Third International Conference on
the Analysis of Mobile Phone Datasets

MIT Media Lab, Cambridge, MA
May 1–3, 2013www.netmob.org

Book of abstracts

Editors
Vincent Blondel
Adeline Decuyper
Pierre DevilleYves-Alexandre De Montjoye
Jameson Toole
Vincent Traag
Dashun Wang

Sponsored by

Contents
Session 5 // Mobility patterns
Frequencies, temporal patterns, and spatial regularity of mobile-phone data . . . . . . . . . . . . . . . . . . . . . . . . . . .
Discovering urban and country dynamics from mobile phone data with spatial correlation patterns . . . . . . . . . . . . . . . .
Location Patterns of Mobile Users: A Large-Scale Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A Multi-Scale Multi-Cultural Study of Commuting Patterns Incorporating Digital Traces . . . . . . . . . . . . . . . . . . . . .

Session 6 // Mobility modelling

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Scaling Theory of Human Mobility and Spatial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Proxy networks for human mobility in Europe: the impact on epidemic modeling . . . . . . . . . . . . . . . . . . . . . . . . .
Human Mobility Modeling at Metropolitan Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographic Similarity Within Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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26
29

Session 7 // Trajectories and regularities
Late for Good . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Do Mobile Phone Data Allow Estimating Real Human Trajectory? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Depict Urban Activities from Real Movement with Auto-GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Aggregated OD tracks of mobile phone data for the recognition of daily mobility spaces: an application to Lombardia region . . .
Understanding Human Mobility Due to Large-Scale Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Social Amplifier – Reaction of Human Communities to Emergencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Session 8 // Social data collection

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Detecting Face-to-face Meetings using Smartphone Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vehicular Traffic Estimation Leveraging Location Area Updates of Mobile Phones . . . . . . . . . . . . . . . . . . . . . . . .
Emergence of Congestion In Road Networks based on Realistic Demand obtained from Mobile Phone Data . . . . . . . . . . . .
Indicators of wealth, economic diversity and segregation in Côte d’Ivoire using Mobile Phone datasets . . . . . . . . . . . . . .
Can Cell Phone Traces Measure Social Development? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Session 9 // Social structure

65
Mobile communication in business networks: structure and leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
A Place-focused Model for Social Network Formation in Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

A Comparative Study of Decentralized Routing in Social Network Based on Mobile Phone Data . . . . . . . . . . . . . . . . . 71
Migration and Ethnic Segregation: Evidence from Estonia’s Mobile Phone Logs . . . . . . . . . . . . . . . . . . . . . . . . . 73
Why does my phone always ring when I am about to make a call? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Session 10 // Privacy

81
Differentially Private Modeling of Human Mobility at Metropolitan Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
Unique in the Crowd: The privacy bounds of human mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Session 11 // Information propagation
The use of mobile phone call record data for malaria control and elimination strategic planning . . . . . . . . . . . . . . . . . .
Time-varying networks and the weakness of strong ties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Limited communication capacity unveils strategies for human interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Is Social Influence Always Positive? Evidence from a Large Mobile Network . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Poster 1 // Mobility

103
Human Mobility and Predictability enriched by Social Phenomena Information . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Pisa Tourism Fluxes Observatory: deriving mobility indicators from GSM calls habits . . . . . . . . . . . . . . . . . . . . . . . 107
Hierarchical Exploration of Human Mobility Regularities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Mining User Mobility Features for Next Place Prediction in Location-based Services . . . . . . . . . . . . . . . . . . . . . . . 113
Extracting People’s Stays from Cellular Network Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Building Energy-Efficient Spatio-Temporal Mobility Profiles for Mobile Users . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Properties of the Positioning Error of Cell Phone Trajectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Privacy in Computational Social Science: An overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Participant Behavior in PHONELAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Exploring the Use of Urban Greenspace through Cellular Network Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Citizen as a Sensor for the SmartCity: The Barcelona Urban Mobility Use-case . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Trace-based Analysis to Identify Popular Locations Visited in Mobile Ad hoc Networks . . . . . . . . . . . . . . . . . . . . . . 134

Poster 2 // Network

137
Simulation of Epidemic Spread using Cell-Phone Call Data: H1N1 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . 138
Network-behavior Dynamics in a Medium Size Mobile Phone Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
On Information Propagation in Mobile Call Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Using telephone network data to study the effects of socio demographics on ego . . . . . . . . . . . . . . . . . . . . . . . . . 145
Understanding Social Influence Using Combined Network Analysis and Machine Learning Models . . . . . . . . . . . . . . . . . 148
Accelerating internet growth in Asia using viral spreading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Determinants of Subscriber Churn in Wireless Networks: The Role of Peer Influence . . . . . . . . . . . . . . . . . . . . . . . 150
Understanding Quota Dynamics in Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Quantitative Analysis of Community Detection Methods for Longitudinal Mobile Data . . . . . . . . . . . . . . . . . . . . . . 156
Calling Patterns in Human Communication Dynamics network structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
Evolution of Communities with Focus on Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Clustering of smartphones ownership in developing countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Mobile phones and digital generations in EU5 countries: a comparison of the 1996 and 2009 survey data. . . . . . . . . . . . . . 166
Protecting Cellular Networks from Attacks Launched Via Mobile Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Customer churn prediction: Integration of sociometric theory of cliques into a diffusion model . . . . . . . . . . . . . . . . . . 171
Characterizing social network spatio-temporal structure through Cell Phone Records . . . . . . . . . . . . . . . . . . . . . . . 174
Ethnic segregation in daily spatial mobility: Call Detail Record based study in Estonia . . . . . . . . . . . . . . . . . . . . . . 175
Reality Commons: Rich Datasets for Mobile Network Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176

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Session

5

Mobility patterns

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Session 5 // Mobility patterns

1Frequencies, temporal patterns, and spatial regularity of mobile-phone data
Philipp Hövel,1, 2, 3, ∗ Filippo Simini,1, 4, 5 Chaoming Song,1, 6 and Albert-László Barabási1, 6, 7
1

2.5⋅106

April 2009count

2.0⋅106
1.5⋅106100

100
mobile-phone data

10-1

probability P(rg)In recent years more and more data have been generated contributing to the rise of network science. In particular, social networks can be studied in greater details
as an increasing number of datasets become readily available in the digital age. The analysis of what is generally
termed big data allows for the inference of human behavior such as mobility or statistics about the social environment. One type of datasets that is especially suited toserve as a proxy for human behavior arises from the usageof mobile-phones [1–5]. The mobile-phone dataset usedin this contribution, for instance, consists of anonymized
call data records (CDRs) of 10 million customers from asingle phone company.

probability P(rg)

Session 5

Center for Complex Network Research, Northeastern University, Boston, USA
2
Institut für Theoretische Physik, Technische Universität Berlin, Germany
3Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Germany
4
Dipartimento di Fisica “G. Galilei”, Università di Padova, Padova, Italy5
Institute of Physics, Budapest University of Technology and Economics, Budapest, Hungary
6Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, USA
7
Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

10-2
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radius of gyration rg / km

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individual-mobility
model

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10-10 010

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radius of gyration rg

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FIG. 2: Normalized distribution P (rg ) of the radius of gyration rg derived from the mobile-phone data (left, observation
period: 1000 hours) and the individual-mobility model (right,
arbitrary spatial units). Reproduction of Figs. 4(a) and (c)of Ref. [6].

1.0⋅106
5.0⋅105
030, Thu
29, Wed28, Tue
27, Mon26, Sun25, Sat24, Fri
23, Thu
22, Wed21, Tue
20, Mon19, Sun18, Sat17, Fri
16, Thu
15, Wed14, Tue
13, Mon12, Sun11, Sat10, Fri
09, Thu
08, Wed07, Tue06, Mon05, Sun04, Sat03, Fri
02, Thu
01, Wed0.0⋅10

FIG. 1: Call activity during one month with an hourly resolution. The colored days mark Easter holidays.

We present a data analysis of CDRs with the purpose to extract various patterns of synchronization andto identify different rhythms of daily life. Our analysis spans multiple time periods, which allows to discoverrhythms on a daily, weekly, and even longer time frames.
For example, we study routines during weekdays and thedeviations from these temporal patterns during weekend
activities. Furthermore time-resolved evaluations of thetotal number of calls provides a simple insight into theheartbeat of the society as a whole. See Fig. 1, whichshows a repeating call activity profile during weekdays
interrupted by weekends or holidays (marked by color).
Systematic sorting of users into pre-defined groups, thatis, the use of additional meta data such as the age of the∗ Electronic address: phoevel@physik.tu-berlin.de8

mobile-phone users, enables further investigation of thesocial behavior of different subpopulations.
Next to the social network (who calls whom), thedataset at hand also provides information about the individual mobile-phone user’s whereabouts (where does acall originates from). The position of the caller is approximated by the location of the nearest mobile-phone towerthat handles the call. We demonstrate that the study ofthis spatial layer of information offers an insightful perspective on human mobility. In particular, it will leadto scaling laws of human travel calculated, for instance,
for the radius gyration [6]. The radius of gyration rg foreach user, who is recorded during L events at positions~r1 , . . . , ~rL , is defined as
v
u
L
u1 X
2
rg = t(~ri − ~rcm )L i=1
PLwith the center of mass ~rcm = L−1 i=1 ~ri . The distribution of the radius of gyration is depicted in Fig. 2.
In addition, we will consider limits of predictability [7].
For this, we use a measure of regularity given by the ratio
Ri of the number of recordings at the most frequently
visited tower to all calls,
Ri =number of appearances at primary location,
total number of appearances

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Session 5 // Mobility patterns

regularity R-1

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mobile-phone dataradiation model

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distance r / km

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probability of trips to population size nprobability of calls to distance r

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10-7 010 101 102 103 104 105 106
population size n

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0
Mon Tue Wed Thu Fri Sat Sun

FIG. 3: Regularity of mobility: (bottom) ratio R to find auser at his/her most frequent location and (top) number ofdifferent locations N with an hourly resolution for each dayof the week. Data: May 2009 for a class of users between 12
and 100 events on the daily average.

for each hour of the week, i = 1, . . . , 168. Figure 3 depicts the averaged regularity R (bottom) and the average
number of locations N (top). That figure confirms an
intuitive expectation: The average person is most regular (largest R) with the least number of locations during
early-morning hours and least regular (smallest R) withthe largest number of locations during commuting times
between home and workplace. Similar to Fig. 1, one cansee that this effect repeats for each weekday, but is lesspronounced on the weekend.

[1] M. C. Gonzalez and A.-L. Barabási: From data to models,Nature Physics 3, 22 (2007).
[2] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabási: Understanding individual human mobility patterns, Nature
453, 779 (2008).
[3] D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L.
Barabási: Human mobility, social ties, and link prediction,
ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining (KDD) (2011).
[4] J. P. Bagrow, D. Wang, and A.-L. Barabási: Collective
response of human populations to large-scale emergencies,
PLoS ONE 6, e17680 (2011).
[5] J. P. Bagrow and Y.-R. Lin: Mesoscopic structure and

FIG. 4: (Left) probability distribution of a call over a distance r and (right) to a municipality with population size n.
Data: number of phone calls between users living in different
municipalities during a period of 4 weeks with a total numberof 38,649,153 calls placed by 4,336,217 users. The data wasaggregated to obtain the total number of calls between every
pair of municipalities. Reproduction of Figs. 3(g) to (h) ofRef. [8].

Finally, we will briefly review simple models like theindividual-mobility model [6] or the radiation model [8]that reproduce the empirical findings to large extent andprovide mechanisms for the discovered scaling laws andscales of human travel. For a comparison of these models
to the empirical findings see Figs. 2 and 4.
Acknowledgments

PH acknowledges support of the German Academic
Exchange Service (DAAD) via a postdoctoral fellowship.

social aspects of human mobility, PLoS ONE 7, e37676(2012).
[6] C. Song, T. Koren, P. Wang, and A.-L. Barabási: Modelling the scaling properties of human mobility, Nature
Physics 6, 818 (2010).
[7] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási: Limitsof Predictability in Human Mobility, Science 327, 1018
(2010).
[8] F. Simini, M. C. Gonzalez, A. Maritan, and A.-L.
Barabási: A universal model for mobility and migration
patterns, Nature 484, 96 (2012).

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Session 5

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Session 5 // Mobility patterns

Discovering urban and country dynamics from mobile phonedata with spatial correlation patterns
Roberto Trasartia, Ana-Maria Olteanu-Raimondb, Mirco Nannia, Thomas Couronnéb,
Barbara Furlettia, Fosca Giannottia, Zbigniew Smoredab, Cezary Ziemlickiba. KDD lab, Istituto di Scienza e Tecnologie dell’Informazione, CNR Pisa, Italyb. Sociology and Economics of Networks and Services dept., Orange Labs, Paris, France

Abstract
Based on data coming from mobile communication infrastructures, this paper proposes an analytical process aimed at
extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of
population local densities. The proposed methods are experimented on two real scenarios of different spatial scale: the Paris
Region and the whole France.

1 Introduction and objectives
In recent years massive mobile phone location data have been studied and shown to have great potential to model humanmobility (González et al., 2008; Song et al., 2010). In particular, studies on long-term mobility data proved how this kind ofdata could be important for urban planning and transportation studies (Reades et al., 2007; Calabrese et al., 2011). The
difficulty to conduct classical travel surveys using self-report records (diaries or questionnaires) as well as the growing needto collect longitudinal data have drawn attention to automatic mobile phone data collection systems (Wang et al., 2010).
However, one of the main difficulties with mobile phone data is the incompleteness of the users’ traces. In fact, people are
localized only when they are using their phone (calling or sending SMS); it leads to several problems in finding mobility
patterns by means of classical data mining algorithms. Recent experiences using mobile phone data have shown that they
can provide a good understanding of how the density of population changes during the day in various regions of a given(urban or larger) area, the existing body of research appears to be mostly focused on the discovery of local phenomena, suchas increases of population, or simple flows of population between pairs of regions. On the contrary, the dynamics of a city
naturally create links of several different natures between regions of the city, sometimes even very far apart: some regions
tend to get congested together as response to some external event (e.g. intense precipitations); others might be connected
through a cause-effect chain, where the population of a region flows from one to the other in exceptional measure when theformer exceeds some levels of saturation. In this paper, we propose a new pattern definition, correlation pattern, whichtackles the problem of finding correlations between areas using the presence of cellphone users. The proposed algorithm
and tools are integrated in an existing mobility data analysis platform: M-Atlas (Giannotti et al., 2011), thus allowing theanalyst to take advantage of all the pre-existing features. Our approach is based on the observation that the density
distribution of population tends to be regular (periodic) in almost all regions. That is the result of the sum of several routine
human activities such as going to work, going to school, etc. which are constantly generated by the same residents, as well
as more random activities due to tourism, use of services (e.g., shopping, leisure activities), generated by different people
yet yielding overall stable densities. Therefore, finding evidence of connections between areas by simply looking at raw
densities would essentially lead to link everything to everything. A more promising approach, therefore, consists in looking
for exceptions to regular behaviors. Following this idea, we start by searching events that represent significant deviations
from regular trends, and locate them in space and time. Then, we try to detect recurrent combinations of events, therefore
extracting frequent patterns of deviations. The types of patterns we look for essentially have the form of sequential patterns
and are defined as follows:
DEFINITION 1. Correlation patterns. A correlation pattern (C-pattern) is a sequence D = <D1,…,Dn> of sets of events,
where Di = {d1,…,dm} is a set of events, each defined as dj = (sj, wj): sj is a spatial region and wj is the weight associated tothe event.
A C-pattern describes a set of regions that often experience a (significant) deviation from their common behavior, and dothat either at the same time (in the case the events belong to the same event set) or at different times (if they belong todifferent event sets) but always in the specific order described by the pattern.

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Session 5 // Mobility patterns

3 Case studies
In this section, we briefly summarize some of the results obtained by applying the methodology to extract patterns from two
different CDR datasets: at a city level, covering Paris, and at a national level, covering all the French territory.
Urban level: Paris. Figure 1 shows an example of results obtained, focused on the C-patterns that had the Charles De
Gaulle Airport (CDG) as starting area. The lines connect the areas belonging to the same pattern. This result shows that all
the major train stations are influenced by the airport, e.g., an increasing of +10% in the presences at CDG airport impacts on
Gare de l’Est (train station) by a +10% in 2 hours at maximum. For better understanding of the phenomena, we analyze thetemporal dimension of patterns (right part of the figure), since this chain of events may happen in different periods of a day.
The temporal distribution shows that the patterns cover several parts of the day, suggesting a further drill-down navigationof the dendogram (possible through the GUI by a simple click on a node) to better separate patterns based on their temporalprofile. On the right, a focus on two subtrees of the dendogram are shown, corresponding to the blue and green nodes.
Beside a first differentiation on the temporal profile, the two subtrees correspond to significantly different spatial area.

112

In order to support the detection of the events and patterns mentioned above, in this section we introduce methods andalgorithms to achieve three basic objectives: first, detect the locations and times where relevant variations of population takeplace; second, infer from them more complex patterns that link regions where variations tend to appear together or in someconstant sequence; finally, navigate the discovered patters along the spatial and temporal dimensions, and enrich patterns
with additional information (derived from raw data) to help their interpretation.
Stop Detection. The main concept behind our work is the population presence in a spatial region. Such presence can be
estimated through mobile phone data in various ways, mainly depending on whether the presence measure for a region(within a time window) should include only users that stopped in the region or it should also count users that simply crossed
it while moving towards a different destination. In particular, we propose a stop detection criterion inspired by Palma et al.
(2008), where spatial positions and time intervals are used instead of the speed: a stop for user uid is any ordered pair (pk,pm) of mobile phone points such that their location is the same ((pi.x, pi.y)=(pk.x, pk.y)), between them there are no points indifferent locations, and the temporal distance between them is longer than a given minimum duration threshold.
Density estimation and events detection. The basic elements of correlation patterns are the single events, which represent
all the relevant variations of population for a given region. In this work, in particular, events are computed by comparing thedensity of population within a region in a given moment against the expected density for that area at that hour of the day.
After partitioning the space into regions and time into time-slots of appropriate size (given by the user), the input data is
divided into a training and a test dataset. For both datasets the spatiotemporal grid of densities is computed. The first is used
to compute the expected densities of a typical period for each region. The second dataset is then compared against such
typical period in order to detect significant deviations. For instance, we might obtain an expected density for each pair(region, hour of the day), i.e., 24 values for each region, assuming 24 one-hour time-slots. Then, for each region and eachtime-slot, the corresponding density is compared against its expected value: if the difference is significant (anotherparameter of the method), an event of form (region, weight, time slot) is produced, representing its spatiotemporal slot and adiscretized measure (weight) of how strong was the deviation.
C-patterns extraction. The extraction of C-patterns focuses on those patterns that appear frequently, i.e. they occur withsome given minimum frequency. In particular, C-patterns are computed as sequential patterns over the dataset of events
obtained in the previous step. To do that, we employ a simple extension of the standard SPAM (Agrawal & Srikant, 1995)
that integrates spatial and temporal constraints, as well as the extraction of maximal and closed patterns. Finally, only the Cpatterns that show a high correlation are retained, the latter being evaluated by an ad hoc variant of the standard lift index
defined for item sets, expressing the ratio between the actual frequency of the pattern and its expected frequency, computed
under the assumption of complete independence between events.
Spatial and temporal navigation of C-patterns. Adopting a hierarchical clustering method we are able to reorganize thepatterns w.r.t. their temporal or spatial distributions in a tree structure. The result is a dendogram where each leaf representsa pattern and the intermediate nodes represent groups of similar patterns in terms of their temporal distribution. An example
is shown in Figure 1(center), as produced by the tool that we implemented to cluster the temporal distributions. The tool canalso visualize the temporal distribution of each intermediate node, as shown in Fig.1(bottom). It represents the typical
distribution in its sub-tree (bold red line) with additional information about the minimum and maximum values of the groupin each time interval (pink shadow). Practically, the analyst can study the dendogram at different levels in order to interpretgroup of patterns by their common temporal distribution, e.g., morning patterns as the patterns having all the occurrences in
the morning. Analogously to the temporal navigation, it is possible to organize the patterns using their spatial component.

