditure on mobile phones. These results are
particularly interesting, as the products from the first groupare mainly bought in the market while the products with

[1] (2014). The world in 2014 : Ict facts and figures. International
Telecommunication Union. http://www.itu.int/.
[2] Deville P, Linard C, Martin S, Gilbert M, Stevens F, et al. (2014)
Dynamic population mapping using mobile phone data. PNAS 111:
15888-15893.
[3] (2013) Mobile Phone Data for Development - Analysis of mobile
phone datasets for the development of Ivory Coast. Orange Data For
Development Challenge, D4D. URL http://perso.uclouvain.be/vincent.
blondel/netmob/2013/D4D-book.pdf.
[4] Berlingerio M, Calabrese F, Di Lorenzo G, Nair R, Pinelli F, et al. (2013)
Allaboard: A system for exploring urban mobility and optimizing public
transport using cellphone data. In: Blockeel H, Kersting K, Nijssen
S, Železný F, editors, Machine Learning and Knowledge Discovery in
Databases, Springer Berlin Heidelberg, volume 8190 of Lecture Notes
in Computer Science. pp. 663-666.
[5] Lima A, De Domenico M, Pejovic V, Musolesi M (2013) Exploiting cellular data for disease containment and information campaigns strategies
in country-wide epidemics. CoRR abs/1306.4534.
[6] Amini A, Kung K, Kang C, Sobolevsky S, Ratti C (2014) The impact ofsocial segregation on human mobility in developing and industrialized
regions. EPJ Data Science 3: 6.
[7] Telecom Italia Big Data Challenge. URL http://www.telecomitalia.com/
tit/en/bigdatachallenge/contest.html.
[8] de Montjoye Y, Smoreda Z, Trinquart R, Ziemlicki C, Blondel V (2014)
D4D-Senegal: The second mobile phone data for development challenge.
arXiv preprint arXiv:14074885 .
[9] Frias-Martinez V, Soto V, Virseda J, Frias-Martinez E (2013) Can cell
phone traces measure social development? In: Third Conference on the
Analysis of Mobile Phone Datasets, NetMob.
[10] Gutierrez T, Krings G, Blondel VD (2013) Evaluating socio-economic
state of a country analyzing airtime credit and mobile phone datasets.
CoRR abs/1309.4496.
[11] Eagle N, Macy M, Claxton R (2010) Network diversity and economic
development. Science 328: 1029-1031.

35

2015

3

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)[12] Frias-Martinez V, Virseda J (2012) On the relationship between socioeconomic factors and cell phone usage. In: Proceedings of the Fifth
International Conference on Information and Communication Technologies and Development. New York, NY, USA: ACM, ICTD ’12, pp.
76–84. doi:10.1145/2160673.2160684. URL http://doi.acm.org/10.1145/
2160673.2160684.
[13] Smith-Clarke C, Mashhadi A, Capra L (2014) Poverty on the cheap:
Estimating poverty maps using aggregated mobile communication networks. In: Proceedings of the SIGCHI Conference on Human Factors
in Computing Systems. New York, NY, USA: ACM, CHI ’14, pp. 511–
520. doi:10.1145/2556288.2557358. URL http://doi.acm.org/10.1145/
2556288.2557358.
[14] WFP. World food programme survey data portal. international household
survey network. URL http://nada.vam.wfp.org.
[15] Decuyper A, Rutherford A, Wadhwa A, Bauer J, Krings G, et al. (2014)
Estimating food consumption and poverty indices with mobile phonedata. CoRR abs/1412.2595.

36

2015

How do daily commuting flows relate to the temporaland spatial distribution of urban economic activity?
Simply knowing where people work will not fully
capture economic activity, because locations vary inproductivity. We show how the entire matrix ofcommuting flows obtained from cell phone data can beused to estimate productivity, which allows for a moreaccurate estimation of economic activity. We achieve
this by estimating a gravity equation derived from a
discrete choice model of job choice. To validate ourapproach, we use the model to predict mean residential
income at commuting origins, which we compare witha new dataset of nighttime lights. We discuss potential
applications.
Human mobility and economic activity are intertwined.
Previous work has established that international migration
flows are responsive to economic activity in thedestination country, as well as to distance [1], [2].
Regional and seasonal migration also respond in a similar
fashion [3], [4]. These results are suggestive of a
relationship between mobility and economic activity atmuch finer temporal and geographic scales. However, todate this relationship has not been studied quantitatively
mainly due to data limitations. This is the goal of our
project. We study the link between daily mobility(commuting) flows and economic activity within cities,
and quantify it using cell phone data.
Our project has three contributions. First, we set up atheoretical model that links individual home- and jobchoice decisions, commuting flows and economic
productivity, and show that it can be implemented

The authors are grateful to the LIRNEasia organization
for providing access to cell phone data and an excellent
working environment, to Sriganesh Lokanathan, research
manager at LIRNEasia, whose dedication and relentlessefforts made this project possible, and to Danaja
Maldeniya for constructive feedback on the project. We
acknowledge funding from the International Development
Research Centre (IDRC).
*
Contact: gek@mit.edu; miyauchi@mit.edu.

Our work builds on a recent literature on urban sensing[5], and the measure of economic activity may eventuallyassist policy in urban areas. This detailed data can help
improve economic policy [6], as well as benefit urbanplanning by making it more responsive to detailed trends
in the concentration of economic activity.
Discrete Choice Model and Gravity Equation
In the model, based on [7], workers choose their work
destination by trading off the wage offered at each
destination, against the distance needed to reach each
destination. Naturally, high wages may compensate for thecost of commuting. Other idiosyncratic factors that
influence job choice are modelled as random shocks froma pre-specified distribution (Fréchet or type II extreme
value). This setup implies that the commuting probability
𝜋𝑖𝑗 that a worker residing in origin 𝑖 commutes todestination 𝑗 satisfies the following gravity equation:

Session 3 :: Economies

Department of Economics, Massachusetts Institute ofTechnology, Cambridge MA
2
LIRNEasia, Colombo, Sri Lanka

Session 2 :: Cities (I)1

Session 1 :: Mobility

Gabriel E. Kreindler 1,2 and Yuhei Miyauchi 1,2*

empirically through a gravity model. Second, we use cell
phone data from Sri Lanka to construct a measure ofcommuting with fine urban spatial and temporal variation,and proceed to estimate the model.1 Third, we validate themodel’s performance using a new and improved data
source of nighttime lights.

Session 6 :: Cities (II)Mobility and Productivity:
Quantifying Urban Economic Activity
using Cell Phone Datalog(𝜋𝑖𝑗 ) = 𝜓𝑗 + 𝛽 log(𝐷𝑖𝑗 ) − 𝜇𝑖 + 𝜀𝑖𝑗 ,(1)where 𝜓𝑗 is a log transformation of the wage at 𝑗, 𝐷𝑖𝑗 is ameasure of distance between 𝑖 and 𝑗, 𝜇𝑖 captures originspecific factors, and 𝜀𝑖𝑗 is measurement error. Intuitively,
𝜓𝑗 captures a destination’s attractiveness, after controllingfor distances to all origin locations, and their respective
sizes. The benefit of using an explicit model of workers’
decisions is that it allows us to back out other useful
economic measures, such as the output and the average
residential income at a particular location.
In the full paper, we study several features and extensionsof the model, such as the importance of the aggregationlevel, (𝑖, 𝑗) pair specific factors, and worker heterogeneity.
The model can also accommodate a variety of assumptions
on how workers choose their home location.
Commuting Flows and Model Estimation
Empirically, the model can be mapped to the real worldusing commuting flows extracted from cell phone data.
We use a simple algorithm to construct daily commuting
flows from CDR data, building on the literature on this
topic [8], [9]. Figure 1 shows the distribution of smoothedcommuting flows from a particular origin location (i.e. agiven cell tower Voronoi cell) in the greater metropolitan
1

Our approach will perform significantly better in urban areas,
due to higher cell tower density relative to less developed areas.

37

2015
Colombo area. What is noteworthy is that there issignificant variation in commuting flows even after
accounting for commuting distance, and this variation
appears to capture anecdotal patterns of commuting in the
Colombo area.

the medium term, economic activity becomes moreconcentrated around city centers, or whether it actually
moves towards city outskirts.

Session 3 :: Economies

Figure 2. The top panel shows a log transformation of VIIRS
nighttime lights. (The bright spot corresponds to an oil refinery.)
The lower panel shows the measure of residential income log(Φ𝑖 )derived from the model (white corresponds to higher values).

38

Session 1 :: Mobility

Instead, we present results from a validation exercise
using a measure Φ𝑖 of mean residential income in origin
location 𝑖 , computed using the model. Intuitively, Φ𝑖captures the average of wages brought “home” by workers
who live in 𝑖 and who work in various other destinations.
To assess whether Φ𝑖 contains non-trivial information, we
compare it with nighttime lights, which is a recognizedmeasure of residential income [10]–[12]. We use a newversion captured by the VIIRS satellites and curated by the
Earth Observatory Group (EOG) at NOAA. The VIIRS
data has higher spatial and temporal resolution than theolder OLS data, and it does not have a saturation point.

Figure 1. Smoothed commuting flows from a given origin towercell (indicated by the green cell). Red lines indicate sub-district(divisional secretariat) boundaries.

Session 6 :: Cities (II)Comparison with Nighttime Lights
Having estimated economic productivity (wages) andeconomic activity (output), exclusively from mobility
flows derived from cell phone data, we would ideallyproceed to validate these measure using independent wage
and output measures. Unfortunately, in our context thistype of data is only available aggregated at province level.

Session 2 :: Cities (I)We estimate equation (1) on commuting volumes between
pairs of towers in Sri Lanka. There are 3,047 towers and≈ 1,7 million pairs of towers in our sample, covering over
300 million commuting trips. We use a linear regression
model with two sets of fixed effects (corresponding toorigin and destination locations). Using the estimated
fixed effects and other coefficients, we construct theresidential income and output measures.

Figure 2 shows a graphical comparison: nighttime light
VIIRS in the top panel, and the log(Φi ) measure (at the
tower cell level) in the bottom panel. The correspondence
is generally good, yet there are clear points where themodel can be improved (e.g. patterns along roads). In thepaper we show using regression analysis that the income
measure is informative after controlling for population
density (interpolated from the census), and various simple
indicators derived from cell phone data.
Ongoing and Future WorkWe plan to exploit the time variation in the cell phone data
to look at changes in the spatial distribution of economic
activity over short and medium time horizons. For
example, we can investigate the effects of short term
disturbances, such as the transportation restrictions
imposed by national events. We can also study whether, in2015

F. Ortega and G. Peri, “The Effect of Income and Immigration Policies on International Migration,”
NBER Work. Pap. Ser., no. 18322, 2012.

[3]

O. J. Blanchard and F. Katz, “Regional Evolutions,” Brookings Pap. Econ. Act., vol. 23, no. 1, pp.
1–76, 1992.

[4]

D. Hare, “‘Push’ versus ‘pull’ factors in migration outflows and returns: Determinants of
migration status and spell duration among China’s rural population,” J. Dev. Stud., vol. 35, no.
January 2015, pp. 45–72, 1999.

[5]

F. Calabrese, L. Ferrari, and V. D. Blondel, “Urban Sensing Using Mobile Phone Network Data:
A Survey of Research,” ACM Comput. Surv., vol. 47, no. 2, pp. 1–20, Nov. 2014.

[6]

J. Mitchell, R. J. Smith, M. R. Weale, S. Wright, and E. L. Salazar, “An indicator of monthly GDP
and an early estimate of quarterly GDP growth,” Econ. J., vol. 115, pp. 108–129, 2005.

[7]

G. M. Ahlfeldt, S. J. Redding, D. M. Sturm, C. Fieler, G. Grossman, B. Honore, U. Mueller, S.
Kortum, E. Morales, and F. Hall, “THE ECONOMICS OF DENSITY :,” NBER Work. Pap. Ser.,no. 20354, 2014.

[8]

F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti, “Estimating Origin-Destination Flows Using
Mobile Phone Location Data,” IEEE Pervasive Comput., vol. 10, no. 4, pp. 36–44, Apr. 2011.

[9]

P. Wang, T. Hunter, A. M. Bayen, K. Schechtner, and M. C. González, “Understanding road usage
patterns in urban areas.,” Sci. Rep., vol. 2, p. 1001, Jan. 2012.

[10]

J. V. Henderson and A. Storeygard, “Measuring Economic Growth from Outer Space,” Am. Econ.
Rev., vol. 102, no. 2, pp. 994–1028, 2012.

[11]

X. Chen and W. D. Nordhaus, “Using luminosity data as a proxy for economic statistics.,” Proc.
Natl. Acad. Sci. U. S. A., vol. 108, no. 21, pp. 8589–8594, 2011.

[12]

C. Mellander, K. Stolarick, Z. Matheson, and J. Lobo, “Night-Time Light Data : A Good Proxy
Measure for Economic Activity ?,” no. 315, p. 33, 2013.

Session 2 :: Cities (I)[2]

Session 1 :: Mobility

A. M. Mayda, “International migration: A panel data analysis of the determinants of bilateralflows,” J. Popul. Econ., vol. 23, pp. 1249–1274, 2010.

Session 6 :: Cities (II)[1]

Session 3 :: Economies

References

39

2015

Cignifi Risk Solutions
An application of mobile phone data for Brazil microcredit approval
Data Science Team∗In the past few decades, microfinance est rates leading the poor into a debt trap.

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)has gained broad attention as a way to The high interest rates are generally caused
promote the financial inclusion of the by: i) lack of relevant information to makepoor population, especially in developing a decision about credit; ii) high transaction
countries.

Microfinance also promotes cost. To overcome these two obstacles, true

economic development and increased innovation is needed, and here we present a
employment.

Popularized through the case study from Brazil, one utilizing alterna-

microcredit movement of the 1970s, a tive data from mobile telecommunications tomovement that included the founding gain credit insight and facilitate lending de-Session 3 :: Economies

Cignifi Inc.

of the Grameen Bank by Muhammad cisions with a much lower transaction cost.
Yunus in Bangladesh, the system of mi-

Despite Brazil having taken the first ini-

crofinance has evolved to a system moretiative (the UNO project early in the 1970s),

comprehensive than one focused on justthe microfinance system development there,

lending. The four main types of financial

after adjusting for the size of the popula-solutions that are offered are: i) credit;

tion, is still lagging behind countries likeii) savings; iii) insurance; iv) access to
payment alternatives.

Bangladesh, India or, in Latin America, Peru

Microcredit re-

and Colombia. This leaves a huge gap in themains the best-known aspect of microfi-

meeting of the potential demand. The Cen-nance, and its development is linked pri-tral Bank reports that only 9% of the po-

marily to the use of innovative mecha-

tential demand for microcredit in the coun-nisms that make it possible to mitigate

try had been addressed in 2011 [1].

different market failures that keep theTheaforementioned obstacles, notably informa-poor outside of the loan market.

tion asymmetry and transaction costs, need
to be surpassed, and this can only be done

Much debate has risen around the mixed

through innovation.

outcome of microcredit. Proponents statethat it helps the borrowers get out of poverty

One of the most promising innovations

through the deploying of loans for small busi- that occurred recently is mobile payments,
nesses and other productive means, while or pagamentos móveis. While by official accritics have mainly focused on the high inter- counts 56% of the adult population has bank∗Contact Qiuyan Xu, qxu@cinifi.com or Adriano A. Massuia, amassuia@cignifi.com for further information.

140

accounts in the country, the number drops significant to differentiate the credit risk.

of calls
to about 33%, even in an era of economic These variables include: i) Number2015
growth and full employment, when this value with standard price; ii) Number of calls tois adjusted by taking into consideration the mobiles from the same carrier, but to difpoorest (20% of low-income) [2]. In com- ferent regions; and iii) Number of SMS reparison, the country reached the milestone ceived. Based on our model, it seems theof 274 million active mobile lines in March population can be segmented to different
2014, with a density of 135.3 active mo- risk profiles through key variables, a probile lines per 100 inhabitants [3], making the cess which will allow operational deployuse of a mobile payments system one of the ment. Hence, creative ways to use CDR canmost promising initiatives for financial inclu- potentially address the issues related to thelack of traditional credit history information

sion.
a

partnershipwith and the high transaction cost.

Session 2 :: Cities (I)(name not to be disclosed due to confiden-

Session 1 :: Mobility

a Brazilian Telecommunication Operator

Global Findex Database 2012:

PolicyReferences

tiality agreement) and Microinvestor (Itau

Microcredit), and from them we received [1] BCB Relatório de Inclusão Financeira
information related to payment history by
2011. Disponível emcontract.

The overlap between the infor-

mation available on the mobile operator and [2] Demirgüç-Kunt, Asli; Klapper, Leora
Measuring Financial Inclusion:The
the product information on microcredit restricted the study period to the year 2013.

Session 3 :: Economies

established

Session 6 :: Cities (II)We

Research Working Paper 6025, World

Because of the nature of the data, with its

Bank, Washington, DC.

many open contracts with outstanding in-stallment, survival analysis is used to esti- [3] Teleco Estatísticas de celulares no Brasil
mate the default risk based on all call detail2014. Disponível em:
Acesso em:
records (CDR).

18/04/2014.

Certain CDR variables are found to be

41
2

2015

Socioeconomic correlations in communication
networks
Yannick Leo1, Eric Fleury1, Carlos Sarraute2, J. Ignacio Alvarez-Hamelin3, andMárton Karsai1,@
1 LIP, Université de Lyon, UMR 5668 CNRS - ENS Lyon - INRIA - UCB Lyon 1, IXXI2 Grandata Labs, Bartolome Cruz 1818 Vicente Lopez. Buenos Aires, Argentina
3 CONICET, Universidad de Buenos Aires, Av. Paseo Colón 850, C1063ACV Buenos Aires, Argentina

In this work we study the socioeconomic structure of a communication network by
combining mobile communication records and bank credit informations of a large number ofindividuals living in Mexico. We provide empirical evidences about present economic
unbalances suggesting not only the distribution of wealth but also the distribution of debts tofollow the Pareto principle. Further we study the internal and interconnected structure ofsocioeconomic groups. Through a weighted core analysis we signal assortative correlations
between people regarding their economic capacities, and show the existence of “rich-clubs”
indicating present social stratification in the social structure.

Our understanding about the structure anddynamics of social systems has beendeveloped considerably during the last years
due to the recent availability of large digital
datasets collecting interactions of millions ofindividuals. One of the most promising
direction of studies involves mobile phone
communication datasets. As mobile phones
became personal items of our everyday life

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)@ Corresponding author: marton.karsai@ens-lyon.fr

they are optimal to collect information about
the location, communication, online, andphysical activities of people, and to map out
their temporally resolved social-communication
network. These advancements give us to theopportunity to study the structure and evolutionof very large social networks and signal
general rules in human behaviour.

Geographical distribution and average economical capacities of mobile phone users in Mexico (left panel). Sample
of users in Mexico City pinned at their most frequented location are depicted together with their social ties extracted
from their call detailed records (middle panel). The social network of selected users demonstrates the existence of“rich-clubs” in the social structure where the wealthiest people appear to be connected (right panel). Colours arecoding economical capacities, the width of links scales with the wealth of the ending nodes, and the size of nodes
reflects the sum of link weights for each individual.

1

42

2015

Here we propose a study, which moves
along this direction by considering information
about the mobile communication, location, andeconomic capacities of people. We use acommunication dataset covering the mobile
phone interactions of millions of people who
are the clients of a single mobile provider in
Mexico [1,2]. The data collects the geolocalised call detailed records (CDRs) for 6
consecutive months. This anonymised dataset
is combined with bank credit informations ofclients of a bank in the same county. The credit
data collects the time, location, and amount ofbank card purchases and the monthly
evolution of incomes, spendings, and debts ofthe cell phone operator clients.
Using these informations we quantify the
economic status of people to estimate theirwealth and debts and infer these measureswith the structure and evolution of the socialcommunication network extracted from the
CDRs. We provide empirical evidences about
present economic unbalances suggesting not
only the distribution of wealth but also theSession 3 :: Economies

Session 1 :: Mobility

Weighted core decomposition of thelargest connected component of themobile communication network of users
with known economic status. Size ofvertices represents the number of contactsof individuals in the social graph, while
colours decode their economic status
ranging from least (magenta) to the most(red) wealthiest ones. The colour of circles
is assigned according to the person's coreshell determined by [5], while link colours
on each end denote the shell of theconnected node on the opposite end.
Social stratification is evidenced by thenarrow shell circles indicating that links
connect mostly nodes from neighbouringshells. Components distributed radially on
the right hand side are ones, whichbecame disconnected from the largest
component during the core decomposition
process.

Session 2 :: Cities (I)distribution of debts to follow the Pareto
principle [3,4]. We categorise individuals into
different economic classes to understand theinternal and interconnected structure ofsocioeconomic groups. Based on theeconomic status measures we perform a core
analysis and show assortative correlations
between people regarding their economic
capacities. Further we provide quantitative
evidences about the existence of “rich-clubs”
and social stratification in the social structure.

Session 6 :: Cities (II)However, although these studies consider
the temporal, structural, and spatial charactersof human interactions they commonly missone important dimension regarding the
economic status of individuals. Economic
capacities of people may largely determine
their communication and social behaviour thus
the emerging structure of the global socialcommunication network. Studies combining
the social network with economic data could
help us better understand spatial, and social
segregation, or economic imbalances evolving
in the society.

References[1] C. Sarraute, P. Blanc and J. Burroni, A Study of Ageand Gender seen through Mobile Phone Usage Patterns
in Mexico. ASONAM IEEE/ACM 836-843 (2014).
[2] J. Brea et. al., Harnessing Mobile Phone Social
Network Topology to Infer Users Demographic Attributes.
SNAKDD’14 ACM (2014).
[3] V. Pareto, Le Cours d’E ́conomie Politique (Macmillan,
London, 1897).
[4] H. Aoyama, et. al.,Pareto’s Law for Income of
Individuals and Debt of Bankrupt Companies. Fractals 8
293-300 (2000).
[5] J. I. Alvarez-Hamelin, L. Dall'Asta, A. Barrat, and A.
Vespignani, Large scale networks fingerprinting andvisualization using the k-core decomposition. In Y. Weiss,
B. Schölkopf, and J. Platt, editors, Advances in Neural
Information Processing Systems 18, 41-50, Cambridge,
MA, MIT Press (2006).

2

43

2015
Understanding the Role of Social Networks on Labor Market Outcomes Usinga Large Dataset from a Mobile Network
Filipa Reis, Pedro Ferreira

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)This study uses call detail records from a major mobile carrier to identify job information networks
within the neighborhoods of a large European metropolitan area. We start by identifying home andwork locations for a random sample of business users using data on the cell towers they use to routecalls. We show that the propensity of two workers to work in the same location (cell tower) is higher
when they also live in the same location (cell tower). We extend this model by adding information on
who calls whom, which allows us to label pairs of workers as likely to know each other – friends inshort. We find that the effect of friendship on the propensity to work in the same location is twoorders of magnitude greater than that associated with living in the same location. Our findings arerobust to alternative specifications of our dependent and independent variables such as the definition
and the strength of friendship, the distance between home and work locations and using working forthe same company in lieu of (just) working in the same location. These specifications are introduced
to reduce our concerns with homophily and reverse causality. We also show that job information
networks are more important in less privileged neighborhoods, measured by mortgages costs andunemployment rates. This finding provides some evidence that social networks may not convey
enough information about job market opportunities to help underprivileged individuals.

The deliberate use of formal and informal social connections for information access and exchange is a
strategy commonly employed by individuals particularly evident during job searches (Ioannides and Loury,2004). Previous literature has shown that social interactions among people who live in close geographicalproximity play a significant role on labor market outcomes (Bayer, Ross, and Topa, 2008). The researchmethodologies traditionally used to study this phenomenon – social experiments (Jacob, 2005; Katz, Kling,and Liebman, 2001), and survey data (Bayer, Ross, and Topa, 2008) – are only rarely produced, extremely
costly to generate and, most importantly, very seldom contain information on actual social networks. The
inability to observe who talks to whom may result in confoundedness between social connections andunobserved neighborhood characteristics.
The recent growth in the use of mobile communications and the subsequent availability of datasets on who
calls whom, when and from where, opens up avenues for novel research approaches that may complement,
or even substitute, the more traditional research methodologies (e.g. Soto, Frias-Martinez, Virseda, FriasMartinez, 2011; Frias-Martinez and Virseda, 2012; Blumstock, 2012). Using data from mobile
communications may help alleviate the above-mentioned concern; though certainly not all communication
occurs over mobile phones, it is likely that one’s most important social connections show up in Call
Detailed Records (CDRs).
In this work, we use eleven months of CDRs from a major European mobile carrier to study how social
connections among neighbors and friends affect their propensity to work together. This carrier is the market
leader in the corporate segment in the country and in particular in the region we analyze. We start ouranalysis by inferring home and work location for a random sample of business subscribers. These locations
are defined as the most used cell tower during nighttime (between 7 pm and 7 am) and daytime (between 1
pm and 5 pm), respectively. Using the geographical information associated to cell towers (latitude andlongitude) we identify the lowest possible statistical region – parishes -- in which cell towers are located.
With this information, we classify subscribers who live in the same cell tower as neighbors. We then assess
the propensity of pairs of neighbors to work in the same location (defined as working in the same cell
tower), compared to the propensity of pairs of people who live in the same parish to do so, interpreting thedifference between these two effects as an indication of job information networks among neighbors.

Session 3 :: Economies

H. John Heinz III College, Carnegie Mellon University

44

1	
  

2015

(4)

wcell

wcell

neighbors*0.0002(0.0001)

0.0020***(0.0002)0.0013***(0.0002)0.0010***(0.0002)0.2601***(0.0075)0.2250***(0.0087)

friends

neighbors9and9friends

intercept

fixed9effects9for9home9parish
NR*sq

0.1075***(0.0165)
0.0061***(0.0001)

*
*

*
*

*
*

no

yes

yes

yes

14951570.000

1495157
0.007

14951570.033

1495157
0.034

Session 3 :: Economies

(3)

wcell

Session 2 :: Cities (I)

(2)

wcell

Session 6 :: Cities (II)(1)
dep.+var:

Session 1 :: Mobility

Our results in Table 1 show that, controlling for parish fixed-effects, pairs of neighbors are 33% more
likely to work in the same location than pairs of subscribers who just live in the same parish (an increase of
0.002, p<0.001, relative to the baseline propensity of 0.006, p<0.001). We then extend our model by adding
information on whether these pairs of individuals use their mobile phones to talk to each other. If they doso, we call them friends. This is where our analysis adds a critical step to what has been previously done in
the literature. The latter has been unable to account for the actual social connections among neighbors. We
find that the propensity of two neighbors who know each other -- friends -- to work in the same location is
two orders of magnitude greater (0.2601, p<0.001) than that observed for neighbors who do not talk to eachother (0.0013, p<0.001). These results are robust to a different specification of the dependent variable,
namely, an indicator of whether the two neighbors work for the same company (which does not actuallyentail working in the same location). These results are also robust to different specifications of friendship.

Standard9errors9in9parentheses+9p<0.199*9p<0.0599**9p<0.0199***9p<0.001

Table 1. Regression results for 1,495,157 subscriber pairs. Our dependent variable (wcell) indicates whether i and j work inthe same cell tower; neighbors indicates whether they live in the same cell. (1) Gives us the baseline propensity for two
subscribers who live in the same parish (but not in the same cell) to work in the same location, 0.0061 (p<0.001). In (2), we
add parish-level fixed-effects to control for potentially time-invariant unobserved parish characteristics. In (3) we add
friends indicating whether i and j call each other, capturing the effect of being friends on the propensity to work in the same
location, 0.2601 (p<0.001), controlling for being (or not) neighbors. In (4) the interaction term, neighbors and friends,
captures how the effect of being friends on the propensity to work in the same location changes for neighbors versus people
who are not neighbors. The positive result (0.1075, p<0.001) indicates that being friends increases the propensity to work inthe same location across neighbors more than across pairs of people that live further apart.

Finally, we also test whether job information networks among neighbors play a more important role in lessprivileged neighborhoods as suggested by prior research (Elliot, 1999; Weinberg, Reagan, and Yankow,
2004). Our results confirm that friendships among neighbors living in neighborhoods with lower mortgage
costs and /or higher unemployment rates play an important role in determining whether people work in thesame location or for the same company. This finding provides some evidence that social connections may
not necessarily help improve the employment conditions for disadvantaged individuals and calls for reallocation programs that may help the poorer find housing in more affluent neighborhoods.
Our research contributes to the body of evidence on the role of social networks on labor market outcomes.
Using only observational data, we extend previous analyses (e.g. Bayer et al., 2008) to gain further insighton the role of job information networks at the neighborhood level and how it may vary according to theneighborhood’s socioeconomic status. Our work provides some evidence of the potential applications of
big data generated by ICTs to explore and analyze social and economic issues.

45

2	
  

2015
Bayer P., Ross, S.L., Topa, G. 2008. Place of Work and Place of Residence: Informal Hiring Networks and Labor
Market Ouctomes. Journal of Political Economy, Vol. 116 (6), pp. 1150-1196.
Blumenstock, J. 2012 Inferring Patterns of Internal Migration from Mobile Phone Call Records: Evidence from
Rwanda. Information Technology and Development 18.
Elliot J. R., 1999. Social Isolation and Labor Market Insulation: Network and Neighborhood Effects on Less-Educated
Urban Workers. The Sociological Quarterly, Vol. 40 (2), pp. 199-216.
Frias-Martinez, V., Virseda, J., Rubio, V., Frias-Martinez, E. 2010. Towards large-scale technology impact analyses:
automatic residential localization from mobile phone-call data. In Proceedings of the 4th ACM/IEEE International
Conference on Information and Communication Technologies and Development (ICTD '10). ACM, New York, NY,USA.

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)Jacob, B. 2004. Public Housing, Housing Vouchers and Student Achievement: Evidence from the Public Housing
Demolitions in Chicago. American Economic Review Vol. 94, pp. 233-58.
Katz, L. F., Kling, J.R., Liebman J.B. 2001. Moving to Opportunity in Boston: Early Results of a Randomized
Mobility Experiment. Quarterly Journal of Economics Vol. 116, pp. 607-654.

Soto, V., Frias-Martinez, V., Virseda, J., and Frias-Martinez, E. 2011. Prediction of Socioeconomic Levels using Cell
Phone Records. User Modeling, Adaption and Personalization Lecture Notes in Computer Science, Vol. 6787, pp.
377-388.
Weinberg B. A., P. B. Reagan, Yankow, J. J. 2004. Do Neighborhoods Affect Hours Worked? Evidence from
Longitudinal Data. Journal of Labor Economics, Vol. 22(4), pp. 891-924.

Session 3 :: Economies

Ioannides Y. M., Loury, L. D. 2004. Job Information Networks, Neighborhood Effects, and Inequality. Journal of
Economic Literature, Vol. 42 (4), pp. 1056-1093.

46

3	
  

2015

Session 4 :: Societies (I)

47

2015
Persistent daily patterns in mobile telephone communication
T. Aledavood1,*, E. López2, S.G.B. Roberts3, F. Reed-Tsochas2,4, R.I.M. Dunbar5, E.
Moro6, J. Saramäki1
Dept. of Biomedical Engineering and Computational Science, Aalto University School of
Science, Espoo, Finland; 2 CABDyN Complexity Center, Saïd Business School, University of
Oxford, UK; 3 Department of Psychology, University of Chester, UK; 4 Department of
Sociology, University of Oxford, UK; 5Department of Experimental Psychology, University of
Oxford, UK; 6 Department of Mathematics, Universidad Carlos III de Madrid, Spain; * email:talayeh.aledavood@aalto.f

Session 4 :: Societies (I)Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)

Human activity patterns are known to follow circadian rhythms 1,2, and thisis also reflected in communication activity. We investigate the daily callpatterns of 24 students over a period of 18 months 3. During this period,
the students finished high school and went to university or work. We showthat while their call patterns are coarsely similar, each individual isdifferent and follows their own rhythm of communication. These individual
rhythms are seen to be persistent despite the major changes in thestudents’ circumstances. Using entropy measures, we show that thepatterns are partially activity-driven – i.e., around certain times
communication is fairly random and shows no preference to specific alters(acquaintances, relatives) – and partially socially driven, such that at given
times certain alters are preferred. While there is more variation here thanin communication rhythms, it is seen that often, daytime calls are more
random and evening/night time calls more alter-specific and focused
towards alters with higher ranks (rank of each alter is determined basedon fraction of calls received by that alter). We also use survey informationon the called alters to determine whether calls to family members or altersof same and opposite gender follow specific rhythms.

Session 3 :: Economies

1

Figure: LEFT: the daily call patterns of two students during three consecutive 6-month time
periods. RIGHT: PDF of the Jensen-Shannon divergence measures of the daily patterns ofstudents, where dself measures the distance between the different 6-month patterns of thesame student, and dref the distance of the patterns between students within one period. It isseen that on average, the distance between the patterns of one student is smaller than thereference distance, i.e. the patterns show persistence in time.

References1.

H.-H. Jo, M. Karsai, J. Kertesz, and K. Kaski, Circadian pattern and burstiness inmobile phone communication, New Journal of Physics 14, 013055 (2012)

48

2015

Session 4 :: Societies (I)Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

3.