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Session 5 // Mobility patterns

Figure 1: A selection of C-patterns obtained on the Parisian area, starting from CDG airport. On the left, a spatial representation of all
patterns selected; on the center, the dendogram (top) and hourly frequencies (bottom) of the temporal distribution of their occurrences;on the right, the temporal and spatial distribution of two subtrees of the dendogram.

National level: France. A second analysis was performed at the national level, using departments as spatial units. The Cpatterns obtained are shown in Figure 2(left), with a focus on the Seine-Saint-Denis department (center), and the temporal
distributions of the corresponding patterns (right). More in detail, we can notice that these patterns can be divided into inand out-coming ones: the groups of patterns a,b,c,d,e move from the neighborhood departments to Seine-Saint-Denis, while
patterns f,g,h move from Seine-Saint-Denis outwards. Also, we can notice that the incoming patterns are more present
during mid-day or early afternoon, while the outcoming one are more present during the evening, with exceptions in e and f,
since they have a two-peeks distribution which most likely follows the systematic movements of the area.

Figure 2: A set of patterns extracted on France at the level of departments (left), a selection of patterns focused on Seine-Saint-Denis andthe neighborhood departments (center), and the temporal profiles of the patterns (right).

4 Conclusion
We presented a new kind of pattern called C-pattern, aiming to discover hidden logic of connections between regions of acity (or other kinds of areas, at different scales), by analyzing frequently co-occurring changes in population densities. We
have developed an extraction process to discover these patterns, and tested it on real cases studies at two different
granularities (urban vs. national), showing examples of results and their temporal and spatial navigation.

References
Agrawal, R. & Srikant, R. (1995). Mining sequential patterns. Proc. 11th International Conference on Data Engineering, Taipei, pp. 3-14.
Calabrese, F., Di Lorenzo, G., Liu, L., & Ratti, C. (2011). Estimating origin-destination flows using mobile phone location data. Pervasive Computing, 10,
pp. 36-44.
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., & Trasarti R. (2011). Unveiling the complexity of human mobility by
querying and mining massive trajectory data. The VLDB Journal, 20, pp. 695-719.
González, M., Hidalgo, C., & Barabási, A.L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), pp. 779–782.
Palma, A. T., Bogorny, V., Kuijpers, B., & Alvares, L.O (2008). A clustering-based approach for discovering interesting places in trajectories. ACMSAC,
ACM Press, pp. 863-868.
Reades, J., Calabrese, F., Sevtsuk, A., & Ratti, C. (2007). Cellular Census: Explorations in urban data collection. IEEE Pervasive Computing, 6, pp. 30-38.
Song, C., Qu, Z., Blumm, N., & Barabasi, A.L. (2010). Limits of predictability in human mobility. Science, 327(5968), pp. 1018–1021.
Wang, H., Calabrese, F., Di Lorenzo, G., & Ratti, C. (2010). Transportation mode inference from anonymized and aggregated mobile phone Call Detail
Record. 13th International IEEE Annual Conference on Intelligent Transportation Systems, Madeira Island, Sept. 2010.

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Session 5 // Mobility patterns

Jean Bolot

AT&T Labs,
asridharan@research.att.com

Technicolor,bolot@technicolor.com

Mobile devices have become a common accessory for alarge fraction of the population who carry and utilize it as
they move. The very nature of the wireless service allows the
network to record location of users on an almost continuous
basis via active and passive means. This has provided a convenient source of location information for large populations,which in turn has spurred a large interest in the study humanmobility from various different perspectives.
In this abstract, our perspective is one of characterizing
the entire footprint or a location pattern of a user. Broadly
speaking, we define a location pattern of a user to be the setof all locations (cell towers) touched by users when making
calls over an observation period. Location patterns possess
significant useful information and hence are interesting anduseful to study. For example, information regarding the range
covered by a user as well as characteristics of how a user
moves within the range, are important inputs for modeling andsizing the paging ’zones’ to optimize paging load. Knowledge
about the size and shape of the footprint of the ensemble ofusers based within a certain locale, e.g a city, allows us todetermine how far they range and in which direction, which
major routes they take, all of which are useful inputs for urban
and traffic planning. Last but not least, clearly such information
can be utilized in building mobility models, e.g. to determine
parameters related to the area covered by users, the directions
they choose, length of major routes etc.,.
In this work, we develop a systematic methodology thatutilizes geometric constructs to capture salient features oflocation patterns such as their size, shape, or internal structure.
We apply these techniques to study the location patterns ofseveral million users extracted from a large nation-wide data
set comprising of several billion call data records, which record
the location of users over a period of 1 month with a spatial
granularity of a cell tower (meaning that the location of theuser is identified by the cell tower the user is associated with).
As explained in detail below, one of our main results is thediscovery that the distribution patterns of users in general,
at both nationwide and city levels, follow a statistical distribution with a simple generative process, namely the Double
Pareto Log Normal distribution(DP LN ). Specifically, theDP LN is an excellent fit to model the distribution of several
features of a location pattern, including its size, shape andstructure. In addition, we also show how these features can beused to discern and compare human footprints across locales.
We characterize the location pattern of a user from two
aspects : a) the coverage spread of the pattern which relates
to its size and shape and b) the arrangement of points within

3

Ashwin SridharanSession 5

Location Patterns of Mobile Users : A Large-ScaleStudy

the pattern which relates to the movement of the user as wellas radio coverage.
A. Size, Shape and Structure of Location Patterns
The size and shape of the area covered by a location patternis one of its most basic features. In order to study this aspect,
we circumscribe the ’hull’ or boundary of each location patternwith a simple geometric shape : a rectangle. We then focus on
studying properties of the rectangle, which are much moreamenable to analysis. The specific rectangle we choose is the
Minimum Area Bounding Rectangle (MABR) [6]. The MABR
covers the convex hull of all the points comprising the celltowers visited by the user (this includes air-travel)1 with theleast amount of area, which allows us to minimize the error
induced by this approximation in estimating the coverage area.
Once we circumscribe the pattern in a MABR , we study threebasic properties of the rectangle (and hence the pattern) : 1)
the area of the rectangle, 2) the skew defined as ratio of width
to length and 3) the orientation of the rectangle (with reference
to true north).
B. Clusters and Trajectories
In order to study the internal arrangement of points within
a location pattern, we classify points into structures that aim
to answer two intuitive questions with regards to movement :•Does a user’s calling/movement pattern occur in acontiguous area or is the movement disparate, i.e., theuser appears at distinctly apart locations?

•

Does a user’s pattern contain trajectories, e.g.a highway route?

The first question, apart from providing an intuitive senseof movement of the user, also has the benefit of characterizing
the radio coverage patterns in an area and can provide important information to design paging ’zones’ since it provides
information about the area in which a user is most likely tomake calls. To address this, we extract ’dense’ clusters of
points that would identify preferred ’areas’ of a user usinga modified version of the well-known dbScan [1] algorithm
that can identify clusters of arbitrary shape and size in thepresence of noise.
The second question is related to major routes that a user
may follow. Our interest is in identifying major routes followed
1 The cell-towers are mapped to a 2 − D space which is reasonably accurate
for small distances. In practice they actually reside on a 3D ellipsoid.

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Session 5 // Mobility patterns

by a user over the entire duration of observation rather than aspecific call. Since such movement would normally be along
roadways that (at least in the continental US) are generally
straight, we approximate the paths a user follows in the realworld as segments in 2D space and extract segments from theset of points by applying a version of the Iterative-End-PointFit (IEP F ) segment extraction algorithm [4] that includes a
pre-partitioning phase presented in [2]. We visually verified
the effectiveness of both the cluster and segment extraction
algorithms over several examples.

(a) PDF

Having defined the geometric constructs that characterize
a location pattern, our objectives in applying them to our large
data-set are two-fold : First, study statistical properties of actual location patterns and in particular determine good models
that can characterize them. Second, utilize these constructs tounderstand how user location patterns compare across various
geographical areas. For the latter, we computed and studied
location patterns of users at the nationwide level, as well as
for eleven cities : New York, San Francisco, Los Angeles,
Chicago, Boston, Seattle, Kansas City, Denver, Albuquerque,Tulsa and Dallas.
C. Results
With respect to the first objective, we find rather surprisingly that a single statistical distribution, the Double Pareto
LogNormal Distribution (or DP LN ) provides an excellent fit
for the area of the location patterns and internal clusters aswell as trajectory lengths of location patterns at both nationaland city level.
The Double Pareto Log Normal Distribution is a fourparameter distribution (α, β, ν, τ ) introduced by Reed ( [3]).
The complete DP LN distribution is given by :αβ h αν +α2 +τ 2 −α−1 log x − ν − ατ 2
f (x) =
e
x
Φ(
)+
α+β
τ
2 2
log x − ν + β 2 τ 2 i)(1)e−βτ +β τ /2 xβ−1 Φc (
τwhere Φ is the Normal CDF of N (0, 1). An attractive propertyof the DP LN distribution is that it arises via exponential
sampling of a generative process that was originally lognormally distributed and then evolves as a Brownian process.
Consequently, this process can also be used as a basis to model
evolution of the empirical process being observed. Qualitatively speaking, on a log-log plot, the DP LN distribution ischaracterized by two linear curves corresponding to the head
and tail of the distribution with slopes β − 1 and −(α + 1)
respectively and a log-hyperbolic middle section.
Our analysis shows that dP LN provides a very good
fit to empirical distributions of the size of location pattern(approximated by M ABR area), size of clusters as well as
trajectory lengths. Furthermore, this is observed to hold at boththe nationwide level as well as city-level.
Fig. 1 plots the empirical density function (1(a)) for thearea of the MABR of location patterns for users nationwide2 .
In the same graph, we also plot the MLE fit of DP LN
distribution computed using the EM algorithm outlined in [3]along with the associated parameters in the legend. Visually,
2 We truncate the range of the x-axis such that y-axis values ≈ 10e − 6.

14(b) QQ Plot
Fig. 1.

MABR Area : Nation

one can see that the DP LN is an excellent fit to the empirical
distribution. To further strengthen this assertion, Figure 1(b)shows a quantile-quantile (QQ) plot between the empirical
data and samples generated from a DP LN distribution withparameters from the M LE fit (the graph is on a log-scale) .
A clear linear relation is evident in the QQ plot between theempirical data and random samples from the fitted DP LN
distribution up to about ≈ 1 million km2 thereby verifying
the goodness of the fit.
Fig. 2 shows the empirical distribution as well as the
DP LN fit and associated QQ plot for the size of location
pattern for users in New York. Again, we see the distribution
provides an excellent fit, albeit with different parameters. We
have observed that the DP LN distribution provides similarly
good fits for other features such as cluster size and trajectory
length at both nationwide and city scale. Further results areavailable in [5].
The second aspect of our work involved comparing andstudying location patterns across different cities. For brevity,
we present a sample of our results in Fig. 3.
Fig. 3(a) compares the average MABR area3 per user
at each city. The graph gives a sense of the range thatusers travel around each . For example, it shows that users
associated with dense urban areas such as New York, Boston,
Chicago, and Los Angeles have a smaller footprint than areas
such as Dallas, Denver, Kansas City, Tulsa or Albuquerque.
Overall, we observe that the typical average MABR area is≈ 20, 000 km2 which translates to user range of about 150 km
in each direction (assuming equal length and breadth of the
MABR ).
The difference in orientation of the M ABR as a functionof geography is explored in Fig. 3(b). We plot the empirical
3 To ensure the measure stays city-specific, we computed the mean over
95% of the CDF mass since the tail values typically correspond to air travel.

3

Session 5 // Mobility patterns

Session 5

3(a) PDF(a) Average MABR Area (95% cut-off)(b) QQ Plot
Fig. 2.

(b) Distribution of orientation of MABR for a
few locations

MABR Area : NewYork

density function of this metric for three representative locales
and compare it against the nationwide orientation. Note that thex-axis in the figure ranges from −π to π and the y-axis is the
PDF. From the figure, the distribution for the ’Nationwide’ data
set is close to uniform indicating that at a global scale there is
no preferred direction, which is as expected: user footprints are
essentially oriented in independent random directions. Next,
the distribution for Dallas shows a few peaks indicating a slight
preference to certain directions of travel. The peaks get more
pronounced with New York and are most evident for Seattle,
suggesting that as an ensemble, the movement pattern in thiscity is highly correlated (eg., everybody uses the same set offew roads).
Finally, Fig. 3(c) provides a sample result from comparisonof the internal structure of the location patterns in the formof average cluster size. Interestingly, we note that some cities,
specifically, Albuquerque, Tulsa, Kansas City, Dallas and San
Francisco have very large clusters which indicates that the
popular calling ’areas’ of users have a large range. In contrast,
New York and Los Angeles have very small clusters pointing
to a user calling pattern that is more concentrated. Thisdifferentiation in behaviour can be useful to optimize network
paging [5].
In summary, we have developed a simple methodology
to analyze the location patterns of users and applied it to alarge nationwide data set of call data records. Our observationthat many salient features of location patterns can be modeledby a single distribution, the DP LN , is one of the main
contributions, of particular interest given the simple generative
process of that distribution. In addition, we have also provided
a sample overview of results that show how our constructs
can be used to compare and understand the difference in user
footprints across different cities.

(c) Cluster SizeFig. 3.

Comparison of Location Patterns

R EFERENCES
[1]
[2]
[3]
[4]
[5]
[6]

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm
for discovering clusters in large spatial databases with noise. In
Proceedings of ACM KDD, 1996.
V. Nguyen, S. Gächter, A. Martinelli, N. Tomatis, and R. Siegwart. A
comparison of line extraction algorithms using 2d laser rangefinder forindoor mobile robotics. Journal of Autonomous Robots, 23, August 2007.
W. Reed and M. Jorgensen. The double pareto-lognormal distribution - a
new parametric model for size distribution. Communications in Statistics-Theory and Methods, 33(8):1733–1753, 2004.
R.O.Duda and P.E.Hart. Pattern Classification and Scene Analysis. John
Wiley & Sons, 1973.
A. Sridharan and J. Bolot. Location patterns of mobile users : A largecase study. IEEE INFOCOM, 2013.
G. T. Toussiant. Solving geometric problems with rotating calipers. In
MELECON, 1983.

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4

A Multi-Scale Multi-Cultural Study of Commuting Patterns Incorporating Digital Traces

Session 5

Session 5 // Mobility patterns

Yingxiang Yang, Marta C. Gonzalez
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology,
Cambridge, MA 02139, USA
yxyang@mit.edu
Abstract
In this paper, we propose an extended radiation model to predict commuting flow OD matrix byperforming a multi-scale study. The extended radiation model overcomes the shortcomings ofboth the gravity model and the radiation model. Unlike the gravity model, it has only one
parameter which can be determined largely just by the study region size, so that the new model
doesn’t necessarily need empirical OD matrices for parameter calibration. The added one
parameter makes the new model more flexible than the original radiation model and thus can beapplied to study regions of different scales. For countries without detailed census data but withrich cell phone data, we propose a cell phone user OD matrix expansion method so that we could
gain insight of these regions’ commuting patterns from cell phone records. This method isvalidated and then tested on regions in three different continents.
The results show that as a combination of the radiation model and the gravity model, theextended radiation model overcomes the shortcomings of each. We show that the radiation
model is applicable to some certain scales. But it’s not flexible enough to adjust to thehomogeneity of opportunities and varying scales. On the other hand, because of the large numberof parameters the doubly constrained gravity model is flexible enough to fit to most given
datasets, but the fitted parameters cannot be applied elsewhere. In the extended radiation model
there is only one parameter , but it’s enough to take into account the effect of the scale and theopportunity heterogeneity. Unlike the doubly constrained gravity model in which the values ofparameters are totally unpredictable, the parameter is to a large extent predictable given the sizeof the study region. The results are validated on countries from four different continents.
We use the Bay Area as an example to demonstrate that cell phone records is an ideal alternative
for movement pattern analysis when traditional data sources are not available. Home and work
locations are inferred at individual level and then aggregated to show its equivalence to thecensus data. The three different models’ prediction results for cell phone users are compared at
different countries. These results show not only the applicability of each model, but also theunique commuting patterns in each country.
The automatically collected geographical information data, the point of interest, is verified to bea suitable proxy for commuting generation rates. The potential of both the cell phone records andthe point of interests can be further exploited in future studies. From cell phone records we can16

4Session 5 // Mobility patterns

Session 5

reconstruct the activity chains and regularities in daily activity patterns at individual level, which
are very hard to acquire by traditional survey methods. Together with digital footprints such as
the point of interest or Foursquare records, non-commuting trips, which have higher flexibility,
can also be traced.

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Mobility modelling19

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Session 6 // Mobility modelling

Scaling Theory of Human Mobility and Spatial Networka
Pierre Deville,1 Dashun Wang,2, 3 Chaoming Song,2, 3 Nathan

1

Eagle,4 Vincent Blondel,1 and Albert-László Barabási2, 3, 5

Session 6

1 Department of Applied Mathematics, University of Louvain, Belgium
2 Center for Complex Network Research, Department of Physics,

Biology and Computer Science, Northeastern University, Boston, Massachusetts 02115, USA
3 Center for Cancer Systems Biology, Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA
4 College of Computer Science, Northeastern University, Boston, Massachusetts 02115, USA
5 Department of Medicine, Brigham and Women’s Hospital,

Harvard Medical School, Boston, Massachusetts 02115, USA

Abstract
With the increasing availability of large-scale datasets that simultaneously capture human movements and social interactions, advances in human mobility and spatial networks have rapidly proliferated during the past years [1, 2], impacting in a meaningful
fashion a wide range of areas, from epidemic prevention and emergency response to urban planning and traffic forecasting [3, 4]. As human mobility and spatial networks havedeveloped in parallel, being pursued as separate lines of inquiry, we lack any known relationships between the quantities explored by them, despite the fact that they often study
the same systems and datasets. Here, by exploiting three different cell phone datasets, wefind a set of scaling relationships, mediated by a universal flux distribution, that link thequantities characterizing human mobility and spatial networks, showing that the widely
studied scaling laws uncovered in the two areas represent two facets of the same underlying phenomena.

a Corresponding authors: pierre.deville@uclouvain.be, dashunwang@gmail.com

1

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Session 6 // Mobility modelling

INTRODUCTION

Our knowledge of the interplay between individual mobility and social network is limited, partly
individual movements and social interactions. This situation is changing rapidly, however, thanks
to the pervasive use of mobile phones. Indeed, the records of mobile communications provide
any two parties and the localization in space and time of the party that initiates the call. These data,collected by telecommunication carriers in a truly objective manner, serve as an unprecedentedsocial microscope helping us scrutinize the mobility patterns together with social structures.

DATA

To demonstrate the practical relevance and universality of our results, we compiled three largescale mobile phone datasets from three different countries in two continents:D1, that contains
1.3 Million users in a western European country and covers a period of one month; D2 is thedataset from another European country that covers one year long period of around 6 Million users;
emphD3 is collected by the largest mobile phone carrier in Africa, covering a period of four years.
These three datasets, of same level of details yet with different demographics and scales, allow
us to assess the universality of previously reported scaling laws on individual mobility and spatial
networks in a systematic manner, and most importantly, lead us to uncover the scaling relationshipbetween these scaling laws, establishing the first formal link between the two fields.

RESULTS

The main result is the discovery of a scaling relationship between the exponent characterizing
spatial networks (β) and the exponent characterizing human movements (α) given by
β = αθ − δ.

(1)

2

21

1

extensive proxy of mobility patterns and social ties, by keeping track of each phone call between

Session 6

due to the difficulty in collecting large-scale data that record, simultaneously, dynamical traces of1

Session 6 // Mobility modellingwhere θ and δ are exponents characterizing the relationship between social and mobility fluxes andare constant across the datasets.

Session 6

1

The values of the exponents in Eq. 1 can be summarized in a table for all datasets and demonstrate a good agreement between empirical measurements and our theoretical results.
Taken together, Eq. 1 offers an explicit link between human movements and social communications, showing that the social exponents characterizing the distance distribution (βr ) and rank
distribution (βs ) can be expressed in terms of the mobility exponents characterizing individual
movements (αr and αs ). The relationship between these classes of exponents is mediated by auniversal flux distribution, which is independent of geography. This scaling relationship directly
bridges two fields that were perceived as distinct, showing that they represent different facets of adeeper underlying reality, and offers us a powerful framework to derive the characteristics of one
field from those of the other.

CONCLUSION

The unexpected duality between human mobility and social communications opens a new avenue in many areas previously improbable, making it possible, for instance, predicting traffic flows
and transportation patterns by using communication volumes alone. Indeed, as technology continues to inundate us with increasingly detailed data on individual activities, the results presented
here are expected to have an increasing value, impacting all phenomena driven by human behavior,from epidemic spreading and emergency response to traffic forecasting, urban planning and more.

[1] Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. (2008) Nature 453(7196), 779–782.
[2] Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., and Barabási, A.-L. (2008) Journalof Physics A: Mathematical and Theoretical 41(22), 224015.
[3] Colizza, V., Barrat, A., Barthelemy, M., Valleron, A.-J., and Vespignani, A. (2007) PLoS Medicine 4(1),e13.
[4] Helbing, D., Farkas, I., and Vicsek, T. (2000) Nature 407(6803), 487–490.

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Session 6 // Mobility modelling

Abstract
Introduction. The modeling of human mobility plays an essential role in the development ofrealistic mathematical and computational models for the spatial spread of infectious diseases[1]. When available, empirical data extracted from official census surveys have been successfully
used to integrate human movements into epidemic models [2, 3]. On the other hand, a rangeof mobility models, such as gravity models [4] and radiation models [5], have been developed to
predict population movements in case of missing empirical data and have been used to fill thisgap in epidemic models [2, 6]. More recently, the use of spatially explicit mobile phone data
as a tool to investigate human mobility patterns has gained great popularity, leading to thediscovery of universal characteristics of individual mobility patterns [7, 8]. Similarly to otherapproaches, mobile phone data can be used as a proxy for people movements and then integrated
into epidemic models, once proper scales of time and space are defined. For instance, recent epidemiological studies on malaria have used mobile phone data to estimate human movements incountries where there is limited availability of official data on human mobility [9, 10]. However,despite the variety of approaches, the impact of using different proxies for human movements inepidemic models is still poorly understood. In particular, a comprehensive comparison between
different methods is urgently needed in order to assess the reliability of mobile phone data as aproxy for human mobility.
Methods. In this paper we use a metapopulation network approach to address two main issues:

23

2Michele Tizzoni1 , Paolo Bajardi2 , Adeline Decuyper3 , Guillaume Kon Kam King4 , Christian
Schneider5 , Vincent Blondel3 , Zbigniew Smoreda6 , Marta C. Gonzalez5 , Vittoria Colizza7,8,9
1 Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Torino,
Italy
2 GECO - Computational Epidemiology Group, Department of Veterinary Sciences, Universityof Torino
3 ICTEAM Institute, Universit Catholique de Louvain, Belgium
4 CNRS, UMR5558, F-69622 Villeurbanne, France
5 MIT - Department of Civil and Environmental Engineering, 77 Massachusetts Avenue,
Cambridge, Massachusetts 02139, USA
6 Sociology and Economics of Networks and Services Department, Orange Labs, France
7 INSERM, U707, Paris, France
8 UPMC Universit Paris 06, Facult de Mdecine Pierre et Marie Curie, UMR S 707, Paris,
France9 Institute for Scientific Interchange (ISI), Torino, Italy
Email: MT: michele.tizzoni@isi.it, PB: paolo.bajardi@unito.it, GKKK:
guillaume.konkamking@gmail.com, AD: adeline.decuyper@uclouvain.be, CS:
schnechr@mit.edu, VB: vincent.blondel@uclouvain.be, ZB:
zbigniew.smoreda@orange.com, MC: martag@mit.edu, VC: vittoria.colizza@inserm.fr

Session 6

Proxy networks for human mobility in Europe: the impact on
epidemic modelingSession 6

2

Session 6 // Mobility modelling

Figure 1: Mobile phone coverage. Map of the ratio between the population estimated bymobile phone records (Nimp ) and the census population (Nic ) in the subdivisions of the European
countries under study. Blue regions are undersampled by the mobile phone dataset, while regionsthat appear in green to red are oversampled.

(i) evaluating the adequacy of mobile phone data as a description of commuting patterns in Europe; (ii) evaluating the impact of using mobile phone data as a proxy for human movements inepidemic models. To this aim, we compare the commuting networks extracted from the official
census surveys of three European countries to the corresponding proxy networks extracted fromthree high resolution mobile phone datasets tracking the daily movements of millions of users.
For the first time we are in the position of examining, through a detailed statistical analysis,
the ability of mobile phone data to match the empirical commuting patterns reported by census
surveys, at different geographic scales in a set of European countries. Then, we directly compare
the outcomes of stochastic epidemics simulated on a metapopulation model that is based either
on the empirical commuting networks or on the mobile phone commuting networks. In thisanalysis, we focus on the differences between the modeling approaches in terms of arrival times(i.e. time of first infection) in a subpopulation and invasion paths from the source of the infection to the rest of the network. In addition, we perform the same analyses on a set of synthetic
networks generated using the radiation model and compare the results to those obtained using

24

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Session 6 // Mobility modellingthe mobile phone networks.

References[1] S. Riley, Science 316, 1298 (2007).
[2] D. Balcan et al., Proc. Natl. Acad. Sci. USA 106, 21484 (2009).
[3] M. L. Ciofi degli Atti et al., PLoS ONE 3, e1790 (2008).
[4] J. Ortúzar and L. Willumsen, Modelling transport (Wiley, Chichester, United Kingdom,2001).
[5] F. Simini, M. González, A. Maritan, and A.-L. Barabási, Nature 484, 96 (2012).
[6] S. Merler and M. Ajelli, Proc. R. Soc. B 277, 557 (2009).
[7] M. C. González, C. A. Hidalgo, and A.-L. Barabási, Nature 453, 779 (2008).
[8] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, Science 327, 1018 (2010).
[9] A. Le Menach et al., Sci. Rep. 1, 93 (2011).
[10] A. Wesolowski et al., Science 338, 267 (2012).

25

2Conclusions. Statistical differences between empirical census data and commuting flows inferred from mobile phone users can be relevant, but they do not essentially alter the outcomes ofsimulated epidemics, especially if we consider those observables that are important for evaluating
strategies for disease control. Epidemic results are comparable to those obtained using synthetic
commuting networks generated by the radiation model. Our results confirm the valuable roleof mobile phone data to estimate population movements which can be integrated into spatial
epidemic models to provide support to public health policies.

Session 6

Results. The statistical analysis reveals that mobile phone data can well predict the total
traffic, both incoming and outgoing, of a given node. Commuting flows estimated from mobile
phones are able to reproduce the topology of the census commuting network to a high level ofdetail. Commuters’ flows are generally found to be in good agreement between the two sources ofdata, however the agreement is not statistically significant because of the discrepancies emerging
in the total number of commuters estimated by mobile phones tracks, due to the presence ofsampling biases in the mobile phone dataset. Indeed, mobile phone data tend to overestimate
the magnitude of commuting flows between residence and workplace, especially in those regionsthat are undersampled; hence, epidemics on mobile phone networks spread usually faster thanon census networks, leading to earlier arrival times for all the subpopulations. Discounting thiseffect, the general epidemic behavior of the mobile phone commuting networks and the census
commuting networks shows a good and statistically significant agreement.

3

Session 6 // Mobility modelling

Human Mobility Modeling at Metropolitan Scales
3

Sibren Isaacman∗ , Richard Becker† , Ramón Cáceres† , Margaret Martonosi‡ ,James Rowland† , Alexander Varshavsky† , Walter Willinger†

Session 6∗

Loyola University

isaacman@cs.loyola.edu†

AT&T Labs

Princeton University{rab,ramon,jrr,varshavsky,walter}@research.att.com

Human mobility models have myriad uses in mobile computing research and other fields of study. Models that faithfully reproduce the movements of real people can help answer questions in areas as varied as mobile sensing, opportunistic networking, urban planning, ecology, and epidemiology. For example, a model of how people move arounda city can help evaluate whether a sensing application running on mobile phones would be able to attain the desired
geographic coverage.

mrm@princeton.edu

input certain spatial and temporal probability distributions
drawn from large populations of real people living acrosswide geographic areas. An especially good source of these
distributions are the Call Detail Records (CDRs) maintainedby cellular network operators. Billions of cellphone users
worldwide keep their phones near them most of the time,
and the networks need to know the rough location of all active phones to provide them with voice and data services.
CDRs contain information such as the time a voice call wasplaced or a text message was received, as well as the identity of the cell tower with which the phone was associatedat that time. When joined with information about the locations of those towers, CDRs can serve as sporadic samplesof the approximate locations of the phone’s owner. A growing body of work has shown that information derived fromanonymized CDRs can accurately characterize many aspectsof human mobility [1, 2, 3, 4, 5, 6, 12].

Our work aims to produce accurate models of how large
populations move within different metropolitan areas. In
pursuit of this general aim, we have a number of more specific goals. Our first goal is to generate sequences of locations and associated times that capture how individuals move
between important places in their lives, such as home andwork. Previous work has shown that people spend most of
their time at a few such places [3, 4, 12]. Our second goal
is to aggregate the movements of many such individuals to
reproduce human densities over time at the geographic scaleof metropolitan areas. A model that operates at these scales
can help address important societal issues such as the environmental impact of home-to-work commutes. Our third
goal is to take into account how different metropolitan areas exhibit distinct mobility patterns due to differences in
geographic distributions of homes and jobs, transportation
infrastructures, and other factors. Previous work has shown
significant differences between cities along metrics such ascommute distances [4, 5, 6, 11].

With cellular network data becoming more available, it is
tempting to think that creating human mobility models fromsuch data should be easy. However, this is not the case. For
example, while CDRs readily yield insights into aggregate
population densities, they do not convey whether their associated locations correspond to home, work, or other important places for particular cellphone users. Without suchsemantic information, it is difficult to abstract CDRs intomodels applicable to scenarios, regions, or populations thatvary from those for which the real-life CDR data was collected. Furthermore, both the spatial and temporal granularity of CDR data is quite coarse. Spatially, CDRs are only accurate to the granularity of celltower spacings. Temporally,
CDRs are only generated when phones are actively involvedin a voice call or text message. Our work makes key contributions in overcoming the challenges stemming from lackof semantic information and coarse granularity, to produce
usefully accurate models for arbitrary metropolitan regions.

Many human mobility models that fall short on one ormore of these goals have been proposed in the past. Somemodels produce random motion that does not correspond toactual mobility patterns, e.g., [8, 10]. Their lack of memory
about recurring movement patterns and of spatiotemporal realism about population densities results in unrealistic motionof modeled individuals. Some models are tailored to a small
geographic area such as a university campus, e.g., [9]. They
do not apply to larger geographic areas with more diverse
populations. Some models aim to be universal, e.g., [3], andthus do not adapt to different geographic areas. There remains a need for a realistic model that matches empiricalobservations for large and distinct geographic areas.

Our modeling approach intelligently samples the spatial
and temporal probability distributions from CDRs, or otherpopulation data, to generate sequences of locations and times
for any number of synthetic people in any region for which
the required distributions can be obtained. A generative model
derived from CDRs has flexibility, compactness, and availability advantages over using CDRs directly. First, our mod-This paper introduces a modeling approach that takes as
1

26

‡

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Session 6 // Mobility modelling

Probability ofHome at each(lat, long)

SelectHome(lat, long)

Distribution ofCommuteDistances at
each (lat, long)

SelectDistanced

CDRs

Previously
Published
Data

Probability ofWork at each(lat, long)

Session 6

CensusData

Get set of(lat, long)’s atradius daround Home
SelectWork(lat, long)

Probability ofcall being
made at each(lat, long, hour)

Select current
location and time(lat, long, minute)3

Probabilities ofa call at each(minute)Get set of(minute)’s

Figure 1: Overview of the WHERE modeling approach.
els offer the option of perturbing the input distributions toevaluate what-if scenarios, for example to consider how theaddition of a new residential or employment area might change
traffic patterns. In contrast, the original CDRs are difficult
to manipulate in meaningful ways. Second, our model fora metropolitan area with a 50-mile radius can be stored asa set of histograms that fit within 2 gigabytes. In contrast,
an anonymized CDR dataset for the same area occupied approximately 100 gigabytes. Finally, our models can be madeavailable to a larger research community because they do
not to reproduce the mobility pattern of any individual real
person. They thus avoid many of the privacy concerns associated with source CDRs.
The final stage of our modeling approach produces locations and times in the form of synthetic CDRs. These synthetic CDRs have the same format and call/text frequency
characteristics of real CDRs. They are modeled to approximate the actual movement patterns of users. Increased model
complexity results in more accurate movement patterns, which
in turn produces higher-fidelity synthetic CDRs. We chose
the CDR output format for several pragmatic reasons. One,
we can compare this output directly against real CDRs, ourbest source of location information for large populations andregions. Two, this output can plug in directly into the growing body of analysis software that uses CDRs as input.
In our full paper [7], we propose and evaluate WHERE(“Work and Home Extracted REgions”), a region-scale modeling approach. First, we identify the key properties of human movement, such as important locations and commute
distances, that need to be represented as probability distributions. Then, we describe how these probability distributions can be used to generate synthetic CDRs for an arbitrary
number of synthetic people. Figure 1 summarizes the overall
flow of our approach.
We validate our approach by comparing the spatiotemporal dynamics of synthetic populations generated by WHERE
to those of real populations. In particular, we use EarthMover’s Distance (EMD) as a metric to compare the spa-tial population densities on an hourly basis for synthetic andreal CDR sequences. Our validation begins with stylized examples that confirm our models’ fidelity both quantitatively
and visually. We validate both at the aggregate level, where
simpler models may perform well, as well as at a finer granularity, which exposes the advantages of WHERE comparedto other models considered. We then scale up our validation
to large datasets containing real anonymized CDRs for theLos Angeles (LA) and New York City (NY) metropolitan
areas. Our LA and NY datasets each span three months ofactivity for hundreds of thousands of phones, yielding billions of location samples.
Recognizing that real CDRs are not available to all researchers, we also evaluate models in which the same input
distributions are derived from publicly available US Census
data [13]. We show that models based on real CDRs closely
approximate the real populations and movements of thesecities. Models based on census data are also viable, but at aloss of significant accuracy.
Finally, we present example applications of our modelingapproach. We create models for the LA and NY metropolitan areas and use the resulting synthetic CDRs to perform
calculations that one may wish to perform on real CDRs.
We show that calculations performed on the WHERE model
produce far more accurate results than those performed on
more naive models. For example, we can calculate daily
ranges of travel that agree with real ranges, as well as perform more complex tasks such as investigating opportunistic
message propagation in large urban environments.
The overall contributions of our work are:
• We introduce an approach to modeling human mobility
patterns by generating fully synthetic CDRs from realworld probability distributions.
• Our approach works at the scale of large metropolitan
areas and accounts for mobility differences between
metropolitan areas.
• We show that our technique is extensible to greater lev2

27

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Session 6 // Mobility modelling

1.

18
16
Error (miles)

14
12

RWP
WRWP
WHERE2
WHERE3

10
8
6

3

4
2

Session 6

0
0

5

10
Hour

15

20

Figure 2: EMD error over time for different models ofhuman movement in the NY area. Our WHERE3 modelshows average errors smaller than 3 miles. WHERE2 fits
between WHERE3 and WRWP.
Miles
0.1

1

10

100

Real NY
0

1.1

0

1.1

3.2

7.1

17.7

Model NY
2.4

7.0

26.4

Real LA
0

1.8

4.5

8.9

20.2

Model LA
0

1.4

3.5

7.5

17.2

Figure 3: Daily range statistics for the NY and LA regions as calculated from real data and the output of our
WHERE2 model. WHERE2 accurately recreates thecharacteristics of NY and LA mobility.
els of precision by providing it more complete input
probability distributions (at the cost of increased model
complexity). For example, we can create WHERE2
models with only home and work locations, and WHERE3
models that include an additional important location.
• We validate our approach against large-scale location
datasets drawn from two major US metropolitan areas.
We compare our generated CDRs against real CDRs,
and show that our location distributions achieve morethan 4 times error reduction compared to a Random
Waypoint (RWP) model. Figure 2 shows these results
for the NY region.
• As an example of how our models can help answer
concrete questions about human mobility, we use our
synthetic CDRs to compute daily ranges of travel. Our
synthetic CDRs exhibit error at the median of less than
0.8 and 1 mile for NY and LA residents, respectively.
This accuracy constitutes a more than 14 times improvement over a Weighted Random Waypoint (WRWP)
model. Figure 3 shows these results for the NY region.
Please see our full paper [7] for additional details.
328

References[1] M. A. Bayir, M. Demirbas, and N. Eagle. Discovering
spatiotemporal mobility profiles of cellphone users.
World of Wireless, Mobile and Multimedia Networks
and Workshops, 2009.
[2] F. Girardin, A. Vaccari, A. Gerber, A. Biderman, andC. Ratti. Towards estimating the presence of visitorsfrom the aggregate mobile phone network activity theygenerate. In Intl. Conference on Computers in Urban
Planning and Urban Management, 2009.
[3] M. C. González, C. A. Hidalgo, and A.-L. Barabási.
Understanding individual human mobility patterns.
Nature, 453, 2008.
[4] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov,
M. Martonosi, J. Rowland, and A. Varshavsky.
Identifying important places in people’s lives from
cellular network data. In 9th International Conf. on
Pervasive Computing, 2011.
[5] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov,
M. Martonosi, J. Rowland, and A. Varshavsky. Rangesof human mobility in los angeles and new york. In
Eighth IEEE Workshop on Managing Ubiquitous
Communications and Services, 2011.
[6] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov,
J. Rowland, and A. Varshavsky. A tale of two cities. In
Workshop on Mobile Computing Systems andApplications (HotMobile), 2010.
[7] S. Isaacman, R. Becker, R. Caceres, M. Martonosi,
J. Rowland, A. Varshavsky, and W. Willinger. Human
Mobility Modeling at Metropolitan Scales. In
Proceedings of the Tenth Annual International
Conference on Mobile Systems, Applications, andServices (MobiSys 2012), June 2012.
[8] D. B. Johnson and D. A. Maltz. Dynamic source
routing in ad hoc wireless networks. In Mobile
Computing, pages 153–181. Kluwer Academic
Publishers, 1996.
[9] M. Kim, D. Kotz, and S. Kim. Extracting a mobility
model from real user traces. In IEEE INFOCOM,
2006.
[10] W. Navidi and T. Camp. Stationary distributions for
random waypoint models. IEEE Transactions on
Mobile Computing, 3(1):99–108, 2004.
[11] A. Noulas, S. Scellato, R. Lambiotte, M. Pontil, andC. Mascolo. A tale of many cities: universal patterns
in human urban mobility. PLoS ONE, 2012.
[12] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási.
Limits of predictability in human mobility. Science,
327, 2010.
[13] US Census Bureau. http://www.census.gov.