T. Louail, Maxime Lenormand, Oliva García Cantú, Miguel Picornell, Ricardo Herranz,
Enrique Frias-Martinez, José J. Ramasco, Marc Barthelemy, From mobile phone data
to the spatial structure of cities, arXiv: 1401.4540 (2014)
Jari Saramäki, E. A. Leicht, Eduardo López, Sam G. B. Roberts, Felix Reed-Tsochas,and Robin I. M. Dunbar, Persistence of social signatures in human communication,
PNAS 111, 942 (2014)

Session 6 :: Cities (II)

2.

49

2015

Inferring social ties
from WiFi scan results

Session 4 :: Societies (I)Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)It has become increasingly popular to study human interactions and mobility through mobile
sensors embedded in smartphones [1, 2]. Most commonly, Bluetooth is used as a ground truth
proxy for face-to-face interactions because of its short physical range (up to ∼10 meters) andrelatively low impact on battery of the sensing device. There are a number of reasons why it
is crucial to investigate alternative approaches to sensing physical proximity. Firstly, relying
on a single channel is prone to error, especially considering frequent hardware and firmware
malfunctions of the sensing devices. Furthermore, while Bluetooth data is rarely collected
outside of controlled social science experiments, other channels—such as WiFi scans—areoften collected during normal operation in many contexts. Such WiFi scan information
may be available for hundreds of millions of people: many of the most popular applications
available in Google Play market require the permission which allows them to read the listof all visible access points in each scan. This situation has important privacy implications,which we investigate here in context of inferring the social network of users based on their
WiFi scan results.
Based on the data collected during a longitudinal study with ∼1000 participants, we
extend previously suggested approaches to WiFi-based proximity inference: we investigate
machine learning methods as well as verify our previous findings [2] by using now-available
information with higher temporal resolution. To validate the idea of using WiFi as a proxyfor physical proximity, and to explore the possibilities provided by WiFi scan information, we
infer social network of users. We train a statistical model exploring various behavioral features(see Figure 1) which, given the WiFi scans of two individuals, estimates the probability thatthe individuals are connected as friends on Facebook. To evaluate the models, we compare
its estimations to the actual online social network of the participants (Figure 2).

Session 3 :: Economies

Piotr Sapiezynski, David Kofoed Wind,
Arkadiusz Stopczynski, Sune Lehmann

References[1] N. Eagle and A. Pentland. Reality mining: sensing complex social systems. Personal andUbiquitous Computing, 10(4):255–268, 2006.
[2] A. Stopczynski, V. Sekara, P. Sapiezynski, A. Cuttone, MM Madsen, J. Eg Larsen, andS. Lehmann. Measuring large-scale social networks with high resolution. PLoS ONE,9(4):e95978, 04 2014.

50

0.2

— P (F riendship | X ≥ x)

0.0

101

102

103
t [minutes]

104

total duration of home visits

1.0

10−4

100

10−2

0.4

10−3

0.2
101

102
103
t [minutes]

104

10−1

100

10−4

101
X

102

103

timed moving

10−4

100
10−1

0.8
0.6

10−2

0.4

10−3

0.2
0.0 0
10

101

Session 6 :: Cities (II)

0.0

0.0

- - P (t ≥ t0)

0.6

10−3

0.2

1.0

10−1

0.8

0.4

N

10−4

102

0.8

0.8
gu
es
do
m
an

0.4

si

0.6

0.2

Precision

1.0

ng

1.0

R

TPR

Figure 1: We extract various behavioral features from WiFi scans of the participants’ phones:
we measure how much time they spend together in total; using the weighted co-occurrencesfeature we incorporate information about intimacy of meetings: the lower the number ofpeople present at a location, the more important the meeting is; home visits are indicativeof friendships, but because of densely populated blocks and dormitories, many false positives
are generated; we also observe instances of two people arriving at a location or leaving it at
the same time.

Session 4 :: Societies (I)

10−3

10−2

- - P (N ≥ N0)

0.4

0.6

Session 3 :: Economies

10−2

10−1

0.8

- - P (X ≥ X0)- - P (t ≥ t0)

0.6

100

Session 2 :: Cities (I)

10−1

0.8

weighted co-occurrences

1.0

Session 1 :: Mobility100— P (F riendship | X ≥ x)

total duration of meetings

1.0— P (F riendship | X ≥ x)

— P (F riendship | X ≥ x)

2015

AUC =0.38

0.6
0.4
0.2

AUC =0.78

0.0
0.0 0.2 0.4 0.6 0.8 1.0
FPR

Random guessing

0.0
0.0 0.2 0.4 0.6 0.8 1.0
RecallFigure 2: We train 100 instances of Random Forest Classifier using the presented features(among others) to infer friendship links on the online social network. Clearly, physical proximity is a very strong indicator of online friendship links, but there are also many links online
which are not reflected in offline relations - a fact highlighted by the saturation of ROC curve
at T P R ≈ 0.5.

51

2015

On the complementary roles of face-to-face and mediatedsocial interactions
Guy Zyskind
MIT Media Lab

Bruno Lepri
Fondazione Bruno Kessler

Alex (Sandy) Pentland
MIT Media Lab

Erez Shmueli
Tel-Aviv University

1

http://www.mobileterritoriallab.eu/

3.7
3.6# of sensor edges

3.5
3.4
3.3
3.2
3.1
3.0
2.9
6

7

(a) Average number of distinct daily calls.
2.0# of sensor edges

1.9
1.8
1.7
1.6
1.5
1.41

2

3

4
mood

5

6

Session 4 :: Societies (I)5

Session 1 :: Mobility

4mood

Session 3 :: Economies

3

Session 6 :: Cities (II)2

Session 2 :: Cities (I)

2.81

7

(b) Average number of distinct daily texts.
10.0
9.5# of sensor edges

Social interaction patterns have long been a subject of greatinterest. Several recent studies (e.g., [3, 2]) have examined
the influence of face-to-face and mediated (e.g., phone, text
and online social networks) communication channels on employees’ mood, but since their motivation stemmed from professional productivity, they did not include interactions that
occurred outside the working environment. In this paper, weseek to understand how mood is affected by face-to-face andmediated interactions during the entire day, focusing on therelationship between the two types of interactions. Unlike
previous studies, our analysis is performed on two distinct
high-resolution datasets that were collected over long periodsof time. We find that in different moods, people prefer one
communication method (either face-to-face or mediated) andthat the two types of interactions have complementary roles
in practice. Moreover, this relationship becomes specificallystrong in cases of extreme mood states – i.e. when people report being inordinately happy or unhappy. Our findings provide initial insights into how mood is affected by different
types of social interactions, and can be used in the future as abasis for predicting and influencing individuals’ mood basedon the pattern of their social interactions.
For our analysis, we used the Friends and Family dataset[1] and the Mobile Territorial Lab dataset1 . The Friends andFamily dataset contains data on n = 130 participants thathas been collected over the course of 12 months. The Mobile
Territorial Lab dataset contains data on n = 70 participantsthat has been collected over the course of 6 weeks. In bothdatasets, call and text logs were collected using a dedicatedAndroid app installed on the mobile devices of the participants. In addition, Bluetooth scans were collected and used asa proxy to face-to-face encounters with other individuals. Finally, mood data was collected via self-reported surveys thatwere completed by the participants on a daily basis. The selfreported surveys were introduced automatically as part of amobile application, thus ensuring the reliability of the selfreported results. While the Friends and Family dataset measured mood directly on a scale of 1−7, the Mobile Territorial
Lab followed the Positive and Negative Affect Schedule measure (PANAS) [4]. In order to make the results in the Mobile
Territorial Lab dataset comparable to those of the Friends andFamily dataset, we computed the average PA portion of thesurvey, rounded to the nearest integer, resulting in 5 distinct
mood states.

9.0
8.5
8.0
7.51

2

3

4
mood

5

6

7

(c) Average number of distinct face-to-face encounters.

Figure 1: Communication patterns effect on mood in
theFriends and Family dataset.
Figure 1 shows the average number of distinct interactions depending on the mood, for all individuals and for all

52

2015
days, in the Friends and Family dataset. As can be seenin the figure, calls and texts, both being mediated types ofcommunication, exhibit similar patterns, while face-to-facefollows a complementary pattern. More specifically, in extreme situations (mood = 1 or mood = 7), people prefer to use mediated communication over face-to-face interaction, and the opposite result holds in intermediate mood states(mood ∈ [2, 6]).
5.5

# of sensor edges

5.0

constructive to note that the Negative Affect (NA) questions
also followed a consistent pattern.
In conclusion, understanding how mood is affected bydifferent types of social interactions provides a mechanism
for inferring well-being. Hence, our results could be used asa first important step in achieving this goal. Further research
could examine more closely different types of subjects basedon their personality traits (e.g., using the Big Five personality
traits model) and see if it affects their communication preferences. Similarly, controlling for the strength of the ties could
also reveal interesting results.
REFERENCES

4.5

1. Aharony, N., Pan, W., Ip, C., Khayal, I., and Pentland, A. Social fMRI:
Investigating and shaping social mechanisms in the real world. Pervasiveand Mobile Computing 7, 6 (Dec. 2011), 643–659.

4.0

2. Counts, S. Understanding Affect in the Workplace via Social Media.

3.5

3. Mark, G., Iqbal, S., Czerwinski, M., and Johns, P. Capturing the M ood :Facebook and Face - to - Face Encounters in the Workplace.

3.01.0

1.5

2.0

2.5

3.0
mood

3.5

4.0

4.5

5.0

4. Watson, D., Clark, L. A., and Tellegen, A. Development and validationof brief measures of positive and negative affect: the panas scales.
Journal of personality and social psychology 54, 6 (1988), 1063.

(a) Average number of distinct daily calls.

2.2
2.1
2.01.0

1.5

2.0

2.5

3.0
mood

3.5

4.0

4.5

5.0

(b) Average number of distinct daily texts.

Session 4 :: Societies (I)2.3

Session 2 :: Cities (I)

2.5
2.4

Session 1 :: Mobility# of sensor edges

2.7
2.6

Session 6 :: Cities (II)

2.8

Session 3 :: Economies

2.9

4.5

# of sensor edges

4.0
3.5
3.0
2.5
2.0
1.51.0

1.5

2.0

2.5

3.0
mood

3.5

4.0

4.5

5.0

(c) Average number of distinct face-to-face encounters.

Figure 2: Communication patterns effect on mood in the Mobile Territorial Lab dataset.
Figure 2 presents the same analysis for the Mobile Territorial Lab dataset, focusing on Positive Affect (PA) questions.
As can be seen in the figure, the results are consistent in thesense that the same complementary relationship between mediated communication (i.e., calls and texts) and face-to-faceencounters is present. In addition, while not shown here, it is

53

2015

Understanding User Attributes from Calling Behavior:
Exploring Call Detail Records through Field Observations and Potential of
Estimating User Attributes of Anonymized Call Records
Ayumi Arai,
Hiroshi Kanasugi, Xiaowei
Shao, Ryosuke Shibasaki

Apichon Witayangkurn

Teerayut HoranontAsian Institute of Technology
Pathumthani, Thailand

Thammasat University
Pathumthani, Thail

University of Tokyo
Tokyo, Japan

In fact, emerging studies are attempting to analyze data derived
from mobile phones in combination with secondary data. [2]observed that the ratio of shared phone usage and call type, suchas incoming and outbound calls, shows significant differences bygender, on average. In addition, the number of usages during a
specified length of time was shown to differ according to income
level. This observation is consistent with another research project,which analyzed social connectivity through social networking
activities [1]. Utilizing sensor data from volunteer mobile users,[10] proposed prediction models based on user demographic
attributes.
In this work, we provide a novel approach for extracting featuresfrom calling behavior, which can be constructed from anonymized
CDRs. This technique can reveal distinctive traits that help
identify the demographic attributes of mobile phone users. This
study is unique, because we focus on extracting lifestyle traits androutines to identify user attributes. We statistically analyze data
This abstract is prepared based on the paper presented at the 12th
International Conference on Advances in Mobile Computing andMultimedia (MoMM’14), December 08 - 10 2014, Kaohsiung, Taiwan
Copyright 2014 ACM 978-1-4503-3008-4/14/12…$15.00•

Prototypes of calling behaviors are provided. We describe
how the prototypes and user attributes are related. Potential of
applying our results to estimate user attributes of anonymized
call records are discussed.

2. DATATo understand the hidden properties of CDRs, we conducted afield survey, named the Survey on Patterns of Activity forComprehensive Explorations of Mobile Phone Users in Dhaka(SPACE). The purpose of SPACE is to understand the calling
behavior, characteristics, and lifestyles of mobile phone users. A
unique characteristic of the survey data is that it includes actual
call records from mobile phone users, as single-day records from922 handsets. The records specify the call’s location type andother basic attributes. The data contain neither mobile phone
numbers nor any other information explicitly specifying
individuals. Our survey site was Greater Dhaka, which iscomposed of Dhaka City Cooperation (DCC), selectedsurrounding municipal areas, and regions outside of the urban area.
SPACE was conducted from November 27, 2013 through January
4, 2014. The survey interviewed 810 households, and included
922 mobile phone users.

Session 4 :: Societies (I)Statistical analysis results are provided for calling behavior
based on field survey data. We extract calling behavior traits
to differentiate gender that correspond to the patterns ofroutine activities. To do so, we introduce the concept ofweekly activity patterns, which specifies whether the day’s
call records correspond to a day where the user is engaged intheir primary routine.

Session 3 :: Economies•

Session 2 :: Cities (I)In addition to such large-scale datasets, there are other types ofdata collected through conventional methods. In the field of urban
planning and transportation, many empirical studies haveattempted to identify the factors that affect human activities andtravel patterns, by analyzing data collected through questionnaire
surveys. Family and social obligations are often presented as
significant factors affecting daily travel and activity behavior [9].
This implies that people’s activity patterns are constrained bysocial ties. This observation is consistent with most previous
analysis of human mobility patterns from large-scale datasets,
where people routinely visit a limited number of locations [13,14].
Though such conventional approaches may be less efficient in
defining human activity and travel patterns in terms of population
size and data period length, it can link activity and travel patterns
with people’s attributes to some extent [12]. To further investigate
the properties of user attributes in anonymized data, it is critical toanalyze this large-scale data in combination with such secondary
data [8].

Contributions of our work are described below:

Session 1 :: Mobility

With the rapid spread of mobile phones, analyses of large-scaledata such as GPS logs and call detail records (CDRs) haveprovided detailed descriptions of human mobility. An increasing
body of human mobility research is focused on modeling theproperties of human mobility patterns, where they are generalized
in a quantitative manner. Although these studies succeed inmining mobility patterns [3,4], the outcomes of large-scale data
analyses describe the movement of crowds, because the data isanonymized.

collected through a field survey that focused on the demographic
attributes, calling behavior, and weekly activity patterns of mobile
users. Several key features are empirically derived to analyze user
attributes.

Session 6 :: Cities (II)

1. INTRODUCTION

3. LIFESTYLE AND CALLING BEHAVIOR
To understand activity patterns, it is vital to identify a certain setof locations where people spend the majority of their time. Many
studies identify two dominant locations for people as their home
and work or school (hereinafter referred to as “Home” and“Primary out-of home location”), which can explain a significant
portion of their activity [13] and thereby their activity patterns andportions of their lifestyles. Therefore, in this section we examine
the time distribution of calls initiated from Home, Primary out-ofhome location, or Others by analyzing the calling behavior of
SPACES’s mobile phone users. Because sequential location
histories can infer similarities between people [7], we expect the
distributions to vary according to differences in user attributes.

54

2015
We extracted calling behavior prototypes according to gender, andexamine how their traits differ.

we conclude that our findings can be utilized to estimate thegender of the user who produced an anonymized CDR.

For the analysis provided this section we introduce the concept ofweekly activity patterns, which specifies whether mobile users areengaged in their primary routine on the day the call was made.
Routines can be any activities users spend the majority of theirtime on during the day. The routine that the user follows on thehighest number of days during the week is considered as theirprimary routine.

3.1 Weekly Activity Patterns

Group
(3)
Group
(4)

%

1

7

56

2

6

30

2

5

9

1 or
more

1 to 65

3.2 Calling Behavior by GenderThis subsection compares the calling behavior of males andfemales by analyzing the call records of the 922 mobile users.
First, we examine calling behavior by analyzing call location
trends. Figure 1 shows percentages for the number of calls at (a)Home and (b) Primary out-of-home location among the total
number of calls on the primary routine day. As the figure shows,males and females exhibit distinctly different call location trends.
Across all patterns, females tend to call predominantly from Home
while males call from both the Home and Primary out-of-homelocation. This indicates that identifying dominant call locations on
the primary routine day is important for determining gender. Thisfeature is particularly distinctive among those classified into
Group (1), where the primary routine is repeated every day. We
conclude that considering the locations of calls and the type ofday (primary or non-primary routine day) while examining callrecords is crucial for extracting gender-wise traits. Assuming thatweekly activity patterns can also be reconstructed from the CDRs,

4.1 Method for Extracting Prototypes

We employed non-negative matrix factorization (NMF) for vector
quantization. NMF was applied to the call records of the 922
mobile users, where the distribution of call records for a single
day is expressed as a 72 x 1 matrix. The first set of 24 elements
out of 72 consists of hourly counts of call records for Home. The
first element is the total number of calls from Hour 0, occurring
between 0:00 and 0:59, and the 24th element denotes Hour 23,
occurring between 23:00 and 23:59. The next set of 24 elements is
structured similarly to the first set, and it accounts for the numberof calls from the Primary out-of home location. In a similarmanner, hourly counts of calls for other locations, which are any
locations except for home and work/school, are captured by anadditional set of 24 elements. As a result, we obtained 922 sets of
72 x 1 column vectors. To employ NMF, we solved the equation
below by following an algorithm, which allows only additive
combinations [9]:V ≈ WH

Session 4 :: Societies (I)

Group
(2)

Those who repeat their primary
routine activity every day. Mainlycomposed of income earners andthose who do household tasks.
Those who repeat their primary
routine activity every day, except
for Friday. Mainly composed ofincome earners or are students.
Those who repeat their primary
routine activity every day except for
Friday and Saturday. Mostly
composed of public sector
employees or students.
Mostly income earners and theiractivity patterns do not follow
Pattern (1), (2), or (3).

Numberof daysfor theprimaryroutineSession 3 :: Economies

Group
(1)

Numberofroutines

In this section, we narrowed the time window from a weekly basis
to an hourly basis, to understand trends in calling behavior acrossgender and occupation types. As a result, we aggregated the calltimes by hour in this section. We extracted parts-based
representations of calling behavior by conducting vector
quantization against the time and location distribution of callrecords. This enabled us to cluster the data into mutually
exclusive prototypes [5].

Session 2 :: Cities (I)Pattern

Description of people classified
into the pattern

4. PROTOTYPES OF CALLINGBEHAVIOR

Session 1 :: Mobility

Table 1. Weekly activity patterns of 922 mobile users in
SPACE data

Figure 1. Percentage of calls from (a) Home and (b) Primaryout-of-home location against total calls on the primary routine
day by gender (a) Upper (b) Lower

Session 6 :: Cities (II)In this subsection, we describe the weekly activity patterns of the
922 mobile users recorded in the SPACE data. Weekly activity
patterns are determined based on the number of weekly routines(primary and non-primary), and the number of days on which theprimary routine is followed. By classifying the activity according
to a pattern, we examine how activity patterns are linked to calling
behavior in the following section. We assume that these patterns
can also be partially extracted from anonymized CDRs, whichallows us to estimate significant locations such as home and work
places. Based on this technique, we consider it possible toreconstruct weekly activity patterns from CDRs, to which we canapply this concept. Table 1 classifies the mobile users into four
patterns, according to the following criteria.

where we obtained non-negative matrix factors W and H given a
non-negative matrix V. Given 922 sets of 72 x 1 column vectors,
the vectors were placed in the columns of a 72 x 922 matrix V.
This matrix was approximately factorized into a 72 x r matrix W,
and an r x 922 matrix H. Here we selected r = 3 to analyze
differences in the major features of calling behavior. We thendefined the cost function that evaluates the quality of
approximation for iterative updates of W and H. We calculated the
distance between two non-negative matrices and measured thesquare of the Euclidian distance [14]. As a result of repeatediterations, we obtained an optimal matrix factorization.
To understand the calling behavior prototypes for males andfemales, we split the call records obtained from SPACE by gender.
Then, we separated the records by primary and non-primary

55

2015

Primary outof-homelocation

Others

0.000

0.002

0.000

0.007

0.001

0.000

0.001

0.010

0.000

0.000

0.005

0.000

0.000

0.003

0.000

0.035

0.003

0.000

0.014

0.019

0.000

0.043

0.030

0.008

0.057

0.080

0.011

0.005

0.183

0.009

0.000

0.241

0.007

0.000

0.458

0.000

0.006

0.512

0.000

0.000

0.315

0.001

0.000

0.280

0.005

0.000

0.266

0.004

0.000

0.221

0.000

0.000

0.203

0.006

0.000

0.341

0.041

0.000

0.128

0.036

0.016

0.075

0.009

0.004
0.000
0.015

0.088
0.061
0.033

0.000
0.000
0.000

Pattern m2: 25%

0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Pattern f1: 15%

0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Home

Primary outof-homelocation

Others

0.000

0.000

0.000

0.000

0.000

0.000

0.001

0.000

0.000

0.017

0.000

0.000

0.010

0.000

0.000

0.028

0.010

0.000

0.000

0.000

0.000

0.064

0.000

0.000

0.074

0.000

0.000

0.182

0.000

0.000

0.108

0.000

0.009

2.875

0.000

0.000

0.000

0.000

0.000

0.241

0.000

0.000

0.159

0.003

0.000

0.101

0.000

0.009

0.059

0.000

0.000

0.010

0.009

0.000

0.109

0.000

0.000

0.000

0.008

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Home

Primary outof-homelocation

Others

0.009

0.000

0.000

0.049

0.000

0.000

0.000

0.000

0.000

0.059

0.000

0.000

0.036

0.000

0.000

0.000

0.000

0.000

0.044

0.000

0.008

0.046

0.000

0.017

0.060

0.000

0.000

0.000

0.075

0.014

0.000

0.036

0.006

0.000

0.044

0.002

0.000

0.000

0.005

0.104

0.000

0.007

0.109

0.041

0.003

0.145

0.012

0.006

0.118

0.185

0.039

0.344

0.083

0.110

0.651

0.000

0.044

0.626

0.002

0.006

Female
0.873
0.013
0.000
0.912
0.000
0.011
Primaryroutine
day
0.983

0.315

0.020

0.000

0.013

0.000

Pattern m3: 29%

0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Pattern f2: 65%

0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Home

Primary outof-homelocation

Others

0.010

0.000

0.000

0.003

0.000

0.000

0.012

0.000

0.000

0.012

0.005

0.000

0.000

0.000

0.000

0.006

0.000

0.000

0.046

0.000

0.001

0.075

0.000

0.000

0.159

0.019

0.008

0.283

0.009

0.005

0.321

0.010

0.006

0.000

0.017

0.000

0.000

0.007

0.001

0.236

0.005

0.000

0.236

0.013

0.000

0.189

0.001

0.004

0.151

0.000

0.005

0.290

0.002

0.002

0.280

0.000

0.003

0.402

0.000

0.002

0.508

0.000

0.005

0.632

0.002

0.000

0.362

0.005

0.000

0.214

0.000

0.000

Home

Primary outof-homelocation

Others

0.003

0.000

0.000

0.021

0.000

0.000

0.000

0.009

0.000

0.013

0.000

0.000

0.000

0.005

0.000

0.021

0.000

0.000

0.020

0.015

0.000

0.038

0.028

0.004

0.282

0.019

0.012

0.544

0.000

0.029

0.688

0.000

0.013

0.731

0.000

0.025

0.679

0.000

0.004

0.279

0.000

0.008

0.159

0.000

0.033

0.045

0.068

0.002

0.052

0.000

0.027

0.161

0.001

0.000

0.007

0.000

0.018

0.055

0.029

0.015

0.000

0.034

0.025

0.000

0.008

0.003

0.000

0.014

0.001

0.026

0.008

0.000

Pattern f3: 20%

0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Home

Primary outof-homelocation

Others

0.000

0.000

0.000

0.005

0.000

0.000

0.010

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.002

0.000

0.000

0.005

0.000

0.000

0.043

0.012

0.000

0.140

0.000

0.000

0.013

0.004

0.005

0.007

0.015

0.000

0.000

0.000

0.000

1.175

0.000

0.000

0.065

0.000

0.000

0.068

0.004

0.000

0.000

0.001

0.000

0.041

0.000

0.000

0.041

0.000

0.000

0.057

0.000

0.000

0.000

0.004

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Figure 2. Time distributions of call locations for (a) male and(b) female on the primary routine day (a) Upper (b) Lower
Pattern m1 shows that males tended to call from Primary out-of
home location around midday, peaking in Hour 12. In contrast,
the most dominant pattern for females, Pattern f2, shows a peak of
Hour 21 at Home. Furthermore, all extracted patterns for females
show a higher intensity of calls from Home. This trend follows theone exhibited in Figure 2, which indicates that call locations could
be a key to distinguishing the gender of users. Although the trend
in call locations partially captures gender differences, frequently
recorded hours for the remainder of males, Pattern m2 and m3, areapparently difficult to distinguish from those for females, Patterns
f1, f2, and f3. For both males and females, intensive calling hours
are Hour 11, Hour 12, Hour 20, and Hour 21 from Home. It is
still fair to conclude that the location of calls around midday could
be a key to identifying the user’s gender. That is, males mostly
call from Primary out-of home location and females call from
Home around midday on their primary routine days.

Figure 3. Distribution of individual income levels for threeprinciple patterns for (a) male and (b) female (a) Upper (b) Lower
Among males, we observed that their common patterns varied
according to their income level. As described in Figure 2(a), thehigher income levels contained larger ratios of Pattern m1.
Conversely, the lower individual income levels contained larger
ratios of Pattern m3. This result is consistent with our fieldobservations where males in lower-income groups tend to beengaged in self-employed jobs whose work locations tend to be
their own home. However, no particular trends were observed
among females across the income levels. This result indicates thatfurther examination is necessary to determine the number ofclusters when we employ NMF to obtain prototypes. Our findings
imply that calling behavior could be a key to understanding theindividual income level for males, but not for females.
It is worth noting that the income level used here is not ahousehold income but individual income, which means we assess
how much each person earns annually. Incidentally, we do not
observe distinctive differences in calling patterns due to different
household income levels when we use the household income level
for analysis. We assume this implies that calling behavior strongly
reflects the characteristics of individuals, rather than those ofhouseholds.

Session 4 :: Societies (I)


0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Home

Session 3 :: Economies

Pattern m1: 46%Session 2 :: Cities (I)Figures 2(a) and 2(b) show three calling behavior prototypes formales and females, whose call records in the SPACE data fall on
their primary routine days. Each prototype is expressed in a 24 x 3
matrix, showing time distributions between zero and 23 hours in
the rows, and call locations of Home, Primary out-of homelocation, and Others in each column from left to right. The
intensity of the color indicates the intensity of calls for the
specified time band and location. For instance, in each matrix, thecell on the top of the left column represents the intensity of callsfor Hour 0 from home. The percentage indicates how significant
each component is for explaining the population’s call record
trends. For example, Pattern m1, whose percentage is 46% and theMalegreatest among the three, Primaryis themost dominant pattern for theroutine day
calling behavior of males on the primary routine day.

Session 1 :: Mobility

4.2 Prototypes of the Primary Routine Day

Figures 3(a) and 3(b) describe the distribution of the calling
patterns illustrated in Figures 2(a) and 2(b) for individual income
levels. We split call records for males into four groups based on
their individual income level. Call records for females were split
into three income groups and a separate non-income group,
because the majority of the female users did not earn an income.

Session 6 :: Cities (II)routine days. We assumed that the calling behavior of the primary
routine days was different from that of the non-primary routinedays, based on the analysis results described in the previous
section.

5. CONCLUSIONSIn this work, we identified calling behavior traits that candistinguish demographic attributes of mobile users. We analyzed
aggregated single-day records by focusing on calling behavior
traits, which can also be generated from anonymized CDRs.
Analysis results suggest that a higher ratio of calls from home canbe a key to distinguishing females from males. Specifically formales on the primary routine day, the higher the individual
income level, the higher the probability that they would initiate
calls from their primary out-of-home locations around midday.
The lower the income level of the user, the higher the probability
that they would initiate calls from home in the morning.
Conversely, there were no distinctive differences in primary
routine day calling behavior for females related to their individual
income level. Our findings suggested that time of day and calllocation distribution were keys to extracting differences in calling
behavior by gender. That is, identifying types of locations, such ashome and primary out-of-home is crucial to estimate the user
attributes of anonymized call records.

56

2015
[5] Lee, D. D. and Seung, H. S. 1999. Learning the parts ofobjects by non-negative matrix factorization. Nature. 401,788-791.
[6] Lee, D. D. and Seung, H. S. 2000. Algorithms for nonnegative matrix factorization. In Advances in Neural
Information Processing 13. Cambridge, MIT Press.

6. ACKNOWLEDGMENTS[7] Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., and Ma, W.
Y. 2008. Mining user similarity based on location history.
In Proceedings of the 16th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems.
298-307. ACM. New York, NY.

We appreciate the telecommunications operator and volunteers
who provided data for the research. Part of this work was
supported by GRENE-ei, funded by Japanese Ministry of
Education, Culture, Sports, Science and Technology (MEXT).

[8] Lu, X., Bengtsson, L., and Holme, P. 2012. Predictability of
population displacement after the 2010 Haiti
earthquake. Proceedings of the National Academy of
Sciences, 109, 29, 11576-11581.

7. REFERENCES[9] Mo, K., Tan, Ben., Zhong, Erheng., and Yang, Q. 2012.
Report of task 3: your phone understands you. Paper
presented at Nokia mobile data challenge 2012 workshop,
Newcastle, UK, 18-19 June 2012.

[12] Pas, E. I. 1984. The effect of selected sociodemographic
characteristics on daily travel-activity behavior. Environ.
Plann. A, 16, 5, 571-581.

Session 3 :: Economies[4] Isaacman, S., Becker, R., Cáceres, R., Kobourov, S.,
Martonosi, M. J., Rowland, J., and Varshavsky, A. 2011.
Identifying important places in people’s lives from cellular
network data. In Pervasive Computing, 133-151. Springer
Berlin Heidelberg.

Session 2 :: Cities (I)[3] González, M. C., Hidalgo, C. A., and Barabási, A. L. 2008.
Understanding individual human mobility
patterns. Nature. 453, 7196, 779-782.

[11] Paatero, P. and Tapper, U. 1997. Least squares formulationof robust non-negative factor analysis. Chemometr. Intell.
Lab. 37, 23-35.

Session 1 :: Mobility[2] Blumenstock, J., and Eagle, N. 2010. Mobile divides: gender,socioeconomic status, and mobile phone use in Rwanda.
In Proceedings of the 4th ACM/IEEE International
Conference on Information and Communication
Technologies and Development, 6. ACM.

[10] Lu, X., and Pas, E. I. 1999. Socio-demographics, activity
participation and travel behavior. Transportation ResearchPart A: Policy and Practice, 33(1), 1-18.

Session 6 :: Cities (II)[1] Aarthi, S., Bharanidharan, S., Saravanan, M., and Anand, V.
2011. Predicting customer demographics in a mobile social
network. In 2011 IEEE International Conference on
Advances in Social Networks Analysis and Mining, 553-554.
IEEE.

[13] Song, C., Koren, T. K., Wang, P, and Barabási, A. L. 2010.
Modelling the scaling properties of human mobility. Nat.
Phys. 6, 10, 818-823.

[14] Szell, M., Sinatra, R., Petri, G., Thurner, S., and Latora, V.
2012. Understanding mobility in a social petri dish. Scientificreports, 2.

Session 4 :: Societies (I)With our work, we exploited the potential of deriving
demographic attributes from anonymized CDRs. Althoughexperiments were performed with a limited number of call records,
the techniques developed in this study are capable of extracting
gender traits from large-scale CDRs. Experimental results inferthat our approach is capable of constructing a demographic
attribute prediction model based on anonymized CDRs.
Considering the importance of specifying call locations to extract
demographic attributes, further studies are necessary to improveexisting CDR location labeling methods.

57

2015
1

The Evolution of Social Strategies across the Lifespan1

Nitesh V. Chawla†,∗ , Jie Tang‡ , Yuxiao Dong†† Interdisciplinary Center for Network Science and Applications, Department ofComputer Science and Engineering, University of Notre Dame, Notre Dame, IN,
United States of America‡ Department of Computer Science and Technology, Tsinghua University, Beijing,
P. R. China∗ Email: nchawla@nd.edu

Session 4 :: Societies (I)Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)Users with demographic profiles in social networks offer the potential to understand the social
principles that underpin the highly connected world, from individuals, to groups, to societies.
In this work [1], we aim to harness the power of computational social science to discover thesocial strategies in human communication by which people use to fulfill social needs, and to infer
users’ demographics based on their daily mobile communication behaviors.
We employ a real-world large mobile network comprised of more than 7 million users andover 1 billion communication records (CALL and SMS) as the basis of our study. In this dataset,around 45% of the users are female and 55% are male. In our study, we focus on users aged
between 18 and 80 years old.
The dynamic social strategies used by people to meet their social needs across their lifetime
indicate more complex and crucial social theories than can be revealed by a static view [2]. Our
research unveils the significant social strategies across one’s lifespan in human communication.
Specifically, we investigate the interplay of communication interactions and demographic characteristics in the perspective of micro-level social structures, including social ego, social tie, andsocial triad.
We found the following social strategies on social ego. First, young people (who have higher
degree) are very active in broadening their social circles, while seniors (who have higher clustering
coefficients) tend to keep small but more stable connections. Second, people tend to communicate
with others of similar age and gender, i.e., age homophily and gender homophily. Third, young
people put increasing focus on the same generation and decreasing focus on the older generation,and the middle-age people devote more attention on the younger generation even along with thesacrifice of homophily.
The social strategies on social tie can be summarized as follows. First, frequent crossgeneration interactions are maintained to bridge age gaps (Cf. Figure 1). Second, young male
maintain more frequent and broader social connections than young females (Cf. Figures 1(b)and 1(c)). Third, opposite-gender interactions are much more frequent than those between
young same-gender users (Cf. Figure 1(d)). However, when people become mature, reversely,
same-gender interactions are more frequent than those between opposite-gender users.
More interestingly, we highlight the social strategies on social triad unveiled from Figure 2.