4

Session 6 // Mobility modelling

Geographic Similarity Within Social Networks
Jameson L. Toole
Engineering Systems Division, MIT, Cambridge, MA 02139

Carlos Herrera

Session 6

Departamento de Matemática Aplicada, Universidad Politécnica de Madrid, Madrid, Spain

Christian M. Schneider and Marta C. González
Civil and Environmental Engineering, MIT, Cambridge, MA 02139

INTRODUCTION

B Mobility

Location

Much research has approached social behavior and mobility separately. Studies have mapped the local andglobal structure of large call networks [1, 2] and used
this structure to predict characteristics and activity patterns of individuals [3]. Geographic data on its own
has revealed universal patterns of human mobility anda surprisingly high amount of predictability to a person’s
movement [4–6]. Recent efforts have combined the twodata types. Correlations between the proximity of two
individuals and the likelihood they are friends [7, 8] have
proven especially important in the diffusion of information [9, 10]. Moreover, predictions of where an individual
will be in the future are improved by incorporating information on the whereabouts of friends [11, 12]. Results
confirm humans as a social species, willing to travel tobe with friends and family [13].
However, these studies have been limited in their approach, seeking to predict movement to and from only asmall subset of places using only a few good friends. This
work addresses builds upon previous studies to more fully
understand the complexities of social behavior and mobility.

B

LocationA

Calls

Each day over 3.5 billion people wake up in a city. They
commute to work, schedule meetings, and convene withfriends for dinner. Mobile phones are increasingly used
to coordinate these behaviors and they are collecting andstoring massive amounts of data along the way. Originally designed as communication devices, mobile phones
are exceptionally well-suited to measure social behaviors
such as who a person talks to, how often, and when. Now
innovations in hardware and software make it possible forphones to provide high-resolution data on the geographic
location of their users. To better understand how cities
function and the behavior of those living in them, thiswork analyzes social and geographic data collected byroughly 800,000 mobile phones in two large cities in a
European country over a 15 month period. The goal is
to shed light on a simple, but important question: How
does a person’s social network affect how much and towhere they travel?

A

Location

C

2

1

E

θ3

θ2
θ1

B

3

A Social NetworkC

C Cosine Similarity

FIG. 1: A) We construct a social network where nodes
are users and links are weighted by the number of calls
between two individuals. For each user, E, we construct an ego-network and rank social contacts basedon the number of calls with the ego. B) A location vector is constructed for each user by counting the number of calls made at each tower in the region. In thisdiagram, four uses (including the ego) record calls at
3 locations. C) Finally, we measure the similarity between the movement of two users by calculating the cosine similarity of their location vectors. Users who visit
the same locations with the same relative frequency
will have locations vectors which point in the same direction, thus giving a cosine similarity value of 1 whileusers that do not share any visited locations in common
have a cosine similarity of 0.

II.

METHODS

Figure 1 diagrams our metrology. For each city, we
begin by constructing a social network where links between two users are weighted by the number of calls between them. Locally, we construct an ego network consisting of all users called by an individual and rank eachof these contacts by the number of calls between them
(Figure 1A). Globally, we assess how the geodesic dis-

29

4

I.

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Session 6 // Mobility modelling

Session 6

4

2tance between two individuals relates to the similarityof two user’s movement. To describe the mobility of aperson in a city, we construct it location vectors for eachindividual. Given a set of T locations (in this case mobile
phone towers) in a city, construct a T-dimensional vector,~u for each user where element ui is given by the numberof times a user calls from location i(Figure 1B). To assess
the similarity between the mobility patterns of two individuals, we compute the cosine similarity between theirvectors in this location space (Figure 1C).

III.

RESULTSWe measure how the similarity of users changes withthe social relationship between them. Figure 2A shows
the average cosine similarity between two users separated
by a given geodesic distance d in the call network. The
similarity of location vectors for direct neighbors is 10
times that of two randomly chosen individuals. Significantly higher similarity values are only measured between
individuals separated by up to three degrees (d = 3) inthe network.
Interpretation of these results is complicated by thefact that individuals who live in an area are likely tohave access to similar places as well as being far more
likely to be friends. In order to measure the contributions of social and geographic proximity to similarity, we
compare four combinations of two variables: sharing or
not-sharing the same home and work locations and arebeing or not-being direct neighbors in the call graph. We
find that sharing the same home and work locations witha user contributes only slightly more to the similarity ofvisits to other places than being direct contacts (TableI).
TABLE I: Four groups are constructed from combinations of (not) sharing the same home and work locations (+/-HW) and (not) making calls between them(+/-F)
Similarity +HW -HW+F 0.44 0.19
-F 0.25 0.04

Next, we explore how similarity changes with tie
strength and relative location importance. The insert
in Figure 2B shows that tie strength is highly correlatedwith location similarity. An individual is nearly twice assimilar to the contact they call the most than to the 30th

[1] J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó,
D. Lazer, K. Kaski, J. Kertész, and A.-L. Barabási,
Proceedings of the National Academy of Sciences of theUnited States of America 104, 7332 (2007).

30

ranked social contact. Furthermore, by randomizing location data we find that having one-half to two-thirds ofthe similarly between contacts is related to placing thesame relative importance on locations rather than simply
visiting the same places (regardless of how often).
The relative importance of a place is also correlatedwith the social rank of a contact. We begin by calculating
the average similarity between a user and each of her 30
people she makes the most calls to. We then successively
remove locations from each user’s location vectors basedon importance and recalculate similarity. For example,
the first step sets the number of visits to each user’s mostvisited location to 0, before recalculating similarity. The
second step sets visits to the top two most visited locations of all users to 0, and so on. Figure 2B shows that removing the most visited location of all users dramaticallydecreases the similarity between a person and their top 4social contacts, but increases their similarity to contacts
5-30 (and to a random user). This suggests that individuals share their most visited location with their top
4 friends. Moreover, we see non-monotonic changes insimilarity over successive removals. We hypothesis that
regimes featuring increasing similarity reflect locations
that are not shared with social contacts (thus removing
them makes a user more similar to a contact), while thereverse is true for regimes featuring decreasing similarity. Moreover, these dynamics appear robust to changes
in the size of the space of possible locations to visit.
Finally, having explored the relationship between an
individual’s social behavior and where he or she moves,
we quantify the correlation between social behavior andhow much movement takes place. We focus on threemobility metrics, the radius of gyration, number of towers
visited, and the entropy of those visits. As the degree ofan individual in the call network increases from 0 to 70,
we find that individuals travel farther, to more places,
and with in less predictability. However, this affect isinverted for users with degree beyond 70. Moreover, wefind that, unlike other forms of social contagion [14], it
is only the number of connections that correlates withmobility and not the structural diversity of them.
IV.

FUTURE WORKFor future work, we hope to relate these findings to
the predictability of users in order to develop an analytic model that reproduces these patterns. We will also
explore the sensitivity of our measurements to the dimension of the location space and the length of the time
periods used.

[2] A.-L. Barabási, Nature 435, 207 (2005).
[3] N. Eagle and A. S. Pentland, Behavioral Ecology andSociobiology 63, 1057 (2009).

4

Session 6 // Mobility modelling
3

B

0.25
0.20

0.15

0.15

0.10

0.10

0.05

10

15

20
0.3

0.05

1

2

3

4 Rand.

Social Distance0

TIVGnFGSFRTIVGnFMPD

0.2

25
0.1
0

0

data
SBOEPNVTFS

0

10

20

30

Social Rank

0

5

10

15

4

0.20

5

Social Rank

A

1

20

25

30
Rand.

30

Number of Locations Removed

FIG. 2: A) We show the average cosine similarity of visited locations between users separated by a geodesic distance d in the call graph. The movement of users who are direct neighbors are 10 times more similar than two users
selected at random. Moreover, we find significant increases in similarity up to 3 degrees removed from an individual. B) We show how similarity depends on the strength of a relationship (ranked by number of calls) and how thissimilarity changes when important locations are systematically removed. The stronger the tie, the more similar thelocations and frequency of visits. Moreover, successively removing the k most visited locations from each user’s vector then recalculating similarity reveals which locations are shared between friends of a given rank.

[4] F. Simini, M. C. González, A. Maritan, and A.-L.
Barabási, Nature 484, 8 (2012), arXiv:1111.0586.
[5] M. C. González, C. A. Hidalgo, and A.-L. Barabási,
Nature 453, 779 (2008).
[6] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, Science
327, 1018 (2010).
[7] D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan, andA. Tomkins, Proceedings of the National Academy of Sciences of the United States of America 102, 11623 (2005).
[8] L. Backstrom, E. Sun, and C. Marlow, North WWW’10, 61 (2010).
[9] J. L. Toole, M. Cha, and M. C. González, PLoS ONE 7,
e29528 (2012), arXiv:1110.0535.
[10] J.-P. Onnela, S. Arbesman, M. C. González, A.-L.
Barabási, and N. A. Christakis, PLoS ONE 6, 7 (2011).

Session 6<Cos(ѡ)>

0.25

0.30

<Cos(ѡ)>0.30[11] M. D. Domenico, csbhamacuk 2012 (2012).
[12] V. Etter, M. Kafsi, and E. Kazemi, in Mobile Data Challenge by Nokia Workshop in conjunction with Int Conf on
Pervasive Computing (2012).
[13] E. Cho, S. A. Myers, and J. Leskovec, in Proceedingsof the 17th ACM SIGKDD international conference on
Knowledge discovery and data mining KDD 11 , KDD’11 (ACM Press, 2011) p. 1082.
[14] J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg,
Proceedings of the National Academy of Sciences 2012,
1 (2012).

31

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Session

7

Trajectories and regularities

33

1

Session 7 // Trajectories and regularities
Late for Good
Vsevolod Salnikov and Renaud Lambiotte
naXys, University of Namur, Belgium

The last few years have witnessed the increasing useof mobile phone devices as a way to collect social data
at a large-scale [1, 2]. Each of us carries a mobile phone,almost continuously, and its sensors and online services
provide a more and more complete view on our life andour environment. Our phone knows our whereabouts,
our friends, where we meet, our taste in music, etc. The
integration and monetization of these data by corporations raises alarming privacy issues. Yet, when treatedethically and used for the common good, they also havethe potential to provide innovative ways to solve problems in areas such as public health, ecology or urban
planning [3, 4]. Examples include the automatic detection of dysfunctions in the transport infrastructure, andthe participatory report of ecological or epidemiologicaldata.
In the particular case of mobility tracking, two different types of approach have been developed to extract
individual trajectories from mobile phone data. The first
approach exploits data already routinely collected by cellular network or by online services. Examples include
Foursquare check-in data [5], or Call Detail Records of aphone connecting to a cell tower [6, 7]. These data havethe advantage of being acquired for free and of being
analyzed at virtually no cost, but they have the disadvantage of being proprietary, often with strict confidentiality agreements, and of variable quality. For instance,
CDRs are known to provide a sparse and heterogeneous
sampling of the trajectories, with a fairly poor spatial
resolution.
The second approach aims at tracking mobility in amore controlled way, either by handing mobile devices toa limited number of users, or by distributing software,
typically downloadable apps, tracking motion. However,this approach either relies on ad-hoc infrastructure or on
the active participation of users, and it has, as a consequence, been limited to small sample sizes so far [8].
The aim of our work is to address this limitation andto explore the possibility to attract a large number ofindividuals in a mobility tracking experiment. Because
the deployment of infrastructure is prohibitively expensive at this scale, we focus on the development of apps
and their adoption by the broader public [9, 10]. To be
installed, an app needs to compete against thousands of

34

4000

delaysregistrations

3500

3000
number of events (cum)Session 7

1

In this work, we perform a large-scale experiment on human mobility by using smartphones as
sensors. To do so, we have developed an app continuously tracking significant displacements ofphones and providing in return to their user an attractive service. Contrary to standard tracking
procedures based on Call Detail Records, our methods offers the advantage of collecting data inreal-time, in an anonymous way, and without the constraints usually imposed by mobile phone
operators.

2500

2000

1500

1000

500

0
11/03/12

11/17/12

12/01/12

12/15/12
date

12/29/12

01/12/13

01/26/13

FIG. 1: Time evolution of the total number of users and submitted delays. After an initial rapid growth, we are in a
steady regime, where the number of new users and delays isroughly constant.

others and, typically, to be run continuously in the background of the phone OS despite its energy cost. To be
successful, the deployment of a tracking experiment thus
requires proper incentives for a user to participate. For
this reason, a majority of experiments have failed to reacha large part of the population, and they have typically
been limited to circles of geeks, students and researchers.
In this work, we have developed an app for iPhones andAndroid devices, called lateforgood [13], where we offer an
attractive service to the user, and not only the prospectof participating in a research experiment. The app is dedicated to Belgian train users, and helps them keep trackof their train delays and submit a form for financial compensation to the national train company [14]. Incentives
for the user are thus: money (paid by the train company
for delays), time and convenience (the otherwise tediousform is automatically filled on our servers) but also theimportant feeling of being heard by the train company
as a collective voice (the punctuality of train delays has
been the subject of animated debates in Belgium in thelast years).
In practice, the app works as follows. When it is first
opened, it starts tracking the motion of the user, in order to detect closeby train stations. The app runs in1

Session 7 // Trajectories and regularities
2
DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy
Office.

Acknowledgment

We acknowledge financial support from FNRS.This
paper presents research results of the Belgian Network

35

1

the background on the mobile device, using a technology
similar to the one used by openpath [11] to minimize impact on battery life: the app does not record the position
continuously via GPS, but tracks only significant changes
in position determined by the device API, via GPS, WIFI access and triangulation of cell phone towers. Whenthe user re-opens the app to declare a train delay, a listof likely official train schedules is proposed, based on recent mobility patterns, and a delay can be declared anduploaded to the user profile. When a sufficient numberof delays has been accumulated, a compensation form ispre-filled and ready for download. Let us also stress thatthe app uploads data to our servers only when the app isused by its user. Moreover, the issue of privacy has been
considered carefully as no personal information is hold:on our side, it is as if mobility patterns of anonymous
mobile phones were collected.
The experiment is still in its acquisition phase. Since
the launch of our service in November 2012, around 3500
users have downloaded it, among which 40% use it actively (Fig. 1). During this time period around 4000 delays have been submitted, leading to 600000 data points
for the position of the users, and providing us with information on the main train lines in Belgium (Fig. 2).
We are currently developing algorithms to distinguishbetween car and train mobility, to properly assign sampled trajectories to train routes [12], including connections between train lines, and to uncover a list of train
lines and/or stations particularly affected by delays.

Session 7

FIG. 2: Geographical representation of train delays. Twotrain stations are connected when a delay has been notified.
Darker lines correspond to more delays.

[1] R. Kwok, “Phoning in data”, Nature 458, 959-961 (2009)[2] J. Reades, F. Calabrese, A. Sevtsuk and C. Ratti, “Cellular Census: Explorations in Urban Data Collection”,
IEEE Pervasive Computing 6, 30–38 (2007)[3] A. Pentland, D. Lazer, D. Brewer and T. Heibeck, “Using
reality mining to improve public health and medicine”,
Stud. Health Technol. Inform. 149, 93–102 (2009)[4] V.D. Blondel, M. Esch, C. Chan, F. Clerot, P. Deville,
E. Huens, F. Morlot, Z. Smoreda and C. Ziemlicki, “Datafor development: the D4D challenge on mobile phonedata”, arXiv:1210.0137 (2012)[5] A. Noulas, S. Scellato, R. Lambiotte, M. Pontil and C.
Mascolo, “A Tale of Many Cities: Universal Patterns in
Human Urban Mobility”, PLoS ONE 7, e37027 (2012)[6] M.C. González, C.A. Hidalgo, A.-L. Barabási, “Understanding individual human mobility patterns”, Nature
453, 779-782 (2008)[7] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, J.
Rowland and A. Varshavsky, “A tale of two cities”, Proceedings of the Eleventh Workshop on Mobile Computing
Systems (HotMobile ’10), 19–24 (2010)[8] N. Eagle and A. Pentland, “Reality Mining: Sensing Complex Social Systems”, Personal and Ubiquitous
Computing 10, 255–268 (2006)[9] http://www.funf.org/[10] M. Wirz, T. Franke, D. Roggen, E. Mitleton-Kelly, P.
Lukowicz and G. Troster, “Inferring crowd conditions
from pedestrians’ location traces for real-time crowd
Monitoring during city-scale mass gatherings”, Proceedings of the IEEE 21st international workshop on enabling
technologies, 367–372 (2012)[11] https://openpaths.cc/[12] R.A. Becker, R. Cáceres, K. Hanson, J. Meng Loh, S.
Urbanek, A. Varshavsky and C. Volinsky, “Route classification using cellular handoff patterns”, Proceeding of
Ubiquitous Computing (UbiComp 2011)[13] More info can be found on http://www.sci-app.com[14] The compensation is equal to 100% of the value of theticket if the delay is of at least 60 minutes. In case ofmultiple delays within a 6 month period, a compensation of 25% (50%) can be claimed if, over a period of 6months, at least 20 (10) delays of at least 15 (30) minutes
are suffered. The compensation is received in the form ofvouchers.

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Session 7 // Trajectories and regularities

1

Do Mobile Phone Data Allow Estimating
Real Human Trajectory?
Sahar Hoteit†§ , Stefano Secci† , Stanislav Sobolevsky§ , Guy Pujolle† , Carlo Ratti§† LIP6/UPMC - University of Paris VI; 4 Place Jussieu, 75005 Paris, France

2§ MIT Senseable City Laboratory, 77 Massachusetts Avenue Cambridge, MA 02139 USA.

Session 7

E-mail: sahar.hoteit@lip6.fr, stefano.secci@lip6.fr, stanly@mit.edu, guy.pujolle@lip6.fr, ratti@mit.edu

Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into agigantic ubiquitous sensing platform, enabling large-scale analysis and applications. In recent years, mobile databased research reaches important conclusions about various aspects of human mobility patterns and trajectories.
But how accurately do these conclusions reflect the reality?
In order to evaluate the difference between the reality and the approximation methods, we study the error between
real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear,
cubic, nearest, spline interpolations) and taking into consideration mobility parameters.
We use for this aim a dataset consisting of anonymous cellular phone signaling data, it consists of location
estimations for about one million devices in the Boston metropolitan area.
To evaluate the error between real human trajectories and the estimated ones, we fine-select data of thosesmartphones holders with a lot of samplings, typically those data-plan users with persistent Internet connectivity due
to applications such as e-mail synch. Then, in order to reproduce artificial “normal user” sampling, we subsample
real data-plan smartphone quasi-continuous traces according to an experimental inter-event statistical distribution.
Therefore, we extract, from the real trajectory, a first random position then the corresponding next positions are
extracted according to the inter-event time distribution values.
Hence, given a real trajectory with a high number of positions, and its subsampling that reproduces normal user’s
activity, we apply an interpolation method to estimate the trajectory across the given points. Given the real trajectory
points Pi , we estimate its corresponding position in time in the estimated trajectory: Pi0 . Then we determine thedeviation between the two points]as the distance separating the exact position Pi to the estimated position Pi0 in
the interpolating curve joining the samples. To take into account mobility habits, we categorise the users dependingon their “radius of gyration” defined by the deviation of user positions from the user centroid position.
From extensive evaluations based on real cellular network data of the Boston metropolitan area, we show thatthe linear interpolation offers the best estimation for sedentary people (with a small radius of gyration) and the
cubic one for commuters (having a big radius of gyration). Moreover, the nearest interpolation appears as the best36

2

Session 7 // Trajectories and regularities2(c) 10km<rg<=32km(d) rg>32km

2(b) 3km<rg<=10km

Session 7(a) rg<=3km

Fig. 1: Boxplots of trajectory error

Fig. 2: Probability density function of error

one for “ordinary people” doing regular stops and standard displacements (Figure 1).
Another important experimental finding is that trajectory estimation methods show different error regimes whether
used within or outside the “territory” of the user defined by the radius of gyration. The distribution of errors over
all users’ positions is approximated by a combination of two power law distributions joined by a breakpoint(approximately equal to 2.2) for the different interpolation methods (Figure 2).
As a future work we aim to estimate the positions of hotspots in a region knowing only the mobility characteristics
of its residents.

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Session 7 // Trajectories and regularities

Depict Urban Activities from Real Movement with Auto-GPS
Teerayut HoranontInstitute of Industrial Science
The University of Tokyo
Komaba, Tokyo 153-8505, JAPANteerayut@iis.u-tokyo.ac.jp

Apichon Witayangkurn
Department of Civil Engineering
The University of Tokyo
Komaba, Tokyo 153-8505, JAPANapichon@iis.u-tokyo.ac.jp

Ryosuke Shibasaki
Center for Spatial Information Science
The University of Tokyo
Kashiwa-shi, Chiba 277-8568, JAPANshiba@csis.u-tokyo.ac.jp

Session 7

3

Abstract
Today, the urban computing scenario is emerging as a concept where human can be used as a component toprobe city dynamics. The urban activities can be described by the close integration of ICT devices andhumans. In the quest for creating sustainable livable cities, the deep understanding of urban mobility andspace syntax is crucial importance. This research aims to explore and demonstrate the vast potential of usinglarge mobile GPS dataset for the analysis of human activity and urban connectivity. The new type of mobile
sensing data called “Auto-GPS” has been anonymously collected from 1.5 million people for a period of one
year in Japan. The analysis delivers some insights on interim evolution of population density, urban
connectivity and commuting choice. The results enable planner to better understanding of urban organismwith more complete inclusion of urban activities and their evolution through space and time.
1

Introduction

How new technology can help cities manage and deliver a sustainable future. In the past few years, it has
become possible to explicitly represent and account for time-space evolution of the entire city organism.
Information and communication technology (ICT) has the unique capability of being able to capture theever-increasing amounts of information generated in the world around us, especially the longitudinal
information that enables us to investigate patterns of human mobility over time. Thus, the use of real-time
information to manage and operate the city is no longer just an interesting experience but a viable alternativefor future urban development.
In this research, the analysis of mobile phone location, namely “Auto-GPS”, has been used to serve as
frameworks for the variety of measures of effective city planning. More specifically, we explore the use of
location information from Auto-GPS to characterize human mobility in two major aspects. First is thecommuting statistics and second is the city activity, how the change of activities in part of urban space can bedetected over times.
2

DatasetThere were two datasets used in this study. The main dataset were collected from approximately 1.5 million
mobile Auto-GPS users of a certain mobile phone service provided by a leading mobile phone operator inJapan. Under this service, handsets provide a regular stream of highly accurate location data, and thereby
enable support services that are closely linked with the user’s behavior. Technically, an Auto-GPS-enabledhandset position is measured within 5 min and sent through a network of registered services. (Fig.1) The data
was recorded from August 2010 to October 2011. In order to preserve user privacy, Auto-GPS data isprovided in a completely anonymous form to ensure privacy of personal information.
Fig. 1 shows a graph of the average number of GPS points per day in this dataset. A small sample of the raw
data is shown in Fig 3.

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Session 7 // Trajectories and regularities

3 Results and Discussions

Fig. 2. Estimate annual visitor to the Odaiba area by prefecture.
We continue observed the number of visitors in Odaiba per day, it appears that the area are more popularduring summer. This is because of the big event arranged by Fuji TV. The highest visit to Odaiba is on
August 14 when the Tokyo Bay Grand Fireworks Festival was held. The second most visit is on theChristmas Eve as the area is known as couple-y place. We notice a significant distinct drop in the number ofvisitors suddenly in March 11. It was the day of the earthquake magnitude 9.0 hit Japan in 2011, followedwith the "radiation leakage" of 2 nuclear power plants in Fukushima prefecture. We could captured 3 weeks
of anomaly reduction in visiting the area before it returned to the normal situation.

39

3

Finding the urban descriptive knowledge such as where/how/when/why of the people who use the area is oneof the most important information for urban planners. Our first result attempted to explain where the people
come from. We constructed multiple criteria to define visitors in the area. We used the minimum stay of 30
minutes and excluded people who have home and work location in the area. The maximum annual visit is set
to 8 times as it is the third quartile of the entire dataset. The total annual visitors to Odaiba area wasestimated at 80,463 people from 1.5 million total samples or 5.36% of the population. Fig. 2 shows thechoropleth map of estimate annual visitor. As expected, the closer prefecture the most likely people comefrom. There are some exception for the big cities such as Nagoya, Osaka, Fukuoka and Hokkaido where airtransport services are operated more often.

Session 7

Fig. 1. The average number of GPS points per day is 37, indicating that Japanese users spent approximately
3 hours traveling with their handset each day.

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Session 7 // Trajectories and regularities

Fig. 3. Estimate daily visit to Odaiba area. The magnitude of anomalies can vary greatly between events,
and this could lead to composite dominated by a few major events.
Next, we visualized how different the activities between weekday and weekend by overlaid weekday staypoints over the weekend. (Fig. 4) Surprisingly, there are several clusters that highly dominate over others atparticular location. By incorporating prior knowledges of the area and collection of news, it revealed clear
evidence how the patterns created. The location marked with “a” are complex buildings where shopping
malls, hotels and restaurants are located in. This yield the similar distribution of both weekday and weekend.
The “b” mark is an open space where we can see half of the area are more active during the weekend. This isbecause of the special events are usually held only on the weekend. The area in upper part are served as
outdoor parking space and is the main area of Fuji TV summer event. This event is usually held for 3 months
in summer regardless weekend or weekday. The “c” areas are event spaces that mainly occupied during
weekend. The “d” areas are office building that is more dominant in weekend. Please note that the d1 area isa construction area during our data collection period.
Fig. 5 provides valuable population countinformation in each building of the entire year. The
hight of 3D building corresponded to the number ofvisitors. It is clear that Shopping malls, restaurantand complex type buildings are the most destination
place in Odaiba area. All of them are 10 times more
visitors than the office area.

6 Conclusions
This study explores the potential of using mobile
Auto-GPS enables in the new context and broader
advances towards the understanding of today’s
excessive mobility. The finding of this remarkabledataset is to capture the urban evolution from the real
movement of people. The results display summarizes
the findings of the comprehensive and creative
process using Auto-GPS data. Finally, theimportance of this research ultimately lies on how it
can be practically applied and utilized massive
amount of high accurate GPS data for futuresustainable urban development.

Fig. 4. Comparison of estimated people activities
between weekday (yellow) and weekend (red).

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Session 7 // Trajectories and regularities
Acknowledgements
This research carried out under the support of DIAS (Data Integration and Analysis System) by MEXT
(Ministry of Education, Culture, Sports, Science and Technology). We would like to thanks ZENRIN Data
Com (ZDC) CO., LTD. for archiving the dataset used in this research.

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Fig. 5 The yearly visit counted in every buildings in Odaiba area. The hight of building represented thenumber of visitors who stop by
References[1]
M. C. Gonza ́lez, C. A. Hidalgo, and A.-L. Baraba ́si. (2009) Understanding individual human mobility
patterns, Nature, vol. 458, pp. 238–238[2]
C. Song, Z. Qu, N. Blumm, and A.-L. Baraba ś i. (2010) Limits of predictability in human mobility, Science,
vol. 327, no. 5968, pp. 1018–1021.
[3]
Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Rowland, J., & Varshavsky, A. (2010). A tale of two cities.
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications - HotMobile ’10, 19.
[4]
Robert J. and Wytse M. (2003). Wearable GPS device as a data collection method for travel research. Working
Paper, ITS-WP-03-02, Institute of Transport Studies, University of Sydney[5]
Hongmian G., Cynthia C., Evan B., and Catherine L. (2011). A GPS/GIS method for travel mode detection in
New York City. Computers, Environment and Urban Systems[6]
Ashbrook D., Starner T. (2003) Using GPS to learn significant locations and predict movement across multiple
users. Personal and Ubiquitous Computing 7:275–286.
[7]
Li, Z., Wang, J., & Han, J. (2012). Mining event periodicity from incomplete observations. Proceedings of the
18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’12, 444.
[8]
Fontana, D., & Zambonelli, F. (2012). Towards an Infrastructure for Urban Superorganisms: Challenges andArchitecture. The IEEE International Conference on Cyber, Physical and Social Computing.
[9] Ministry of Internal Affairs and Communications. Retrieved January 27, 2013,
from http://www.soumu.go.jp/english/index.html

41

Session 7 // Trajectories and regularities

4

Netmob 2013 “Third conference on the Analysis of Mobile Phone Datasets”
May 2-3, 2013, MIT

Aggregated OD tracks of mobile phone data for the recognition of daily
mobility spaces: an application to Lombardia region

Session 7

4

Paolo Tagliolato, Fabio Manfredini, Paola Pucci
Dipartimento di Architettura e Studi Urbani – Politecnico di Milano
paolo.tagliolato@polimi.it, fabio.manfredini@polimi.it, paola.pucci@polimi.it

Introduction
Interpretative tools for the identification of mobility
practices in the contemporary metropolis are needed,
not only for the some known limitations of traditional
data sources but also because new forms of mobility
are emerging, describing new city dynamics and timevariations in the use of urban spaces by temporary
populations. In Italy, the traditional data sources forurban and mobility investigations (ie surveys, census)
have some known limitations, including the high costof surveys, the difficulty of data updating, thedifficulty of describing city dynamics and timedependent variations in intensity of urban spaces
usages by temporary populations at different scales.
New forms of mobility are changing the way in whichurban spaces are used. They are characterized both bybeing based on the use of transportation system, andby the efficient appropriation of information
technologies (internet, mobile phones). As underlined
by some authors (Ehrenberg, 1995; Urry, 2000;
Kaufmann, 2002; Ascher, 2004; Bourdin, 2005;
Scheller et al. 2006), changes in management ofmobility in contemporary cities are a useful key forunderstanding the transformations of times, places
and modes of social life and work programs,
structuring the metropolitan areas.
Considering the role of mobility practices in social andspatial differentiation, it becomes important toformulate pertinent analytical approaches, aimed at
describing the different densities of use of the city as a
new challenge and a prerequisite for understanding
the city and its dynamics.
Hence, from an analytical point of view, it becomes
important to accompany the traditional quantitative
approaches referred to a geographic displacementthat tends to focus on movement in space and time, inan aggregate way and for limited periods, with data
sources able to describing fine grain over-time
variation in urban movements.
In this direction, an interesting contribution may
come from mobile phone network data as a potential
tool for the development of real-time monitoring,useful to describe the urban dynamics as it has been42

tested in several experimental studies (Ahas et al.,2005; Ratti et al., 2006; Gonzales et al., 2008).
In this general context, we used mobile phone data
provided by Telecom Italia, the main Italian operator,
in form of aggregated mobility traces in order to test
the potentialities of this information to identify
temporary populations and different forms of mobility
that structure the relationships in the contemporarycity and to propose diversified management policies
and mobility services that city users require,increasing the efficiency of the supply of public
services.
Methodology
Milan is placed in an urban region which goes far
beyond its administrative boundaries (fig.1). The core
city and the whole urban area have been affected in
the last 20 years by changes in their spatial structuresand have generated new relationships between thecentre and suburbs. At the moment, the urban regionof Milan is a densely populated, integrated area where
4.000.000 inhabitants live, where there are 370.000
firms and large flows of people moving daily in thiswide area (Balducci et al., 2010).

Figure 1 - Map of built-up areas in the Milan urban
region (2007).
We used localized and aggregated tracks ofanonymized mobile phone users. The data set wascollected in different working days (five Wednesday
in July, August, September, October and November2012). In this case the available information was thegeolocation of users’ mobile phone activity in timeand in space. With mobile phone activity we intend
each interaction of the device with the mobile phone
network (i.e. calls received or made, SMSs sent or4

Session 7 // Trajectories and regularities

Figure 2 - Aggregated flows of mobile phone users: 9
am – 2011-10-19

Figure 3 - Aggregated flows of mobile phone users: 5
pm – 2011-10-19434Aggregated tracks of mobile phone users
The analysis of the activity of mobile phone users
permitted to put in evidence the main hourly
distribution of origin – destination movements of ahuge sample of people (more than one million perday).
We started from an hourly origin destination matrix(OD) of mobile phone users among the 526 zones
obtained through the automatic clustering ofantennas. For each zone it was available a set of
directed connections towards the other zones of theaggregation and for each connection it was available
the number of traced users.
Our goal was to display prevalent fluxes of mobility at
different hour of a typical working day through avisualisation of the sum vector moving from eachzone. The sum vector is the single vector resulting
from the sum of all the single connections between
each zone and the others and is characterized by twodimensions: the magnitude, which is function of themagnitudes of the original vectors and the angle
which expresses the direction of the flux. The sum
vectors have been finally applied to each zone of theautomatic clustering of antennas.
A set of maps of the sum vector moving from eachzone at different hours has been produced in order tohighlight the main patterns of mobility during atypical working day. In the maps the dimension of thearrow is proportional to the absolute value of the sum
vector and is therefore related to the prevalent fluxesof mobile phone users at specific hours.

The convergence of travels toward the main centres
during the morning, the more complex direction of
movements during the afternoon, are someinteresting phenomena emerging from our analysis.
A broad use of the territory and an articulation ofdaily moves are visible every hour. The maps can beused as meaningful tools for monitoring the use of theinfrastructural networks and of the urban spaces. On
the one hand, the morning map (9pm; fig 2) confirm apolarization of movements towards the main centres
offering job opportunities and highlights also the mostcommonly used infrastructures. On the other hand,the aggregated flows of mobile phone users in theafternoon (at 5 pm; fig 3, fig. 4) allow to recognize
significant places for shopping and leisure, that are
attended after work. This type of information isdifficult, if not impossible, to monitor throughconventional data at a comparable spatial andtemporal resolution.
Our analysis shows, in synthesis, a wide and dense useof the territories of the Milan urban region, a region inwhich the attractiveness of new places emerges fromthe mobility practices. The multi-directional mobility
intensifies and describes a complex network of
relationships such as the growing transversal travels
that define a not hierarchical system of relationships
in the most dynamic territories of the Milan urban
region.

Session 7

received, internet connections, etc..). This information
was available at the level of the antenna whichhandled the activity.
We performed an aggregation of the information
related to individual cells (antennas) in order toobtain useful polygon elements which could gave us
the possibility to map and to interpret main spatial
patterns of mobile phone users’ mobility. The
aggregation was determined by means of theapplication of an algorithm of hierarchical clusteringof the location of the antennas, resulting in 526
polygons. The final zoning has been obtained bycalibrating the algorithm in order to reach sufficiently
balanced clusters (i.e. with an homogeneous numberof antennas per polygon). Through a process oftessellation, we defined a set of polygons and it wastherefore possible to map the direction and theintensity of mobile phone users’ movements at anhourly basis. Using this data, we performed ananalysis aimed at evaluating the overall mobility ofcell phone users in the Lombardia region.

Session 7 // Trajectories and regularities

4

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4

nomadism that characterizes the contemporary
practices, that surveys on mobile phone data havealready undertaken (Gonzales et al., 2008).

Figure 4 – Milan city aggregated flows of mobile
phone users: 5 pm – 2011-10-19
The interested reader can visit our interactive web
version of the map showing prevalent fluxes ofmobility in a working day at the following URL:
http://www.ladec.polimi.it/maps/od/fluxes.html .
Implications for Policies
The research allowed us to test the potential of mobile
phone data in explaining relevant urban usage andmobility patterns at the Milan urban region scale thatcan be hardly intercepted through traditional data
source. This opens new implications for the urban
research community which needs to elaborate newstrategies to integrate traditional data with user
generated data, such as mobile phone activity, inorder to achieve a better comprehension of urban
usages, in time and in space. Describing the trends ofuse of the urban spaces, the maps of mobile phonedata give important information for mobility policies:
the lack of coincidence between the mobility practices
in the peak hours in the morning and in the afternoon
when the chains of displacements are very articulate
and complex, allows to recognize not only thevariability in mobility practices, but also the places
where these practices are occurring.
The commuters between 8 am and 9 am, become cityusers between 5 pm and 7 pm. This phenomenon
strictly affects land use and can pose new questions
and indications also for transport policy.
Indeed, if we overlaid the boundary of theinstitutional management of local public transport in
the Milan area with the areas of mobility practices,taken from the mobile phone data, we could observethe "deep structural effects of the mobility of people
on urban policies" and the obvious disconnection
between fixed jurisdictions and “mobile factors"(Estèbe, 2008).
The same data helps us to question someinterpretations in the literature on the erratic
44 of metropolitan populations and on the
behaviors

References
Ahas, R., Aasa, A., Silm, S. and Tiru, M. (2010). Daily
rhythms of suburban commuters’ movements in the
Tallin metropolitan area: case study with mobile
positioning data. Transportation Research Part C:
Emerging Technologies 18(1), 45–54.
Ahas, R., Mark, Ü. (2005). Location based services–
new challenges for planning and public
administration?. Futures 37(6), 547–561.
Ascher F. (2004). Les sens du mouvement :modernités et mobilités. in Allemand S., Ascher F.,
Lévy J. (eds). Le sens du mouvement, Belin Bourdin,
21-34.
Balducci, A., Fedeli, V. and Pasqui, G. (2010)
Strategic planning for contemporary urban regions:city of cities : a project for Milan, Ashgate, Burlington,
VT.
Bourdin A. (2005). Les mobilités et le programmede la sociologie . Cahiers internationaux de sociologie,
1 (118), 5-21.
Ehrenberg, A. (1995). L’individu incertain. Paris,
Calmann-Lévy.
Estèbe, P. (2008). Gouverner la ville mobile, PUF,
Paris 2008.
Gonzalez, M. C., Hidalgo, C. A. and Barabási, A.-L.
(2008). Understanding individual human mobility
patterns. Nature 453 (7196), 779–782.
Kaufmann, V. 2002. Re-thinking mobility.
Aldershot: Ashgate.
Manfredini F., Pucci P., Tagliolato P. (2012). Mobile
phone network data. New sources for urban studies?
In Borruso G., Bertazzon S., Favretto A., Murgante B.
and Torre Cm (eds), Geographic Information Analysis
for Sustainable Development and Economic Planning:
New Technologies, IGI Global.
Nuvolati G.(2003). Resident and Non-resident
Populations: Quality of Life, Mobility and Time
Policies. The Journal of Regional Analysis and Policy,
33 (2), 67-83.
Ratti, C., Pulselli, R. M., Williams, S. and Frenchman,
D. (2006). Mobile landscapes: using location data fromcell phones for urban analysis. Environment andPlanning B: Planning and Design 33(5), 727–748.
Reades, J., Calabrese, F., Sevtsuk, A. and Ratti, C.
(2007). Cellular census: Explorations in urban datacollection. IEEE Pervasive Computing 6(3), 30– 38.
Scheller M., Urry J. (2006). The new mobilities
paradigm. Environment and Planning A, 38, 207-226.
Urry, J. (2000). Sociology Beyond Societies.
London: Routledge.