Session 3 :: Economies

Abstract1This work was published in the 20th ACM SIGKDD International Conference on Knowledge Discovery andData Mining (KDD0 14) [1] and also is submitted to International Conference on Computational Social Science(IC 2 S 2 ).

58

2015
2
20

70
16

16

14

14

12
50

10
8

40

60
Age (Male)

Age

60

12
50

10
8

40
30

4
2

20
20

40

60

40
60
Age (Male)Age(a) #calls per pair

18
70

16

16
14

60

12
50

10
8

40
30

2
20

20

18

14

60

12
50

10
8

40

6

6

4

20

80

80

20

70

6

6
30

80

18

18

Age (Female)

80

20

70

Age (Female)

80

4
2

20

80

20

(b) #calls per M-M pair

30

40
60
Age (Female)

4
2

20
20

80

(c) #calls per F-F pair4060
Age (Male)80(d) #calls per M-F pairFigure 1. Strength of social tie. XY-axis: age of users with specific gender. The spectrum
color represents the number of calls per month. (a), (b), and (c) are symmetric.
MMM

−3

−3

x 10
3

80

x 10
3

70

2.5

70

2.5

70

2.5

60

2

60

2

60

2

50

1.5

50

1.5

50

1.5

40

1

40

1

40

1

30

40

50

60

70

80

0

Min Age of FFF(a) FFF demog. dist.

20
20

30

40

50

60

70

80

0.5

20

0

20

30

Min Age of FFM(b) FFM demog. dist.

40

50

60

70

80

0

Min Age of MMM(c) FMM demog. dist.

(d) MMM demog. dist.

Figure 2. Demographic distribution in social triad. X-axis: minimum age of three users ina triad. Y-axis: maximum age of three users. The spectrum color represents the distributions.

People expand both the same-gender and opposite-gender social circles during the dating active
period. However, people’s attention to opposite-gender groups quickly disappears after entering
into middle-age (Cf. Figures 2(b) and 2(c)), and the insistence and social investment on samegender social groups lasts for a lifetime (Cf. Figures 2(a) and 2(d)).
Based on these discovered social strategies, we further study to what extent users’ demographic information can be inferred by mobile social networks. The objective is to infer users’
gender and age simultaneously by leveraging their interrelations. We present a computationalmodel—that is, the WhoAmI framework, a Multiple Dependent-Variable Factor Graph model,
whereby the social interrelations between users with different demographic profiles can be modeled. On both CALL and SMS networks, the WhoAmI method can achieve an accuracy of 80%
for predicting users’ gender and 73% for their age according to their daily mobile communication
patterns, significantly outperforming several alternative data mining methods.

Session 4 :: Societies (I)

20

30

0.5

Session 2 :: Cities (I)20

30

Session 1 :: Mobility

0.5

Session 6 :: Cities (II)30

Max Age of MMM

80

Session 3 :: Economies

FFM

−3

x 10
3

Max Age of FFM

Max Age of FFF

FFF
80

References
1. Y. Dong, Y. Yang, J. Tang, Y. Yang, and N. V. Chawla. Inferring user demographics andsocial strategies in mobile social networks. In KDD’14, pages 15–24. ACM, 2014.
2. V. Palchykov, K. Kaski, J. Kertész, A.-L. Barabási, and R. I. M. Dunbar. Sex differences
in intimate relationships. Scientific Reports, 2:370, 2012.

59Temporal dynamics of intra and inter-city social networks

2015

Alejandro Llorente,1 Manuel Cebrian,2 and Esteban Moro1
1
Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganés, Spain
National Information and Communications Technology Australia, University of Melbourne, Victoria 3010, Australia

Session 2 :: Cities (I)• At the region level, we have corroborated that inour database the aggregated number of ties kα
within a geographical area α does indeed scale
super-linearly with those areas population Nα . In
fact we get that kα ∼ Nα1.14 in line with other works[8]. However, tie dynamics is much more superlinear and the number of ties created or destroyed
in region α scales like nα ∼ Nα1 .33. Our result
suggest part of the super-linear character of social
connectivity within cities comes from the more dynamical nature of relationships in larger cities.

Session 4 :: Societies (I)Session 3 :: Economies

lion users in an industrialized European country during
19 months. Users are geolocated according to their postcode (our regions) billing address and thus we can study
the geographical extent of ties within the country. Using the method presented in [5] we were able to detect
when ties are created and/or destroyed during our observation period. Each user i is then characterized by his
social strategy γi , namely the ratio between his social activity ni (the total number of links created or destroyedin the period) to his social capacity κi the number of
instantly maintained relationships. Users with γi  1have a large turnover in their social neighborhood (social explorers), while those with γi  1 keep a stablelocal network around them (social keepers). Using thisdefinitions we have studied the geographical features ofindividual’s strategies and of ties. Our main results are:Session 1 :: Mobility

During recent years, the analysis of different city properties as a function of their size has attracted the attention of economists, sociologists and environmentalists[1–3, 8]. Cities are important economic and cultural centers, and account for significant and increasing part ofreal-world and online activity in a modern urbanized society. Thus it is important to understand their properties
and how they scale with population. While some factors
such as wealth creation and innovation have been shownto scale superlinearly with the city size, other quantities, such as infrastructure, scale sublinearly. A possible
origin of the non-linear relationship between city societal properties and their size lies in the number of social
ties within cities. In recent papers it has been suggested[7] and found [8] that average connectivity of people inlarger cities is higher than in smaller cities. As a consequence, contagious disease rates, information diffusion
speeds or innovation propagation spreading is higher onlarger cities [7], possibly leading to the superlinear scaling of societal properties with city size.
However, this aggregated static analysis of city social
connectivity contradicts the highly dynamical nature ofsocial relationships [4]: ties are formed and destroyed
at a constant rate in our society and individuals show
very different strategies regarding the way they manage
their social connectivity, mostly due to time or cognitive
limitations [6]. In particular, recent works [5] showedthat either individuals keep their relationships constantin time (social keepers) or they vary the people they areconnected to (social explorers). On top of that tie persistence is old, with an average of 10% of total ties decaying
per month [5]. A highly dynamical evolution of networkties might be misinterpreted with larger social connectivity in the aggregated static picture of social networks.
Thus, it is of interest to elucidate whether the aggregated super-scaling of number of ties within cities is also
observed instantaneously in time and what is the influence and heterogeneity of social strategies in that superscaling. Moreover, tie dynamics has a great impact on
the way information or disease spread. In general, thebursty nature of tie contacts and their formation/decay
dynamics slows down propagation [4] and thus network
dynamics within cities will play an important role on theway information/disease spread.
In this work we investigate the dynamical propertiesof connections within and across regions to characterize
and understand the impact of network dynamics in the
super-scaling properties of regions and how information
spreading is affected by the stability of ties within andacross regions. To this end we have analyzed a mobile
phone database consisting on 9000 million calls of 20 mil-Session 6 :: Cities (II)2

• At the users’ level we found that the explanation
for the more dynamical nature of networks withinregions is due to the the higher proportion of social explorers in larger regions. In fact we find that(exp)
scales superthe number of social explorers Nα
(exp)
1.27
linearly with city size Nα∼ Nα(see figure2a). Thus, human social strategies are different
depending on whether individuals live in small orlarger areas, with a higher tendency to become social explorer if we live in larger urban areas.
• Away from region boundaries we have studied also
the stability of links as a function of the distance
between individuals. As it is well known, the probability to have a link between two persons i andj that are at a geographical distance dij decays
slowly with that distance P (dij ) ∼ dηij . We also
observed that result in our database, but also thatthe stability of links (the tie lifetime) depends onthe geographical distance. Specifically, ties become more and more unstable (short) with increasing dij up to the geographical scale of 50km from60N↵ (Population)

Er = c 2 N

100
103

104

Real
Random105

Population= 1.33N↵ (Population)
FIG. 1. a) Scaling of the aggregated number of ties kα withina geographical area α and its population Nα . Each circle
represents a postcode in our database. Blue line is an runningaverage over the data, while the red line is a nonlinear fit tokα ∼ N β . For comparison we also show the linear relationshipkα ∼ Nα (black line). b) Scaling of the aggregated activitynα (number of ties destroyed/formed within the region, grey
symbols) and social capacity κα (number of instantly observed
ties within the city, orange symbols) as a function of the region
population Nα . Black lines are nonlinear fits κα , nα ∼ Nαβ .

which stability of ties remains constant. Thus, network dynamics is strongly affected by geography:
whereas estable links appear at different geographical distances (with higher probability at smaller
distances), short duration ones are more likely toappear at higher distances.
• Finally, we have investigated the geographical nature of network dynamics in the process of informa-

2500
2000

1.9e+072.0e+072.1e+07

Time
Time

Session 2 :: Cities (I)↵ , n↵x4

N↵ (Population)
Er = c1 N 1.27

2500
2000
1500
1000

N↵ (Population)

Session 6 :: Cities (II)= 1.12N↵ (Population)

b)

0

b)

101

N1.33
p==
0.6

1500

= 1.33

102

105

1000

N↵ (Population)

104

500

a)

103

103

Population↵x4

↵ , n↵k↵# explorers104= 1.12N↵(exp)⇠ cN1↵1.27
Er =
N 1.27N↵(exp)⇠ cN2↵N
Er =100# infected
regions<# postcodes>b)= 1.14= 1.330

101

N↵ (Population)

a)

x4

102

Session 1 :: Mobility

k↵

x4

3

2.2e+072.3e+07

FIG. 2. a) Relationship between the region size and the number of social explorers living there, exhibiting superscaling. b)
Number of infected regions as a function of time in the SI process for the real and the randomized data. We consider thata region is infected when a fraction p = 0.6 of its inhabitants
is infected.

Session 4 :: Societies (I)↵ , n↵= 1.14k↵↵ , n↵= 1.14# infected
regions<# postcodes>

b)

(exp)
explorers
N# ↵

a)

104

a)
b) 10 = 1.12
= 1.12

a)

500

2015

Session 3 :: Economies

lation)2tion diffusion at the country level. The larger proportion of social explorers in larger regions and thefact that long-ranged ties are more unstable might
have a paramount impact in the way information
spreads at larger distances. To analyze this we have
performed simulations of simple contagious models (Susceptible-Infected, SI) on the original call
database and compare it to the randomized versionof the calls timestamps. Similary to other previous
works, an acceleration of the number of infected individuals is provoked by the time randomization asa consequence of the uniformization of the times between interactions, breaking their burstiness. Moreinterestingly, not only the number of total infections is accelerated but also the number of different
areas where a breakout has been declared, understood as those regions where the proportion of infected individuals is higher than a threshold p. We
conclude that this effect is provoked by the increaseof instantaneous relationships within the cities, accelerating the internal infection processes without

61

Session 3 :: Economies

Session 2 :: Cities (I)[6] Giovanna Miritello, Esteban Moro, Rubén Lara, Rocı́oMartı́nez-López, John Belchamber, Sam GB Roberts, andRobin IM Dunbar. Time as a limited resource: Communication strategy in mobile phone networks. Social Networks, 35(1):89–95, 2013.
[7] Wei Pan, Gourab Ghoshal, Coco Krumme, Manuel Cebrian, and Alex Pentland. Urban characteristics attributable to density-driven tie formation. Nature communications, 4, 2013.
[8] Markus Schläpfer, Luı́s MA Bettencourt, Sébastian
Grauwin, Mathias Raschke, Rob Claxton, Zbigniew
Smoreda, Geoffrey B West, and Carlo Ratti. The scaling of human interactions with city size. Journal of The
Royal Society Interface, 11(98):20130789, 2014.

Session 1 :: Mobility[1] Michael Batty. The size, scale, and shape of cities. science,
319(5864):769–771, 2008.
[2] Luı́s MA Bettencourt. The origins of scaling in cities.
science, 340(6139):1438–1441, 2013.
[3] Luı́s MA Bettencourt, José Lobo, Dirk Helbing, Christian Kühnert, and Geoffrey B West. Growth, innovation,scaling, and the pace of life in cities. Proceedings of the
National Academy of Sciences, 104(17):7301–7306, 2007.
[4] Petter Holme and Jari Saramäki. Temporal networks.
Physics reports, 519(3):97–125, 2012.
[5] Giovanna Miritello, Rubén Lara, Manuel Cebrian, andEsteban Moro. Limited communication capacity unveils
strategies for human interaction. Scientific reports, 3,
2013.

Session 6 :: Cities (II)In conclusion we have found that network dynamics
does depend on the geographical scale they happens. In
particular we find that larger cities are more dynamical
and tend to have larger number of unstable (sort) ties dueto the larger proportion of individuals with social exploring strategies. At larger distances we also observe thatlinks are shorter, which yields to a slower infection of different geographical areas. Thus, it is not only that information propagates slower in time because of network dy-

namics it also does it slowly in geographical terms. Our
results suggest that social networks do behave differently
depending on city properties: most of network dynamics
happens in larger, denser urban areas, while small, countryside areas display less evolution in social connectivity.
That rapid network evolution might be behind the existence of more opportunities, connections, meetings, etc.
that yield to better performance (in terms of wealth, innovation, etc.). But it also challenges our understandingof how individuals manage their social connectivity at
different time and spatial scales.

slowing the inter-region dissemination.

Session 4 :: Societies (I)

2015

3

62

2015

Session 5 :: Societies (II)

63

2015
While theoretical work provides intuition about network effects, there is little empirical work to guidepolicy choices.1 Empirical work has been limited forthree reasons. It is costly to measure an entire network using traditional data sources. It is also difficult
to identify network effects: one individual may adopt
after a contact adopts because the contact provides
network benefits, or because connected individuals
share similar traits or are exposed to similar environments. And even if these two issues are overcome,it is difficult to evaluate policies, which can cause
effects to ripple through the entire network. As aresult, there remain open questions about how to design policies that better capture the spillover benefits associated with network effects, as well as policies that overcome suboptimal provision arising fromhigh concentrations in industries providing network
goods.
In this project, I overcome previous limitations using a new empirical approach and 5.3 billion transaction records from Rwanda’s dominant mobile phone
operator as the network expanded from 300,000 to 1.5
million subscribers. I estimate a structural model ofdemand for mobile phones, and then use this model
to simulate the effects of two policies.

THE ADOPTION OF NETWORK GOODS:EVIDENCE FROM THE SPREAD OF
MOBILE PHONES IN RWANDADANIEL BJÖRKEGREN∗
BROWN UNIVERSITYThis project develops a method to estimate andsimulate the adoption of a network good. I estimate
demand for mobile phones as a function of individuals’ social networks, coverage, and prices, using transaction data from nearly the entire network of Rwandan mobile phone subscribers over 4.5 years. I thensimulate the effects of an adoption subsidy and a requirement to provide coverage in rural areas.
Full version of paper: http://dan.bjorkegren.com
1. Introduction
Improvements in communication, through mobile
phones as well as associated services such as mobile
money and mobile internet, have the potential to knit
even remote villages into the global economy. Although these goods can generate large efficiency gains(Jensen, 2007; Jack and Suri, 2011), their allocations
are likely to be inefficient due to network effects. Individuals are unlikely to internalize all the benefits
their adoption generates, so adoption is likely to be
suboptimal unless the firms operating the network
use sophisticated pricing mechanisms. Also, in competitive markets, any single firm will internalize onlya small share of the benefits it generates. If insteada market is so concentrated that these benefits areinternalized by a small number of firms, the abilityof these firms to exert market power raises welfare
concerns.
That mobile phones have spread rapidly acrossvaried environments suggests that, in this case, failures by consumers and firms to internalize network
effects have not prevented widespread adoption. Yet,because adoption is widespread, even small inefficiencies can have large welfare consequences. Also, there
may be many beneficial network goods that we do
not observe, because they would diffuse only withina narrow band of policy conditions. The fact thatmobile phones have spread makes it possible to observe the network of benefits they generate, and toempirically study policies that may lead to more efficient adoption of other network goods.
Firms and governments use many different policies
to guide the provision and adoption of network goods.

Session 3 :: Economies

Session 2 :: Cities (I)Session 5 :: Societies (II)Conference∗
E-mail:danbjork@brown.edu,
Web:http://dan.bjorkegren.com
Revision January 6, 2015. 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.

Session 1 :: Mobility

Session 6 :: Cities (II)My method has three steps:
First, acknowledging that the utility of owning amobile phone is derived from its usage, I model theutility of using a phone. I observe every connection
between subscribers, as well as the calls placed acrosseach connection. Because 99% of accounts are prepaid and the person placing a call pays for it by thesecond, a subscriber must value a connection at least
as much as the cost of calls placed across it. Because calling prices changed over this period, I canestimate the underlying demand curve for communication across each link, and thus the value of eachconnection.
Let G be the communication graph. Each individual i has a set of contacts Gi ⊂ G, where a directed
link ij ∈ G indicates that i has a potential desire
to call j over the mobile phone network. Let St be
the subset of nodes subscribing in month t. At eachperiod t, individual i can call any contact j that currently subscribes, j ∈ Gi ∩ St , to receive utility uijt .
Each month, i draws a communication shock ijt representing a desire to call contact j. Given the shock,i chooses a total duration d ≥ 0 for that month, solving:

Session 4 :: Societies (I)

2. Model1
Early theoretical work includes Rohlfs (1974), Katz andShapiro (1985), and Farrell and Saloner (1985). Most empirical work on network goods measures the extent of network
effects; see for example Saloner and Shepard (1995), Goolsbee
and Klenow (2002), and Tucker (2008). The paper closest in
spirit to this one is Ryan and Tucker (2012), which estimates
the adoption of a videoconferencing system over a small corporate network, and evaluates policies of seeding adoption.

64
1

2015

2

uijt = max vij (d, ijt ) − cijt dd≥0

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

3. Results
I use this approach to answer two sets of policyquestions, by simulating how adoption would haveproceeded if conditions were less favorable. Because
individuals tend not to internalize adoption spillovers,
it is common for firms or governments to subsidize
adoption of network goods. I analyze a rural adoption subsidy program implemented by the Rwandan
government in 2008. I first use phone data to determine how subsidized handsets were ultimately used,
and then use the simulation method to determine
how the policy affected the entire network. I findthat a substantial fraction of the subsidy’s impact
arises from its impact on nonrecipients—in particular, contacts of recipients account for more than 62%of the effect on revenue. Although the bounds arewide, the subsidy improved welfare, in a low case by$191,108 (representing a social return of 33%), and ina high case by $5.6 million (a social return of 888%).
I also analyze the welfare implications of providing coverage to rural areas. A social planner would
expand coverage until the point where building any
additional towers would not improve welfare. Firms
may stop building before reaching this point: in a

4. Conclusion
This project introduces a new method for estimating and simulating the adoption of network goods. I
overcome measurement issues that have limited empirical work on network goods using rich new data on
the adoption and usage of nearly an entire network ofmobile phone users. (For more details, find the full
paper at http://dan.bjorkegren.com )Session 5 :: Societies (II)t=τI estimate the parameters of this model using maximum likelihood and moment inequalities.
Finally, to evaluate the impact of policies, I use
a simulation method that allows each individual toreact directly to a policy change, and to each other’s
responses, capturing effects that ripple through the
network and across physical space.

competitive market, some of the benefits of expanding coverage will spill over into competitors’ networks.2
And regardless of market structure, firms are unlikely to internalize all of the value generated for consumers. Depending on the shape of private and social
benefits from expansion, it may be optimal for a government to require the provision of coverage to areasthat are unprofitable to serve. I find that in Rwanda,
a government coverage obligation led to the buildingof a handful of rural towers that were unprofitable forthe firm but slightly welfare improving for the country. The impact shifted bounds on welfare in a focal equilibrium upward by roughly $179,381 (0.06%).
The impact was also extremely dispersed: over 65%
of the gain in consumer surplus accrued to individuals
living in areas where coverage was not affected; someof these individuals called in to the covered areas andothers were affected indirectly. The impact on revenue by geography is presented in Figure 1: muchof the revenue comes from areas far from the towers.
Because of this dispersion, it would have been difficult for local communities to raise the funds to build
the towers themselves.

Conference∞Xδ t Euit (pt , φt ) − δ τ βprice phandset
τ

Session 4 :: Societies (I)Uiτ =Dropped towers are denoted by triangles; difference inrevenue between the baseline and counterfactual in January
2009 is shaded.

Session 3 :: Economies

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

Session 2 :: Cities (I)j∈Gi ∩St

Session 1 :: Mobility

X

Session 6 :: Cities (II)uit =Figure 1. Geographic Distribution of Revenue from Rural Expansion

2A fraction of these benefits can be internalized using interconnection fees, but some will spill into the interiors ofcompetitor networks.

65

2015
3

Session 5 :: Societies (II)Conference

Session 4 :: Societies (I)Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

Farrell, J. and G. Saloner (1985): “Standardization, Compatibility, and Innovation,” The RAND
Journal of Economics, 16, 70–83.
Goolsbee, A. and P. J. Klenow (2002): “Evidence on Learning and Network Externalities inthe Diffusion of Home Computers,” Journal of Law
and Economics, 45, 317–343.
Jack, W. and T. Suri (2011): “Risk Sharing andTransactions Costs: Evidence from Kenya’s Mobile
Money Revolution,” Working Paper.
Jensen, R. (2007): “The Digital Provide: Information (Technology), Market Performance, and Welfare in the South Indian Fisheries Sector,” Quarterly Journal of Economics, 122, 879–924.
Katz, M. L. and C. Shapiro (1985): “Network Externalities, Competition, and Compatibility,” The
American Economic Review, 75, 424–440.
Rohlfs, J. (1974): “A Theory of Interdependent
Demand for a Communications Service,” The Bell
Journal of Economics and Management Science, 5,
16–37.
Ryan, S. P. and C. Tucker (2012): “Heterogeneity
and the dynamics of technology adoption,” Quantitative Marketing and Economics, 10, 63–109.
Saloner, G. and A. Shepard (1995): “Adoption ofTechnologies with Network Effects: An Empirical
Examination of the Adoption of Automated Teller
Machines,” The RAND Journal of Economics, 26,
479–501.
Tucker, C. (2008): “Identifying Formal and Informal Influence in Technology Adoption with
Network Externalities,” Management Science, 54,
2024–2038.

Session 6 :: Cities (II)References

66

2015
The Strength of the Strongest Ties in Collaborative Problem Solving
Yves-Alexandre de Montjoye, PhD Candidate, Media Lab, MIT, Cambridge, MA, USA; Arkadiusz
Stopczynski, PhD Candidate, DTU Compute, Technical University of Denmark, Copenhagen, Denmark;Erez Shmueli, PhD, Media Lab, MIT, Cambridge, MA, USA; Alex 'Sandy' Pentland, Prof., Media Lab,
MIT, Cambridge, MA, USA; Sune Lehmann , DTU Compute, Technical University of Denmark,Copenhagen, Denmark;Corresponding Author Contact: yva@mit.edu, (617) 324-3842

Session 5 :: Societies (II)Conference

Session 3 :: Economies

Session 2 :: Cities (I)Fig 1 (A) Network of strongest expressive (blue) and instrumental (green) as
well as the groups participants where assigned to for the first project. Color
saturation is the performance of the groups where darker is higher. (B)Correlation between expressive tie strength and group performance. (C)Correlation between instrumental tie strength and group performance. For
both expressive and instrumental ties, the position in the network of strong
ties is more important than other a priori characteristics of the group such as
self-evaluated and measured proficiency or personality. The gray areas
indicates values with p > 0.05.

Session 1 :: Mobility

Session 6 :: Cities (II)Abstract: Complex problem solving in science, engineering, or business has become a highlycollaborative endeavor. Groups of scientists or engineers are collaborating on projects but also using theirsocial networks to gather new ideas and feedback. Here we bridge the literature on group work andinformation networks by studying groups' problem solving abilities asa function of both their within-group networks but also their members'
extended networks. We show that while groups' performance isstrongly correlated with its networks of expressive and instrumental
ties, only the strongest ties in both networks have an effect on
performance, as shown in Figure 1. Both networks of strong ties
explain more of the variance than other factors such as measured or
self-evaluated technical competencies or personality of the groupmembers. In fact, the inclusion of the network of strong ties renders
these factors non-significant in the statistical analysis. Our results
have consequences for the organization of groups of scientists,
engineers, or other knowledge workers tacking our current mostcomplex problems.

Session 4 :: Societies (I)Keywords: social networks; collaboration; groups; strong ties; complex problem solving

67

2015
Investigating Social Influence Through Large-Scale Field
Experimentation
†​
†​
†​
†‡​
Johannes Bjelland,​
Geoffrey Canright,​
Asif Iqbal,​Rich S. Ling,​
Kenth
†​
†​
§​
†​
Engø-Monsen,​Taimur Qureshi,​​
Christoph Riedl,​​Pål Roe Sundsøy,​​David§
Lazer​†​

Telenor Research, Oslo, Norway; ‡​​Nanyang Technological University, Singapore;§​
Northeastern University

Session 5 :: Societies (II)Methods. ​Here we report the results of two country­level experiments in which we exposed 19,352 and27,420 users, through mobile phone text messages, to unique voucher codes that offered them 100MB offree traffic for their data plans. Each user exposed to a code could (a) adopt the code themselves (i.e.,
redeem the voucher) and (b) pass the code on to their friends. In the experiments we manipulated thenumber of users within a dyad and closed triangle of “friends” that were exposed to the market offering. Thatis, in some randomly selected dyads, one randomly selected user was exposed, while in other dyads bothwere exposed (N=9,318 dyads with one code; N=5,017 dyads with two codes). Conversely, in somerandomly selected closed triangles, one randomly selected user, two randomly selected users, or all threeusers were exposed to the product offering (triangles with one, two, or three codes: 8,408, 4,697, 3,206). We
then tracked adoption of voucher codes over two weeks.

Conference

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)Theory​. This is the core reasoning behind the “strength of weak ties”—weak ties connect nodes thatprovide non­redundant information (Granovetter, 1973; Onnela et al., 2007). However, more recent work
(Centola & Macy, 2007; Centola, 2010) suggests that the probability of behavioral contagion increases morethan linearly with marginal adoption. That is, having two friends that adopt X more than doubles ego’s odds
of adopting X. This has direct implications for mass efforts for mass behavior change, either by marketers or
policymakers: that efforts to change behavior should NOT be randomly disseminated through the population
but aimed at clusters of connected individuals.

Session 4 :: Societies (I)The recent emergence of computational social science research methods has enabled us to conduct
large­scale, population level experimentation that allow us to estimate causal effects of different policy
alternatives. This new experimental paradigm is particularly suited to study patterns of social influence overreal­world networks in which social embeddedness and tie strength are often correlated with each other andwith homophily. Networks of interaction among individuals provide the primary pathways along which viralcontagion spread. This viral information and behavior spreading is relevant to understand fundamental
aspects of social influence in the adoption of behavior like smoking or exercising, and economic productadoption. Given limited resources, policy makers and marketers confront a strategic choice: who to initiallytarget with information? Traditional models of contagion suggest that targeting neighboring nodes would be
inefficient, because there is the possibility that one node would infect the other virally. More recent work on
complex contagion suggests that behavioral confirmation by alters will increase probability of adoption. Here
we report on a marketing experiment in which the clustering of targeting is varied, and we find support thatclustered targeting is more effective than non­clustered targeting.

68

2015

References
Centola, D. (2010). The spread of behavior in an online social network experiment. ​Science​,​329​(5996),
1194­1197.
Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties1. ​American Journal of
Sociology​,​
113​(3), 702­734.
Granovetter, M. S. (1973). The Strength of Weak Ties. ​American Journal of Sociology​,​
78​(6), 1360–1380.
Onnela, J­P., et al. (2007). Structure and tie strengths in mobile communication networks. ​
PNAS​
,​
104​
(18),
7332­7336.

Session 5 :: Societies (II)Conclusion​. Our results suggest that there is a marginal benefit of exploiting existing ties between
individuals to increase behavioral contagion. We show that not only may peer’s adopted behavior affect own
adoption rates, but simply peer’s exposure may already be enough. This has direct implications for efforts toaffect mass behavior change and suggests that efforts to change behavior can be more effective if they are
aimed at clusters of connected individuals rather than disseminated randomly.

Conference

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)Results. ​We find strong causal evidence that increased exposure to the offering through friends
significantly increases adoption probability by the focal node in the triad experiment. In dyads, the baseline
probability of code adoption remains largely unchanged if a “friend” is equally exposed to a code (adoption
probabilities of 4.56% and 4.68%, respectively; not significantly different). However, in the triad experiment,
we find that the baseline adoption rate of 6.28% increases to 7.18% and 7.08% if one or two “friends”,
respectively, have also been exposed to a code (3­sample test for equality significant at p < 0.5).

Session 4 :: Societies (I)Figure 1. What is the probability of adoption by the focal node (in red)?​In the experiment we had
two dyad level treatments, and three triad level treatments, varying the number of nodes that received theoffer. In experiment 1 probability of focal node adopting was 4.56% in the baseline condition and 4.68% in
the treatment condition where both nodes received a code—i.e., employing clustered targeting (notsignificantly different). In the triangles experiment, the probability of adoption in the baseline condition was
6.28%. In the treatment condition in which two of the three nodes received a code it was 7.18% and in thesecond treatment condition where all nodes received a code it was 7.08% (3­sample test for equality
significant at ​p​
< 0.5). This represents an increase in adoption probability of 13%­15% from being
exposed to clustered treatments.

69

2015

Asymmetric Role of Social Influence in Smartphone
Adoption in Large Mobile Networks

Heinz College
Carnegie Mellon University
Pittsburgh, PA 15213
United States

DATA

We use CDR to construct the social network as an undirected call graph. Specifically, we denote two subscribers
to befriend each other if they exchange at least one call inthe same calendar month. The mutual relationship between
subscribers ensure that we preclude communications thatare unlikely to represent the social ties, such as customer
services and PBX machines. The resulting network consistsof 5,535,388 subscribers and 66,717,468 edges with mean,
standard deviation, and median of degree being 24.1, 25.7
and 16, respectively.

3.

SUBPOPULATION EXTRACTION

Recent researches on statistical properties of real world social networks provide evidence of the existence of community
structure within the network [6, 16]. These findings does not
only validate the theoretic role of homophily and influencein tie formation, but also provide several important insights
into the problem of community structure inference when we
only observe the resulting network as follows. First, uncovered communities should exhibit real social meaning, as
individuals in the same community have some natural affinity for each other or some fundamental characteristics incommon. Meanwhile, they should be more likely to connect
to each other than those who belong to different communities. Hence community discovery method should consider
1
The municipal location is defined as Nomenclature of Units
for Territorial Statistics (NUTS) III, which is a geocode standard across European countries by Eurostat for statistical
purposes

Session 5 :: Societies (II)EURMO dataset includes call detail records (CDR) for over
5 million subscribers between August 2008 and June 2009.
Subscribers are identified by their anonymized phone numbers. For each call we know the caller and the callee, thetimestamp, and the GPS coordinates of the connected cell
tower. By aggregating GPS coordinates over the entire period, we can approximate subscriber’s home location at municipal level as where they spend most of their days1 . We
further infer the socio-economic indicators (e.g. wage) by
cross-referencing the latest census. We also have an (incomplete) set of subscriber characteristics such as gender andusage history since their subscription to EURMO, which includes tariff plan, handset and supplementary services (e.g.
mobile broadband). In our period of analysis, there are20,570 iPhone 3G adopters with complete profiles. Table
1 lists relevant variables and short descriptions that we extract from EURMO.

Conference

Social influences have been acknowledged the important rolein determining consumer’s decision making process for various mobile communication products and services (e.g. [8,9, 12, 13]. According to [5], the positive (negative) wordof-mouth message in the mobile phone market can increase(decrease) a company’s market share by as much as 10 (20)percent. In particular, with regard to smartphone adoption,
both [13] and [17] found positive effect, meaning the propensity for a focal customer to adopt smartphone increases with
the number of adoptions in her ego network. However, identifying social influence in large networks, especially in observational studies, still remains as serious concerns to researchers [7], as it is known to confound with endogenous
factors such as homophily - the tendency for individuals tochoose friends with similar tastes, among others [14]. Whencorrelated behavior between ego and her direct neighbors
can be explained by both influence and their inherent similarities, misattribution of homophily to influence may leadto significant overestimation of the latter [3]. Moreover,[19] argued that social influence and homophily are generically confounded and can not be readily separated fromeach other, because latent homophily may still remain as acomponent of the estimated influence. Nevertheless, they
proposed several constructive suggestions that may help alleviate the issue, one of which our work is motivated: touse community structure as the proxy to control for latenthomophily. We intend to estimate the effect of social influence on adoption of iPhone 3G, an iconic smartphone, using
dataset from a major wireless carrier operated in one European country (hereinafter EURMO). iPhone 3G is releasedin July 2008 and EURMO is the sole partner with Apple inthis country, so we are able to capture the full cycle of its
adoption.