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Session 7 // Trajectories and regularities

Understanding Human Mobility Due toLarge-Scale Events
Lucas M. Silveira

Jussara M. Almeida

Pontifical Catholic University of
Minas Gerais (PUC MINAS)
Brazil – 30.535-901
lmsilveira@sga.pucminas.br

Universidade Federal de
Minas Gerais (UFMG)
Brazil – 31.270-010
jussara@dcc.ufmg.br

Artur Ziviani

Humberto T. Marques-NetoOi Telecom
Brazil – 20.230-070
malab@oi.net.br

National Laboratory for Scientific
Computing (LNCC/MCTI)
Brazil – 25.651-075
ziviani@lncc.br

Pontifical Catholic University of
Minas Gerais (PUC MINAS)
Brazil – 30.535-901
humberto@pucminas.brI.

I NTRODUCTION

Analyzing the mobility patterns of cellphone users is achallenging task, but also a great opportunity to better understand the human dynamics in a covered area. Although
recent studies show that human mobility in urban areas canbe predictable considering daily routines [1], cellphone carriers
still have difficulties for planning the necessary communication
infrastructure to support the unusual workload that arises
during large-scale events [2]. Such events typically involvea large number of people within an urban area, such as the
final match of a soccer championship, a major rock concert(e.g., Rock in Rio), New Year’s Eve celebrations, a religious
pilgrimage, political manifestations, or the Olympics.
We here consider a large-scale event to be characterized
by a huge number of people with similar interests directly
related to the event’s main subject who move towards/froma specific place in order to participate on a set of collective
activities during a period of time. Even though many of theselarge-scale events are scheduled and planned in advance, andare expected to cause collective changes in the mobile phone
workload [3], it remains common to notice the congestion of
the carrier’s resources during them.
In this paper, we present our on-going work towards understanding the human mobility and the workload dynamics ofmobile phone networks due to large-scale events. To that end,
we analyze the impact of some types of large-scale events on
the workload of a mobile phone network. We use our recently
proposed methodology [4], applying it to real anonymized
mobile phone datasets provided by a major mobile operator in
Brazil. Whereas in [4] the methodology was applied to datasetscollected during major Brazilian soccer matches, we here apply
it to a different type of large-scale event: New Year’s Evecelebrations in three large Brazilian cities. Our results could
be used to improve the understanding of human mobility inurban areas due to large-scale events, thus contributing to the
network management of the mobile phone operators and also
to the development of new applications and devices.

II.

R ELATED W ORK

Better understanding the dynamics of the workload imposed on mobile phone networks is increasingly gaining
attention from different research efforts [5]. The predictionof user mobility patterns can help, for instance, detecting
routine patterns [6], urban planning efforts (e.g., urban trafficplanning [7]), and public health management (e.g., disease
spread control [8]). Other authors argue that characteristics ofthe network workload can be exploited to identify the kindof events users are experiencing (e.g., an emergency or aconcert) [3].
Two previous studies are particularly related to our project.
Batty et al. [9] analyze human dynamics and social interactionsof mobile phone users during large-scale events and Calabrese et al. [10] investigate the relationship between different
types of events and the home area of the attendees.
We have recently developed a methodology to analyze theworkload dynamics of a mobile phone network during largescale events, with an initial focus on soccer matches [4]. In
that work, we characterized how mobile phone users move
during part of the day, around the time and location where
major soccer matches took place. We analyzed soccer matches
in the same location (one soccer stadium) on different days,
aiming at comparing the workloads imposed on the carrier’s
infrastructure (the same set of antennas near the stadium) on
days with and without matches. The analysis from a single
point of interest also makes it easier to determine how people
move towards the stadium before the match starts and where
they go afterwards, following an approach similar to [10].
In this paper, we apply our proposed methodology toanalyze another kind of large-scale event, i.e. New Year’s Evecelebrations, similar to what has been done by [9]. We also
include in our study the analysis of three new metrics: (i) calldurations, (ii) call inter-arrival, and (iii) call inter-departure
times, extending what was done in our previous work [4].

455Carlos Henrique S. MalabSession 7

Faber Henrique Z. Xavier
Pontifical Catholic University of
Minas Gerais (PUC MINAS)
Brazil – 30.535-901
faber.xavier@sga.pucminas.br5

Session 7 // Trajectories and regularities

TABLE I.

Fig. 1.

Timeline adopted in our methodology.

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III.

P ROPOSED METHODOLOGY

Our proposed methodology [4] aims at providing insights
for better management and capacity planning of the carrier’s
infrastructure to support the demands of cellphone users dueto their mobility during large-scale events. Basically, ourmethodology is designed with the purpose of answering threemain questions: (i) who moved towards the surroundings ofthe large-scale event when it took place? (ii) where did theycome from? and (iii) where did they go after the event?
Towards answering such questions, we restrict our analysis
to cellphone calls made during a period of time around theevent time. Specifically, we adopt the timeline notation shownin Figure 1, which is discussed next.
The first step of our methodology is to identify the antennas
that cover the region where the event was held. We then selectthe users who made at least one cellphone call in one of
the selected antennas before and after the event. To that end,
we define the Pre-Event and Post-Event periods as the timeintervals that cover the k minutes preceding the beginningof the event and following its end, respectively. We referto the period from the beginning of Pre-Event until the endof Post-Event, including the event’s total duration, as tevent .
Users who made at least one call during tevent in one of theselected antennas are considered attendees. The durations ofthese time intervals, i.e., determining k, somewhat depends on
local aspects and on the event characteristics.
In the second step, we identify the subset of event attendees
who also made calls before the beginning (i.e., before PreEvent) or after the end (i.e., after Post-Event) of the event.
These calls allow us to track the movements of these users
before or after the event duration. Thus, our analysis of mobility patterns focuses on these selected users. To identify theseusers, we define two other periods (tarrival and tdeparture )corresponding to the time intervals before and after the tevent
period, respectively, during which people are moving towards
and arriving at the event place as well as departing from thelocation.
At this point, the third and last step of our methodology,
we use the geolocation of the antennas to locate where eachcall started and ended, thus determining from where the event
attendees (identified in the previous step) came and to where
they moved after the event. Ultimately, this enables us toanalyze the workload dynamics of the antennas located along
the main routes to and from where the large-scale event
takes place. To visualize such dynamics, we use heat maps1
to represent the intensity of activity in the mobile phone
network (the darker the color in the heat map, the larger thenumber of calls received by the antenna in this area).
1 Heat maps are generated using the Google Maps Javascript API V3.

46

OVERVIEW OF THE D ATASETS ( EVENT DAYS IN BOLD ).

City

Day

BH
BH
RecifeRecifeSalvador
Salvador
Rio
Rio

Dec 31, 2011
Jan 03, 2012
Dec 31, 2011
Jan 03, 2012
Dec 31, 2011
Jan 03, 2012
Dec 04, 2011
Oct 30, 2011

IV.

# Calls
done byAttendees
5187
779
9951
924
12826
1019
4284
1270

# Attendees
1938
365
3566
444
7458
689
1754
691

Average
Calls perAttendees
2.7
2.1
2.8
2.1
1.7
1.5
2.4
1.8

R ESULTS

Our methodology was applied to datasets containing mobile phone calls made during the 2012 New Year’s Evecelebrations in three large Brazilian cities: Belo Horizonte(BH), Recife, and Salvador. The datasets contain for each call aunique user identifier2 , the geographical locations (latitude andlongitude) of the antennas where the call started and ended, as
well as the time instants when it started and ended.
To define the event location and associated antennas, weconsidered large-scale New Year’s Eve celebrations organizedin each city, such as a celebration on a beach in Salvador which
received, reportedly, one million attendees. Six antennas covered this area. The celebrations in BH and Recife, hosted in anarea covered by 3 antennas each, received around 100,000 and
10,000 people, respectively. Regarding the proposed timeline,
we considered the total period from 9:45PM to 2:30AM, withthe event starting at 11:15PM and lasting for 105 minutes.
We set the durations of tarrival , tdeparture , Pre-Event andPost-Event to 45 minutes each. For comparison purposes, we
also analyzed other datasets from the same cities, collected
on January 3rd, 2012 (a day without event) using the sameantennas and timeline. Similarly, we compared these resultswith those obtained for another type of large-scale event - asoccer match in Rio on December 4th 2011 - reported in [4].
The antennas covering the location (stadium) of the match andthe timeline were defined using the same methodology, taking
the time and soccer match stadium into account. As basis forcomparison, we also analyzed data collected on October 30th
corresponding to the same antennas and timeline of the match.
Table I summarizes the analyzed datasets, presenting thetotal numbers of calls and attendees. Note that the numbersof calls are increased by a factor between 6.7 and 12.6 during
the New Year’s Eve celebrations (BH, Recife, and Salvador),comparing with the numbers of a day without event at eachcity. This could be expected if we consider the time (late atnight), the event nature, and the huge number of attendees.
Note also that the growth factor of the number of calls during
the soccer match, comparing with a day with no events in
Rio (two last rows of Table I), is lower (factor of 3.4).
This indicates the importance of the event nature for better
understanding the human mobility and the workload dynamics
of the mobile phone network.
To further understand the characteristics of the workload
imposed on the selected antennas during each event, Figure
2 shows the numbers of calls made by attendees of the2 This id is generated by the cellphone carrier. It is completely anonymized,
and thus, cannot be used for identifying the user; although, it allows us to
identify multiple calls made by the same user in different points in time.

5

Session 7 // Trajectories and regularities

Dez 31, 2011
Dez 04, 2011
Out 30, 2011
Jan 03, 2012

1000

# Calls

800

600

400

200

Fig. 3.

Heat map of New Year’s Eve at Salvador during its tarrival .

Fig. 4.

Heat map of New Year’s Eve at Salvador during its tevent .

0

2

4

6

8

10

12

Session 7

014

Periods of 15 minutes

Fig. 2. Calls made by attendees of Recife’s New Year’s Eve (Dec 31, 2011),
at one soccer match in Rio (Dec 04, 2011), and on two days with no large-scale
event in Rio (Oct 30, 2011) or in Recife (Jan 03, 2012).

Call durations, inter-arrival times (IAT), and inter-departure
times (IDTs) are key metrics for capacity management andplanning as they allow us to assess whether an antenna
throughput is meeting its imposed load. We computed allthese metrics over 15-minute intervals for each New Year’s
Eve celebration and soccer matches. In general, all events are
typically composed of a large volume of short calls. We also
observed that the average of IATs and IDTs are similar for eachcelebration and slightly different of days with soccer matches.
This also points out the difference of the workload imposedon a mobile phone network during events of distinct nature.
Finally, in our methodology, we use heat maps to analyze
the mobility of event attendees before, during and after the
analyzed events, which allows us to infer the most used accessroutes to/from the event’s location. Figures 3 and 4 show theheat maps produced for the tarrival and tevent periods of the
New Year’s Eve celebration in Salvador. The observation ofthese heat maps in sequence illustrates human mobility towards
the location of the large-scale event.
V.

C ONCLUSIONS AND F UTURE W ORK

In this paper, we extended our recently proposed methodology [4], applying it to analyze human mobility and theworkload dynamics of a mobile phone network during largescale events. Our results show that user behavior patterns thatarise during different kinds of events can be used to analyze
phenomena related to human mobility due to such events. As
future work, we intend to expand our analysis to include othertypes of large-scale events, like major concerts. We believethat identifying mobility patterns based on cellphone calls
during large-scale events can drive the development of target
applications and gadgets tailored for such events.

5celebration in Recife, in successive 15-minute time bins during
tevent . For comparison purposes, corresponding results for a
day with a soccer match (Dec 04, 2011) and two days without
event (Oct 30, 2011 in Rio and Jan 03, 2012 in Recife) arealso shown in Figure 2. This figure shows that, for the day of
Recife’s New Year’s Eve, the number of calls peaks at around
midnight (bins 6–8), dropping quickly after the celebration
finishes. For a soccer match and regular days, instead, thenumber of calls shows different patterns, decreasing sharply
in the ending of tevent . Results for the other cities are very
similar, being thus omitted.

ACKNOWLEDGMENT
This work is supported by FIP-PUC MINAS, InWeb(MCT/CNPq 573871/2008-6), CNPq, CAPES, FAPEMIG, and
FAPERJ.
R EFERENCES[1] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, “Limits of predictability
in human mobility,” Science, vol. 327, no. 5968, pp. 1018–1021, 2010.
[2] A. Bleicher, “The on-demand olympics,” IEEE Spectrum, vol. 49, no. 7,
pp. 9–10, jul 2012.
[3] J. P. Bagrow, D. Wang, and A.-L. Barabási, “Collective response ofhuman populations to large-scale emergencies,” PLoS ONE, vol. 6,
no. 3, p. e17680, 03 2011.
[4] F. H. Z. Xavier, L. M. Silveira, J. M. Almeida, A. Ziviani, C. H. S.
Malab, and H. Marques-Neto, “Analyzing the workload dynamics ofa mobile phone network in large scale events,” in Proceedings of the
UrbaNe Workshop – ACM CoNEXT 2012, 2012.
[5] N. Eagle, A. Pentland, and D. Lazer, “Inferring social network structureusing mobile phone data,” Proceedings of the National Academy of
Sciences (PNAS), vol. 106, no. 36, pp. 15 274–15 278, 2009.
[6] M. C. González, C. A. Hidalgo, and A.-L. Barabási, “Understanding
individual human mobility patterns,” Nature, vol. 453, pp. 779–782,
2008.
[7] J. Yuan, Y. Zheng, and X. Xie, “Discovering regions of different
functions in a city using human mobility and POIs,” in Proceedingsof the 18th ACM SIGKDD international conference on Knowledge
discovery and data mining, ser. KDD ’12. New York, NY, USA:ACM, 2012, pp. 186–194.
[8] D. Balcan, V. Colizza, B. Gonçalves, H. Hu, J. J. Ramasco, and
A. Vespignani, “Multiscale mobility networks and the spatial spreading of infectious diseases,” Proceedings of the National Academy of
Sciences, vol. 106, no. 51, pp. 21 484–21 489, 2009.
[9] M. Batty, J. Desyllas, and E. Duxbury, “The discrete dynamics ofsmall-scale spatial events: agent-based models of mobility in carnivals
and street parades,” International Journal of Geographical Information
Science, vol. 17, no. 7, pp. 673–697, 2003.
[10] F. Calabrese, F. Pereira, G. Di Lorenzo, L. Liu, and C. Ratti, “Thegeography of taste: Analyzing cell-phone mobility and social events,”
in Pervasive Computing, ser. Lecture Notes in Computer Science,
P. Floréen, A. Krüger, and M. Spasojevic, Eds. Springer Berlin /Heidelberg, 2010, vol. 6030, pp. 22–37.

47

6

Session 7 // Trajectories and regularities

The Social Amplifier – Reaction of Human
Communities to Emergencies
Yaniv Altshuler, Michael Fire, Erez Shmueli, Yuval Elovici,
Alfred Bruckstein, Alex (Sandy) Pentland and David Lazer

Session 7

6

I. I NTRODUCTION
Imagine a scenario where some set of individuals witness
an extraordinary event which impels them to communicate
regarding that event to other individuals, who in turn willcommunicate with yet others. In this scenario, it is possible
for an external observer to witness the fact of communication,
but not the content. How might that observer effectively makethe inference that an extraordinary event has occurred?
This is in fact a plausible scenario, with the existence ofcommunication systems (most notably phones) where timing
and volume of traffic is observed, but (typically) not content.
Mobile phones are particularly notable in this regard, becauseof how pervasive they are. Here we build on work examining
detection of anomalous events in networks [2], but with thefocus on how to aggregate those signals in a computationallyefficient fashion. That is, if one cannot observe all nodes andedges, how best to sample the network?
Analyzing the spreading of information has long been thecentral focus in the study of social networks for the last
decade [4], [5]. One of the main challenges associated withmodeling of behavioral dynamics in social communities withrespect to anomalous external events stems from the fact that
it often involves stochastic generative processes. A further
challenge is the trade off that exists between coverage and prediction accuracy [1]. While simulations on realizations fromthese models can help explore the properties of networks [3],a theoretical analysis is much more appealing and robust. The
results presented in this work are based on a pure theoreticalanalysis, validated both by extensive simulations as well as byreal world data derived from a unique dataset.
Contribution: In this work we present an innovative approachfor studying the network dimension of the changes that takeplace in social communities in the presence of emergencies.
We do so using a mechanism we call a “Social Amplifier” –a method for analyzing local sub-networks spanning certain
high-volume network nodes. The innovation in our proposed
approach is twofold: (a) using a non-uniform sampling of the
network (namely, focusing on activity in the social vicinity of
Y. Altshuler, E. Shmueli and A. Pentland are with MIT Media Lab. E-mail:{yanival,shmueli,sandy}@media.mit.edu.
M. Fire and Y. Elovici are with Deutsche Telekom Lab & Department of Information Systems Engineering, Ben-Gurion University. E-mail:{mickyfi,elovici}@bgu.ac.il
A.M. Bruckstein is with Computer Science Department, Technion. E-mail:freddy@cs.technion.ac.il
D. Lazer is with College of Computer and Information Science & Department of Political Science, Northeastern University. E-mail: d.lazer@neu.edu

48

network hubs), and (b) projecting the network activity into a
multi-dimensional feature space spanned around a multitude of
topological network properties. We show using both simulation
and real world data that starting certain coverage level of the
network, our method outperforms the use of either random
sampling, as well as single signal analysis.
II. T HE S OCIAL A MPLIFIER
The proposed method is comprised of three stages asfollows.
In the initial stage, we track the traffic volume in the
network’s nodes, looking for hubs – nodes with high traffic(either incoming or outgoing). The rationale behind the useof hubs is that hubs are highly likely to be exposed to new
information, due to their high degree.
Given available resources , we select network nodes,
v1 , . . . , vn such that those nodes have
S the highest degrees in
the network and the set SM = 1≤i≤n E 1.5 (vi ) does notcontain more than  portion of the edges, where E 1.5 (v) denotethe 1.5 ego-network around node v, that is – the edges betweenv and all of v’s neighbors, as well as the edges between v’s
neighbors and themselves.
The use of the 1.5 ego-network is required in order toanalyze not only the overall number of calls in the network(sampled by the hubs), as done in works such as [2], butrather to generate the actual networks around the hubs, inorder to enable their in-depth analysis. More specifically,
analyzing only the overall number of calls, can only detectmassive global events, but not local ones (unless the local
events are known in advance, and the local data is analyzed
in retrospective).
In the second stage, for each day during the test period, andeach phone social network, we extract a set of 21 topologicalfeatures, such as the In Degree, Out Degree, Number of Strong
Connected Components, Subgraph Density, etc.
In the third stage, we detect anomalies in the dynamics ofthe social network around the network hubs, using the LocalOutlier-Factor (LOF) algorithm. Applying the LOF algorithm
on each hub, detects days which anomaly features occurred.
Then, by using ensemble of all the hubs, we detect whichdates have the highest probability for anomaly.
We do so by ranking each day according to the number ofhubs that reported it as anomalous. Then, for each day welook at the 29 days that preceded it, and calculate the finalscore of the day by its relative position in terms of anomalyscore within those 30 days. Namely, a day would be reported

6

Session 7 // Trajectories and regularities

as anomalous (e.g., likely to contain some emergency) if it is“more anomalous” compared to the past month, in terms of thenumber of hubs-centered social networks influenced during it.
Each day is given a score between 0 and 1, stating its relative“anomaly location” within its preceding 30 days.
III. VALIDATION
A. Analytic Evaluation

EAM P LIF IER ≈≈λ · EDET ECT≈< k > +α(kM AX − < k >)λ · EDET ECT< k > (1 − α) + αkM AX

We shall therefore rewrite Equation 1 as follows :EDET ECT =E · (1 − α)λ1 + <k>(1−α)+αk
M AX

6
(2)

Figure 1 illustrates the behavior of EDET ECT as a functionof the changes in the exposure coefficient α and in the portionof edges being monitored . Notice how EDET ECT has a nonmonotonous dependency on α, obtaining a global maximum
for intermediate values.