2.

Instituto Superior Técnico
University of Lisbon
Lisbon, 1049-001
Portugal

Session 4 :: Societies (I)As the number of mobile subscriptions worldwide is reachingnearly 7 billion by the end of 2014 [10], the rapid growth ofmobile handset adoption has evolved from communication
devices (a.k.a. feature phones) with basic telephony service
to advanced multifunction computing devices (a.k.a. smartphones) that provide one-stop solution to meet popular dailyneeds. Hence, smartphones have become platforms enablinga multitude of services and applications, through which thebusiness of mobile market is delivered through these devices.
Understanding the adoption pattern of this high-technology
product is of great interest to not only handset manufacturers and mobile operators, but also to service providers
and application developers, and it may also reveal significant managerial and policy implications [11].

†Session 3 :: Economies

1. INTRODUCTION‡

Session 2 :: Cities (I)Department of Engineering and Public Policy
Carnegie Mellon University
Pittsburgh, PA 15213
United StatesJoão Paulo Costeira†jpc@isr.ist.utl.pt

Session 1 :: Mobility*

Pedro Ferreira*‡
pedrof@cmu.edu

Session 6 :: Cities (II)Qiwei Han*†
qiweih@cmu.edu

70

2015
VariableTypegendercategoricalwage

categoricalprepaidbinaryphone technology

categoricalmobile internet

binaryphone agecontinuous

tenurecontinuous

regioncategorical

Description
Self-reported gender (male,
female, unknown)
Inferred wage level (verylow, low, average, high, veryhigh)prepaid tariff plan (yes, no)handset technology (2G,
2.5G, 3G, 3.5G, other)
mobile broadband (yes, no)age of currently owned
handset (year)
tenure since subscription(year)
home location at municipal
level

4.

CORE-PERIPHERY STRUCTURE

From the extracted subpopulation, we observe that over 70%
of subscribers belong to only one community and nearly 90%of those belong to two, while only about 5% of subscribers
belong to more than 5 communities. This is consistent withthe findings suggested in [18, 20] that the intersection of
overlapping communities may reveal core-periphery structure which complements current views of network organizations. In general, core nodes refer to set of central nodes
that are connected to other core nodes as well as peripheral
nodes, while peripheral nodes, by contrast, are only loosely
connected to the core nodes but not to each other [4]. In
this sense, following the measure proposed in [20] that unify
both organizing principles of the network, we validate theexistence of core-periphery structure in our subpopulation(see Fig. 1).

Table 1: List of covariates extracted from EURMO
1.00

0.50

0.25

0.00
1

2

3

4

5

α, Node community memberships

Yt = 1{Ut > 0}
Ut = α + β · X + γ · Zt + δ · Core+ µ1 · Core F riend Adoptt−1+ ρ1 · Core · Core F riend Adoptt−1+ µ2 · P eri F riend Adoptt−1+ ρ2 · Core · P eri F riend Adoptt−1 + t

Session 5 :: Societies (II)We describe our reduced form latent utility model as follows(index i are dropped for simplicity):(1)

where X and Zt are time invariant and time variant subscriberspecific characteristics listed in table 1. Dummy variable
Core indicates the incremental network position effect whensubscriber is deemed as core. For social influence, we include the main effects of number of core friend adopters
Core F riend Adoptt−1 and number of peripheral friend adopters
P eri F riend Adoptt−1 and the interaction effects between
number of friend adopters and subscriber’s network position. A significant coefficient for the latter terms captures

Conference

MODEL AND RESULTS

Session 3 :: Economies

Session 2 :: Cities (I)5.

Session 1 :: Mobility

We then define a subscriber as core node if she belongs to
at least five communities and as periphery node if otherwise. Among 9,194 core nodes, 5,548 (60%) are iPhone 3G
adopters, whereas only 5% of peripheral nodes are adopters.
This provides us with extra implications that iPhone 3G
adopters tend to form densely connected groups that havethe most shared properties with others thorough overlapping
communities.

Session 4 :: Societies (I)Figure 1: The fraction of nodes C(α) in the largest connected component of the induced subgraph on the nodes
who belong to at least a communities. A high C(α) meansthat there is a single dominant core.

Session 6 :: Cities (II)We employ the method of discovering Communities from
Edge Structure and Node Attributes (CESNA) for our purpose [21]. CESNA is a community discovery algorithm thatconsider both node attributes and network structure as wellas the interactions between these two sources of information.
It can detect overlapping communities with high accuracy
and scalability over many existing community detection algorithms, particularly on large scale networks. For sake ofspace, details beyond the mechanics of CESNA can be foundin [21], and we only note the following implementation procedures: i) for each iPhone 3G adopter, we construct theego-network that contains adopter and their direct neighbors, similarly as [17]; ii) for each subscriber in the egonetwork, we extract a list of 0-1 valued covariates specified in table 1 that represent pluralistic homophily including gender and wage (socio-demographic homophily); tariffplan, phone technology and mobile broadband (contextual
homophily); and home location (spatial homophily); iii) we
apply CESNA on each ego-network using both node andedge information with the optimal number of communities
identified through cross-validation; iv) we remove duplicatedand nested communities and only retain communities that
contain iPhone 3G adopters. As a result, we obtain 11,454
communities with 202,743 subscribers, 14,685 of which are
iPhone 3G adopters. After detecting community structurefrom networks, we are able to “naturally” extract subpopulations with half of the original network size which include
over 70% of adopters and their cohesive groups of neighborswith whom are both similar and strongly connected.

0.75

C(α)

two different sources of information together, i.e. individual characteristics and social connections among them. Second, in many actual networks, individuals may belong tomultiple overlapping communities [1, 15], e.g. families, coworkers and friends. This is also aligned with the notion of
homophily across different social dimensions. Third, as also
noted in [19], misspecification of community structure (e.g.
simple modular and/or disjoint structure) may even worsen
the problem and lead to biased model estimation. Fourth,
as the complexity of network structure grows exponentiallywith the size, computational costs still remain challenging
for the analyses on large scale networks. Therefore, extracting subpopulation via community discovery does not only
significantly reduce group-level heterogeneity that may potentially confound the result but also help lessen the computational cost [22].

716. REFERENCES[1] Ahn, Y.-Y., Bagrow, J., and Lehmann, S. Link
communities reveal multiscale complexity in networks.
Nature 466 (2010), 761–764.
[2] Ai, C., and Norton, E. Interaction terms in logit
and probit models. Economic Letters 80, 1 (2003),
123–129.
[3] Aral, S., Muchnik, L., and Sundararajan, A.
Distinguishing influence-based contagion from
homophily-driven diffusion in dynamic networks.
Proceedings of National Academy of Science 106, 51(2009), 21544–21549.
[4] Borgatti, S., and Everett, M. Models ofcore/periphery structures. Social Networks 21, 4(2000), 375–395.

Session 5 :: Societies (II)By computing the average partial effect using delta method,
we find that for core subscribers, their propensity to adopt
iPhone 3G is over 10% higher than peripheral subscribers,
indicating that subscribers who are in the higher network
position (maybe also social status) are more likely to adopt
the smartphone. Meanwhile, having one more core friend
adopter and one more peripheral friend adopter will increase
the focal subscriber’s adoption probability by 0.46% and0.25%, respectively. However, interpreting the coefficientsof interaction terms are far more straightforward [2], as theinteraction effect needs to be calculated as the cross-partial
derivatives. We find asymmetric interaction effect on coreand peripheral subscribers when having core and peripheral
adopter friends. Specifically, on average with one more core(peripheral) adopter friend, the changes between core andperipheral subscribers is about -1.52% (-1.76%). The implications of this finding are twofold: 1) peripheral subscribers
are more likely to get influenced than core subscribers; 2)core subscribers are slightly more likely to be get influenced
by a core adopter friend rather than a peripheral adopter
friend.

Conference

Table 2: Regression results of Probit Model

Session 3 :: Economies

Probit1.522∗∗∗(0.021)
Core F riend Adoptt−1 (µ1 )0.363∗∗∗(0.008)P eri F riend Adoptt−1 (µ2 )0.187∗∗∗(0.008)Core · Core F riend Adoptt−1 (ρ1 ) 0.092∗∗∗(0.01)
Core · P eri F riend Adoptt−1 (ρ2 ) 0.244∗∗∗(0.013)***p < 0.001, ** p < 0.01, * p < 0.05

Session 2 :: Cities (I)Dependent Variable: Adoptedt
Core(δ)Session 1 :: Mobility

We organize the subpopulation data into a panel where eachindividual is a subscriber and each period is a calendar
month and observations after the first adoption need tobe removed from the sample. We use the resulting sample(size=2,116,855) to empirically estimate the social influenceon iPhone 3G adoption. Table 2 summarizes the estimatesof covariates of interest from equation 1 using Probit model.
The full regression table is available upon request.

[5] Bughin, J., Doogan, J., and Vetvik, O. A new
way to measure word-of-mouth marketing. Mckinsey
Quarterly (April 2010), 1–9.
[6] Girvan, M., and Newman, M. Community structure
in social and biological networks. Proceedings of
National Academy of Sciences 99 (2002), 7821–7826.
[7] Goldsmith-Pinkham, P., and Imbens, G. Social
networks and the identification of peer effects. Journalof Business & Economic Statistics 31, 3 (2013),
253–264.
[8] Han, Q., and Ferreira, P. Role of peer influence inchurn in wireless networks. In Proceedings of 7thInternational Conference on Social Computing,SocialCom 2014, Beijing, China (2014), pp. 176–183.
[9] Hill, S., Provost, F., and Volinsky, C.
Network-based marketing: Identifying likely adopters
via consumer networks. Statistical Science 21, 2(2006), 256–276.
[10] ITU. The World in 2014: ICT Facts and Figures,
2014.
[11] Kauffman, R., and Techatassanasoontornb, A.
Understanding early diffusion of digital wirelessphones. Telecommunications Policy 33, 8 (2009),
432–450.
[12] Ma, L., Krishnan, R., and Montgomery, A.
Latent homophily or social influence? an empiricalanalysis of purchase within a social network.
Management Science 57, 9 (2014), 1623–1639.
[13] Matos, M., Ferreira, P., and Krackhardt, D.
Peer influence in the diffusion of the iPhone 3G over a
very large social network. MIS Quarterly 38, 4 (2014),
1103–1133.
[14] McPherson, M., Smith-Lovin, L., and Cook,
J. M. Birds of a feather: Homophily in social
networks. Annual Review of Sociology 27 (2001),
415–444.
[15] Palla, G., Derenyi, I., Farkas, I., and Vicsek, T.
Uncovering the overlapping community structure ofcomplex networks in nature and society. Nature 435(2005), 814–818.
[16] Park, J., and Barbasi, A.-L. Distribution of nodecharacteristics in complex networks. Proceedings of
National Academy of Sciences 106, 46 (2007),
17916–17920.
[17] Risselada, H., Verhoef, P., and Bijmolt, T.
Dynamic effects of social influence and direct
marketing on the adoption of high-technology
products. Journal of Marketing 78 (March 2014),
52–68.
[18] Rombach, M. P., Porter, M., Fowler, J., andMucha, P. Core-periphery structure in networks.
SIAM Journal of Applied Math 74, 1 (2014), 167–190.
[19] Shalizi, C. R., and Thomas, A. C. Homophily andcontagion are generically confounded in observationalsocial network studies. Sociological Methods &Research 40, 2 (May 2011), 211–239.
[20] Yang, J., and Leskovec, J. Overlapping
communities explain core-periphery organization of
networks. Proceedings of the IEEE 102, 12 (December2014), 1892–1902.
[21] Yang, J., McAuley, J., and Leskovec, J.
Community detection in networks with node
attributes. In Proceedings of the IEEE 13rd
International Conference on Data Mining, Dallas TX,
USA (2013), pp. 1151–1156.
[22] Zhang, B., Krackhardt, D., Krishnan, R., andDoreian, P. An effective and efficient subpopulation
extraction method in large social networks. In
Proceedings of the 32rd International Conference on
Information Systems, Shanghai, China (2011).

Session 6 :: Cities (II)the social influence from core friend adopters and peripheral
friend adopters, relative to focal subscriber’s network position. Besides, we also introduce month and location fixed
effects to control for heterogeneity across time and region.

Session 4 :: Societies (I)

2015

72

2015

Using Mobile Phone Data to Predict the Spatial Spread of Cholera
Linus Bengtssona,b, Jean Gaudartc, Xin Lud,a,b, Sandra Mooree, Erik Wetterb,f, Kankoe Sallahc, Stanislas
Rebaudete and Renaud Piarrouxe
Corresponding author: Linus Bengtsson (linus.bengtsson@flowminder.org)
aDepartment of Public Health Sciences. Karolinska Institutet,
Stockholm, Sweden;b
Flowminder Foundation, Stockholm, Sweden;c
Aix-Marseille University, UMR 912 SESSTIM (INSERMIRD-AMU), Marseille, France;dCollege of Information System and Management, National
University of Defence Technology, Changsha, China;e
Aix-Marseille University, UMR MD 3, Marseille, France;fStockholm School of Economics, Stockholm, Sweden;

Session 5 :: Societies (II)Empirical data has provided key insight into thespatial spread of measles in England13 andNiger14 as well as into influenza spread in the
USA and Europe.11,12,15 While population
mobility plays a key role in such modellingstudies,10,16 it has not been possible, until now, tostudy detailed and concurrent data on both
population
mobility
andspatiotemporal
distribution of cases. Instead, empirical studies
have used either models of population mobility,
preferentially gravity models,17 or census data on
work-home commuting as proxies for total
mobility during outbreaks.11 It is also not clear
how to choose and properly parameterizemobility models across contexts in new
outbreaks. This is especially problematic during
the critical early outbreak phases, wheninterventions have the greatest effect, but limited
data are available to fit transmission models.
Mobile operator data is a promising source ofnational mobility patterns and has notably beenused as in malaria modelling studies.18,19
However, the extent to which this type of data
accurately reflect movements of infectious

Conference

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobilityof case reports11 and limited access to accurate
and spatiotemporally resolved case data.12

Session 6 :: Cities (II)Introduction: Re-occurring infectious disease
outbreaks due to cholera, measles and otherpreventable infectious diseases contribute to amajor disease burden affecting low- and middleincome countries.1,2 Concurrently, outbreaks of
new infectious diseases with pandemic potential
pose a considerable threat to human life anddevelopment.3,4 Response to, and ideallycontainment of,5 an infectious disease outbreak
can be greatly improved if health care response
and outbreak control measures can be focused toareas predicted to be at the highest risk ofexperiencing new outbreaks.6,7 Accurate modelsof the geographic distribution of epidemic risk
could significantly enhance the population-level
effects of interventions implemented to control
thespreadoftransmittablediseases.8
Considerable progress has been made in
predicting temporal evolution of epidemics once
outbreaks have progressed beyond a small initialgroup of cases.7,9 However, predicting spatialtransmission routes of epidemics has proven to beremarkably difficult, due to the importance ofrare, long-distance transmission events,10 limited
data on population mobility, unknown population
immunity levels,9 low sensitivity and specificity

Session 4 :: Societies (I)Effective response to infectious disease epidemics requires focused control measures in areas
predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could
predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysedfor 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous
mobile phone SIM cards were used to create a national mobility network. Two gravity models of
population mobility were implemented for comparison. Both were optimized based on the complete
retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area
experiencing an outbreak within seven days showed strong dose-response relationship with themobile phone-based infectious pressure estimates. The mobile phone-based model performed better(AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively).
Infectious pressure at outbreak onset was significantly correlated with reported cholera cases
during the first ten days of the epidemic (p<0.05). Mobile operator data is a highly promising data
source for improving preparedness and response efforts during cholera outbreaks. Findings may beparticularly important for containment efforts of emerging infectious diseases, including highmortality influenza strains.

73

2015

!,!!!

𝑐! 𝑡 − 𝑘                         (Eq. 1)!!!

Figure 1: Mobile phone mobility network. The
average absolute number of mobile phones moving
between the study areas (October 15 to December 19,2010). The original outbreak location (Mirebelais),
the Artibonite River (dark blue) and Port-au-Prince(PAP) are depicted.

Session 5 :: Societies (II)Conference

In the absence of detailed mobility data,
parameter values for the gravity model areunknown and needs to be assigned. We chose to
optimize the model based on the retrospective
case data from the complete study period. Notethat this optimisation thus could not have been
performed until after the spatial spread of theepidemic was complete. Our comparison model
thus performs better than a model that could havebeen developed during the epidemic. We
produced two separate optimisations. In the first
we optimised the gravity model by choosing
values for 𝜇!   and 𝛿 (0.154 and 122 respectively),
which minimised the residual sum of squares
between reported daily cholera cases in eachstudy area and the estimated pressure from thegravity model.25 In the second we choseparameter values (0.158 and 3.5 respectively)that maximised the area under the curve (AUC),
among all possible ROC curves. ( 𝑃 !"#$! and𝑃 !"#$! , respectively).

Session 4 :: Societies (I)For comparative purposes, we implemented agravity model of population mobility.

Session 3 :: Economies

We used the mobile phone data to construct a!!!"#
mobility matrix 𝑀 !!!"# , with elements 𝑚!",indicating the average daily proportion of mobile
phones relocating from study area 𝑖 to 𝑗 ,comparing their last registered location on day twith their last registered location on day t-1. The
mobility network built on the basis of 𝑀 !!!"#
displays strong connectivity both between Portau-Prince and large parts of the country as well as
between other urban areas and their surrounding
countryside (Fig. 1). We calculated the infectious
pressure 𝑃! 𝑡 , sustained by each study area 𝑗during the period from October 21 (from sevendays after the disease onset of the first case in
Haiti) to December 16, according to Eq. 1, in

!
!!!"#

𝑚!"

Session 2 :: Cities (I)Methods: The daily case reports per healthfacility enabled us to determine daily casenumbers per commune while the epidemic spreadthroughout the country (October 14 and 64 days
onwards). We defined the end of the study period
as December 16, when the peak of the epidemic
was reached and all but one commune hadreported at least one case. In 62 communes out of
140 communes, including the eight communes
within the Port-au-Prince metropolitan area, there
may have been patients who sought healthcare inneighbouring communes.
Mobile phone data: The analysed anonymous
mobile phone data consisted of the last outgoingcall or text message each day from October 15 to
December 19, 2010 for all 2.9 million users
belonging to the largest mobile operator, Digicel
Haiti.24!

𝑃! 𝑡 =

Session 1 :: Mobility

The largest cholera epidemic to strike a single
country in recent history was the 2010 Haitianoutbreak.20 The 2010 Haiti cholera epidemic
provides a unique opportunity to explore theinfluence of population mobility on the spatial
evolution of a large-scale cholera outbreak. First,
cholera had not previously affected the country
for at least a century, thereby rendering epidemic
development unbiased by differential population
immunity. Second, the circumstances andlocation of the onset of the cholera epidemic are
well understood.21-23 Third, daily case reporting
based on WHO criteria was initiated very earlythroughout the country, and the notification
system was highly effective.20-23

which 𝑐! 𝑡 is the number of reported cases instudy area 𝑖 on day 𝑡:

Session 6 :: Cities (II)persons and its utility in predicting spatial spreadof infectious agents have not been evaluated.

Results: The risk of a study area experiencing a
new outbreak correlated closely with theinfectious pressure. Over a pressure level of 22

74

2015

0.3

0.6

0.4

0.2

0.2
0.1
0

<-3

-3 to -2

-2 to -1
-1 to 0
Infectious Pressure (log10)

0 to 1

>1

0

0

(a)

References
1 Black, R. E. et al. Global, regional, and nationalcauses of child mortality in 2008: a systematic analysis.
Lancet375,
1969-1987,
doi:10.1016/S01406736(10)60549-1 (2010).
2 Murray, C. J. et al. Disability-adjusted life years(DALYs) for 291 diseases and injuries in 21 regions,
1990–2010: a systematic analysis for the Global Burdenof Disease Study 2010. The Lancet 380, 2197-2223(2013).
3 Murray, C. J., Lopez, A. D., Chin, B., Feehan, D. &Hill, K. H. Estimation of potential global pandemic

0.2

0.4
0.6
1-Specificity
(b)

0.8

1

influenza mortality on the basis of vital registry datafrom the 1918-20 pandemic: a quantitative analysis.
Lancet 368, 2211-2218
4 Smith, R. D., Keogh-Brown, M. R., Barnett, T. &Tait, J. The economy-wide impact of pandemic.
5 Longini, I. M., Jr. et al. Containing pandemic
influenza at the source. Science 309, 1083-1087,
6 Ferguson, N. M. et al. Strategies for containing an
emerging influenza pandemic in Southeast Asia. Nature
437, 209-214, doi:10.1038/nature04017 (2005).

Session 5 :: Societies (II)Sensitivity

Risk

0.4

Conference

0.8
0.5

Figure 2: a) Relationshipbetween infectious
pressure, calculated fromthe mobile phone data
and the risk of areas
experiencing a newoutbreak within sevendays. b) ROC curve forpredicting outbreak atincreasing thresholds ofinfectious pressure (red:𝑃 !!!"# , green: 𝑃 !"#$! ,black: 𝑃 !"#$! ).

Session 4 :: Societies (I)0.6

Session 3 :: Economies

1

0.7

Session 2 :: Cities (I)Discussion: Our results show that the risk of
epidemic onset of cholera in a given area and theinitial intensity of local outbreaks could havebeen anticipated during the early days of theSession 1 :: Mobility

Building upon this strong correlation between
infectious pressure and outbreak risk, we createda binary test to predict an outbreak occurring
within the upcoming seven days, based solely on
thresholds of infectious pressure. We predicted
an outbreak to occur at a given pressure
threshold) and plotted the corresponding
sensitivity and specificity of each threshold (Fig.
2b). We compared the model based on the mobile
operator mobility data (𝑃 !!!"# ) with the gravity
models (𝑃 !"#!! and 𝑃 !"#$! ), for which 𝑃 !"#$!
was optimised specifically to yield the maximum
possible area under the curve (AUC) in thisanalysis. Comparing these ROC curves, the𝑃 !!!"# model clearly performs better than the
𝑃 !"#$! model and slightly better than the 𝑃 !"#$!
model, yielding a higher specificity for a given
level of sensitivity. Note that both gravity models
rely on parameter optimisations that could nothave been performed until the epidemic spread
was completed.

Haitian epidemic using case reports and themobility patterns of mobile phones. We showthat the specificity and sensitivity of predictionsof epidemic spread was improved or comparable
to currently available optimized mobility models.
Most importantly, the predictions based on themobile operator data did not rely on retrospective
optimization of parameter models and could thus
be available from the start of an outbreak. This isimportant as gravity model parameters are highly
context specific.26,27 Although this study focuses
only on the influence of human mobility, future
mobile phone based models focusing on cholera
may benefit from including data on spatial
distributions of access to water and sanitation,28
bacterial
transmission
via
waterways,25
29
agricultural practices, differential population
immunity levels30 and interactions between
infectiousness and mobility and between
infectiousness and phone use.
These results indicate that outbreak preparedness
and response to epidemic agents, such as cholera,
can be enhanced. The findings may haveparticular importance for improving early
containment efforts of emerging infectious
diseases, such as high mortality strains ofpandemic influenza, and the response to vaccinepreventable diseases, such as measles, in lowincome settings.

Session 6 :: Cities (II)(𝑃 !!!"# ), all areas (six study areas) experienced
outbreaks within seven days (see S5 for 𝑃 !"#$! ,𝑃 !"#$! and sensitivity analyses).

75

Session 5 :: Societies (II)Conference

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

19 Le Menach, A. et al. Travel risk, malaria
importation and malaria transmission in Zanzibar. Sci
Rep 1, 93 (2011).
20 Barzilay, E. J. et al. Cholera surveillance during theHaiti epidemic--the first 2 years. The New England
journal of medicine 368, 599-609 (2013).
21 Piarroux, R. et al. Understanding the cholera
epidemic, Haiti. Emerg Infect Dis 17, 1161-1168,
doi:10.3201/eid1707.110059 (2011).
22 Frerichs, R. R., Keim, P. S., Barrais, R. & Piarroux,
R. Nepalese origin of cholera epidemic in Haiti. Clin
Microbiol Infect 18, E158-163 (2012).
23 Frerichs, R. R., Boncy, J., Barrais, R., Keim, P. S. &Piarroux, R. Source attribution of 2010 cholera
epidemic in Haiti. Proceedings of the National Academyof Sciences of the United States of America 109, E3208;
author reply E3209 (2012).
24 Lu, X., Bengtsson, L. & Holme, P. Predictability of
population displacement after the 2010 Haiti
earthquake. PNAS 109, 11576-11581 (2012).
25 Rinaldo, A. et al. Reassessment of the 2010-2011
Haiti cholera outbreak and rainfall-driven multiseason
projections. PNAS 109, 6602-6607, (2012).
26 Garske, T. et al. Travel patterns in China. PLoS One
6, e16364 (2011).
27 Alberto Rubio, V. F.-M., Enrique Frias-Martinez
and Nuria Oliver. Human Mobility in Advanced andDeveloping Economies: A Comparative Analysis.
Telefonica Research, Madrid, Spain.
28 Waldman, R. J., Mintz, E. D. & Papowitz, H. E. The
cure for cholera--improving access to safe water andsanitation. N Engl J Med 368, 592-594,
doi:10.1056/NEJMp1214179 (2013).
29 Gaudart, J. et al. Spatio-temporal dynamics ofcholera during the first year of the epidemic in Haiti.
PLoS Negl Trop Dis 7, e2145 (2013).
30 Chao, D. L., Halloran, M. E. & Longini, I. M.
Vaccination strategies for epidemic cholera in Haiti
with implications for the developing world. PNAS 108,7081-7085 (2011).

Session 6 :: Cities (II)7 Anderson, R. M. & May, R. M. Infectious diseasesof humans : dynamics and control. (Oxford University
Press, 1991).
8 Tuite, A. R. et al. Cholera epidemic in Haiti, 2010:
using a transmission model to explain spatial spread of
disease and identify optimal control interventions. Ann
Intern Med 154, 593-601
9 Grassly, N. C. & Fraser, C. Mathematical models of
infectious disease transmission. Nature Reviews
Microbiology 6, 477-487 (2008).
10 Riley, S. Large-scale spatial-transmission models of
infectious disease. Science 316, 1298-1301,
11 Viboud, C. et al. Synchrony, waves, and spatial
hierarchies in the spread of influenza. Science 312, 447451
12 Eggo, R. M., Cauchemez, S. & Ferguson, N. M.
Spatial dynamics of the 1918 influenza pandemic in
England, Wales and the United States. J R Soc Interface
8, 233-243, doi:10.1098/rsif.2010.0216 (2011).
13 Grenfell, B. T., Bjornstad, O. N. & Kappey, J.
Travelling waves and spatial hierarchies in measles
epidemics. Nature 414, 716-723
14 Bharti, N. et al. Explaining seasonal fluctuations ofmeasles in Niger using nighttime lights imagery.
Science 334, 1424-1427 (2011).
15 Crepey, P. & Barthelemy, M. Detecting robust
patterns in the spread of epidemics: a case study of
influenza in the United States and France. Am J
Epidemiol 166, 1244-1251 (2007).
16 Balcan, D. et al. Multiscale mobility networks andthe spatial spreading of infectious diseases. PNAS 106,21484-21489, (2009).
17 Truscott, J. & Ferguson, N. M. Evaluating theadequacy of gravity models as a description of humanmobility for epidemic modelling. PLoS Comput Biol 8,e1002699 (2012).
18 Wesolowski, A. et al. Quantifying the impact ofhuman mobility on malaria. Science 338, 267-270,
doi:10.1126/science.1223467 (2012).

Session 4 :: Societies (I)

2015

76

2015

Micro Dynamics of Social Interactions:
Quantifying Human Life
Vedran Sekara1 , Arkadiusz Stopczynski1,2 & Sune Lehmann1,3
1 Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyn-

1

Session 5 :: Societies (II)Conference

Session 3 :: Economies

Session 2 :: Cities (I)Session 1 :: Mobility

Session 6 :: Cities (II)Community structure is a key property in many real world social networks and is thought to playa crucial role in both network function1 and resilience2 . Due to frequent changes in the communication and activity patterns of individuals3 the underlying network topology is under constant evolution4 , but our understanding of the basic features that facilitate this evolution is quite
limited5, 6 . Based on a dataset of minute-to-minute social interactions between approximately 1 000
densely connected individuals, spanning more than a year7 , we study the micro dynamics of an
entire social system. Slicing temporal data into micro-snapshots we show that isolated motifs
directly appear (Fig. 1a-c)—thus conventional community detection heuristics become redundant.
By tracking the evolution of motifs across time, we demonstrate a new method that identifies social
gatherings. On the shortest time-scale, we find that gatherings are fluid, with members coming andgoing, but organized via a stable core of individuals that are persistent throughout the entirety ofmeetings. Some cores exhibit a pattern of recurring meetings across weeks and months, each withvarying degrees of regularity. In terms of network structure, we find that cores are highly overlapping, and large cores contain rich inner structure with hierarchically nested sub-cores. From anindividual perspective, these cores can be used to summarize social contexts, where each person
can belong to multiple cores (Fig. 1d). In Fig. 1e we observe the temporal patterns of core participation from late January to late April for the ego-network shown in Fig. 1d—displaying complex
participation patterns where regularity is mixed with randomness. Applying cores as a vocabularyof states we summarize human social life by drawing on previous work that has studied the predictability of individuals’ trajectories8 . We show that in analogy to human mobility, social contexts
can be predicted with high precision based on past behavior. Combining social behavior of eachindividual with his/her time resolved geographic mobility we can in fact describe a persons fulllife. Results show that there is no overall correlation between social and location predictability,
meaning that you can be highly predictable with respect to location but not with respect to your social context or vice versa. However, in real life, we have varying degrees of predictability, at night
we have low entropy because we sleep in the same location, while during days and evenings ourentropy is higher—because we occupy more states. Illustrating that predictability has a inseparable
temporal component. During periods when the ability to predict location based on past behavior issystematically lowered, social contexts provide a new way to predict location for individuals.

Session 4 :: Societies (I)gby, Denmark,2 Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA,
3 The Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.

77

2015

2. Albert, R., Jeong, H. & Barabási, A.-L. Error and attack tolerance of complex networks. Nature 406, 378–382(2000).

Session 5 :: Societies (II)Session 3 :: Economies

1. Fortunato, S. Community detection in graphs. Physics Reports 486, 75–174 (2010).

Session 2 :: Cities (I)References

Session 1 :: Mobility

Session 6 :: Cities (II)varying temporal windows (1 day, 1 hour, and 5 minutes). Below, adjacency matrices, colored in agreement with networks, and sorted according to the largest component. d, Ego-centric view of the community
structure for a single individual (red node) that belongs 9 temporally distinct groups. In the aggregatedsense communities are both overlapping and hierarchically organized. Only frequently occurring communities (appearing on average at least one per month) are shown. e, Temporal dynamics of each individual
community shown in panel d, colored accordingly, displaying some degree of order but also randomness.

Session 4 :: Societies (I)Figure 1: Dynamics of social behaviour. a-c Network slices obtained by slicing the social dynamics using

3. Cattuto, C. et al. Dynamics of person-to-person interactions from distributed rfid sensor networks. PloS one 5,
e11596 (2010).

5. Kossinets, G. & Watts, D. J. Empirical analysis of an evolving social network. Science 311, 88–90 (2006).
6. Palla, G., Barabási, A.-L. & Vicsek, T. Quantifying social group evolution. Nature 446, 664–667 (2007).
7. Stopczynski, A. et al. Measuring large-scale social networks with high resolution. PLoS ONE 9, e95978 (2014).
8. Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327, 1018–1021(2010).

2

Conference

4. Holme, P. & Saramäki, J. Temporal networks. Physics reports 519, 97–125 (2012).

78

2015

Session 6 :: Cities (II)

79

2015

NEIGHBORHOOD AND NETWORK SEGREGATION:ETHNIC HOMOPHILY IN A ‘SILENTLY SEPARATE’ SOCIETY∗
Joshua Blumenstock, University of Washington
Ott Toomet, Tartu University
Rein Ahas
Erki Saluveer
December 31, 2014

Abstract
We examine the relationship between geography and ethnic homophily in Estonia, a linguistically divided country. Analyzing the physical locations and cellular communicationsof tens of thousands of individuals, we document a strong relationship between the ethnic
concentration of an individual’s geographic neighborhood and the ethnic composition of
the people with whom he interacts. The empirical evidence is consistent with a theoretical model in which individuals prefer to form ties with others living close by and of thesame ethnicity. Exploiting variation in the data caused by migrants and quasi-exogenous
settlement patterns, we find suggestive evidence that the ethnic composition of geographic
neighborhoods has a causal influence on the ethnic structure of social networks.
Keywords: residential segregation, homophily, social segregation, minorities

1

Introduction

Ethnic segregation is a prominent feature of most contemporary and historical societies. Such
fractionalization has been tied to patterns of economic development and growth, investment
in human capital, the efficiency of inefficient labor markets, violence and corruption, as well
∗ The authors are grateful for thoughtful comments from Mark Ellis, Ira Gang, Matthew Jackson, Štěpán
Jurajda, Ramona Angelescu-Naqvi, Xu Tan, and participants of Tartu 2014 Xmas seminar. We also thank Siiri
Silm for help with geodata analysis. We gratefully acknowledge ﬁnancial support from GDN RRC and ESRC andEstonian Science Foundation grants IUT2-17, IUT20-49, and 9247. All errors are our own.

Session 6 :: Cities (II)JEL codes: J15, J61

1

80

2015

as broader patterns of inequality, prejudice, and discrimination (cf. Easterly and Levine 1997,
Collier 1998, Cutler and Glaeser 2007, Bayard, Hellerstein, Neumark, and Troske 1999, Migueland Gugerty 2005).
Ethnic homophily, or an individual’s preference for co-ethnics, plays an important role inshaping patterns of ethnic segregation. When individuals prefer to associate with others of thesame type, this influences where people choose to live, where people choose to work, and withwhom they choose to interact (Massey 1985). At the same time, the reverse process may also
obtain: when people are physically surrounded by others of their own type, they may choose
to associate with them to a higher degree The relationship between neighborhood segregation,
as defined by the structure of geographic communities, and network segregation, as determinedby actual patterns of interaction, is thus theoretically ambiguous. Empirical adjudication issimilarly complicated by the difficulty of separately observing both types of segregation on asingle population.
Here, we study the relationship between neighborhood segregation and network segregation
using a novel dataset that allows us to disentangle the ethnic composition of an individual’s
physical surroundings from the ethnic composition of people with whom he interacts. The
context for this study is Estonia, a country with a long and complex history of ethnic strife
and resettlement. Prior to World War II, roughly 94 percent of the Estonian population was
ethnically Estonian; however, the Estonia’s incorporation into the USSR created a large influx of
Russian immigrants into Estonia, and by 1989 roughly 39 percent of the Estonian population was
ethnically Russian. Due to Stalin’s brutal regime, and the anti-Russian backlash that followed
Estonian independence, strong feelings of animosity exist between the two groups. Modern-day
Estonian society has been described by Heidmets (1998) as one of “silent separation” where theEmpirically, we analyze a large dataset of anonymized mobile phone communications datathat allows us to observe, for tens of thousands of individuals, all locations inhabited be eachindividual over a five-year period, as well as all phone-based interactions with other individuals inthe dataset. Critical to our current analysis, we also observe the language spoken by each mobile
phone subscriber. In Estonia, linguistic preference remains a core component of ethnic identity,
with most ethnic Russians choosing to speak the Russian language, and most ethnic Estonians
choosing to speak the Estonian language (Tammaru 2001). Using these data, we are thus able

Session 6 :: Cities (II)two ethnic groups occupy the same physical spaces but rarely interact.

to separately measure, for each individual, the extent to which she is physically surrounded bycoethnics, and the ethnic composition of her social network.
2

81

2015

We observe a strong and robust relationship between neighborhood and network homophily.
In other words, while the average individual is more likely to interact with coethnics than others,
those individuals who are physically surrounded by coethnics are even more likely to interact
with coethnics. This relationship exceeds what would be expected under a naive model ofgeographically constrained random attachment, where individuals randomly interact with thosein their geographic network irrespective of ethnicity. This effect persists even when controllingfor a range of demographic characteristics.
We further find suggestive evidence that the ethnic composition of an individual’s network
exerts a causal influence on the social connections formed by the same individual. In particular,when we study the homophilic tendencies of migrants,1 we find that they are less sensitive to their
physical surroundings; while they still interact more with coethnics the more they are surroundedby coethnics, this relationship is less pronounced than it is in the population of people who nevermigrate. It appears that this differential effect for migrants is primarily driven by the fact that
migrants remain connected to their place of origin, and do not immediately form connections intheir new neighborhood.
The above results are consistent with a simple model of social preferences where geographically close coethnic ties are most likely to form. If interethnic ties are possible, the number ofinterethnic friends will depend on the “local” ethnic composition. The relationship is weaker for
migrants who have just recently arrived in the region and have not yet had time to adapt their
networks to the new local environment.
In analyzing the behavior of migrants, we cannot rule out the possibility that individuals
with different homophilic tendencies select into migration. However, we exploit several plausibly
exogenous sources of variation to help assuage such concerns of endogeneity. First, we use
homophily for migrants of different cohorts. Here, we find that recent migrants are indeed
more likely to be in contact with their origin community than migrants who have lived in thedestination community for several years; these recent migrants are also less likely to interact withindividuals in their new neighborhood.
As a second robustness test, we analyze the relationship on Soviet-era housing estates. As the
housing market was virtually non-existent during the communist period, the residential location
was largely exogenous and the segregation preferences play little role accordingly. The find theSession 6 :: Cities (II)the high-resolution mobility data to infer the exact date of migration, and look separately at

1 Migrants are identiﬁed in our data based on the set of geolocated mobile phone towers used to route their calls.
Migrants constitute roughly 10% of the sample population, and our results are robust to a variety of plausible
ways of classifying migrants.

3

82

2015

effect being almost as strong on these estates as in the full sample, suggesting that neighborhoods
that play a substantially stronger role in determining the social networks.
Our paper thus documents the strong relationship between neighborhood and network segregation, and provides suggestive evidence on causality. Taken further, these results imply thatphysical integration may lead to social integration.
The remainder of the paper is organized as follows: Section 2 describes the backgroundand institutions of Estonia, the country we analyze. Section 3 provides a simple theoreticalframework for interpreting our results. The data and the empirical approach are described ingreater detail in Section 4, and Section 5 discusses the results. In Section 6 we discuss andinterpret the results in greater detail before concluding.

2

Estonia: Background and Context

The focus of our empirical analysis is Estonia, a country uniquely suited for the empirical analysis
of ethnic segregation. Before World War II, roughly 94 percent of the population was ethnic
Estonians, with the remainder largely comprised of ethnic Russians (Katus 1990). During WWII,
Estonia was incorporated into the Soviet Union, and fell under the brutal Stalinist regime fornearly a decade. As part of the post-war reconstruction and industrialization effort, it experienced
large-scale immigration from other parts of the Soviet Union, mainly from Russia. This process
resulted in an increase of the population of the country to 1.57 million by 1989, 39% of whomwere ethnic minorities (Tammaru and Kulu 2003).
Most of the migrants from the Soviet Union were Russian-speaking, and were regarded by theethnic Estonian population as the “Russians” and associated with the harsh regime. Relations
was sharply divied along linguistic lines. Estonians and Russians attended different schools,
worked in different establishments and followed different media outlets (Kalmus and Pavelson
2002, Vihalemm 2010). Russian language functioned as the lingua franca of the Soviet Unionand most Estonians possessed a working knowledge of the language. However, the commandof the Estonian language was poor among the minorities (Kulu and Tammaru 2004). Despite
the official Soviet policy, Estonians never considered themselves a part of the Soviet nation, anddistinguished clearly between in-group (i.e. “Estonians”) and out-group (i.e. “Russian”) members.

Session 6 :: Cities (II)between the two dominant ethnic groups deteriorated rapidly, and by the 1970s the society

This linguistically divided society where two ethnic communities live rather parallel lives has thus
been characterized as a“silently separated society” (Heidmets 1998).
4

83

2015

After the fall of the USSR, Estonia began a nation-building that has been widely regarded
as discriminatory towards ethnic Russians (Pettai 2002). First, the newly elected parliament
granted citizenship only to nationals of the pre-WWII republic and to their offspring (Everly1997). As a result, a sizeable part of the current minority population does not have Estonian
citizenship. Second, the Estonian language was made the sole official language of the country,
causing a gradual deterioration of Russian language skills among Estonians, particularly among
the younger generation. However, a large percentage of Russians are still not able to communicate
in Estonian (Kulu and Tammaru 2004). For this reason there is no universally shared languagein the country today. The shift in the roles of the languages was also accompanied by relative
deterioration of the economic position of Russian speakers (Leping and Toomet 2008). In this way
the historic animosity between the two language groups, the high levels of segregation in many
important spheres, and the lack of a lingua franca contribute to the low number of interethnic
contacts and general lack of social integration today. While the attitudes toward the otherethnic groups have been improving through the previous decade, interethnic engagement is still
relatively infrequent (Lauristin, Uus, and Seppel 2011). The tensions do occasionally rise to thesurface as, for instance, during the large-scale riots in Tallinn in the spring of 2007.2
The fall of the Soviet regime also had a signifant impact on patterns of migration and settlement. The Russian immigration of the Soviet era come to a rapid halt, while both urbanization and sub-urbanization gathered momentum. The main mechanism that shaped the ethnic
composition of the urban neighborhoods, including in the capital Tallinn, is related to historic
immigration and residential construction. Between 1950 and 1989, the population of the countryrose by more than 40%, from 1,097,000 to 1,565,000, mainly through immigration from elsewherein the USSR (Tammaru 2001). In the absence of a housing market, immigrants were usuallywhich nowadays provide accommodation for a large part of the total population. These are often
dominated by ethnic Russians, whereas Estonians are over-represented in pre-WWII (and also
in the small post-1991) housing stock, and also in detached houses. In this way, the current ethnic composition across urban neighborhoods largely reflects the immigration patterns during theconstruction periods, rather than factors such as socio-economic status. Recent suburbanization,and the fact that a substantial part of the immigrant population left after the collapse of Soviet
2 The riots were caused by the relocation of a Soviet World War II monument, popularly referred to as the“Bronze Soldier”, from central Tallinn to a military cemetery. From the perspective of ethnic Estonians, themonument was considered to glorify oppressive Soviet rule, while for the Russian-speaking population it was a
symbol of victory over the Nazis in the “Great Patriotic War.” See Schultze (2011).

Session 6 :: Cities (II)granted flats in newly built, standardized, high-rise housing estates (Kährik and Tammaru 2010)

5

84

2015

Union, has not radically changed this picture (Hess, Tammaru, and Leetmaa 2012).

3

Theoretical Framework

We develop a simple model that includes two different groups of people, two regions, and migration between these regions. The model allows us to describe the expected number of ties within
and across groups, and illustrates two important results: first, homophily, or the preference forindividuals of the same group, is positively correlated with the neighborhood ethnic composition;and second, this relationship is stronger for people who remain in a single location and weaker for
people who migrate. We formalize these results as propositions below and explain the intuition.
We consider a world containing two regions, A and B, and two (ethnic) groups, 0 and 1. The
A A
population in region A is nA and in region B it is nB . There are nA
0 = π n group-0 members
A A
A
and nA
1 = (1 − π )n group-1 members in region A where π is the fraction of group-0 members

in region A. The expressions for region B are analogous.

Assume that ties between two individuals, located at geographic distance dg and “ethnic
distance” de is created by a Poisson process with intensityµ = φ(dg ) · χ(de ).

(1)

The first term φ(·) describes how the intensity depends on the geographic distance and χ(·)
describes the dependence on “ethnic distance”, where de = 0 for members of the same group andde = 1 for members of the different group. As we only have two regions and two groups, we
can label the corresponding function values as φ0 and φ1 for local ties and distant ties, and χ0χ0 > χ1 . Ties are destroyed with Poisson process with intensity δ, independent of their “length”.
Assuming that the number of actual ties is much smaller than the number of potential ties, wehave the following expression for the expected number of individual ties, ν. For instance, for agroup 0 member in region A we have:
dν
1 A
1
0 B
1 B
= φ0 (χ0 nA
0 + χ n1 ) + φ (χ n0 + χ n1 ) − δν.
dt(2)

Session 6 :: Cities (II)and χ1 for in-group and out-group ties. We assume “short” ties arise more easily: φ0 > φ1 and

The first term describes creation of new local ties, the second term that of distant ties, and thelast term the destruction of ties.
6

85

2015

If people who remain in one region have lived there long enough, their expected number ofties correspond to these in the steady-state, ν ∗ and homophily (for group 0) h∗ = ν0∗ /(ν0∗ + ν1∗ ).3
4

Here ν0∗ and ν1∗ are ties to group 0 and group 1, i.e. in-group and out-group ties for group 0.

Proposition 1. Homophily in social ties is positively related to the ethnic composition of local∗∂hgeographic neighborhoods: ∂π
A > 0.

Proof. See Appendix A.
Intuitively, as our model explicitly allows for a greater likelihood of local tie formation, thelocal population composition influences substantially the actual homophily.
As migrants do not posses the the steady-state equilibrium networks, we solve the dynamic
equation (2) and have the following result:
Proposition 2. Conditional on the average level of neighborhood homophily, the homophily of
migrants is less sensitive to the neighborhood composition than that of non-migrants.
Proof. See Appendix A.
This effect exists because local network ties develop over time. In-migrants, arriving fromneighborhoods of different ethnic composition, have only partially adapted to the ethnic composition of their new neighborhoods.

4

Data and Empirical Approach

To analyze the relationship between neighborhood and network segregations, we exploit a large
set of data on mobile phone use in Estonia. This dataset permits us to simultaneously observe
individuals, as well as the extent to which those individuals interacts with others of the same ordifferent ethnicity.
3 Homophily is a measure of exposure dimension of segregation (Massey and Denton 1988). Here we focus on
homophily based on the percentage of contacts in an individual’s network, but our empirical results are robust toother common deﬁnitions of homophily.
4 Empirically we observeh∗ = [ν0∗ /(ν0∗ + ν1∗ )] instead of ν0∗ /( ν0∗ + ν1∗ ). However, under mild assumptions these two expressions are equal. See Appendix A.

E

E

E

E

E

Session 6 :: Cities (II)the locations of thousands of individuals over a period of several years, the ethnicity of those

7

86

2015

4.1

Data

We employ cellphone usage data from the largest mobile service provider in Estonia, EMT, which
has roughly 60% market share. We obtained two related datasets from this operator.
Passive Positioning Data: The first dataset contains the locations of each individual overthe period from 2007–2012. As is typical for such positioning data, we do not observe the actual
location but rather the Cell Global Identity (CGI), i.e. the network antenna which processed
the outgoing call.5 This gives us a spatial resolution of a few hundred meters in dense urban
environments, and up to five kilometers in rural areas. The data include the time of each call
activity and the corresponding location (CGI). Every network user (as identified by a SIM cardwith a unique phone number) is assigned a random identification tag, making it possible to track
the same user over time.6 Based on timing, location and regularity of the calls, we attach a placeof residence to each cellphone (Ahas, Silm, Järv, Saluveer, and Tiru 2010). We focus on yearlymodal place of residence in order to avoid places that are too unstable, or seasonal migration.
Call Graph: The second dataset contains a complete 10-day call graph, which allows us toobserve in a fixed window who is communicating with whom. This call data records (CDR) aresimilar to the passive positioning data, but for each call or SMS event we also observe the ID of
the second party.
For each subscriber in our dataset, we additionally observe whether the subscriber prefers theEstonian or Russian language. Since the correlation between ethnicity and language is almost
complete (Kulu and Tammaru 2004) we use language as a proxy for ethnic background. All ofthese data use shared anonymized identifiers which allow us to link long-term location information
to the network communication data, and in this way to relate the segregation in communication
network to segregation in space.
subscribers. Of these, 42,604 are Estonian and 6,178 are Russian; 46,835 have a known residence
location, and 18,716 live in the metropolitan area.
5 In a cellular network, a “cell” roughly corresponds to an area where all the network traﬃc goes through a single
antenna. Usually, several antennas are located in one transmission tower and are oriented in diﬀerent directions.
We know the location of the transmission towers and the direction of the antennas. Based on this information,
we can construct “typical” cell boundaries; however, the actual boundaries may ﬂuctuate due to network load,
obstacles and noise.
6 The individuals and real phone numbers cannot be identiﬁed using the tag in our data. The collection, storage,
and processing of the data complies with all European Union requirements regarding the protection of personal
data (European Commission 2002). Approval was also obtained from the Estonian Data Protection Inspectorate
and the University of Washington Human Subjects Division.

Session 6 :: Cities (II)We perform our empirical analysis on a random sample of 48,781 individual mobile phone

8

87

2015

4.2

Empirical Approach

In our empirical analysis, we examine the relationship between the ethnic composition of anindividual’s immediate physical neigborhood, and the ethnic composition of his call graph. The
passive positioning data allows us to determine where individuals live, which in turn makes it
possible to observe the physical neighborhood. We will begin by defining physical neighborhood
along political boundaries, but our results are robust to several alternative definitions.7 Similarly,
we will initially measure the ethnic composition of the call graph as the fraction of contacts ofthe same ethnicity, but our results obtain when we define network homophily as the fraction ofcommunication events (i.e., the weighted call graph).
Below, we will separately analyze the relationship between neighborhood and social network
homophily, as well as the extent to which people are connected to current and historical regionsof residence. In both cases we show the nonparametric relationships graphically, then test thestatistical relationship in a regression specification.
The basic regression equation for estimating the relationship between social network homophily hi and neighborhood own-group percentage Pi for individual i is
hi = α0 + βEi + γPi + ηPi · Mi + ǫi

(3)

where Ei indicates the ethnicity of individual i, and Mi indicates whether i is a migrant.8 Notethat as Pi is defined at region level, we cluster the standard errors within regions.
Later, we will also introduce several variants on model (). First, to analyze the geographic
structure of networks in greater detail, we will split the connections into local and distant ones,
depending on whether these cross a county border. For migrants, we will also separately analyze
location, or the location from which they migrated. Additionally, to assess the robustness of ourresults, we will restrict our sample to specific types of individuals, for instance those who were
7 We perform our analysis using diﬀerent types of spatial units. Calculation of neighborhood homophily is basedon city tracts inside of the capital city. These are spatial units, based on access roads and housing type. Elsewherein the metropolitan area we rely on municipalities, the area contains 18 suburban municipalities. Finally, outsideof the metropolitan area we use counties, there are 14 of these outside of the metropolitan area. Counties are ofroughly equal size (though of very unequal population) and broadly correspond to commute-to-work area aroundan urban center. We choose such an approach to account for diﬀerent population density and also to take into
account the uneven distribution of network antennas. Finally, we analyze migration and geographic tie distanceat county level.
8 As people show a heterogeneous pattern of spatial mobility, we use several deﬁnitions of migrants. The
strictest deﬁnition requires a valid residence region for all 6 years, and only a single move during this period. This
gives us 2,614 migrants. The most ﬂexible deﬁnition allows up to three missing yearly locations and up to two
moves (we analyze the last of these). This gives us 6,592 migrants. All our central results are robust with respect
to the deﬁnition of migrants.

Session 6 :: Cities (II)the extent to which their current social network is comprised of people residing in their current

9

88

2015

likely to be assigned to their current place of residence through a Soviet-era natural experiment.
Finally, we will also disaggregate migration by cohort, to determine whether the effects of migration are different for recent migrants. Formally, if b is the proportion of given type connections
and Y SM is years since migration, we estimate:bi = α0 + β · Y SMi + ǫi .

5

Results

5.1

Neighborhood and Network Homophily(4)

We start by presenting the relationship between the regional homophily and the average network
homophily for the residents of these neighborhoods. Figure 1 shows the nonparametric relationship between geographic neighbordhood composition and observed homophily in the call graph.
Each point indicates the average values for a municipality, with each municipality appearing
once for Estonians (hollow circles) and once for Russians (filled circles). The horizontal axis
indicates the proportion of a given ethnicity in the municipality and the vertical axis indicatesthe average homophily of that ethnicity in that region. We see a clear positive relationship forboth Estonian speakers and Russian speakers. We also see that while the slope for both groups
is similar, Russian speakers are substantially less homophilous.
Next we estimate the same relationship at the individual level, using variants of model (),described above. Table 1 presents four different specifications, where in addition to neighborhood
composition we add different combinations of migrant and minority status. All models confirm
the visual impression that network composition is strongly related to that of neighborhoods. Inof the neighborhood Pi . The estimates indicate that a 10 percentage point increase in co-ethnics
in a geographic neighborhood corresponds to a 3.5 percentage point increase in co-ethnics in thecall network (column 1). This figure is highly significant and robust to the inclusion of several
control variables (columns 2-4).
In Column 2 of Table 1, we note that migrants are in general more homophilous than non
migrants (by 12 percentage points), but that critically the relationship between the neighborhoodand the network is weaker. For migrants, a 10 percentage point incresae in co-ethnic share is

Session 6 :: Cities (II)the first column, we simply regress individual network homophily hi on the ethnic composition

only associated with a 10 × (0.36 − 0.12) = 2.4 percentage point increase in co-ethnics in the call

network.

10

89

0.2

Homophily h
0.4
0.6

0.8

1.0

2015

0.0

Hd E
Hd R
0.0

0.2

0.4
0.6
Relative group size

0.8

1.0

Figure 1: Average homophily as a function of group size across regions. Empty circles denote
Estonian-speakers, filled circles Russian speakers. The dashed and solid lines are correspondingsmoothed averages.
Model 3 adds controls for ethnicity. We see that while Russians are generally less homophilous
than Estonians, they are more sensitive to the neighborhood environment: here, 10 percentage
points more co-ethnics in the neighborhood corresponds to 10 × (0.20 + 0.22) = 4.4 percentage

points higher share in call network. The lower average homophily levels are presumably related
correlation is related to neighborhoods with very low group size. The housing type may also playa role as that Russian speakers are overrepresented in Soviet-era high-rise estates.
The above results are evidence of the strong relationship between neighborhood and network
segregation. Whether this relationship is causal, however, is not clear. The model presented
in Section 3 assumed that neighborhood composition would influence tie formation, and theseresults are consistent with the two Propositions from that model. Below, we provide additional
empirical evidence that appears to indicate there is indeed a casual effect of physical segregation

Session 6 :: Cities (II)to smaller country-wide numbers of Russian speakers while the figure suggests that the stronger

on network homophily.

11

90

2015

Outcome: individual homophily h (percentage of co-ethnic contacts in call network)
Model 1% coethnics

∗∗∗

0.35
(0.00)

Migrant
Migrant×% coethnics

Model 2∗∗∗0.36
(0.00)
0.12∗∗∗(0.02)−0.12∗∗∗(0.02)

RussianModel 3∗∗∗0.20
(0.00)−0.32∗∗∗(0.01)
0.22∗∗∗(0.01)

Russian×% coethnics
Migrant×Russian
Migrant×Russian×% coethnics
Intercept
R2
Num. obs.
∗∗∗

0.67∗∗∗(0.00)
0.14
40819

0.66∗∗∗(0.00)
0.14
40819

0.80∗∗∗(0.00)
0.22
40819

Model 40.21∗∗∗(0.01)
0.10∗∗∗(0.02)−0.11∗∗∗(0.02)
0.34∗∗∗(0.00)
0.22∗∗∗(0.01)−0.04(0.03)
0.02(0.06)
0.68∗∗∗(0.00)
0.22
40819

p < 0.001, ∗∗ p < 0.01, ∗ p < 0.05

Table 1: Full sample results: relationship between individual homophily and neighborhood ethnic
composition.

5.2

Soviet-Era Neighborhoods as a Natural Experiment

If the decision to migrate were exogenous, the fact that the network structure of migrants isless strongly correlated with the geographic struture of their current surroundings could beinterpreted as evidence of a causal relationship in which physical surroundings determine network
structure. However, since migration and patterns of settlement are not generally exogenous, it
about their physical surroundings are the ones who choose to migrate. To disentangle these two
possibilities, we examine the same relationship on a sample of individuals for whom the current
location choice is more plausibly exogenous.
As discussed in Section 2, the constant shortage of housing and lack of choice in the housing
market created quasi-exogenous variation in patterns of settlement. During the period of highest
Russian immigration, 1970s and 80s, individuals had little choice over where to live in cities.
We treat this as a natural experiment and limit our analysis here to neighborhoods that are

Session 6 :: Cities (II)may also be the case that people select into migration, and in partcular that people who care lessdominated by Soviet-era housing estates. We focus on the capital city Tallinn only. Note that

12

91

2015

our setup does not constitute a perfect experiment as we do not know who in our sample did
actually live in these neighborhoods during the Soviet period. However, we exclude all the
migrants into these areas we are able to identify in the sample.
Outcome: individual homophily h (percentage of co-ethnic contacts in call network)% coethnics

Model 1Model 20.05
(0.04)∗∗∗

RussianRussian×% coethnics
Intercept
R2
Num. obs.
***

0.85∗∗∗(0.02)
0.00
2362

Model 30.20
0.17∗∗∗(0.04)(0.04)−0.25∗∗∗ −0.33∗∗∗(0.01)(0.05)
0.13(0.09)
0.83∗∗∗
0.85∗∗∗(0.02)(0.02)
0.16
2362

0.16
2362

p < 0.001, ** p < 0.01, * p < 0.05

Table 2: Soviet-era neighborhoods: relationship between individual homophily and neighborhood
ethnic composition.

The results (Table 2) are rather similar to the estimates in the full sample. In particular, theestimate for co-ethnics, 0.17, in Model 3 is statistically indistinguishable to that of Model 3 in
the full sample, 0.20 (in Table 1). However, as the sample is smaller, the standard errors arecorrespondingly larger. The fact that in this sample, who were plausibly exogenously settled into
their currently location, we see the same strong relationship between neighborhood and network
segregation, lends support to the causal nature of the relationship, i.e., that social networks areat least in part determined through the residential neighborhoods, and that a portion of network

5.3

Local and Distant Ties

The homophily-related predictions in Section 3 are based on the fact that local ties are createdmore easily than distant ones. Unfortunately, as our observations of network structure are basedon a single 10-day period, we cannot directly observe the process of tie formation. However, we
do observe the geographic structure of ties, which allows us to make two observations that areconsistent with our model of tie formation. First, for for migrants, we observe that distant ties

Session 6 :: Cities (II)segregation is caused by geographic segregation.

are primarily linked to their former place of residence: most of the distant ties were formed when

13

92

2015

the distant place still was “local”. Second, when we separate the migrants into cohorts by timesince migration (we can observe migration 1-6 years ago), we observe an increasing number of
local ties and a decreasing number of ties to the previous home region.
Specifically, we split the connections to local and distant ones based on counties (Estonia
consist of 15 counties, roughly similar in terms of area but very unevenly populated). We compare
the number of ties in the current county of residence, in the previous county of residence (formigrants only), and in all other counties. We select residents of the metropolitan area as of2012. Table 3 presents their average number of contacts (based on the 10-day callgraph) in
selected counties: the Metro area, Ida-Viru (code 44), Pärnu (67), and Tartu (78).9 The rows
correspond to the previous (2011) residence: Metro are those who were living in the metropolitan
area in 2011 as well, i.e. “stayers”; 44 are those who lived in Ida-Viru and hence they are recent
migrants to the metro area, and analogously for the other rows. Columns represent the countyof contact.10 In case of Estonian-Estonian ties (left panel), we see that those who have been in
the metropolitan area both for 2011 and 2012 (row labeled “Metro”) clearly posses the largest
number of the connections in that area. The average number of connections in other counties(columns labeled “44”, “67” and “78”) is very small. For movers (rows labeled “44”, “67” and “78”)the picture is different. All of them possess a substantial number of connections in the metroarea (after all, they are living there as of 2012) while the number of contacts to their previous
county of residence is also relatively large (left panel, main diagonal). However, the number ofcontacts in the other counties is negligible, exactly as in case of those who never migrate. The
Russian-Russian ties (right panel) paint a similar picture. There are too few observations forany inference on interethnic ties (not shown).
To summarize, Table 3 strongly suggests that ties form locally. People who never migrate areof connections to their previous county.

5.4

Evidence on Tie Creation and Destruction

Analyzing differences in tie structure by year of migration allows us to indirectly test the theoryof tie formation posited above. Figure 2 shows the relationship between migration year andthe geographic structure of the current social network. The figure indicates that the numberof contacts in the current county (circles) is lower for the recent migrants while the number

Session 6 :: Cities (II)almost exclusively connected to their current county while migrants have a substantial number

9 The results for other counties are qualitatively similar.

10 The numbers are low because we do not observe valid county of residence for the contacts outside of thesample.

14

93

2015

connections to
44
67
78

Residence 2011

Metro

Metro
44
67
78

Estonian-Estonian
0.91
0.01 0.03 0.05
0.35
0.28 0.02 0.02
0.45
0.00 0.32 0.06
0.72
0.00 0.01 0.39

Metro
0.45
0.20
0.00
0.14

connections to
44
67
78

Russian-Russian
0.03 0.00 0.00
0.39 0.00 0.02
0.00 0.17 0.00
0.00 0.00 0.29

Notes: Residents of the metropolitan (capital) area 2012 depending on their 2011 residence (in rows), and their numberof contacts (degree) in columns. The county codes are 44 = Ida Viru; 67 = Pärnu; 78 = Tartu.

0.5

1.0

Table 3: Number of contacts in the current residence county, previous residence county, andother counties. Estonian-Estonian and Russian-Russian ties.

avg # of contacts
0.1 0.2 0.3 0.4

Current
Prev

0.0

0.0

avg # of contacts
0.2 0.4 0.6 0.8

Current
Prev

Stayers

2008

2009
2010
migration year

2011

2012

(a) Estonian-Estonian

Stayers

2008

2009
2010
migration year

2011

2012

(b) Russian-Russian

Figure 2: Average number of contacts to the current and previous county of residence. The lines
represents the corresponding linear fit (with stayers excluded).
of contacts in the previous county (triangles) is decreasing. The figure for Estonian-Estonianties (left panel) is less noisy and suggests that the ties to the former county are rather resilientand may be related to family or others in-kin (Phithakkitnukoon, Calabrese, Smoreda, andTable 4 gives similar results using individual-level regressions. We estimate the percentage of
connections to the current and previous county of residence as a function of years since migration.
The table indicates that the share of contacts in the former place of residence falls by about 4
percentage points per year and are replaced by a corresponding growth in connections in thecurrent place of residence.
In summary, our contact distance analysis strongly suggests that ties arise locally over timeand also fade away over time when individuals move elsewhere. These outcomes fit to our

Session 6 :: Cities (II)Ratti 2011).

theoretical framework and suggest that neighborhood population composition is an important
determinant of social networks.
15

94

2015

Outcome: percentage of contacts in the region
Region:Home 2007 Home 2012
Years since migration−0.04∗∗∗(0.01)
0.40∗∗∗(0.03)

Intercept
R2
Num. obs.

0.01
1203

0.04∗∗∗(0.01)
0.44∗∗∗(0.03)
0.01
1203

Notes: *** p < 0.001, ** p < 0.01, * p < 0.05

Table 4: 2007 and 2012 connections percentage, by migrant status and ethnicity

6

Discussion

Our theoretical framework suggests that a number of our findings are compatible with the causality running from neighborhoods to networks. The ethnic composition in residential neighborhoods is related to that in social networks, and the relationship is stronger for stayers and weakerfor migrants. In addition, the relationship is similar in Soviet-era high-rise estates, the neighborhoods that were populated in a period with little residential choice. We also show that cellular
calls are mostly connecting individuals living in the same commuting area, and if stretching alonger distance, these are likely connections to the former place of residence. All these outcomes suggest that ties typically arise between individuals who are living close to each other.
Most importantly, the trade-off between ethnic and geographic distance results in out-group ties,
likelihood of which increases along the number of out-groups living in the same neighborhood.
Our central outcome, the positive correlation between network and neighborhood compositionin particular individuals with certain amount of inter-ethnic contacts may choose to live in acorrespondingly mixed environment. However, in this case one expects migrants to be equallysensitive to the neighborhood composition than the non-migrants, and second, one also expects
the relationship to be substantially weaker in the Soviet-era neighborhoods. Neither of thesepredictions is true. Unfortunately, the current cross-sectional network data does not allow totest this hypothesis directly.
Alternatively, both network and neighborhood composition may be jointly determined by a

Session 6 :: Cities (II)can be explained in other ways as well. First, networks may influence neighborhood choice,third variable, such as segregation preferences. If this is true, we would not expect the geographic

16

95

2015

distance to be a strong determinant of networks, and in addition, we would also expect therelationship to be much weaker in Soviet-era neighborhoods.

7

Conclusion

We use mobile telecommunication data to analyze the relationship between physical segregationand social network network. The data originates from Estonia, a linguistically divided country where the relationship between the corresponding ethnic groups has been characterized by
distrust and animosity. These data allow us to compute network homophily and analyze its
relationship with the place of residence, separately for people who migrate and those who donot. We document a strong positive relationship between ethnic composition in the residential
neighborhood and network homophily. We also show that communication networks are largely
local, except for migrants who possess a substantial number of contacts in their previous placeof residence. As more time passes since the date of migration, migrants have more contacts intheir new location and fewer contacts in their previous location.
A simple theoretical framework suggests that all these outcomes can be explained by neighborhood ethnic composition being an important determinant of social network homophily. While
we cannot conclusive rule out alternative explanations, we test several additional specifications
that are consistent with this causal relationship.
More speculatively, our results suggest that physical integration might help generate social
integration. By contrast, ethnic or racial ghettos may harm social integration, even if suchseparation might increase the efficiency of local labor markets (Edin, Fredriksson, and Åslund
2003, Damm 2009). Of course, our context, where two well-established ethnic groups shareethnic groups are separated by much more than just language. Nonetheless, we believe theseresults provide empirical support for policies designed to promote physical integration.

Session 6 :: Cities (II)similar socioeconomic and cultural backgrounds, may not be generalizable to contexts where the

17

96

2015

A

ProofsExpected Homophily The observed communication process does not include the complete
network data. A number of links are missing, either because they connect to out-of-sample peers,
or because no calls were made during our 10 days of network sampling. Here we show that under
independent link sampling, E h∗ = E[ν0∗ /(ν0∗ + ν1∗ )] = E ν0∗ / E(ν0∗ + ν1∗ ).