(1)

whereas E is the “energy” supplied to the system for monitoring some k edges, EIN IT IAL is the overhead spent on monitoring the first few hubs until we achieve good topographicalcoverage of the network, EAM P LIF IER is the energy spent
on maintaining a 1.5 ego-network closure (that is, the numberof edges of the 1.5 ego-network minus the number of edges
at the 1 ego-network), and EDET ECT denotes the resources
spent on the actual detection of the signal.
We note that EIN IT IAL decreases with the time it takes thedetection process to complete. In other words, as the event tobe detected is more explicit and broadly observed, it will be
detected using a shorter time, which implicitly increases therelative portion of EIN IT IAL . We can therefore write :EIN IT IAL ≈ α · E
for α ∈ [0, 1] the exposure coefficient of the event.
Notice that as the exposure coefficient of an event decreases,
it means that additional edges (and nodes) are required in orderto detect the event. For extreme low values of the exposure
coefficient there is no longer much difference between adding“hubs” and adding random nodes (in terms of their degrees) tothe monitored set of nodes. This means that the ratio between
the number of edges between hubs’ neighbors and the edges
to and from the hubs increases, resulting in an increase in
EAM P LIF IER .
Namely, for high exposure coefficient values the ratio
between EAM P LIF IER and EDET ECT is proportional to
the ratio between the average aggregate degrees of hubs’
neighbors and the average degree of the hubs themselves. For

0.15
EDETECTE = EIN IT IAL + EAM P LIF IER + EDET ECTλEAM P LIF IER
λ
≤
≤
kM AX
EDET ECT<k>denoting by kM AX the maximal degree, and for λ ≥ 1 being
the Social Amplification Constant of the network.
The same effect is obtained when the portion of the edges
being monitored  changes, as low values for  cause the ratio
EAM P LIF IER
to decrease, and very high values of it cause it
EDET ECT
λto converge to <k>. We can therefore write :Session 7

Alongside its increased sensing capability, our proposedmechanism has also an additional overhead, in terms ofadditional edges that should be monitored, compared to thestandard approach of “number of calls analysis”. This is theresult of the following two reasons:• Hubs: Due to their high degree, whenever the edges associated with an additional hub are added to the monitorededges set they increase its size substantially (unlike theaddition of a randomly selected node, that is expected to
be of a much lower degree).
• 1.5 Ego-Network: For some node v, although the numberof nodes in its 1 ego-network equals exactly the numberof nodes in its 1.5 ego-network, the latter is usually
expected to have substantially larger amount of edges.
We therefore write the utilization of the Social Amplifier
mechanism as follows :1
low exposure coefficient values this ratio converges to <k>(denoting by < k > the average degree of the network) :

0.1
0.05
0
0

1
0.8

0.2
0.6

0.4
0.4

0.6
0.2

0.8
Portion of edges under coverage

1 0

Exposure coefficientFig. 1. The dependency of EDET ECT on the exposure coefficient α andon the number of edges being monitored. The illustration assumed kM AX =10· < k >.

B. Simulation
The goal of our simulation was to check how the two
methods for selecting the subset of monitored edges (i.e. Social
Amplifier vs. Random) influence the time required to detect anevent. In order to achieve this goal we simulated the spreadingof events in generated scale-free graphs and measured thetime taken to detect those events when using the two different
methods for selecting the subset of monitored edges.
Our simulation included a tremendously large number of
executions (≈ 106 ) and used different parameters:• cp - the coverage percentage of the mobile operator.
• w - the number of initial witnesses to the event.
• c - the confidence level, i.e., the minimum number of“spreading edges” that need to be sensed in order to be
confident that an event has occurred.

49

6

Session 7 // Trajectories and regularities

6

Following the analysis in Section III-A, we defined theexposure coefficient, denoted by α, as α = log2 (c)/w.
Fig. 2 shows the influence of α and cp on ∆(α, cp) where∆(α, cp) is the mean difference in detection time (between
the two methods) over all executions with the given cp
and log2 (c)/w = α. (Note that the original results weresmoothened with R = 0.804 and R2 = 0.646.) As shown in
the figure, for medium α values, the Social Amplifier method
outperforms the Random method. In addition, we observe thatin this range of medium α values, the advantage of the Social
Amplifier method increases with larger cp values.

0.06
0.04

δ|E| = CORR(VAM P LIF IED , V̂) − CORR(VBASE , V̂)

0.02
0
−0.02
−0.04
−0.06
−0.08
−0.1
0

0.5

1

1.5

2

0

0.2

0.4

0.6

0.8

1

Coverage Percentage
Exposure CoefficientFig. 2. The influence of α and cp on ∆(α, cp) as evaluated using simulativeenvironment. ∆(α, cp) is represented by the red area in the figure. The dark
grid represents the fixed z = 0 plane. Positive values of ∆(α, cp) mean anadvantage for the Hubs method.

Note that the efficiency of our method as illustrated in
Figure 2 closely resembles that of our analytic model, as
discussed in Section III-A (Equation 2 and Figure 1).
C. Real World Data
We also validated our method using a comprehensive
dataset, containing the entire internal calls as well as many of
the incoming and outgoing calls within a major mobile carrier
in a west European country, for a period of roughly 3 years.
During this period that mobile users have made approximately
12 billion phone calls. We used the company’s log files,
providing all phone calls (initiator, recipient, duration, andtiming) and SMS/MMS messages that the users exchange
within and outside the company’s network. All personal details
have been anonymized, and we have obtained IRB approval
to perform research on it.
For evaluating the Social Amplifier technique as an enhanced method for anomalies detection we have used a seriesof anomalous events that took place in the mobile networkcountry, during the time where the call logs data was recorded.
We have divided the anomalies into the following threegroups : (1) “Concerts and Festivals” Events that are anomalous, but whose existence is known in advance to a large
enough group of people; (2) “Small exposure events” Anomalous events whose existence is unforeseen, and that werelimited in their effect; and (3) “Large exposure events”
Anomalous events whose existence is unforeseen, that affecteda large population.

50

for (VBASE , VAM P LIF IED )|E| , and for CORR(x, y) the
correlation coefficient function.
Notice that whereas δ|E| measures the delta in detection
accuracy, it has somewhat similar meaning to ∆(α, cp), whichmeasures delta in detection speed.
Figure 3 presents the values of δ|E| for number of monitorededges between 300 and 800, for the three types of events.
Notice how the results strongly coincide with the analytic
model as is illustrated in Figure 1, as concerts and events havethe highest exposure value a, and the small exposure events
have the lowest value.
0.09
0.08
Delta of correlation coefficient

Delta of Required Time Ticks

Session 7

0.1
0.08

For each of the events we used the method described in Section II in order to rank each day between 0 and 1, according to
its “anomalousness”. This was done for increasingly growing
number of monitored edges, in order to track the evolutionof the detection accuracy. The result of this process was aseries of numeric vectors pairs: (VBASE , VAM P LIF IED )|E| ,corresponding to the two networks used (e.g. the random
network sampling for VBASE and the social-amplified hubssampling for VAM P LIF IED ), for |E| edges which were monitored. In addition, we created a binary vector V̂ having ‘1’
for anomalous days and ’0’ otherwise.
For |E| edges which were monitored we denote by
δ|E| the difference between the correlation coefficient of
VAM P LIF IED and V̂, and the correlation coefficient of
VBASE and V̂, namely :Large exposure events
Small exposure events
Concerts and Festivals

0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
300

350

400

450

500

550

600

650

700

750

800

Number of edges under coverageFig. 3. The changes in the value of δ|E| for growing numbers of edges being
analysed, segregated by the type of event detected. Notice how concerts andfestivals that have high exposure value a generate relatively lower values of
δ|E| (but still monotonously increase with |E|), while the small exposure
events are characterized by the highest values of δ|E| , specifically for low
values of |E|. It is important to note that a low value of δ|E| does not imply
that the accuracy of the detection itself is low, but rather that the differencein accuracy is small.

R EFERENCES[1] Altshuler, Y., Aharony, N., Fire, M., Elovici, Y., Pentland, A.: Incremental
learning with accuracy prediction of social and individual properties frommobile-phone data. CoRR (2011)[2] Bagrow, J., Wang, D., Barabási, A.: Collective response of humanpopulations to large-scale emergencies. PloS one 6(3), e17,680 (2011)[3] Herrero, C.: Ising model in scale-free networks: A monte carlo simulation.
Physical Review E 69(6), 067,109 (2004)[4] Huberman, B., Romero, D., Wu, F.: Social networks that matter: Twitter
under the microscope. First Monday 14(1), 8 (2009)[5] Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the
dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD
international conference on Knowledge discovery and data mining, pp.
497–506. Citeseer (2009)

Session

8

Social data collection51

1

Session 8 // Social data collection

Detecting Face-to-face Meetings using Smartphone
Sensors
Piotr Sapiezynski
s091728@student.dtu.dk

Arkadiusz Stopczynski
arks@dtu.dk

Sune Lehmannsljo@dtu.dk

Session 8

1

Techical University of Denmark

Figure 1: The network composed of 466 most active dyads. 366 of them are among most active both on and outside of the campus(orange edges), while 50 are only significantly active on (blue edges) and 50 outside of the campus (green edges). The campuscentered subset is a good approximation of the whole network, but note that the bridging edges between distinct communities
occur only outside of the campus
ABSTRACT

Face-to-face meetings are used in computational social science as one of the most prominent signals for discoveringsocial ties. Different methods have been used for recording
such meetings, for example video analysis, infrared sensors,or more recently Bluetooth scanning using mobile phones. In
this paper we examine data collected during large computational social science study deployment (N=130 people). The
data is recorded using Funf framework[1] running on participants’ smartphones. We perform Bluetooth and WiFi scanning, location readings, and cell tower tracing, as well as datacollection from a campus-wide WiFi system.
Using a curated Bluetooth signal as a ground truth for faceto-face meetings, we examine how this signal can be recovered from other channels. Our main result is that interactions,which occur on campus (and can thus be discovered using
the system WiFi), constitue a relevant approximation of all
the interactions among the participants. These findings may
be beneficial for 1) discovering co-location networks in contexts, where a WiFi system is already deployed, and using
additional mobile devices is not feasible, such as university
and company campuses, schools, and other institutions, 2)planning more energy-efficient deployments of experiments
involving mobile devices, and 3) understanding how different
signals may introduce bias into analysis and conclusions.

52

Further, this contribution reports on a thorough examinationof the data in search of quality deficits, biases, and interplay
between the information channels and argue this to be crucial step, before the data can be used to generate and verify
scientific hypotheses.
RESULTS OVERVIEW

We use our real world data to show the following:• Interactions between the students, which can be observed
through a campus-only system WiFi network, can be successfully used to estimate the proximity networks emerging between the participants also outside of campus, see
Figure 1. This finding indicates a possibility of deploying
co-location based studies in environments featuring a WiFi
network, without a need for buying additional hardware.
• User perspective WiFi is a rich source of information, because of inherent oversampling (Android forces often WiFi
scans whenever WiFi is enabled), giving comparable results accross participants (contrary to GSM towers wherethe visibility dependents on the provider, users see thesame WiFi access points in the same location), and easy
recognition of dynamic and static contexts.
• GPS based location estimation has a low recall and precision in face-to-face meeting detection, due to transient
nature of contexts where the GPS signal is available.

1

Session 8 // Social data collection• GSM towers are a poor medium to study social interaction,
due to lack of data about their geographical position, inconsistent identifiers across providers, and very low precision.
• In an experiment where three phones located close to eachother periodically scan Bluetooth environment, a successful diadic discovery between them occurs with a rate of approx. 75%. Thus, Bluetooth scan results cannot be directly
treated as ground truth without sensible preprocessing.
REFERENCES

1. Aharony, N., Pan, W., Ip, C., Khayal, I., and Pentland, A.
The Social fMRI: Measuring, Understanding, andDesigning Social Mechanisms in the Real World. In
Proceedings of the 13th International Conference on
Ubiquitous Computing, ACM (2011), 445–454.

Session 8

1

53

2

Session 8 // Social data collection

Vehicular Traffic Estimation Leveraging Location Area
Updates of Mobile Phones
Andreas Janecek

Research Group Entertainment Computing
University of Vienna, Austria

andreas.janecek@univie.ac.at
Karin A. Hummel
Communication Systems GroupETH Zurich, Switzerland

Danilo Valerio

Telecommunications Research Center (FTW)
Vienna, Austria

valerio@FTW.at
Fabio Ricciato

Telecommunications Research Center (FTW)
Vienna, Austria

fabio.ricciato@FTW.at
karin.hummel@tik.ee.ethz.ch
Helmut Hlavacs

2

Research Group Entertainment Computing
University of Vienna, Austria

Session 8

helmut.hlavacs@univie.ac.at

1.

INTRODUCTION

Being a large-scale ubiquitous mobility sensor, the mobile cellular
network provides valuable data for human mobility modeling [3]and vehicular traffic analysis. So far, road traffic has been mainly
monitored by means of static sensors or derived from floating car
data. These approaches are either costly or significantly suffer fromthe low fraction of vehicles captured.
In our study, we analyze the mobile cellular network signaling
data to infer road status and, specifically, congestion events in realtime. One major novelty compared to most previous studies on the
topic [5] that considered only mobile phone data related to ‘active’terminals (Call Detail Records), is the use of the more complete
signaling data provided by the network links near the Radio Access Network (RAN) of the cellular network. This way, also theposition of ‘idle’ terminals can be observed on Location Area (LA)level. On the downside, the LA level provides only coarse grained
location information. However, the overwhelming majority of themobile terminal population can be included leading to a much better coverage of moving vehicles. This property is crucial, as wehave shown in [2, Section 4] that the exclusive observation of ‘active’ terminals produces a biased picture of the overall human mobility.
By validating our approach against four different data monitoring datasets on a sample highway in Vienna, Austria (see Figure 1), over one month, we show that our method can indeed detectcongestions very accurately and in a timely manner. The cellular
dataset used comprises anonymized mobile cellular data of the signaling traffic from 2G/3G cells of a real operational network over
31 days. For details of our work, refer also to [4].

2.

METHODOLOGYOur method builds mainly on leveraging location area and routing
area (for simplicity, we use LA to refer to both) updates to detectcongestion events; this part is termed ‘stage 1’. Additionally, we
introduced also a second stage (‘stage 2’) to include cell handoversof ‘active’ terminals in order to localize congestions events more
precisely [4]. This second stage has the potential to differentiate
between congestions types as identified in road traffic research [6].

54

Figure 1: Excerpt of the sample highway, Vienna, Austria: topography, cellular network, and traditional road sensors. Devices are tracked across LA2 based on events triggered in theentry cells (south) of LA2 (start border area LA1 /LA2 ) and theentry cells (south) of LA3 (arrival border area LA2 /LA3 ).

The stages differ in type and amount of cellular network signaling
events that are considered, and in terms of length and granularityof the considered road section. Stage 1 offers a continuous estimation of the travel times at large-scale with good terminal coverage
but with spatial accuracy limited by the large radii of LAs (up to
several kms), in urban areas comparatively small (5-10 km on ourtarget highway). Stage 2 offers higher spatial accuracy (up to few
hundred meters) but less terminal coverage. We remark that, ingeneral, it is not sufficient to rely exclusively on active terminals as
the coverage is too low. Stage 2 can be used to further localize thearea of congestion, but only after the congestion has been detected.
We will now focus on stage 1. The calculation of the travel time tof one mobile terminal is processed as follows: Every time the mobile terminal attaches to a cell belonging to a new LA, it emits an
LA update event. In order to identify the terminals traveling along
a target road segment, a suitable set of cells is pre-selected meetingthe following criteria: (i) the cells are located at the border of two
LAs and (ii) they are in close proximity of the target highway. We

2

Session 8 // Social data collection

Figure 2: Visualization of one day (June 30th, 2011): comparison of estimated travel times for mobile cellular data and speed
measured through fixed sensors during the day.

3.

DATASETS

We apply or methods to anonymized traces of 2G/3G signaling traffic observed in an operational cellular network of a major Austrian
mobile operator. We evaluate our results by comparing them toresults achieved by major traditional sources for traffic estimation.

Cellular data

The anonymized traces consist of 400–500 million events on average per day, and contain signaling messages for both the packet
switched and circuit switched domains. To preserve user privacy,
all sensitive identifiers are removed from the traces. Distinct users
are discriminated only by means of pseudonyms computed via aone-way hash function. The pseudonyms are changed every 24
hours. The event-based tickets used contain information such as:anonymous_ID of the user generating the event, timestamp of
the event, cell information including the LAC (Location Area Code)and Cell_ID, the coordinates, type, etc. of the base station
antenna, information about the event such as type_of_event,etc.

We validate the results of our approach against various datasets
originating from traditional road monitoring sources. We use roadsensors as point-based road monitoring data (measuring speed ofpassing vehicles, in our investigation nine road sensors covered thehighway and the measurement frequency is once per 60 seconds)
and toll gantries monitoring trucks via RFID-based transponders toestimate the average travel time of trucks between two toll gantries(every 15 minutes). Further, taxi floating car data provide GPS positions of taxis and, thus, average taxi speed. Additionally, we use
radio events, that are radio broadcasts about road incidents reported
by registered drivers.