Look at individual i. Assume that in the complete network she has degree Ni = Si + Di
(communication links to different alters), comprising of Si same type links and Di different typelinks. Assume that the alters are observed independently with probability p. Hence, the observed
number of contacts Ñi is a random variable and can be expressed as a sum of Ni realizations of
independent Bernoulli random variables Aij ∼ Bernoulli(p) where Aij = 1 denotes that the alterPj of individual i is observed. The observed number of same type alters is S̃i = j∈S ASij andPthat of different type alters D̃i = j∈D AD
ij (S and D denote the set of same and different typefriends of i). The true homophily is Hi = Si /Ni while we observePS
S̃ii∈S Ai
P
=P
.
H̃i =
S
D
Ñi
i∈S Ai +i∈D Ai(5)

As AS and AD have equal i.i.d distribution, we can apply lemma 3 by Heijmans (1999) andconclude thathi

E H̃i |Ñi > 0 =E S̃i = Si .
E S̃i + E D̃i Si + Di

(6)

Accordingly, we can easily base our inference on the individual homophily on the observed ties
in the data.

Proposition 1 For stayers, individuals who spend long time in one region, we observe theexpected equilibrium number of ties. For group 0:
1 0 0 A
1
0 B
1 B
φ (χ n0 + χ1 nA1 ) + φ (χ n0 + χ n1 ) =δ
1 0 0 A
φ [χ π + χ1 (1 − π A )]nA + φ1 (χ0 π B + χ1 (1 − π B )]nB .
=
δ

ν∗ =

(7)
(8)

Session 6 :: Cities (II)Propositions

18

97

2015

This relationship can be used to express the individual network homophily in equilibrium:h∗ =ν∗
ν0A∗ + ν0B∗
ν0∗
= ∗ 0 ∗ = A∗=∗ν
ν0 + ν1
ν0 + ν1A∗ + ν0B∗ + ν1B∗=

1
0 0 A A
1 0 B Bδ φ χ π n +φ χ π n
=
1
0 0 A
1
A
A
1
0 B
1
B
B
δ {φ [χ π + χ (1 − π )]n + φ (χ π + χ (1 − π )]n }0 0 A A
1 0 B B=φ χ π n +φ χ π n

φ0 [χ0 π A + χ1 (1 − π A )]nA + φ1 [χ0 π B + χ1 (1 − π B )]nB,

(9)where ν0 and ν1 are ties to group 0 and 1 respectively, ν A and ν B are ties to regions A and B,
and ∗ denotes the corresponding equilibrium values.
Look at the region A with the local ethnic composition π A . As the number of ties in regionB, ν B∗ , does not depend on π A , we have
∂
∂h∗
=
∂π A
∂π A



ν0A∗ + ν0B∗
ν0A∗ + ν1A∗ + ν0B∗ + ν1B∗
=

∂ν0A∗
∂π A
ν∗



=



ν0∗ ∂ν A
1 φ0 0 A φ00
1 A
− ∗ 2 A =
χ n − ∗ h(χ − χ )n .
(ν ) ∂π
δ ν∗
ν

(10)

The first term in brackets describes the growth of ν0∗ while π A grows, the second one the corresponding growth of ν ∗ . As χ0 > χ1 > 0 and 0 ≤ h ≤ 1, the derivative is positive. It increases in
the local interaction rate φ0 and in the local population size.

Proposition 2 Look at movers from the region A to B. Assume they initially posses theties, corresponding to the equilibrium in A. At time t = 0 they relocate to B. As ties are neither
created nor destroyed instantly, we have at the moment of move ν0B (0) = 1δ φ1 χ0 π B nB which isν0B (t) =
1 0 0 B B
11
φ χ π n + e−δt φ1 χ0 π B nB − φ0 χ0 π B nB =
δ
δ
δ

1
1 0
φ + e−δt (φ1 − φ0 ) χ0 π B nB ≡ φ10 (t)χ0 π B nB .
=
δ
δ

(11)

Analogous expressions for the other types of ties will be
1
1
φ01 (t)χ0 π A nA
ν1A (t) = φ01 (t)χ0 (1 − π A )nA
δ
δ
1
B
0
B
B
ν1 (t) = φ10 (t)χ (1 − π )n ,δ
ν0A (t) =(12)

Session 6 :: Cities (II)not the equilibrium value. Solving the differential equation (2) we find

19

98

2015

where φ01 (t) = φ1 + e−δt (φ0 − φ1 ). Note that φ0 > φ01 (t) > φ1 and φ0 > φ10 (t) > φ1∀t > 0.

The relationship between migrant’s homophily and the new local ethnic composition, ∂h(t)
,
∂π B

is
∂h(t)
∂
=
∂π B
∂π B



ν0B (t) + ν0A (t)B
ν0 (t) + ν1B (t) + ν0A (t) + ν1A (t)=∂ν0B (t)
∂π B

ν(t)

−



=



h(t) ∂ν(t)1 φ10 (t) 0 B φ10 (t)
0
1 B
.
=
χ
n
−
h(t)(χ
−χ)nν(t) ∂π Bδ ν(t)
ν(t)(13)

This is positive, by similar argumentation as used for the steady-state equilibrium.
Next, we show that φ0 /ν ∗ > φ10 (t)/ν(t). It is determined by the sign of
δ · φ0 ν(t) − φ10 (t)ν ∗ =


1 A
0 B
1 B
= φ0 φ01 (t)χ0 nA
0 + φ01 (t)χ n1 + φ10 (t)χ n0 + φ10 (t)χ n1 −


1 1 A
0 0 B
0 1 B
− φ10 (t) φ1 χ0 nA
0 + φ χ n1 + φ χ n0 + φ χ n1 =
1 A
0
0 B
1 B
= φ01 (t)φ0 (χ0 nA
0 + χ n1 ) + φ10 (t)φ (χ n0 + χ n1 )−

1 A
0
0 B
1 B
− φ10 (t)φ1 (χ0 nA
0 + χ n1 ) − φ10 (t)φ (χ n0 + χ n1 ) == e−δt ((φ0 )2 − (φ1 )2 ) > 0(14)where we have used the definition of φ10 (t) and φ01 (t). Accordingly, if homophily levels arecomparable, h(t) = h∗ , then the ∂π∂B h∗ > ∂π∂B h(t) as φ0 > φ10 (t). Intuitively, the stayers’
networks are primarily determined by the local population composition while the other regions
weight more in the movers’ networks.

Ahas, R., A. Aasa, A. Roose, Ü. Mark, and S. Silm (2008): “Evaluating passive mobile
positioning data for tourism surveys: An Estonian case study,” Tourism Management, 29(3),469–486.
Ahas, R., S. Silm, O. Järv, E. Saluveer, and M. Tiru (2010): “Using Mobile Positioning
Data to Model Locations Meaningful to Users of Mobile Phones,” Journal of Urban Technology,
17(1), 3–27.

Session 6 :: Cities (II)References

20

99

2015

Bayard, K., J. Hellerstein, D. Neumark, and K. Troske (1999): “Why are Racial andEthnic Wage Gaps Larger for Men than for Women? Exploring the Role of Segregation,”
Working Paper 6997, National Bureau of Economic Research.
Collier, P. (1998): “The political economy of ethnicity,” in Annual World Bank Conference on
Development Economics, pp. 387–405.
Cutler, D. M., and E. L. Glaeser (2007): “Social interactions and smoking,” Working Paper
13477, NBER, 1050 Massachusetts Avenue, Cambridge, MA 02138, USA.
Damm, A. P. (2009): “Ethnic Enclaves and Immigrant Labor Market Outcomes: QuasiExperimental Evidence,” Journal of Labor Economics, 27(2), pp. 281–314.
Easterly, W., and R. Levine (1997): “Africa’s growth tragedy: policies and ethnic divisions,”
Quarterly Journal of Economics, 112(4), 1203–1250.
Edin, P.-A., P. Fredriksson, and O. Åslund (2003): “Ethnic Enclaves and the Economic
Success of Immigrants—Evidence from a Natural Experiment,” The Quarterly Journal of Economics, 118(1), 329–357.
European Commission (2002): “Directive 2002/58/EC of the European Parliament and of theCouncil of 12 July 2002 concerning the processing of personal data and the protection of privacyin the electronic communications sector (Directive on privacy and electronic communications),”
Official Journal of European Communities, L201, 37–47.
Everly, R. (1997): “Ethnic assimilation or ethnic diversity? Integration and Estonia’s citizenship law,” in The Integration of non-Estonians into Estonian Society: History, problems andHeidmets, M. (1998): “The Russian Minority: Dilemmas for Estonia,” Trames, 3(2), 264–72.
Heijmans, R. (1999): “When does the expectation of a ratio equal the ratio of expectations?,”
Statistical Papers, 40(1), 107–115.
Hess, D. B., T. Tammaru, and K. Leetmaa (2012): “Ethnic differences in housing in postSoviet Estonia,” Cities, 29(5), 327–333.

Session 6 :: Cities (II)trends, ed. by A. Kirch, pp. 106–121. Estonian Academy Publishers, Tallinn.

Kalmus, V., and M. Pavelson (2002): “Schools in Estonia as Institutional Actors and asa Field of Socialisation,” in The Challenge of the Russian Minority: Emerging Multicultural
21

100

2015

Democracy in Estonia, ed. by M. Lauristin, and M. Heidmets, pp. 227 – 236. Tartu University
Press, Tartu.
Katus, K. (1990): “Demographic trends in Estonia throughout the centuries,” Yearbook of
Population Research in Finland, 28, 50–66.
Kulu, H., and T. Tammaru (2004): “Diverging views on integration in Estonia, determinantsof Estonian language skills among ethnic minorities,” Journal of Baltic Studies, 35, 378–401.
Kährik, A., and T. Tammaru (2010): “Population composition in new suburban settlementsof the Tallinn metropolita area,” Urban Studies, 45, 1055–1078.
Lauristin, M., M. Uus, and K. Seppel (2011): “Kodakondsus, kodanikuühiskond ja rahvussuhted (Citizenship, civil society and ethnic relations),” in Integratsioonimonitor 2011, pp.
9–50. Kultuuriministeerium, Tallinn, EE.
Leping, K.-O., and O. Toomet (2008): “Emerging ethnic wage gap: Estonia during politicaland economic transition,” Journal of Comparative Economics, 36(4), 599–619.
Massey, D. S., and N. A. Denton (1988): “The Dimensions of Residential Segregation,”
Social Forces, 67(2), pp. 281–315.
Miguel, E., and M. K. Gugerty (2005): “Ethnic diversity, social sanctions, and public goodsin Kenya,” Journal of public Economics, 89(11), 2325–2368.
Pettai, I. (2002): “Mutual tolerance of Estonians and non-Estonians (in Estonian),” in Estonia
and Estonians in comparative perspective (in Estonian), pp. 213–233. Tartu University Press.

Out of Mind–How Our Mobile Social Network Changes during Migration,” in Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee thirdinternational conference on social computing (socialcom), pp. 515 –520.
Schultze, J. (2011): “Contact and crisis in interethnic relations,” in The Russian Second
Generation in Tallinn and Kohtla-Järve: The TIES Study in Estonia, ed. by R. Vetik, and
J. Helemäe, pp. 165–182. Amsterdam University Press, Amsterdam, NL.

Session 6 :: Cities (II)Phithakkitnukoon, S., F. Calabrese, Z. Smoreda, and C. Ratti (2011): “Out of Sight

Tammaru, T. (2001): “Suburban growth and suburbanisation under central planning: The caseof Soviet Estonia,” Urban Studies, 38(8), 1341–1357.
22

101

2015

Tammaru, T., and H. Kulu (2003): “The ethnic minorities of Estonia: changing size, location,and composition,” Eurasian Geography and Economics, 44(2), 105–120.
Vihalemm, T. (2010): “To learn or not to learn? Dilemmas of linguistic integration of Russians

Session 6 :: Cities (II)in Estonia,” Russian Minorities in the Baltic States, 2, 74–98.

23

102

2015

Untangling the effects of residential segregation on individual
mobility
Suma Desu1 , Lauren Alexander2 , Marta González1,2

1 Center for Computational Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA,2 Department of Civil Engineering, , Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA,

January 30, 2015

1 ∗ Introductionsocio-economic demographics, but also move according
to these as well. With the rapid migration into urban areas,
understanding how social decisions influence mobility hasconsequences in many domains such as epidemiology andurban planning. But perhaps more interestingly, this work
also servers as a platform to link individual mobility datawith social theories that posit racial isolation and concentrated disadvantage heighten exposure to criminality andreduce access to resources and opportunities [7, 8].

Physicists and sociologists have longstanding interests in
identifying the mechanisms by which individual dynamics lead to collective outcomes. In the social domain, tipping point or threshold models provide one useful framework for connecting the actions of individuals to population processes [1, 2]. In the physics domain mobility
models can account for individual preferences that resultin population level commuting fluxes at multiple scales[3, 4]. Behavior models, such as models of social interaction, have demonstrated great potential for understanding
the dynamics of both residential mobility and segregation
by race and ethnicity. In his work [1] , Thomas Schelling
laid the conceptual groundwork for understanding the relationship between individual preferences and the evolution of neighborhood compositions. He demonstrated thatminute racial preferences in individual residential choices
could result in aggregate patterns of residential segregation over time.
Residential choices are often the result of careful planning and consideration, thus the importance of individual
preference in deciphering patterns of segregation is wellestablished. On the other hand, daily mobility choices,such as choosing a grocery store, are often ephemeral. Although these daily decisions might seem random, individual mobility data has revealed to be highly non-random,
governed by simple laws, and greatly predictable [3, 5, 6].
Accordingly we can use individual trajectories as a proxy
to understand the environments to which individuals are
exposed.
In this work, we use call detail records (CDRs) generated over multiple months, which are treated and validated, to create average daily origin destination (OD) networks. We use these networks to study the difference
in daily mobility choices arising from residentially segregated areas.This represents the first data driven study
to investigate whether racial preferences can be discerned
from individual mobility choices. If so, results would indicate that not only do we organize residentially by our

Our CDR data set contains more than 8 billion
anonymized mobile phone records, obtained from several phone providers, covering 2 million users overtwo months in the spring of 2010. For each record weare given the following information: an anonymized user
identification number, the latitude and longitude of therecord, and finally the timestamp at the moment of phone
activity, these activities encompass calls, text messing,
and web browsing. Typical cell record datasets are given
with respect to cell towers, in our case, the provider estimates the location of each record using a triangulation
scheme, resulting in location accuracy of 200-300m. Using the stay-point extraction method detailed in [9], we
identify ’stay locations’ which are the locations whereusers engage in some activity. Then using the same
methodology developed in [10] we assign labels to thestay regions, thereby generating trips by purpose. The
stay process yields a timestamp and duration for each staylocation, using these and frequency of visits we are able
to assign an activity type of ‘home’, ‘work’, or ‘other’ toeach stay location for each user. To capture only thoseusers whose home location is adequately represented in
the CDR data, we filter out users with less than 8 records.
We further filter census tracts that do not have enough residents identified from the CDR data. Next we upscale user
trips to represent population level travel patterns by calculating expansion factors for each tract as the ratio of resi-

Session 6 :: Cities (II)2 Methods∗ corresponding author: sdesu@mit.edu

1

103

2015
dents as identified in the CDR data and the census population. We validate these ODs against the Massachusetts
Travel Survey at various scales of aggregation; in 1 weshow that the accuracy of our data improves at sparser
scales.
Residential segregation metrics are also particularly
sensitive to which scale is used. The reason is segregation
measures using census tract data tend to report higher values for cities with high population densities, where census tracts are smaller and cover only one neighborhood,as compared to smaller, sparser cities where census tracts
can cover many neighborhoods. This bias is reduced atsmaller levels of spatial aggregation. Consequently, it isimportant to be careful when comparing the level of residential segregation over larger spatial areas. Moreover, it
is advisable to carry out a multi-scale analysis. In [11] amethodology to compute residential segregation on various scales is presented but it has not been widely used in
the subsequent literature. Here we creating a segregation
profile to measure how scale affects segregation in ourstudy region. To ensure adequate correlation and smallenough clusters to capture variation in racial composition
we choose to use 350 k-means clusters as the local environments of our users.

Figure 2: Home Other Networks, we will use this color
scheme throughout

Figure 1: the correlation increases with higher levels of
aggregation but the H-index decreases
Using these 350 clusters we construct OD-networks tomeasure aggregate patterns arising from residentially segregated areas. Since our goal is quantify the effects of residential segregation we assign all users the dominant raceof their home tract. Then we construct the race networks
2 as follows: each node represents one particular cluster,and edges are present between nodes if a trip has occurred
between the two clusters. The weights on the edges tirj
are the sum of daily flows from i → j, hence the network
is directed. We focus on average daily home other networks because we believe these trips represent individual
choices and preserve individuals’ residences.

Figure 3: Every dot represents 100 people of a particularrace. For each census tract, dots are generated and labeledwith races according to their respective racial population
distribution in the census. For the visualization dots are
uniformly distributed within the borders of corresponding
census tracts and colored by the race they represent. We
are left with a visual approximate of the racial composition of our study area. This map shows our study region
is predominately white and fairly segregation.

Session 6 :: Cities (II)Using the static racial composition in 3 as a reference,
we see in 2 some long range connections in minority networks are between other areas that have higher concentrations of the same respective minority.

2

104

2015
3

Metrics

local environments. What we see in the right hand sideof seems intuitive, each race has a higher probability of
We reformulate traditional measures of segregation, visiting a cluster that has a larger proportion of their own
namely entropy and exposure to include dynamic prop- race.
erties of individual mobility. These measures are often
cited as proxies for understanding the local environmentsof minorities, but a more accurate understanding would 4 Model
incorporate data on where these individuals go throughout the day. Sociologists use entropy to measure diversity From 5 we see a stark pattern emerge: even during dailyof racial distributions in locations and this value is the lo- travel races are likely to visit locations that are dominatedcation’s contribution to the information-theory based H- by their own race. To untangle whether these are effects
index. Instead of measuring the static distributions of lo- of individual preference or simply spatial segregation wecations, we measure the diversity of visits to each location test two models. Both are rank based variants of the raformulated as follows:
diation model in [4]. In both models we predict tirj using
Let L be the set of all locations. Firstly, for each lo- the outgoing strength sr as production. In the race blindication i ∈ L: we compute sri , the incoming strength in the model we use total population as attraction and in the race
OD-network defined by race r. Next, we normalize by to- aware model we only use the population of race r to prer
tal incoming strength in r’s network , stotal, this gives us dict t r . These models allow us to account for the distanceij
the overall importance of location i in each race network. decay in trip lengths in addition to discerning if individBecause we have many more white user’s than minorities ual’s mobility is race blind or race aware.
normalization ensures we weight each network equally.
Finally we compute the entropy, Ei , of these normalized
values.

References[1] Thomas C Schelling. Dynamic models of segregation. Journal of mathematical sociology, 1(2):143–
186, 1971.

(1)

j∈L

sri
log
4 r
r
stotalr∈R stotal

Ei = ∑sri(2)[2] Mark Granovetter and Roland Soong. Thresholdmodels of diffusion and collective behavior. Journal of Mathematical sociology, 9(3):165–179, 1983.

In 4 Downtown Boston and South Cambridge areshown to be important locations in all race networks but
there is not much diversity elsewhere.
Exposure is traditionally a measure used to capture theaverage percentage of race m present in the local environments of race n, denoted n Pm . Here we measure the exposure of individual’s destinations as a function of distance.
To measure the distance between location i and location
j we use the haversine function, h(i, j). Most trips in our
OD networks occur between 0 - 5 km with very few above
25km, so we measure exposure in the following intervals
d = [0, 1, 5, 10, 25, ∞], shown in the left hand side of .

[3] Marta C Gonzalez, Cesar A Hidalgo, and AlbertLaszlo Barabasi. Understanding individual human mobility patterns. Nature, 453(7196):779–782,
2008.
[4] Filippo Simini, Marta C González, Amos Maritan, and Albert-László Barabási. A universal
model for mobility and migration patterns. Nature,484(7392):96–100, 2012.

(4)[5] Christian M Schneider, Vitaly Belik, Thomas
Couronné, Zbigniew Smoreda, and Marta C
González. Unravelling daily human mobility motifs.
Journal of The Royal Society Interface,10(84):20130246, 2013.

Threshold models used in segregation literature often
cite that there is tolerance parameter governing residential
movement. Inspired by this, we create probability of visitation functions with respect to the racial composition of[6] Chaoming Song, Zehui Qu, Nicholas Blumm, andAlbert-László Barabási. Limits of predictability inhuman mobility. Science, 327(5968):1018–1021,
2010.

sri =

∑

j∈L

n Pm =

tirj

if dk ≤ h(i, j) < dk+1
sn

∑ sn ii∈L total

∗ πim

(3)

Session 6 :: Cities (II)sri = ∑ t rji

3

105

2015

Figure 5: Left: each subplot represents the exposure for one race, in the top left we measure white exposure, the yellow
line represents the exposure of white to white and the black line represents the exposure of white to black. etc. For
all minorities as distance increases exposure to white increases and exposure to their own race decreases but remains
higher than exposure to any other minority group Right: utility functions for each race

Session 6 :: Cities (II)Figure 4: Location Visitation Diversity. Darker colors represent lower diversity in visitation ex. dark green means thatlocation is mostly important in the black network, light green means mostly important in the black network but somediversity in visitation. Grey represents high diversity of visitations.

4

106

2015
[7] Robert J Sampson, Jeffrey D Morenoff, and Thomas
Gannon-Rowley. Assessing” neighborhood effects”:
Social processes and new directions in research. Annual review of sociology, pages 443–478, 2002.

opportunities. In Proceedings of the 2nd ACM
SIGKDD International Workshop on Urban Computing, page 2. ACM, 2013.
[10] Lauren Alexander, Shan Jiang, Mikel Murga, andMarta C. González. Validation of origin-destination
trips by purpose and time of day inferred from mobile phone data. submitted to Transportation Research Part C, 2014.

[8] Robert D Dietz. The estimation of neighborhood
effects in the social sciences: An interdisciplinary
approach. Social Science Research, 31(4):539–575,
2002.

Session 6 :: Cities (II)[9] Shan Jiang, Gaston A Fiore, Yingxiang Yang,
Joseph Ferreira Jr, Emilio Frazzoli, and Marta C [11] Sean F Reardon and David OSullivan. Measures
González. A review of urban computing for moof spatial segregation. Sociological methodology,
bile phone traces: current methods, challenges and34(1):121–162, 2004.

5

107

2015

Spatial and Social Homophily at a Massive Religious Gathering

The Kumbh is a religious Hindu festival that has been celebrated for centuries. The 2013
Kumbh Mela, a grander form of the annual Kumbh, had an estimated 40 to 100 million visitors(Sugden, 2013). These estimates, if correct, would make it the largest gathering of people inhuman history. We used call detail records (CDRs) from one of India’s largest cellular service
providers to study the spatial and social dynamics of Kumbh attendants. Here, we focused ourinvestigation on homophily, the common finding that individuals who frequently interact with one
another tend to be similar in their attributes. What separates our investigation from previous
studies in this area is that we examine spatial and social components of homophily
simultaneously and, further, do so in the context of a massive gathering of millions of people.
Our data consisted of 390 million communication events among a sample ofapproximately 4 million people whose communication activities were observed for a three-month
period from January 1 to March 31, 2013, a time period that encompassed the 2013 Kumbh
Mela. The festival took place within a designated venue that was served by approximately 200
cell towers, which divided the Kumbh area into 200 small spatial regions (technically known as a
Voronoi tessellation where each Voronoi cell is centered on a cell tower). We examinedcommunication events that were transmitted by any one of these towers with known spatial
locations. Past investigations of homophily have focused on individual covariates, such as age,sex, race, and ethnicity. India is a culturally diverse nation with many languages and customs
that vary widely across its many states, and here we investigated individual-level homophily interms of callers’ regions, available as their phone area codes. We defined spatial homophily asa tendency for individuals from a given region of India to be spatially co-located, while at the
Kumbh, with other individuals from the same region over and above that expected by chancealone. Similarly, we defined network homophily as a statistical excess of social ties, while at the
Kumbh, here measured as phone calls and text messages, between people who shared an areacode.
We compared how spatially and socially homophilous the different regions of India werewith respect to one another. Overall, we found many of the regions to exhibit relatively stronglevels of both types of homophily. Further, we found that states with smaller representation at
the Kumbh tended to show significantly stronger spatial and social homophily than the stateswith greater Kumbh representation (Figure 1). In other words, smaller groups at the Kumbh
appeared to display greater cliquishness, both spatially and socially. This phenomenon ofminority groups being more homophilous than larger ones has been observed in studies of raceand social structure in school settings (Gonzalez, 2007; Vermeij, 2009; Currarini, 2009), butthese earlier findings tend to be based on relatively small interaction contexts that are longlasting. In contrast, the Kumbh is a massive event in which most attendees stay a relatively
short period of time, seldom more than a couple of weeks. In this very different type of
behavioral context we find that homophilous tendencies persist.
Our findings could be helpful in understanding the dynamics of organic or unplanned
displacements of populations. It would be helpful to be able to identify the social factors thatlead people to seek out others like them in these types of settings, because these behavioral
tendencies can lead to unpredictable and devastating outcomes, such as human stampedes.
More broadly, we anticipate that the present work could be used in the context of disasters, likeearthquakes and floods, when large numbers of people are forced out of their homes.
1
2

Session 6 :: Cities (II)Ian Barnett1
Tarun Khanna2
Jukka-Pekka Onnela1

Department of Biostatistics, Harvard School of Public Health, Boston, MA
Graduate School of Business Administration, Harvard University, Boston, MA

108

2015

Figure 1: We have divided India into 23 different states, ranked first by representation at the Kumbh (leftpanel) and then by degree of spatial homophily (right panel). The heat map colors correspond to therankings. The yellow star is the city of Allahabad, the location of the 2013 Kumbh Mela. The near inversionof colors when comparing the two panels demonstrates a clear negative association between state
representation and spatial homophily.

References

Session 6 :: Cities (II)Currarini, S. a. (2009). An economic model of friendship: Homophily, minorities, andsegregation. Econometrica, 77(4), 1003--1045.
Gonzalez, M. a. (2007). Community structure and ethnic preferences in school friendshipnetworks. Physica A: Statistical mechanics and its applications, 379(1), 307--316.
Sugden, J. (2013). How the Kumbh Mela Crowds Are Counted. Wall Street Journal.
Vermeij, L. a. (2009). Ethnic segregation in context: Social discrimination among native Dutch
pupils and their ethnic minority classmates. Social Networks, 31(4), 230--239.

109

2015

Predicting Gender from Mobile
Phone Metadata
Eaman Jahani1 , Pål Roe Sundsøy2 , Johannes Bjelland2 , Asif
Iqbal2 , Alex Pentland1 and Yves-Alexandre de Montjoye1
1

MIT Media Lab, Cambridge, USA
2
Telenor Group, Oslo, Norway
Email: eaman,pentland,yva@mit.edu
pal-row.sundsoy,johannes.bjelland,asif.iqbal@telenor.com

Over the past decade, international organizations,research institutes and governments in rich countries
have heavily invested in data collection for evaluating
their performance in development areas such as childmortality, spread of diseases and poverty.
Noveland scalable analysis techniques have been specificallyintroduced for the better usage of big data in policy
making. As rich countries collect and analyze more andmore data from a broad range of applications, the poorand developing countries have lagged behind. Many
countries in the developing world have not conducteda census in many years due to infrastructure andresource limitations and market inefficiencies [1]. For
example, Afghanistan is still using census data from1979. The lack of reliable government statistics andcensus data hinders development and evaluation ofpolicies in countries which need it the most. Given thevital role of census and demographic data in measuringsocial and economic development, their shortage needs
to be addressed.
Researchers have noted that the emergence ofthe data-driven “computation social science” andthe large scale adoption of new technologies o↵er
an unprecedented opportunity to better understand
individual behavior and societies at scale [2]. The
rise of communication technologies, and in particular
the high penetration rate of mobile phones acrossthe globe (90% in the developing countries), has thepotential to address the scarcity of development data.
While many economic, demographic and population
data are missing in developing countries, the abundanceof metadata collected by mobile phone operators can fillthis gap, thus enable a more informed policy making
process [3].
Many international organization, such as UN andworld bank, have already started tapping into thegreat potential of big data for development.
A
report recently published by the UN data revolutiongroup calls for greater embedding of ubiquitous

data collection and analysis into its 2015 sustainable
development action plan [4] .
In addition, thebig data and people project alliance (Data-Pop)
has focused on developing methods for collecting
and analyzing indicators of human welfare, such as
poverty, inequality, human mobility, economic statistics
and inflation patterns, mostly in underdeveloped andconflict regions [5]. Recently, a series of research studies
have investigated the applicability, accuracy and coste↵ectiveness of geographical and temporal aggregationof communication logs in novel applications such as
census prediction. Deville showed how mobile phone
metadata can be used to generate detailed maps of
population distribution on a country-wide scale [6].
Using mobility and communication patterns obtained
from mobile phone metadata, Enns constructed ageographic network of regions with low and highmalaria transmission and proposed a more cost-e↵ective
approach to the malaria control e↵ort [7]. Similarly,
researchers have utilized mobile phone mobility andCall Detail Records (CDR) for mapping and containing
the spread of Ebola [8].
There are two main challenges with the applicationof call and mobility data for development purposes.
First, while the CDRs in their raw form provide us
with precise information on who called whom from
where and for how long, they provide very little
insights about the individual phone users and theirhabits. This is especially important in development
policy-making which heavily relies on temporal andgeographic aggregates of demographic and behavioral
data. Second, the lack of subscribers demographic
information is even more pronounced in developing
countries where this data has the most potential but
the vast majority of mobile phones are prepaid. Here,
we address these challenges by extracting insightful
behavioral indicators from the raw CDRs which canbe used for prediction of individual demographic
characteristics. Our behavioral indicators range from

Session 6 :: Cities (II)Keywords: Metadata, Gender Prediction, CDR, Development, Behavioral Indicators

110

2015
mobility patterns of individual users. We developeda software package which can easily compute thesevariables from raw carrier logs. We believe these
indicators can account for a diverse set of behavioral
patterns by only considering the activities of individual
users, and not of their neighbors. The indicators canbe roughly divided into five di↵erent categories:
1.

2.

3.

4.

5.

Basic phone usage: Some examples are number ofcalls, number of texts or number of active days per
week.
Active user behavior: These indicators aim tocapture user’s level of proactiveness.
Some
examples are the percentage of call or text
interactions initiated by the user or the delay timein answering a text. We consider a text from user A
to user B as a response if it is sent within one hourof receiving a text from user B. The response delayis the expected time it takes for a user to answer atext. Text response rate is another example of user
active behavior.
Diversity: Entropy or diversity is a quantitative
measure capturing how many di↵erent values arandom variable can take and how evenly they are
distributed across the range of all possible values.
For example, as a user interacts more evenly with alarger number of contacts, her entropy of contacts
increases. In this work, we considered the entropyof call, text, call and text contacts and the uniqueplaces a user visits.
Regularity: Some examples are weekly deviationsin temporal calling pattern, call and text inter-timeor the times a user comes back home.
Location: These features aim to capture user’s
mobility pattern. Some examples are the absolute
number of di↵erent places a user has sent or
received a call or text from or the distance between
these places. Another example is the mean radiusof gyration which is the radius of the smallest circle
that contains all the places a user has visited in aday.

Our data set consists of a random sample ofmore than 550,000 subscribers in a European country
and 40,000 subscribers in a South Asian country,
whose raw CDRs were collected for a period of 3
months. Longer periods of user data result in moreaccurate representation of behavioral indicators since
the features are aggregated weekly. The dataset isanonymized by encrypting the phone numbers of thecallee and the caller in the raw CDRs. An explorative
study of the features reveals stark di↵erences and yet
remarkable similarities between the two countries withdi↵erent culture and economic status. For example,
figure 1 illustrates the kernel density of mean durationof calls across these two countries along with the same
distribution per gender in each country. While theaverage duration of calls in the European country is 200
minutes longer than the South Asian country, women in

Session 6 :: Cities (II)radius of gyration to average response delay or thediversity of contacts.
The motivation of this study is to investigate theextent to which our behavioral indicators can replace
the expensive and missing census data. Gender is abasic, yet an essential, demographic variable in census
data. In this paper, we focus on how behavioral
indicators derived from standard mobile phone metadata, CDRs, can be used as input features to machinelearning techniques for predicting the gender of themobile phone user. By combining large-scale CDR metadata with the mobile phone subscriber information,
we evaluated the prediction power of these behavioral
indicators across two di↵erent countries, developed anddeveloping.
Several recent studies have investigated the application of mobile phone usage data to infer the demographic structure of a population. Di↵erent variables including age, gender, personality traits and socioeconomic status have been the focus of these studies. The prediction approaches range from networkbased collaborative filtering to standard machine learning algorithms. Herrera-Yagüe introduced a method
which exploits the homophily in the network connections and uses the available demographic informationof a user’s contacts along with her SMS, call usage pattern and ego-network structure as the input features toseveral machine learning algorithms [9]. Brea proposeda graph-based algorithm for predicting the age of mobile
phone users. Given a seed of known user ages, the algorithm extracts topological information from the network to infer the user’s age group. Brea also discoveredthat the topology of the seed set relative to the prediction node plays an important role in performance of thealgorithm [10]. These methods require the whole communication network around each user along with theinformation about her neighbors to be available before
making any prediction. In contrast to these approaches,
our method solely relies on the activities of individual
mobile phone users for prediction.
Sarraute employed several individual-based “behavioral variables” and network-based “social variables” asinput features to a double-layered learning algorithm
called “Population Pyramid Scaling”. The algorithm
admits a hyper parameter that determines the proportion of nodes to predict, thus adjusting the trade-o↵
between precision and recall [11]. The nature of these“behavioral” and the “social” variables extracted frommobile phone usage patterns is similar to ours, withthe di↵erence that we greatly expanded the set of input
features to cover new behavioral categories. In contrast
to Sarraute’s work, we did not limit our testing to themore predictable users and evaluated our model on allof the test data.
We developed a range of novel indicators capturingdi↵erent aspects of individual habits and behaviors.
The complete set of behavioral indicators consisted of
94 features that are extracted from texting, calling and

111

2015
Algorithm
Linear SVM
SVM with Polynomial Kernel
SVM with RBF Kernel

Random Forest

Best Configuration
Penalty = L2, Loss = L2,
Cost = 10
Degree = 2, Cost = 10
Kernel Coefficient = 0.01
Cost = 100,
Kernel Coefficient = 0.1
Split criterion = Entropy,
Min samples for split = 5,
Features searched for best split = 50%,
Number of trees = 400

European Accuracy

South Asian Accuracy

71.9%

73 %

74%

74.9%

74.1%

74.8%

73.2%

78%

TABLE 1: Gender Prediction Accuracy with Best Classifier Configuration

Herra-Yagüe [9]65.3%Sarraute [11]66.3%

Our European Data
74.1%

FIGURE 1: The mean duration of calls per country (left), mean
duration of calls per gender in the south Asian country (top right) andmean duration of calls per gender in the European country (bottom
right)

both countries are expected to have approximately 30%
longer call duration compared to men. This similarity
is in fact confirmed by our final machine learning model
where mean duration of calls appears as one of thestrongest predictors of gender in both countries.
We tested the prediction power of our behavioral
indicators on users who were active on at least 3 daysof the week and were able to correctly predict genderwith 74.1% accuracy in the worst-case scenario. We
also observed that the strongest predictors of gender
are mostly the same across these two countries, despite
cultural, social, geographical and economic di↵erences.
In particular, we observed that diversity of contacts
and places visited is strongly correlated with genderin both of these countries. We examined SVM withpolynomial and radial basis function kernels, linear
SVM and random forest and concluded that SVM with
RBF kernel and random forest yield the highest level ofaccuracy. To achieve equal precision and recall for bothgenders in presence of data imbalance, we modified thepenalty term for each gender inversely proportional to
its relative frequency in the training set. Furthermore,
our input features were standardized to the [0,1] intervalin the case of SVMs. Table 1 summarizes the accuracy
results from various learning models we tested. Ourmodels were tuned through a grid search of stratified 5fold cross validation scenarios. The accuracy numbers
reported in table 1 are from unseen test data held
separate from the cross validation set.
Table 2 compares our worst case accuracy (from theEuropean data set) with equivalent accuracy metrics
from [9, 11]. Herra-Yagüe [9] accuracy is taken fromthe best model which uses both isolated link andego-network features. The accuracy from Sarraute[11] is from the case where a prediction is generated
for all nodes (i.e.
q = 1).
We attribute thesubstantial improvement in our prediction accuracy tothe expanded set of learning features. In fact, a close
examination of the learned models reveals that someof the most predictive features in both countries areones that are missing from these previous studies (e.g.
interactions per contact, entropy of contacts and places,
percent initiated interactions).
These results show that behavioral indicators directly
derived from mobile phone metadata can be combinedwith standard machine learning techniques for accurate
and robust prediction of gender.
Further work
is required to investigate the applicability of these
indicators in prediction of other demographic variables
such as age and socio-economic status. We believethat combining our individual behavioral features withinformation obtained from the ego-networks (such asneighbor similarity) can result in even higher accuracy.

Session 6 :: Cities (II)TABLE 2: Comparison of Previous Gender Prediction Accuracies
with our Worst Performance

112

2015

[1] Jerven, M. (2013) Poor Numbers: How We Are Misled
by African Development Statistics and What to Do
about It. Cornell University Press.
[2] Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabsi,
A.-L., Brewer, D., Christakis, N., Contractor, N.,
Fowler, J., Gutmann, M., Jebara, T., King, G.,
Macy, M., Roy, D., and Van Alstyne, M. (2009)
Computational social science. Science, 323, 721–723.
[3] de Montjoye, Y.-A., Kendall, J., and Kerry, C. (2014)
Enabling humanitarian use of mobile phone data.
Brookings, Issues in Technology Innovation, 26.
[4] Data Revolution Group (2014) A World that Counts.
Mobilising the Data Revolution for Sustainable Development. Prepared by United Nations Independent Expert Advisory Group on Data Revolution.
[5] (2015). Data-pop alliance. www.datapopalliance.org.
Accessed: 01-20-2015.
[6] Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens,
F. R., Gaughan, A. E., Blondel, V. D., and Tatem,
A. J. (2014) Dynamic population mapping using mobile
phone data. Proceedings of the National Academy of
Sciences, 111, 15888–15893.
[7] Enns, E. A. and Amuasi, J. H. (2013) Human mobility
and communication patterns in côte d’ivoire: A
network perspective for malaria control. Mobile Phone
Data for Development: Analysis of Mobile Phone
Datasets for the Development of Ivory Coast, 1.
[8] Wesolowski, A., Buckee, C., Bengtsson, L., Wetter, E.,
Lu, X., and Tatem, A. (2014) Commentary: Containing
the ebola outbreak the potential and challenge ofmobile network data. PLOS Currents Outbreaks, 1.
[9] Herrera-Yagüe, C. and Zufiria, P. J. (2012) Predictionof telephone user attributes based on networkneighborhood information.
Proceedings of the 8thInternational Conference on Machine Learning andData Mining in Pattern Recognition, Berlin, Heidelberg
MLDM’12, pp. 645–659. Springer-Verlag.
[10] Brea, J., Burroni, J., Minnoni, M., and Sarraute, C.
(2014) Harnessing mobile phone social network topology to infer users demographic attributes. Proceedingsof the 8th Workshop on Social Network Mining andAnalysis, New York, NY, USA SNAKDD’14, pp. 1:1–
1:9. ACM.
[11] Sarraute, C., Blanc, P., and Burroni, J. (2014) A
study of age and gender seen through mobile phone
usage patterns in mexico. Advances in Social Networks
Analysis and Mining (ASONAM), 2014 IEEE/ACM
International Conference on, Aug, pp. 836–843.

Session 6 :: Cities (II)REFERENCES

113

2015

Session 7 :: Crowds (II)

114

2015

SPATIOTEMPORAL DETECTION OF UNUSUAL HUMAN POPULATIONBEHAVIOR USING MOBILE PHONE DATAADRIAN DOBRA, NATHALIE E. WILLIAMS, AND NATHAN EAGLE
Abstract. With the aim to contribute to humanitarian response to disasters and violent
events, scientists have proposed the development of analytical tools that could identify emergency events in real-time, using mobile phone data. The assumption is that dramatic anddiscrete changes in behavior, measured with mobile phone data, will indicate extreme events.
In this study, we propose an efficient system for spatiotemporal detection of behavioral anomalies from mobile phone data and compare sites with behavioral anomalies to an extensive
database of emergency and non-emergency events in Rwanda. Our methodology successfully
captures anomalous behavioral patterns associated with a broad range of events, from religious
and official holidays to earthquakes, floods, violence against civilians and protests. Our results
suggest that human behavioral responses to extreme events are complex and multi-dimensional,
including extreme increases and decreases in both calling and movement behaviors. We alsofind significant temporal and spatial variance in responses to extreme events. Our behavioral
anomaly detection system and extensive discussion of results are a significant contribution to
the long-term project of creating an effective real-time event detection system with mobile
phone data and we discuss the implications of our findings for future research to this end. The
complete version of this paper will appear in PLoS ONE and it is available online [1].
KEYWORDS: Big data, call detail record, emergency events, human mobility

Discrete emergency events, such as terrorist attacks and natural disasters, occur frequently
around the globe and regularly cause massive destruction. With the ultimate aim to decrease
the humanitarian toll of post-event disasters, scientists have recently begun to understand thatseveral relatively new sources of organically collected data, such as cell phone records, internet
blogs, and Twitter, could provide real time or very quick identification of emergency events.
While these data are continuously collected by service providers and could ostensibly be madeavailable, the tools for using such data for real-time event identification are still under construction. The broad purpose of this article is to contribute to the long-term goal of developmentof analytical tools for using mobile phone data to identify emergency events in real time. Thiscan ultimately contribute to quicker humanitarian response and decreases in the severity ofdisasters. Specifically, we create a system for identifying anomalies in human behavior as manifested in mobile phone data, and discuss the correspondence between these anomalies and actual
emergency and non-emergency events that might have caused them.
Previous research has demonstrated that such analytical tools might be possible, by showingthat natural and man-made emergency events, such as earthquakes or bombings, can be seenin dramatic increases in calling and mobility behaviors. These studies are able to show suchanomalies by comparing behaviors to events where the character, time, and place of these
events are already known. We build on these studies, but develop a blind system that is closer
in nature to an actual event detection system. Instead of starting with the time and locationof an event, then looking for anomalous calling behavior, we develop a behavioral anomaly
detection system that identifies days with unusual calling or mobility behavior, as well as thelocation and geographic extent of these disruptions. Our detection system is scalable as it isable to efficiently process years of country-wide mobile phone records.
For illustration we use mobile phone records from a single cellular services provider from
Rwanda. We connect the identified anomalous days and locations with extensive records ofviolent and political events and natural disasters. Results of this exercise reveal that some days

Session 7 :: Crowds

1. Introduction

1

115

2015
with anomalous increases in calling and mobility behavior match well with several different
kinds of events. In other cases, days with decreases in calling and/or mobility match with
events. These cases were surprisingly more numerous than events matched with increases incalling and mobility. In still other cases, we do not find good event matches for days withanomalous behavior and we also find cases where emergency events occurred without resulting
in anomalous behavior that our system could detect. Notably, we learn as much from theunmatched events and behavioral anomalies as from the matched cases.
We argue that further quantitative and qualitative research into the exact and possibly multidimensional nature of human response to emergency events is needed. In this regard, our carefulanalysis of both the matched events and the events and instances of anomalous behavior that
do not match reveal some key insights into further developments needed to better understandhuman response to emergency events. In fact, it is this outcome, namely the demonstrationthat human behavioral responses to emergency events are much more complex than previously
assumed, that is the most important contribution of this paper. Future research must address
this complexity and can benefit from using existing social and psychological theories of behavioral response to threat. We conclude this article by setting out a clear pathway of research
aimed at the goal of a creating an effective system of identifying emergency events in real-time(or close to real time) from mobile phone data.

Our anomalous behavior detection system identified many days with unusual calling andmovement behavior across multiple sites. Here we describe some of these disturbances in which
the largest spatial clusters of sites were identified, match some of them with key events that
occurred in Rwanda, and discuss what we learn from each matched and unmatched event.
Violence against civilians – September 16, 2005. Our system identified a spatial clusterof four sites with higher than usual call volume and movement frequency on September 17, 2005.
These sites are located in the vicinity of the city of Goma, along the Rwanda-DRC border andnear the Rwanda-Uganda border. We find no event, emergency or otherwise that occurred on
September 17, 2005. However, a violent event, reported by ACLED, occurred in the same area
on September 16, 2005. The distance between the event’s reported location (latitude -1.67,
longitude 29.22) and the centroid of the closest site in the spatial cluster is 1.7 km. RadioFrance reported that this event involved five armed attacks in which 1 person was killed, 7others were wounded, and was part of the general trend of violence against civilians.
Violence against civilians – January 15, 2006. We identify 16 sites (eight in the Kigaliarea) with lower than usual call volume on January 15, 2006. Eight of these sites also had
lower than usual movement frequency. A violent event was recorded by ACLED in Kigalion the same day. The distance between the event’s reported location (latitude -1.96, longitude
30.04) and the centroid of the closest site with unusual call volume is 3.8 km. Reporters Without
Borders and Amnesty International report that four armed men invaded and ransacked the homeof Bonaventure Bizumuremyi, the editor of the independent fortnightly Rwandan newspaper,
Umuco. Mr. Bizumuremyi was the target of intimidation and harassment, demanding that hestop publishing articles criticizing the ruling Rwandan Patriotic Front (RPF). The armed forcesof the RPF forced many independent journalists and human rights activists to leave Rwanda
during that same general time period by intimidation, harassment or arbitrary arrest.
Protest – November 25, 2006. Our system identified seven sites with unusually low
call volume and movement frequency, and seven additional sites with unusually low movement
frequency on November 26, 2006. One of these sites is far from the other 13 and belongs to a
separate spatial cluster. This suggests that there were two events on this day, one that createdthe anomaly in a single site and another that created behavioral anomalies in the remaining 13
sites. The 13 cluster sites are in Kigali and slightly to the east of the city. The single separate
site is in the southern part of Rwanda on the Burundi border. We do not have record of any
events that occurred on November 26, 2006 in these areas. However, a large protest was recordedon November 25, 2006 in Kigali. The distance between the event’s reported location (latitude

Session 7 :: Crowds

2. Results2116

-1.96, longitude 30.04) and the centroid of the closest site with unusual call volume (movement
frequency) is 3.38 (1.83) km. The New Times of Rwanda reports that 15,000 demonstrators
flooded the streets of Rwandas capital Kigali in protest of France’s role in the Rwandan genocide,and their call for the arrest and trial of the Rwandan President Paul Kagame.
Protests – November 19, 2008. Our system identified 45 sites in and extending wellbeyond Kigali that had unusual low call volume and movement frequency. Four additional sites
recorded unusually low call volume. The sites are grouped in a large spatial cluster which isindicative of a major common cause of the disturbances at all these sites. On the same day,reports indicate that tens of thousands of Rwandans participated in a series of protests over
the arrest in Germany of Rose Kabuye, a prominent Rwandan military and political figure, on
alleged involvement in the plane crash that led to the 1994 genocide. The distance between thereported location of the protests (latitude -1.96, longitude 30.04) and the centroid of the closest
site with unusual call volume and movement frequency is 1.8 km.
Floods – September 19, 2007. Our system identified 53 sites that had unusually low callvolume and movement frequency on September 19, 2007. During the previous week, startingon September 19, torrential rains in the northwest of the country led to severe floods, leaving
15 people dead, 7000 people homeless and displaced, and more than 1000 houses uninhabitable.
Floods also contaminated clean water supplies and decimated field crops, leading to concerns
about waterborne diseases and food insecurity in the area. On September 18, floods dramaticallyswept away 42 homes and forced families to evacuate in the middle of the night. The following
day, September 19, is when we find behavioral disruptions of decreased calling and movement.
Notably, the behavioral anomalies occur across the country, instead of concentrated in thearea most affected by flooding. In this case, the date of the behavioral disruption suggests a
good match with the flooding event, but the spatial range of behavioral reaction decreases ourconfidence that the dramatic floods created the dramatic behavioral anomalies. It is possiblethat other areas of the country were also affected by flooding, that roads were damaged ortransportation infrastructure was disrupted, or that families were busy rebuilding homes andcrops that were destroyed by the rains. All of these possibilities are plausible explanations
for decreased mobility and calling. However, further qualitative and quantitative research on
behavioral reactions to similar flood disasters will be necessary to understand if and exactly
how people change their communication and movement in response to natural disasters. The
contribution of this case study is an indication that reactions to flood disasters might be much
more complicated than we currently understand.
Christmas Eve – December 24, 2007 and 2008 . We identified 26 sites with unusuallyhigh call and movement frequency on December 24, 2007 and 59 such sites on December 24,
2008. Still more sites recorded only higher than usual call volume (21 in 2007 and 17 in 2008),
or only higher than usual movement frequency (1 in 2007 and 2 in 2008). Given that about
90% of Rwandans identify as Christians, it is not surprising that we find behavioral anomalies
on Christmas Eve in 2007 and 2008. We expect that people called and visited their families
to celebrate the holiday, resulting in the increases we find in both behaviors. The particularfeatures of the behavioral anomaly we find on these two days (large spatial extent, higher thanusual calls and mobility) match well the characteristics of this major planned religious event.
New Year’s Eve and New Year’s Day – January 1 and December 31, 2008, andJanuary 1, 2009. Our system identified more than 20 sites spread throughout Rwanda withunusually high call and movement frequency on each of January 1, 2008, December 31, 2008,
and January 1, 2009. Given that New Year’s is a national holiday that affects all people in
Rwanda (regardless of religion) and given the wide spread of the behavioral anomalies we find,
we believe that these anomalies are due to this holiday. Just as with Christmas, it is likely that
Rwandans call and visit family and friends more often on New Year’s Eve and Day.
International treaty – November 9, 2007. Behavioral anomalies were identified overa large area of Rwanda on November 9, 2007: 52 sites recorded unusually high call volume
and movement frequency, three additional sites recorded unusually high call volume and one
other site recorded unusually high movement frequency. One political event might explain

Session 7 :: Crowds

2015

3

117

2015
this anomalous behavior: on that day, the governments of the Republic of Rwanda and of the
Democratic Republic of Congo (DRC) signed the “Nairobi Communiqué” which defined a joint
approach to end the threat to peace and stability in both countries and in the Great Lakes
region posed by the Rwandan armed groups on Congolese territory. It is plausible that people
made more calls to spread information and discuss this major treaty, but it is unclear why suchas event would cause increased mobility. We do not find any other event that could plausibly
have caused a nationwide response such as this.
Major unknown event – April 24 and 25, 2008. Our system identified unusually low
call volume and movement frequency in 61 sites on April 24, 2008 and in 53 sites on the nextday. On both days additional sites recorded unusually low call or movement frequency. We
have been unable to find an event on or just before these days that could explain anomalous
human behavior that lasted at least two consecutive days, affected almost the entire country
and led to a significant decrease in the routine behaviors in Rwanda.
Commemoration of the genocide against the Tutsi – April 7 and 8, 2007, andApril 7 and 8, 2008. Our system identified 26 sites with unusually low call volume andmovement frequency on April 7, 2007 and 24 such sites on April 7, 2008. Our system also founda smaller number of sites with unusually low call volume and movement frequency on April 8,2007 and 2008. April 7 is an official annual Rwandan holiday which marks the start date of the1994 genocide. It is a planned event which affects most Rwandans. The behavioral anomalies
spread across the country on these days for two years in a row suggest that the remembrance
day could be the cause of decreased call volume and mobility frequency.
3. Discussion
In this paper, we contribute to the process of creating a system of detecting emergency eventsusing mobile phone data. An effective event detection system could make significant contributions to humanitarian response and reducing the toll of disasters on human well-being. Towards
this end, we develop a method for using mobile phone data to identify days with anomalous
calling and mobility behavior, including days with high call volume and/or mobility, and low
call volume and/or mobility. Our method also identifies the location of these anomalies andthe geographical spread of the disturbances. We compare the days we identify with anomalous
behaviors to a database of emergency and non-emergency events. Some days and places withbehavioral anomalies match well with events and others do not. We learn from both cases.
Our analysis makes clear that detecting dramatic behavioral anomalies is only part of thework required to create an effective system of emergency event detection. The remaining workthat is necessary is serious social-behavioral analysis of the exact types of behaviors that canbe expected after different kinds of events and the exact time scales on which they occur. This
will require intensive qualitative as well as quantitative analysis. It is only through a thoroughunderstanding of these underlying differential behavioral patterns that an effective detection
system can be developed.
References

Department of Statistics, Department of Biobehavioral Nursing and Health Systems, Center
for Statistics and the Social Sciences and Center for Studies in Demography and Ecology, University of Washington, Box 354322, Seattle, WA 98195
E-mail address: adobra@uw.edu
Department of Sociology and Jackson School of International Studies, University of Washington, Box 353340,Seattle, WA 98195
E-mail address: natw@uw.edu
Department of Epidemiology, Harvard University, Boston, MA 02115
E-mail address: nathan@mit.edu

Session 7 :: Crowds[1] Dobra A, Williams N, Eagle, N (2015) Spatiotemporal Detection of Unusual Human Population Behavior
Using Mobile Phone Data. PLoS ONE to appear. Available at http://arxiv.org/abs/1411.6179

4

118

2015

Estimating Attendance From Cellular Network Data
Marco Mamei

Massimo Colonna

marco.mamei@unimore.it

massimo.colonna @telecomitalia.it

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

Engineering & Tilab
Telecom Italia, Italy

1. INTRODUCTION

45000

40000

We present a methodology to estimate the number ofattendees to events happening in the city from cellular
network data. In this work we used anonymized Call
Detail Record (CDR) comprising data on where andwhen users access the cellular network. Our approachis based on two key ideas: (1) we identify the network
cells associated to the event location. (2) We verify
the attendance of each user, as a measure of whether(s)he generates CDR data during the event, but not
at other times. We evaluate our approach to estimate
the number of attendees to a number of events ranging
from football matches in stadiums to concerts and festivals in open squares. Comparing our results with thebest groundtruth data available, our estimates provide
a median error of less than 15% of the actual numberof attendees.
While a number of existing works deal with the problem of discovering and analyzing events con the basis ofcellular network data, the problem of actually estimating the number of attendees is largely unexplored. In
particular, to the best of our knowledge, there are notpublished results of the accuracy of attendance estimation using CDR data.

35000

Groundtruth attendance

30000

25000

20000

15000

10000

5000
Juventus Stadium (TO)
Stadio Silvio Piola (NO)Parco Dora (TO)
Piazza Castello (TO)

Piazza Vittorio (TO)
Piazza San Carlo (TO)

Stadio Olimpico (TO)

0
0

500

1000

1500

2000

2500

3000

3500

4000

4500

CDR user countFigure 1: Correlation result using the naive approach. It is easy to see that there is almost no
correlation (r2 = 0.016) among CDR count andgroundtruth.
Our goal is to identify a mechanism to create a strong
correlation between groundtruth and CDR counts. Once
this result is achieved, a simple linear regression canscale up CDR counts to the actual attendees estimate.

3.

METHODOLOGY

To overcome the above limitations, we developed a
specific methodology to deal with attendance estimation. In particular: (3.1) We identify the radius within
which are all the cells whose traffic can be associated tothe area where the event takes place. (3.2) On the basisof the identified cells, we count the number of users who
generate CDR data at the event time, but who do not(usually) generate CDR at other times. (3.3) On thebasis of such data from a number of events, we set up a
linear regression to estimate the number of attendees.

A naive approach to estimate the number of people
attending an event would be to just count the numberof users who generate CDR data in cells covering theevent’s location during the event time. In particular,
we defined the area associated to each event as a circle
centered in the event place with a fixed radius of 100m.
Then, we record all the CDR produced in the network
cells that overlap with the area at the event time. We
then counted the number of individual users.
Figure 1 shows correlation results – using the naive
approach – for a number of events covered by our dataset.
Each point represents an event: the x-coordinate is the
CDR estimate for attendance, while the y-coordinate is
the groundtruth attendance. It is easy to see that there
is almost no correlation (r2 = 0.016) between the two
estimates, so the naive approach is highly ineffective.

3.1

Event Radius Identification

Determining the network cells handling the traffic foran event is a fundamental task. Otherwise it is possible that the cells being considered will include CDR
data actually produced elsewhere, or will miss CDR
data that were actually produced in the proper area.
We model the event area as a circle with center c- where the event takes place, and with radius r. A
1

Session 7 :: Crowds

2. NAIVE APPROACH

119average Z-score during the event

average Z-score during the event

2015

Event at Stadio Olimpico on 24-3-2012

Event at Juventus Stadium on 22-4-2012

radius of the event area

radius of the event area

Figure 2: Graph showing for each rk how muchthe area had an unusually high number of people
during the event.
Figure 3: Estimated VS. groundtruth attendance. It is possible to see a good linear correlation between data (r2 = 0.78).

cell with center b and radius rc is considered relevant
for the event if: dist(c, b) < r + rc. Where dist is thegeographic distance between the points. We could also
select r < 0 to impose the fact that a cell has to overlap
to the center of the event by a certain amount to beconsidered as relevant.
Our approach starts from the basic consideration thatthe plot of the number of CDR generated from the event
area should have a spike (i.e., an outlier) when the event
take place, as the events – we are interested in – will
typically attract a large number of people.
Accordingly, we scan different values rk for the radiusof the area of the event and compute a z-score zk of thenumber of CDR generated from that area at the event
time. The result is a graph showing for each rk how
much the area had an unusually high number of people
during the event. Figure 2 shows the result for two
events. It is possible to see that once the area is properly
identified, the z-score clearly identifies that something
unusual is taking place there (z = 3.7 with a radius of
about 300m for the event on the left, z = 2.2 with aradius of about -200m for the event on the right).
We compute the best radius as the average of the rk
values weighted by the associated z-scores.

Figure 4: Results obtained by using piecewise
linear regression.
ysis. In any case, as we will show in the next section, it
has a strong positive correlation with groundtruth headcounts. Accordingly, a simple linear regression can scale
up the above count to the actual attendees estimate.
We assume the availability of a training set of events
for which attendance estimate is known to be used to fit
the parameters of the linear regression. The resulting
coefficients are then used to scale CDR estimates of attendance in a testing set of events. specifically, we used
piecewise linear regression: for each testing sample, we
consider the n closest samples in the training set, createa linear regression on that n points, and use it to scalethat predicted testing sample.

Attendance Estimator

Once the event area has been identified, we need amechanism to count the number of users who attended
the event. Since we do not know what the user was
doing in the event area, we estimate the probability of
the user presence as proportional to the fraction of timein which the user was there during the event f 1, andinversely proportional to the fraction of time in which
the user was there outside of the event time f 2.
p = f 1 · (1 − f 2)
We then add all such probabilities p together to obtain a raw attendance estimator of the event.

3.3

6057
4072
68%
14%

4.

ANALYSIS AND RESULTS

We applied our approach to estimate the number ofattendees to events ranging from football matches instadiums to concerts and festivals in open squares. The
analysis spans large events with ground truth attendance of more than 80000 persons to smaller ones witha ground truth attendance of less than 2000 persons.
Overall, we considered a dataset comprising 43 events.
Fig. 3 shows correlation between our estimate andgroundtruth values (r2 = 0.78), note the improvement
over Fig. 1. Fig. 4 presents mean/median absolute error (i.e., mean/median of the actual difference between
estimated attendees and groundtruth). The percentage
error divides the actual difference by the groundtruth
to understand the relative error to the attendees’ size.
It is possible to see that the use of CDR data produces
rather good estimates of the number of attendees.

Piecewise Linear Regression

The above estimator is typically much lower than theactual attendance. This can be naturally explained by
the fact that not all the users will use the phone during the match, and by the fact that not all of them
adopts the same carrier providing the data for this anal2

Session 7 :: Crowds

3.2

Piecewise Linear R.
Mean abs. error
Median abs. error
Mean % error
Median % error

120

2015

Seasonal Decomposition of Cell Phone Activity Series and
Urban Ecology
Blerim Cici, Minas Gjoka, Athina Markopoulou, Carter T. Butts
University of California, Irvine, USA{bcici, mgjoka, athina, buttsc}@uci.edu

NamePeriodSourceMilan Activity
Nov.4-Dec.1 2013 Telecom Italia [2]Milan Square-to-SquareNov.4-Dec.1 2013 Telecom Italia [2]Universities, Businesses,
Parks, Population per area, Jan.1-Dec.31 2013 City of Milan [3]Sport Centers, Bus stops
Table 1: Data Sets
Data: The data that we use in this study (summarized in Tab. 1)
consists of time series of aggregate cell phone traffic sent or received
by persons within small areal units in the city of Milan, made available for the Big Data Challenge competition [2]. In particular, thecity of Milan, an area of 550 km2 , was divided into a 100 × 100
square grid. Each grid square has the same dimensions: a side lengthof 0.235 km and an area of 0.055 km2 . This is the areal unit we use
throughout the paper, and we refer to it as a “square”. The temporalunit is the 10-minute interval. In addition, we collected ground truth
data from the municipality of Milan and online sources, containing
elements such as universities, residential areas, sport centers, parks,etc (last row of Tab. 1). This latter data is used here for external
evaluation of our methodology.
Methodology and Findings: We apply our approach to the cell
phone activity series, using the decomposed series to characterize

Figure 1: Cell phone activity series (normalized) for two gridsquares of Milan, for the week of 11/4/2013–11/10/2013. Square
5640 is located close to the San Siro stadium, while square 4961
is located in a university region. Differences in seasonal patterns(weekday/weekend) reflect stable differences between the university and stadium environments.

Figure 2: Power spectrum of activity aggregated over all
grid squares (blue line indicates mean, shaded area ±1 stddev); marks indicate high-amplitude frequencies (e.g. daily (1cycles/day) and weekly (0.14 cycles/day).
distinct aspects of urban ecology. We proceed as follows:First, we begin by decomposing the original series, using FFT.
For each grid square i ∈ S, we decompose the original time-series
Oi (T ), into seasonal and residual through the following steps:
1. We select its k highest-power frequencies1 (see Fig. 2).
2. We regenerate the seasonal communication series SCSi (T )using only the top k frequencies of the square, where SCSi (T ) ={scsi (t1 ), ...., scsi (tm )}.
3. We obtain the residual communication series RCSi (T ) by subtracting the basic series from the original series:
RCSi (T ) = {oi (t1 ) − scsi (t1 ), ...., oi (tm ) − scsi (tm )}.
Second, we perform hierarchical clustering of different squares
based on the different time series and we validate the results using
the ground truth data; we used a correlation based distance function,i.e. dist(i, j) = 1 − correlation(SCSi (T ), SCSj (T )), wherei, j ∈ S. Then, we used the cluster-size skewness distribution inorder to cut-off the deprogram and get a single clustering; the cut-off
is the point where the distribution has the lowest skewness.

Session 7 :: Crowds

Motivation: It is estimated that within the next forty years, twothirds of the world’s population will be living in expanding urban
centers, and the level of urbanization is expected to increase in all
major areas of the developing world, with Africa and Asia urbanizing more rapidly than the rest [1]. Given this, there is clearly aneed for methods that will cheaply yield up-to-date information on
urban environments, without extensive on-the-ground investigation.
A promising tool in that regard is the use of aggregate geo-locateddata on communication activity, a resource that is increasingly available given the near-universal penetration of mobile devices within
urban populations. Such data is inexpensive and can be collected anddistributed in a privacy-preserving manner.
The question, then, is how aggregate mobile data can be used toprovide information on urban ecology — the distribution of population density, social and economic activities, and social interaction. In
this abstract, we present an approach to this problem that draws on
the notion of seasonal decomposition; by decomposing communication data we create distinct time series that provide information on
routine activities and on deviations from those routines.
Key idea: The key to our approach is the spectral decomposition of
the original cell phone activity series (see Fig. 1) into seasonal communication series (SCS) and residual communication series (RCS).
SCS captures regular patterns of socio-economic activity within anarea and can be used to segment a city into distinct clusters. RCS
across areas enables the detection of regions that are subject to mutual social influence and of regions that are in direct communication
contact. The RCS and SCS thus provide distinct probes into the structure and dynamics of the urban environment, both of which can beobtained from the same underlying data.

1
We use k = 30, since RCS autocorrelation function does not differ
significantly from that of a white noise sequence for that value.

121

2015

(a) Segmentation of Milan
Figure 5: 10 largest strongly connected components for the directed graph of RCS cross-correlations with lag = 1.

Figure 3: Fig. 3(a) shows the city of Milan segmented in different clusters; for clarity we show only the top-10 largest clusters.
Fig. 3(b) shows the cluster densities for two of the ground truth
elements. The red dashed line indicates the Milan average. SCS
clustering separates regions with distinct urban environments(e.g., university vs. green space).
Category

Entropy for hierarchical SCS

Entropy for [4]Universities
Green (%)
Businesses
Population

0.96
0.94
0.82
0.97

0.97
1.27
1.33
1.34

6
5
4
3
2
1
0
1
2
0.0

Typical days

0.5

1.0
days

SCS

1.5

sms & call activity

sms & call activity

Table 2: Segmentation performance via Entropy (lower value isbetter). Hierarchical SCS clustering produces more functionallydistinct clusters for all categories.

2.0 0

5

10

15
days

20

25

Figure 4: The left figure shows how the typical weekday/weekend
approach, of [4], summarizes the cell phone activity series for the
San Siro area. Notice that the high peaks are lost when trafficis aggregated. The right figure shows the SCS traffic in the San
Siro area for one month (our method).
Finally, we show that our SCS clustering scheme successfully segments areas dominated by distinct types of socio-economic activity,
and allows for discovery of regions whose activity patterns differ
markedly from the rest of the city (see Fig. 3). Also, the resultscompare favorably with state-of-the-art approaches such as [4] (seeTab. 2), since SCS is able to flexibly and parsimoniously incorporate
regular patterns occurring on any time scale; by contrast, state-of-

1. REFERENCES[1] World urbanization prospects: The 2011 revision. United Nations
Department of Economic and Social Affairs/Population Division.
[2] Big data challenge. http://www.telecomitalia.com/tit/
en/bigdatachallenge.html, 2014.
[3] Milan’s public data. http://dati.comune.milano.it/, ’14.
[4] V. Soto and E. Frias-Martinez. Automated Land Use Identification using
Cell-Phone Records. In Proc. of HotPlanet, 2011.
[5] D. Krackhardt. QAP Partialling as a Test of Spuriousness. Social
Networks, 9:171–186, 1987.
[6] B. Cici, M. Gjoka, A. Markopoulou, and C. T. Butts. On thedecomposition of cell phone activity patterns and their connection withurban ecology.

Session 7 :: Crowds(b) Densities per cluster

the-art methods estimate regular patterns using average weekday andweekend days (evaluated using binned averages), and cannot therefore exploit temporal patterns across multiple days (see Fig. 4).
In addition to using the SCS to identify regular patterns, we showthat RCS enables the detection of regions that (i) are subject to mutualsocial influence or (ii) in direct communication contact; this was notpreviously possible using mere activity data.
First, we examine how squares affect each other’s RCS activity,
i.e. for two cross-correlated squares, a perturbation in one square attime t will be associated with a change in the other square at a later
point in time. We calculate a cross-correlation digraph for a given
time lag = i and compute its strongly connected components to findareas that are subject to mutual social influence. Using this approachin Fig. 5 shows a different, but interesting, segmentation of the cityfrom that obtained by SCS, with a clear structure becoming apparent. The center of the city is a connected component (green color),
completely separate from the rest. This means that perturbations occurring inside a square on the center, will most likely propagate toother grid-squares in the center. Another example is the dark blue
connected component on the center-right side which corresponds to
the Milan Linate airport.
Second, we show that the RCS allows for the detection of areasthat are in direct communicative contact. We validate the latter byshowing that RCS correlations between areas are significantly related to the volume of inter-area cell phone traffic, and that this relationship is substantially stronger than for SCS: 0.27 for RCS versus
0.05 for SCS; this is done using the Quadratic Assignment Procedure (QAP) [5], which is a technique for testing an matrix correlation
against a null hypothesis of no association, while controlling for theunderlying structure of the matrices being compared.
Due to space constraints, this abstract does not contain all the details of our methodology. A more detailed version of this work iscurrently under submission in MobiHoc 2015 [6].

122

2015

Social Events in a Time-Varying Mobile Phone Graph
Grandata Labs

Grandata Labs

Jorge Brea

Javier Burroni

charles@grandata.com

jorge@grandata.com

javier.burroni@grandata.com

Klaus Wehmuth

Artur Ziviani

J.I. Alvarez-Hamelinklaus@lncc.br

ziviani@lncc.br

ihameli@cnet.fi.uba.ar

LNCC

LNCC

Introduction
The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use ofmobile phones in urban populations [1, 2]. Studying the activity of mobile phones allows us, not only to infer social
networks between individuals, but also to observe the movements of these individuals in space and time [4]. In thiswork, we investigate how these two related sources of information can be integrated within the context of detecting
and analyzing large social events. We show that large social
events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of theevent, but also by an increase in social relationships of theattendants present in the event. Moreover, having detecteda large social event via increased antenna activity, we canuse the network connections to infer whether an unobserved
user was present at the event. More precisely, we address thefollowing three challenges: (i) automatically detecting largesocial events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users
were in the event.

Data Source and Methodology
Our data source is an anonymized traffic dataset from a
mobile phone operator in Argentina, collected mostly in the
Buenos Aires metropolitan area, over a period of 5 months.
The raw data logs contain about 50 million calls per day.
Call Detail Records (CDR) are an attractive source of location information since they are collected for all active cellular users (about 40 million users in Argentina). Further,
additional uses of CDR data incur little marginal cost.
Together with the location of the clients of the mobile
phone network, the CDRs allow us to reconstruct a social
graph derived from the communications among users. We
first define a contact graph G composed of all users participating in the mobile network, where an edge between users
exist if they have communicated at any point within the
5 months period. We note that G includes non client usersthat have communicated with client users.
Then, we define a time series of subgraphs of G composed
only of users present at a given location at a given time.
In this work, these subgraphs are restricted to client nodes(users for which we have mobility data) using the event antenna at a specified day, for a given time window. Note thatthe existence of a link between such nodes in any subgraphdepends on whether there is a link between them in G. These
subgraphs allow us to focus on the social dynamics of a set

Grandata LabsUBA–CONICETof users present at a particular location at a given time asis the case of client users attending a large-scale event.
We formalize this temporal sequence of subgraphs within
the context of a Time-Varying Graph (TVG) based on the
representation encountered in [6]. We consider a TVG as an
object H = (V, E, T ), where V is a vertex set, T is a time
instant set, and E is an edge set. In this context, an edgee ∈ E is primarily a quadruple of the form hu, ta , v, tb i, whereu, v ∈ V are vertices and ta , tb ∈ T are time instants. In thisrepresentation, an edge hu, ta , v, tb i then shows a relation
between vertex u at time ta and vertex v at time tb , i.e.
a directed relation, where the pair hu, ta i is the origin andthe pair hv, tb i is the destination. Note that the represented
relation is between the composite objects u = hu, ta i andv = hv, tb i. In this representation, a TVG edge e ∈ E ⊂V × T × V × T can be thus seen as an ordered pair hu, vi ∈(V × T ) × (V × T ) of composite vertices.
In this work, each edge in a subgraph is represented asa tuple h(x, tx ), (y, ty )i, where user x is the caller, user yis the callee, tx = ty is the date and time of the call. In
addition to the origin and destination composite vertices
present on the TVG edge, we also wish to identify the local
base station antenna (BSA) used on the call represented byeach TVG edge. In order to achieve this, we represent the
BSA identity as an edge attribute, so that the TVG edge
used in this particular work becomes an ordered 5-tuple ofthe format hx, tx , y, ty , li where in addition to the knownedge elements, l represents the BSA identity.

Automatic Event Detection
In this section, we show how we can use the mobile phonedata in an endogenous manner to detect past events automatically (as [3, 5] did with social media such as Twitter).
The events we are interested in involve a large amount ofpeople, hence they significantly increase the usage demandof antennas nearby, as evidenced by their traffic time series.
Our strategy thus is to locate time windows where the antenna activity is significantly higher than some appropriate
baseline. Defining an appropriate activity baseline is not atrivial task since this is possibly dependent on the size ofthe event one is trying to detect and on the regularity of thenormal antenna activity patterns.
In this work, the antennas activity is given by the numberof calls per hour registered in each antenna, according to theweek wi (0 ≤ i < n, where n is the number of weeks in thestudied dataset), the day of the week dj (0 ≤ j ≤ 6), andthe hour of the day hk (0 ≤ k ≤ 23). For each time slot
(wi , dj , hk ), we denote the number of calls as C(i,j,k) which

Session 7 :: Crowds

Carlos Sarraute123

we then normalise be the average activity over a period of 3
months, defining an event index E(i,j,k) given by
C(i,j,k)E(i,j,k) = 1 P` C(`,j,k)n

for the ith week in the 3 month period, j th day of the week,
kth hour of the day, and where ` sums over all weeks in thewhole time period considered (0 ≤ ` < n). Figure 1 showsthe event index E for an antenna near the stadium of River
Plate, a major soccer team in Buenos Aires, for each timeslot (hour) from January 1 to March 31, 2012. We consider
a large-scale event to have occurred when the event index iswithin the highest 99-percentile while considering the whole
3 months period.
During January and February, we observe the event index smoothly oscillating around the mean value except forsmall sharp increases due to normal peak daily activity. As
we approach March, the oscillations’ amplitude starts to increase reaching its peak value by mid March. On top of
this large oscillations we see very large sharp peaks reaching values of E = 9.98 on March 20. Additional high peaks
are also observed on March 7, 9, 10, 12, 14, 15, 17, and 18.
Retrospectively, we corroborated that a sequence of 9 Roger
Waters concerts took place at this location during March on
the same days of the observed peaks. We thus highlight thatthe event index has clearly spotted the Roger Waters eventson the exact days the concerts took place.
An open question is to understand the slight increase in
the background oscillation during these peak periods. A
plausible explanation is an increase in activity due to gatherings before the event (e.g., fans trying to get tickets, organizers working at the stadium, January and February arevacations month in Argentina, etc.) or it might be a statistical fluctuation one could verify if one had access to amuch longer dataset. In any case, further study of this background activity can prove very useful, for instance, allowing
for the forecasting of large-scale events. Identifying theseearly precursors could allow us not only to automaticallydetect events retrospectively, but also give us the possibilityof anticipating a future event.

Social Dimension of Events

2015

Having detected a large-scale event, we now take a look atsocial relationships of users attending these events. We consider all communications from the event antenna for a period of 5 months from November 1, 2011 to March 31, 2012,
therefore including the Roger Waters concert series fromMarch 7–20, 2012. For each day, we compute the implicit
social subgraph induced by G restricted to users observedat the event antenna during the time window from 18:00 to
22:00 (time window for the Roger Waters concerts).
Figure 2 shows the induced subgraphs for February 7 and
17, in which no event took place (a, b), along with March
7 and 17 for which two Roger Waters concerts took place(c, d). The total number of nodes on February 7 and 17
were 107 and 130, respectively, whereas on March 7 and 17
we observe 716 and 740 attenders, respectively. Most nodes
are singlets and are thus not shown in the plots, leaving only
nodes for whom at least one of his/her contacts in G was alsopresent at the event location, at the same day, and during
the defined time window.
Therefore, one can expect that it is somewhat likely thatthe nodes present in these subgraphs are attending this location together. We call these nodes social attenders and allnodes, singlets included, attenders. An immediate observation is the larger number of social attenders at the days ofthe concerts than on a normal day. This is expected given
the larger attendance during the event. More interestingly,
for the days of the event, especially on March 17, we observe
more complex group relationships of up to 7 nodes, possibly indicating larger groups of people attending the concert
together.

(a) Feb 7(b) Feb 17(c) March 7(d) March 17

Figure 2: Implicit social graphs: (a), (b) two days
with no large event; (c), (d) two days of the Roger
Waters concerts.

10

Probability of Being in an Event
Event Index E

64p(x ∈ U |k contacts ∈ U ) =

2

0
Jan
2012

Feb

Mar

Apr

Figure 1: Event index E for an antenna near theRiver Plate soccer stadium.

users ∈ U with k contacts ∈ U,users with k contacts ∈ U

where U is the set of all users in the event. The results
are shown in Fig. 3, where we also plot a linear regression
curve showing how strongly the expectation of finding a user
in the event depends on the amount of graph contacts thisuser has in the event. The scatter plot and the regression
curve (R = 0.83) show that the probability that a given
user attended the event grows linearly with the number ofhis/her contacts in the event. If several of the user contacts
in G represent real social relationships, such as friends orfamily members, and a significant fraction of such contacts

Session 7 :: Crowds

The increased social structure suggested in Fig. 2 motivates
the following question: can we estimate the probability thata user attended the event, given that k of his/her contacts
in graph G also attended the event? We compute this probability as follows:

8

124

0.35

Probability of x ∈ event

0.30

0.25

0.20

0.15

0.10

0.05

0.00
0

2

4

6
8
10
12
14
Number of contacts in event

16

18

Figure 3: Scatter plot and linear regression curvefor the conditional probability of a user being in an
event given that k of his/her contacts in graph G
were in the event.
We also computed the ratio between the number of users
in the event and the number of users with contacts in theevent (such users can be at the event or not), both having
at least K contacts in the event U . This ratio is displayed inFig. 4, which can be interpreted as the conditional probability that a user is in the event given that at least K of his/hercontacts are in the event, as a function of K. This probability remarks the social character of the event because, as
the number of contacts in the event increases, the probability of users with such contacts also increases up to K = 10.
Then, it starts to decrease due to the less significant proportion of people with more than 10 contacts. Notice thatthe sample space, where each probability is taken, is definedindependently for each K.

p(x ∈ U |x with k ≥K contacts)

0.25

0.20

0.15

0.10

0.05

0.00

0.05
0

2

4

6

8
10
12
K (contacts ∈ U)

14

16

18

Figure 4: Probability of a user being in the event U ,given the user has at least K contacts in the event.

Conclusion and Future Work2015

In this work, we have shown how we can combine the mobility and social network information present in mobile phonedatasets to detect large social events and characterize theirsocial features, such as an increase in local community structures of users present at an event. We have also shown that
it is plausible to infer the probability of a user (in the contact graph) attending an event, given the number of his/hercontacts who attended the event.
Finding useful inference algorithms of users attendance toa particular location given the mobility of their contacts canbe of tremendous use for mobile phone companies. Mobile
phone carriers have no mobility information about users in
the contact graph G who are not clients of the carrier. Therefore, having the possibility of making at least weak inferences
about the locations and mobility of these users can add avaluable dimension for the carrier to better understand these(non client) users.
We have presented this work within the formalism of timevarying graphs (TVG). Even though this work has not yet
explored the potential uses of the TVG to analyse the dynamics of the graph structure, we believe this is a usefulformalism to extend this work into further understanding
the changes in social dynamic networks as one approaches
large-scale events.

Acknowledgements
Authors are grateful to the STIC-AmSud program. Brazilian authors thank CAPES, CNPq, FAPERJ, and FINEP for
their support.

1.

REFERENCES[1] 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.
Communications of the ACM, 56(1):74–82, Jan. 2013.
[2] F. Calabrese, L. Ferrari, and V. D. Blondel. Urban
sensing using mobile phone network data: A survey ofresearch. ACM Computing Surveys, 47(2):25:1–25:20,
Nov. 2014.
[3] F. C. T. Chua and S. Asur. Automatic summarizationof events from social media. In International AAAI
Conference on Weblogs and Social Media – ICWSM,
2013.
[4] N. Ponieman, A. Salles, and C. Sarraute. Human
mobility and predictability enriched by social
phenomena information. In Proceedings of the 2013
IEEE/ACM International Conference on Advances in
Social Networks Analysis and Mining, pages 1331–1336.
ACM, 2013.
[5] A. Ritter, O. Etzioni, S. Clark, et al. Open domain
event extraction from twitter. In Proceedings of the
18th ACM SIGKDD international conference on
Knowledge discovery and data mining, pages
1104–1112. ACM, 2012.
[6] K. Wehmuth, A. Ziviani, and E. Fleury. A Unifying
Model for Representing Time-Varying Graphs.
Technical Report RR-8466, INRIA, Feb. 2014.

Session 7 :: Crowds

are in a social event, such as a music concert, one could
expect an increase in the likelihood the user is also attending
the event. Nevertheless, it is worth noticing that the linear
relation does no saturate even for high values of k.

125Real-Time Social Event Analytics

2015

Francesco Calabrese∗ , Giusy Di Lorenzo∗ , Gavin McArdle∗† , Fabio Pinelli∗ , Erik Van Lierde‡∗ IBM Research - Ireland, Dublin, Ireland{fcalabre, giusydil, gavinm, fabiopin}@ie.ibm.com† National Centre for Geocomputation, Maynooth University, Maynooth, Co Kildare, Irelandgavin.mcardle@nuim.ie‡ Mobistar, Brussels, Belgium

erik.vanlierde@mail.mobistar.be

We use data from a Belgium telecommunication operator
who provided details about the distribution and azimuth oftheir cell towers in the city of Mons. Using this information
the city is divided into cells using a Voronoi tessellation [7].
In total, 319 distinct cells were created (figure 1). These
formed the basic unit on which crowd and vehicle density
are analysed. Prior to the opening ceremony, the police andorganisers provided details of specific public squares in the citywhere events were taking place along with a list of important
access routes to the city and our analysis focuses on these
spatial features.
Anonymous CDR data for individuals connected to cell
towers in Mons and the surrounding area are supplied directlyby the telecommunication operator. Each row of CDR dataconsists of an anonymous user ID, a time-stamp, the ID of
the cell tower, the home country of the device and the typeof record (call, SMS, data). Typically, data connections are an

Fig. 1. 319 Voronoi cells were computed from the cell tower distribution
and azimuth data provided by the telecommunication operator in Mons.

always on service and lead to generation of comprehensive
CDRs. In this case the data are recorded at a rate of morethan 250 records every second. By analysing the cell IDs,
it is possible to determine the number of users connected toeach cell tower and determine the Voronoi cell they are in.
By combining this data with the known area of each Voronoi
cell and the known customer penetration rate of the specificmobile operator, the density of crowds in any given Voronoi
cell is estimated. For the public squares which are of particularinterest to the police, we also produce a count of the number ofindividuals in these spaces. Furthermore, an estimation of thenumber of people in the whole city is calculated by summing
the amount people connected to the relevant cell towers.
While the police are interested in public safety and crowd
control, organisers are also interested in assessing the successof the event and the marketing campaigns used to attract
people. In addition to the numbers attending the event, theorganisers also wanted to know where people were travellingfrom in order to attend the events in the city. Therefore, thenumber of users by nationality are calculated using the homecountry of the device which is available directly from each
CDR. In order to obtain the home city of Belgian users, further

Session 7 :: Crowds

Managing public safety at large events is important. Crowd
control and traffic management are particularly relevant for
non-ticketed events in public spaces. In such cases, it canbe difficult for organisers to anticipate the number of people
who will attend and to validate the event’s success [1]. Given
the ubiquitous nature of mobile phones, Call Detail Records(CDRs), which are the logs of user transactions with a mobile
phone service provider, have been widely used to study urban
processes [2], [3], [4]. By building on the work of [5] and[6] our research explores the use of real-time CDR data
as a proxy to estimate the density of crowds in different
areas of a city while events are taking place. The research
has also been extended to estimate the density of vehicles
on the main access routes to a city. This has led to thedevelopment of an application entitled Social Event Analytics(SEA) which provides both real-time and historic information
about crowd and vehicle densities. The application can beused by authorities and event organisers to manage the eventand gauge its success. The application was used in January
2015 for monitoring city wide events in Mons, Belgium whichmarked the launch of Mons as the European City of Culture
for 2015. Using SEA local police simultaneously monitored
the density of vehicles on the road network and the crowd
density in different areas of the city. Below, we briefly describe
the new real-time data analysis which we carried out for thisspecific case in which over 20 million CDRs were analysedeach day. The results are useful for authorities but will also
help to further our knowledge of human processes in urban
environments.

126

2015

Fig. 3. A screen capture from the SEA application showing the estimated crowd density for the Voronoi cells in Mons. Additional bar charts show the estimatedvolume of people at several locations in the city and the home country of visitors.

In addition to crowd estimation, the density of vehicles
travelling on the main access roads to the city is estimatedusing a minimal computational approach. Each road of interestis segmented so each segment is contained within a single
Voronoi cell. This produces 86 road segments (figure 2). Whilethe density of crowds in these cells can be estimated using
the techniques described above, we are interested in those
travelling through the cell. Historic CDR data was analysed todetermine users who regularly spend time in that cell. These
typically represent people who live or work in the area. Theseusers are removed from the analysis of the density for that cellwhich allows us to estimate the density of the major roads inthe cell. When two or more major roads are covered by a single
Voronoi cell, it is necessary to estimate the density of theseroads by averaging the known densities of the road segmentson either side of the particular cell.
Fig. 2. 86 road segments were computed based on their intersection with
the Voronoi cells.

analysis of the complete CDR data for Belgium was carriedout off line. Several weeks of CDR data were analysed in orderto determine the city where each device generally connected

The analysis above was used to produce an application,
called SEA, which monitors vehicle and crowd density innear real-time. The data is supplied by the telecommunication
operator every 15 minutes and contains the CDR data forthe previous 15 minutes. Based on the analysis, dynamic
visualisations are produced to present the results to the policeand organisers. The main visualisation, as seen in figure 1

Session 7 :: Crowds

to network during the night time hours between Midnightand 6 AM. This is similar to approaches for calculating thesignificant places people visit [8], [9].

127

2015

A screen capture from the SEA application showing the estimated traffic density on several access routes for Mons.

consists of a map with the Voronoi cell structure superimposed.
The cells are coloured based on the crowd density. The Voronoi
cells containing the squares where events are taking place arefurther highlighted. A bar chart shows the count of the crowd
for each of these squares. An additional bar chart shows thehome country of international visitors along with an estimateof the total number of people in the city. A separate interactive
map shown in figure 4 presents the roads of interest. Eachroad segment is coloured based on the estimated traffic density.
Roads which share cells are coloured with 50% transparency
to signify an average of the surrounding road segments wasused to calculate its density.
The real-time application was used by the police andorganisers to monitor the events during the opening ceremonyof Mons as the European City of Culture 2015. Preliminary
feedback has been positive and we are awaiting the results ofa more detailed evaluation. We will also use the CDR data to
determine the amount of time individuals spend at events andto understand the fine grained mobility occurring in the city.
These will serve as indicators regarding the success of events
while also furthering our understanding of urban processes.

R EFERENCES
[1]
[2]
[3]
[4]
[5]

[6]

[7]
[8]

ACKNOWLEDGMENTS
This publication has emanated from research conductedwith the financial support of Science Foundation Ireland underGrant ”SFI 13/IF/I2783”. The authors would like to thankMobistar for providing access to the anonymized data.

[9]

F. Calabrese, F. C. Pereira, G. Di Lorenzo, L. Liu, and C. Ratti, “Thegeography of taste: analyzing cell-phone mobility and social events,” inPervasive computing. Springer, 2010, pp. 22–37.
F. Calabrese, L. Ferrari, and V. D. Blondel, “Urban sensing using mobile
phone network data: A survey of research,” ACM Computing Surveys(CSUR), vol. 47, no. 2, p. 25, 2014.
J. Reades, F. Calabrese, and C. Ratti, “Eigenplaces: analysing cities using
the space-time structure of the mobile phone network,” Environment andPlanning B: Planning and Design, vol. 36, no. 5, pp. 824–836, 2009.
R. Cáceres, J. Rowland, C. Small, and S. Urbanek, “Exploring the useof urban greenspace through cellular network activity,” in Proc. of 2nd
Workshop on Pervasive Urban Applications (PURBA), 2012.
T. Sohn, A. Varshavsky, A. LaMarca, M. Y. Chen, T. Choudhury, I. Smith,
S. Consolvo, J. Hightower, W. G. Griswold, and E. De Lara, “Mobility
detection using everyday gsm traces,” in UbiComp 2006: Ubiquitous
Computing. Springer, 2006, pp. 212–224.
C. Ratti, S. Sobolevsky, F. Calabrese, C. Andris, J. Reades, M. Martino,
R. Claxton, and S. H. Strogatz, “Redrawing the map of great britain froma network of human interactions,” PloS one, vol. 5, no. 12, p. e14248,
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.
S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi, J. Rowland, and A. Varshavsky, “Identifying important places in peoples lives
from cellular network data,” in Pervasive computing. Springer, 2011,
pp. 133–151.
F. Calabrese, Z. Smoreda, V. D. Blondel, and C. Ratti, “Interplay between
telecommunications and face-to-face interactions: A study using mobile
phone data,” PloS one, vol. 6, no. 7, p. e20814, 2011.

Session 7 :: Crowds

Fig. 4.

128

2015

Inferring spatio-temporal changes in urban areasfrom mobile phone data

Sofia.Nikitaki@neclab.eu

Mobile phone data represents a key source of information
for understanding human mobility at a scale and, thank to
its ubiquity, has shown that human trajectories exhibit ahigh degree of temporal and spatial regularity [1]. When we
consider the mobility patterns of a set of individuals whoshare a given area over a common timeframe, we can modelthe dynamics behind the interactions of people in the area
and within the neighbor areas, with several practical applications: public safety, e.g., crowd detection and planning in
extreme situations [2]; urban and transportation planning,e.g., forecasting traﬃc congestions [3]; information diﬀusion,e.g., spreading of deseases and detecting economic flows [4].
In this paper we focus on the definition of a supervised
method that, during a training phase, profiles the communication patterns of users in a given geographical area overtime based on antennas’ traﬃc information, in the form ofCall Detail Records (CDR). A CDR is produced during acommunication transaction and contains attributes such as
the caller and callee identification number, date and time,
type of service (e.g. voice, text), and duration. We thenuse such profiles to continuously monitor for future changes
in the communication patterns over time within that area.
The detection of changes can be correlated with third party
sources of information such as news and social media, toidentify the reasons of these changes.
The proposed method builds on the Non-Negative Matrix Factorization technique (NNMF) that considers nonnegative observed data and explains the observations as alinear combination, represented by the coeﬃcient matrix ofa base. NNMF technique has been applied in order to discover latent structuring patterns of sensed data and theirchanges in time. To this aim, the traﬃc matrix is defined,that contains the captured information at a specific location
over diﬀerent time intervals. Specifically, NNMF techniquesolves an optimization problem in order to decompose thetraﬃc matrix into a basis matrix and a coeﬃcient matrix:
the bases represent the latent factors, the coeﬃcients thestrength of these factors.
Figure 1 outlines the NNMF technique as applied to ourcase. In particular, we apply the technique to cell phoneantenna data1 collected in Côte d’Ivoire between December
2011 and April 2012 [5]. We split the dataset into ten subsets
of two weeks chronologically ordered: we use the first L(1 ≤ L ≤ 10) sets to gather the profiles (training set T ) andthe remaining sets to test the ability in detecting changes incommunication patterns by using such profiles (evaluation
1
This Data was made available by France Telecom/Orange
Côte d’Ivoire within the framework of the D4D Challenge.

Maurizio Dusi

NEC Laboratories Europe, Heidelberg, Germany

Maurizio.Dusi@neclab.eu

Figure 1: Non-Negative Matrix Factorization technique. The traﬃc matrix is decomposed into thecoeﬃcient matrix and the basis matrix. Each rowof the basis matrix represents a communication pattern. Each component of the coeﬃcient matrix represents the strength of each pattern.
set E).
In formulating the optimization problem needed to gather
the communication patterns during the training phase, ourmethod takes into consideration the spatial and temporalcorrelations of the calls of the users, to find global communication patterns of an area, which are stable over time.
Specifically, we formulate the problem as follows:min{∥X − U · V T ∥2F + α(∥U ∥2F + ∥V ∥2F )++ β(∥S(U · V T )∥2F + ∥(U · V T )T ∥2F )}(1)

where S(·) is the spatial constrain, T (·) is the temporalconstrains. Specifically, the spatial constraint is captured
by creating the weighted communication graph of the matrix X and the matrix S is the Laplacian matrix of thatgraph. With respect to the temporal constraint, we apply the Toeplitz matrix that captures the temporal smoothness of the collected data. The Frobenius
of a matrix
∑
∑norm
n
2 1/2
X ∈ Rm×n is defined as ∥X∥F = ( m.
i=1
j=1 |xij | )To cope with the scalability challenge given by the amountof data to consider, we solve the optimization problem byapplying a distributed stochastic gradient descent technique,which allows for good convergence guarantees and can be
distributed [6].
During the detection phase, we project the traﬃc matrix
Xi of each subset of the evaluation set onto the basis matrix, and we estimate the values of the resulting coeﬃcientmatrix Ui . We then compute the pairwise diﬀerences of twoconsecutive coeﬃcient matrixes to trigger an alert wheneverSession 7 :: Crowds

Sofia Nikitaki

NEC Laboratories Europe, Heidelberg, Germany

129

Method:
Spatio-temporal NNMF

0.25

Method:
NNMF

0.40

2015

0.35
Normalized reconstruction error

0.20
0.30
0.25

0.15

0.20

0.10

0.15
0.10

0.05

Vi ∈ T0.05

a change is above a pre-defined threshold.
Figure 2 provides an overview of the system.
In the following we show the two main properties of themethod: (i) it can gather profiles that are stable over time;and (ii) it can detect changes based on such profiles.
Stability of the bases over time. We start by solving
the problem of Equation 1 for each one of the ten subsetsthat compose the dataset, ending up with ten basis matrix{Vi }i=1,...,10 . We then normalize each row of Vi , so that the{Vi } can be compared among each other.
We define as training set T the first L basis matrixes,
where 1 ≤ L ≤ 10, and as evaluation set E the remaining
basis matrixes.
We compute the common basis Vc as the average of theVi ∈ T :∑L
i=1 ViVc =,(2)
Land finally, evaluate the normalized reconstruction error
defined as the normalized Frobenius norm between Vc and{Vi } ∈ T and between Vc and {Vi } ∈ E. In the first case, thereconstruction error gives an indication on how good is thealgorithm in finding the common bases; in the latter case, it
indicates on how stable are the bases when comparing themto the call patterns of timeframe other than the ones used
to gather the profiles.
Figure 3 reports on this, with a comparison between our
method, which consider the spatio-temporal constrain among
the calls across the antennas, and the basic NNMF.
Detecting spatio-temporal changes. We exploit theabove technique to look for changes in call patterns that happen across the areas of Ivory Coast during the four-month
period of the dataset. Here we report the changes spottedby our technique when considering as training the first four
two-week periods (cell phone antenna data from December
2011 and January 2012), and evaluating the changes on theremaining dataset.
We project each traﬃc matrix Xi , 5 ≤ i ≤ 10, onto thebasis matrix Vc , computed from {Vi }i=1,...,4 , thus ending
up with the corresponding normalized coeﬃcient matrixes{Ui }i=5,...,10 , which contains the strenghts (proportions) of
the communication patterns for each antenna. We then look
for the antennas which exhibit changes in the proportion of
the patterns.
Interestingly, we found that the biggest diﬀerences happen
to clusters of neighbor antennas, as if the antennas reveal
macro behavior that are common to a wide geographicalarea. Figure 4 reports the cluster where we register the

0.001

2

3

4

5

6

7

0.001
8
9 10
2
3
#sets used for computing Vc

Vi ∈ E
4

5

6

7

8

9

10

Figure 3: Stability of the common bases Vc . On
the left, NNMF with spatio-temporal constrain. On
the right, the basic NNMF. By adding the spatiotemporal constrain, the method exhibit a reconstruction error which tends to become similar in case
Vi belongs either to the training or to the evaluation
sets, in the order of 0.18. Conversely, when using
basic NNMF, Vc appears to be more biased towards
the Vi used for creating it, and diverges with the{Vi } that belong to the evaluation set,{Vi } ∈ E.

Figure 4: Clusters of antennas which exhibit thebiggest diﬀerences in communication patterns during the firts half of February as revealed by our technique.
biggest diﬀerences during the first half of February, showing two clusters of antennas in the area around the city of
Abidjan.
We further inspect the evolution in the communication
patterns for the antenna that exhibits the biggest changes(the green antenna in Figure 4). As shown in Figure 5,
during the second half of February the area covered by thisantenna show a completely diﬀerent predominant patternwith respect to the previous period: a shift from the diurnal
pattern to a morning and nocturnal pattern emerges, andthe technique spots it. The following period shows thatthe communication patterns in the area return to behave as
before.
We believe that the proposed technique which allows forcontinuously monitoring changes in communication patterns
presents several advantages. The method is based on a nonnegative matrix factorization approach and accounts for theinherent correlation structure of the traﬃc matrix both inSession 7 :: Crowds

Figure 2: System architecture: overview.

130

0.12
0.10 16-31 January
0.08
0.06
0.04
0.02
0.00
0.12
0.10 1-15 February
0.08
0.06
0.04
0.02
0.00
0.12
0.10 16-29 February
0.08
0.06
0.04
0.02
0.00
5

1.

15
10
Time [hour]

20

Figure 5: Evaluation of the communication patterns
for the antenna that exhibits the biggest changes(green antenna in Figure 4).
time and space in order to find stable global communication
patterns, as opposed to the basic formulation of the problem via NNMF. Furthermore, we use such patterns in orderto detect for future changes in the communication profiles
over time in a given area. Finally, the detection of changes
can be associated with third party sources of informationsuch as news and social media, to infer the reasons of thesechanges. The proposed method is solved via stochastic gradient descent that can be distributed and thus making it
appropriate for large-scale learning data.

REFERENCES

2015[1] M.C. González, C.A. Hidalgo, and A.-L. Barabási,“Understanding individual human mobility patterns.,”
Nature, vol. 453, pp. 479–482, 2008.
[2] Bagrow JP, Wang D, and Barabsi A-L, “Collective
response of human populations to large-scale
emergencies.,” PLoS ONE, vol. 3, pp. e17680, 6 2011.
[3] Chaogui Kang, Stanislav Sobolevsky, Yu Liu, and Carlo
Ratti, “Exploring human movements in singapore: A
comparative analysis based on mobile phone andtaxicab usages,” in Proceedings of the 2Nd ACM
SIGKDD International Workshop on Urban
Computing, New York, NY, USA, 2013, UrbComp ’13,
pp. 1:1–1:8, ACM.
[4] Francesco Calabrese, Massimo Colonna, Piero Lovisolo,
Dario Parata, and Carlo Ratti, “Real-time urban
monitoring using cell phones: A case study in rome.,”IEEE Transactions on Intelligent Transportation
Systems, vol. 12, no. 1, pp. 141–151, 2011.
[5] Vincent D. Blondel, Markus Esch, Connie Chan,
Fabrice Clérot, Pierre Deville, Etienne Huens, Frédéric
Morlot, Zbigniew Smoreda, and Cezary Ziemlicki,“Data for development: the D4D challenge on mobile
phone data,” CoRR, vol. abs/1210.0137, 2012.
[6] Rainer Gemulla, Erik Nijkamp, Peter J. Haas, andYannis Sismanis, “Large-scale matrix factorization with
distributed stochastic gradient descent,” in Proceedingsof the 17th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, New York, NY,
USA, 2011, KDD ’11, pp. 69–77, ACM.

Acknowledgement

Session 7 :: Crowds

The research leading to these results has received funding
from the European Union under the FP7 Grant Agreementn. 318627 (Integrated Project mPlane).

131