4.

ANALYSIS RESULTS

The sample period stretches over 31 days. While the mobile cellular data are available for all days, the validation data are only
partially available due to the nature of the sensors or temporary
faults: toll data are partly not available on weekends (due to ban oftrucks), sensor data are missing for eight days (due to a problem
in the recording system), and concerning the taxi data, only half ofperiod is provided. Radio data are available throughout the whole
period. The target highway stretches over 32 km from a rural area
into the center of Vienna, Austria. It is covered by four different
LAs.

Detecting congestions

As there is no general agreement in the literature about the definition of a traffic congestion [1], we adopt the following definition: aroad segment is marked as congested if the estimated speed of thefastest vehicles considered falls below half the speed limit (i.e., thetravel time is doubled). We mark a congestion event in our dataset
if at least one data source out of toll, sensors, or taxi triggers theabove condition. In this way, 74 congestion events could be identified over the sample period, and 58 of them were sent as broadcast
also via radio. All events in the radio dataset could be detected byat least one of the other three validation sources. We refer to our approach with the term mobile. We analyze the quality of congestion
estimation based on cellular data vs. other data sources in terms ofthe number of detected congestions and the estimation delay.
Let us first exemplify the analysis with a comparison related toa single congestion. Figure 2 shows the estimated travel times
through a given area on our target highway over a whole day for

55

2

A congestion is detected by evaluating the travel time of fastest
users. Due to the nature of the cellular network and to the heterogeneity of mobile terminals, one cannot predict exactly at which
position a cell change occurs, and thus, the length of the segment
under investigation. As a consequence, it is difficult to determine
the minimum travel time. Instead of using a possibly imprecise
static estimate for the minimal travel time, we adapt the set of
fastest users by introducing an adjustable parameter κ: The travel
time of the κ-quantile of all users is used to estimate the minimum
travel time.

Validation data

Session 8

term these cells entry cells. Let now LA1 , LA2 , and LA3 be threeadjacent LAs crossed in sequence by a mobile terminal. The set ofentry cells of LA2 indicating a change from LA1 to LA2 is termedborder area LA1 /LA2 while the set of entry cells of LA3 indicatinga change from LA2 to LA3 is termed LA2 /LA3 .The mobile terminal (vehicle) traveling from LA1 to LA3 will generate at least one
event in LA1 /LA2 and one in LA2 /LA3 . The travel time estimateof the terminal is now simply calculated as t = ta − ts , where ta
is the time of the first event in LA2 /LA3 and ts is the time of thelast event in LA1 /LA2 . Hereby, we assumed that the highway is
the fastest connection between the start and arrival area, i.e., thefastest mobile device users are all traveling on the target highway(see Figure 1).

2

Session 8 // Social data collection

cellular data vs. the speed estimates for fixed sensor data. A severe congestion is visible in the afternoon and a less severe in themorning. The morning and afternoon incidents were also visible intoll and taxi data, the incident in the afternoon was broad-casted onradio at 15:33 h (‘heavy traffic’).

Table 1: Delay of mobile vs. validation data [in s]: number ofcomparable events (i.e., identified by both data sources) is given
in brackets. Correct, false positive (FP), and false negative (FN)classifications are shown at the bottom of the table.

Session 8

2

Our results of the full month period are summarized in Table 1. The
average advance (“-”) or delay (“+”) of mobile against each validation data source is reported. κ determines the fraction of users considered as being in the set of fastest users (upper κ-quantile of allusers). Here, smaller values of κ (0.03 or 0.04) make the approachaggressive as it identifies congestions fast, but with an increase infalse positive classifications. Setting κ = 0.05 is a good trade-off
between FPs, FNs and advance over other data sources.

κ

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Toll:

-533
(66)

-282
(66)

-152
(65)

0
(62)

+72
(62)

+175
(61)

+196
(61)

Sensors:

-586
(25)

-377
(25)

-259
(24)

-93
(22)

-15
(22)

+37
(22)

+79
(22)

Taxi:

-583
(25)

-383
(25)

-311
(25)

-182
(24)

-113
(24)

-11
(23)

+7
(23)

Radio:

-451
(58)

-195
(58)

-177
(57)

+5
(57)

+77
(57)

+197
(56)

+217
(56)

Numbers of identified congestions for mobile data
Correct:
FN / FP:

5.

74
0 / 6574
0 / 19CONCLUSION

73
1/3

70
4/2

70
4/1

68
6/1

68
6/0

We presented a novel approach for estimating vehicular travel times
based on anonymous location area updates of mobile phones collected from an operational mobile cellular network. Although spatially coarse, mobility data from all terminals (most of which inidle state) can be exploited to detect speed deviations at long roadsections and congestion events.

56

Experiments on a major traffic route in Austria showed that our
method yields higher detection success rates and at least a similar
detection delay when compared to traditional road monitoring sensory technologies. Thus, we conclude that cellular network data
are a valuable source for accurate traffic monitoring. At the sametime, our approach does not require investments in a new infrastructure but leverages the mobile cellular network – as a large-scale
mobility sensor.

Acknowledgments

We acknowledge the support of the FTW project RoadCell by thepartners ASFiNAG, A1 Telekom Austria, Kapsch CarrierCom, andNast Consulting. Further, we acknowledge the support of the Austrian Government and the City of Vienna under the COMET program and the Commission of the European Union under the FP 7
Marie Curie project MOVE-R, contract PIEF-GA-2010-276336.

6.

REFERENCES[1] R. Bertini. You are the traffic jam: an examination ofcongestion measures. Technical report, Department of Civil
and Environmental Engineering, Portland State University(2005), 2005.
[2] P. Fiadino, D. Valerio, F. Ricciato, and K. A. Hummel. Steps
towards the extraction of vehicular mobility patterns from 3G
signaling data. In Traffic Monitoring and Analysis (TMA)
Workshop, 2012.
[3] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi.
Understanding individual human mobility patterns. Nature,453(7196):779–782, 2008.
[4] 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, 2012.
[5] G. Rose. Mobile phones as traffic probes: Practices, prospects
and issues. Transport Reviews, 26(3):275–291, 2006.
[6] M. Treiber, A. Kesting, and D. Helbing. Three-phase traffic
theory and two-phase models with a fundamental diagram in
the light of empirical stylized facts. Transportation Report
Part B, (44):983–1000, 2010.

3

Session 8 // Social data collection

Emergence of Congestion In Road Networks based on Realistic Demandobtained from Mobile Phone Data
by Serdar Çolak1, Christian M. Schneider2, Pu Wang3, Marta C. González4
Emails: serdarc@mit.edu, puwang@mit.edu, schnechr@mit.edu, martag@mit.edu
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, USA

1,2,4

3School of Traffic and Transportation Engineering, Central South University, China

The first requirement a road network needs to fulfill is overall connectivity: there must be anadequately functioning path between any two places. Roads have limited capacity and queues ofvehicles accumulate; therefore it is of great interest to study the effect of both network topology andtravel demand on traffic flow leading to the emergence of congestion. Upon a certain threshold in thewhole network. In analyzing congestion, estimation of the demand is of prime importance.
Transition to congestion emerges for the rate of trips starting per time step, R, exceeding a certain

travelling at any time is reached. First, we study the influence of the network topology on theemergence of congestion. Inspired by models investigating network resilience in the context of
information packets and of the Internet, we propose a model to analyze the resilience of urban road
networks described as follows: At each time step R vehicles with assigned sources and destinations
enter the system. Roads have different capacities for delivering vehicles, at each time step every
segment can deliver at most C vehicles one step towards their destinations following a fixed routing
table. A vehicle, upon reaching its destination, is removed from the system.
We are interested in the critical value Rc, measured by the number of trips beginning at each time step,at which a phase transition takes place from free flow to congested traffic. This critical value reflects
the network’s capability of handling its traffic demand. Particularly, for R < Rc, the numbers of startingand completed trips are balanced, leading to a free traffic flow. For R > Rc, traffic congestion occurs as
the number of accumulated vehicles increases with time simply because the capacity of the roads is
exceeded.
The travel time dimension is incorporated into this model using a point-queue (PQ) macroscopic linkmodel. Vehicles move along the road at the speed limit before they reach its end where a point queueis formed if the traffic arriving is greater than the capacity at the exit. The PQ model is enhanced to a
spatial PQ model (SPQ) by limiting the number of vehicles a road segment can contain at once.
We analyze both theoretical and real road networks by performing simulations on periodic and nonperiodic lattices, random networks as well as on the real San Francisco road network. Our analyticaland numerical findings indicate that the critical point of the transition is determined by the ratio of the

57

3

threshold Rc. A network is considered to remain functional if an equilibrium in the number of vehicles

Session 8

number of vehicles, the capacity of the roads is exceeded, retarding the efficient functioning of the3. (a)Timespansthrough which the congested element operates
3 at its
Session 8 // FIG.
Socialdata
collection

timespans for the IM and (c) PQM model. Distributions exhibit no charac
specified exponents.

capacity to a modified maximum betweenness centrality. Moreover, the network response can beanalytically obtained by iteratively solving a set of coupled equations. We also show that at Rc, the els as a ctimespans during which the congested road operates at its flow capacity exhibit no characteristic timein the lite
portation
simulated
To estimate realistic demand, we use a dataset of mobile phone activity for the Bay Area, California.
the netwo
Using the tower scale and the timestamps we generate trajectories, which are then matched onto thetained. T
road network. The trajectories are scaled using census data and aggregated to form realistic originhibited n
destination matrices. The real demand distribution is applied on the San Francisco road network toat their oanalyze congestion and its emergence. It is found that the emergence of congestion based on demand
lowed a p
FIG.
4. Theloading
ratenotRfrom
lattices
c for
distributions
obtainedfrom criticalmobile phoneusers arises
lacknon-periodicof outflow capacitiesof the road
SPQM wwith n = 400, n = 625, n = 900 and for various VC simultaneously
values.
segments but from lack of volume capacities, namely, the number of vehicles that can
The relationship is linear for V < C, and then settles to thetices for v
use the segment. This suggests that congestion is inherent in downtown areas where road segments are
critical value of the PQM. Rc is found to decay exponentiallythe critica
short andfor
dense.
decreasing volume levels.
for volum
SPQM wand resul




arose, nofrom lack

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

the role oareas in t



Session 8

3

scale, as the timespan distribution follows a power law with an exponent around -0.5.


































⇤

FIGURE.
The transitions
the San Francisco
roadSannetworkfor (a) PQM
withnetwork
Rc=40 (14400
FIG.
5. The fortransitions
for theFranciscoroadvehicles/hr)
and
(b)
SPQM
with
R
=32
(11520vehicles/hr).
(c)
Network
response
as red points
c (b) the SPQM.
for (a) the PQM andrepresenting the locations of the queues of links that are above 90% of their volume capacities at thetime of this snapshot, for R=0.98Rc and (d) R=1.02Rc, respectively. A slight increase in travel demand
results in an explosion of queues in the downtown area, and the spillback of congestion to the arteries
that entercapacities
the downtown area
observed.
at canthebe timeof this snapshot, for R = 0.98Rc

58

and 1.02Rc , respectively. A slight change in travel demand results in an explosion of queues in the downtown
area, and the spillback of congestion to the arteries thatenter the downtown area can be observed.

Corre[1] M. Ba
dense[2] U. Br[3] S. Ca(Euro[4] Z. Wu
Physi[5] E. To
Revie65, 06[6] A. Ar
and F
ences
[7] L. Zh
E (St
(2005
[8] P. Ho4

Session 8 // Social data collection

Indicators of wealth, economic diversity and segregation in Côte
d’Ivoire using Mobile Phone datasets
Thoralf Gutierrez1,*, Gautier Krings1,2 and Vincent D. Blondel1,†1Institute for Information and Communication Technologies, Electronics and Applied Mathematics(ICTEAM), Université catholique de Louvain, Avenue Georges Lemaître, 4, 1348 Louvain-la-Neuve,
Belgium
2
Real Impact Analytics, 30 Grand Rue, 1660 Luxembourg, Luxembourg*
thoralf.gutierrez@student.uclouvain.be,†vincent.blondel@uclouvain.be

We notice that a user’s top-up history is often quite stable; users tend to buy credit in chunks of thesame amount. This stable behavior allows us to assign an average top-up amount to every user
without losing much information. We hypothesize that the wealth of a user is correlated with his or
her recharging habit: someone richer will be able to buy larger amounts of phone credit at a time.
Further analysis would be needed to assess the quality of this indicator.
We represent on Figure 1 the average user top-up in the different regions of the country. We may
also quantify the wealth diversity in the regions by computing the variance of the average top-up(Figure 2). As expected, the region of Abidjan is the wealthiest and most diverse. Some borderregions also stand out as wealthier than the rest of the country but with a lower diversity. The
administrative capital Yamoussoukro stands out in terms of its economic diversity but not by its
wealth. Finally, the typical rural regions are poor and have limited diversity.
After constructing a social graph based on the CDRs, we identify communities in the social graphusing the Louvain Method described in [2]. We then look at how many people have an average topup close to their community’s average top-up. This gives an indication of how economically diverse‡

Although the dataset is for Côte d’Ivoire, this is not the dataset used for the D4D challenge organized byOrange. The data comes from another mobile phone operator in Côte d’Ivoire.

59

4

We use anonymized Call Detail Records (CDRs) and top-up history from a major cellular network
company for a period of seven months‡. Most mobile phone subscriptions in Côte d’Ivoire are prepaid subscriptions. The top-up history of a user gives a complete description of every credit additionon the user’s subscription. The top-up amounts and frequency may be used as an indicator of a user
wealth. The CDRs include, for each call or text message, the caller and callee number, the location of
the tower where the call originated, the date and hour of the call/text message and, if it is a call, its
length in seconds. These CDRs allows us to use the method described in [1] to localize a user’s home
cell and to construct a social graph of users.

Session 8

In this study, we demonstrate how mobile phone datasets can be used to generate indicators ofwealth, economic diversity and economic segregation in the African country of Côte d’Ivoire. We are
missing statistical information for many African countries. We illustrate in this paper how mobile
phone data sets can be used to infer rough economic parameters without having to carry large andexpensive surveys.

Session 8 // Social data collection

Session 8

4communities are. In order to give a representation of this diversity in the country, we consider only
those communities whose members all live in the same region and we compute for every region the
average of diversity of all communities in the region. The result is represented on Figure 3. We findthat regions with high variability in top-up behavior often show higher levels of diversity withincommunities. In other regions, high variability in top-up behavior is accompanied by low diversity
within communities. This may be seen as an indication of segregation between social classes in thecorresponding regions.

Figure 1 – Average of top-up behavior for each region, which we interpretas an indicator of wealth for each region.

Figure 2 – Standard deviation of top-up behavior for each region, which
we interpret as an indicator of wealth diversity for each region.

60

4

4

Session 8 // Social data collection

Session 8

4

Figure 3 – Average Coefficient of Variation (CV) of the top-up
behavior within all communities in each region, darker means that
communities are less economically diverse.

References[1] Sibren Isaacman, Richard Becker, Ramón Cáceres, Stephen Kobourov, Margaret Martonosi, James
Rowland, Alexander Varshavsky "Identifying important places in people’s lives from cellular network
data." Pervasive Computing (2011): 133-151.
[2] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte and Etienne Lefebvre "Fast unfoldingof communities in large networks."Journal of Statistical Mechanics: Theory and Experiment 2008.10(2008): P10008.
[3] Jukka-Pekka Onnela, Samuel Arbesman, Marta C. González, Albert-László Barabási, Nicholas A.
Christakis "Geographic constraints on social network groups." PLoS one 6.4 (2011): e16939.
[4] Ott Toomet, Siiri Silm, Erki Saluveer, Tiit Tammaru, Rein Ahas, "Ethnic Segregation in Residence,
Work, and Free-time Evidence from Mobile Communication." (2011).

61

5

Session 8 // Social data collection

1

Can Cell Phone Traces Measure Social
Development?
Vanessa Frias-Martinez ∗ , Victor Soto ∗† , Jesus Virseda ∗‡ , Enrique Frias-Martinez ∗∗ Telefonica Research, Spain † Columbia University, USA ‡ Carlos III University, Spain

Session 8

5vanessa,vsoto,jvjerez,efm@tid.es

Abstract—Census maps contain important socio-economic information regarding the population of a country. Computing
these maps is critical given that policy makers often times makeimportant decisions based upon such information. However, thecompilation of census maps requires extensive resources andbecomes highly expensive, especially for emerging economies withlimited budgets. On the other hand, the ubiquitous presenceof cell phones, both in developed and emerging economies, isgenerating large amounts of digital footprints. These footprints
can reveal human behavioral traits related to specific socioeconomic characteristics. In this paper we propose a new tool,
CenCell, to approximate census information from behavioral patterns collected through cell phone call records. The tool provides
affordable census information by accurately classifying socioeconomic levels from cell phone call records with classification
rates of up to 70%.

I. I NTRODUCTION
Census maps gather large amounts of information regarding
the socio-economic status of households at a national scale. These
maps contain information that characterizes various social andeconomic aspects like the educational level of the citizens or
the access to electricity. Such information is aggregated andreported at various granularity levels, from a national scale,to states, all the way down to urban geographic areas of afew square kilometers. The accuracy of these maps is criticalgiven that many policy decisions made by governments andinternational organizations are based upon variables measured
through census maps. National Statistical Institutes compute suchmaps every five to ten years, and typically require a large
number of enumerators that carry out interviews gathering
information pertaining the main socio-economic characteristics
of each household. All these prerequisites make the computationof census maps highly expensive, especially for budget-constraint
emerging economies. To reduce costs, countries have made cuts
both in the number of interview questions and in the number ofcitizens interviewed, which unfortunately impacts the quality of
the final census information.
On the other hand, the ubiquitous presence of cell phones
in emerging economies is generating large datasets of digital
footprints. Data mining techniques applied to such datasets canbe used to reveal cell phone usage patterns specific to socioeconomic levels. Previous research has already shown that cell
phone-based behavioral patterns might be correlated to specificsocio-economic characteristics [1], [2]. For example, Eagle etal. showed correlations between the size of a cell phone social
network and the socio-economic level of a person, and Friaset al. observed strong relationships between mobility and socioeconomic indices [3].
In this paper, we propose a new tool for governments andpolicy makers that allows to compute affordable census maps
by decreasing the number of geographical areas that need to beinterviewed by the enumerators. The tool, called CenCell, is designed to allow institutions to approximate the census information

62

Fig. 1.

CenCell Architecture.

of areas not covered by the enumerators using anonymized cell
phone call records gathered by telecommunication companies.
At its core, CenCell consists of a classification algorithm that
determines the socio-economic level of a region based on theaggregated cell phone behavioral patterns of its citizens. Thus,
CenCell significantly decreases the workload of the enumeratorsthat carry out the interviews and as such, allows to reduce thebudget allocated for the computation of census maps.

II. C EN C ELL : G ENERAL A RCHITECTURE
Figure 1 shows the general architecture of the tool. It consistsof two main components: (1) the calibration phase, which needs
to be executed only once to set up the system for a region;and (2) the classification phase, which is executed every time
census information is required for a specific geographical areain the region that was not covered by the enumerators throughhousehold surveys.
The calibration phase needs two datasets, one containing
anonymized cell phone call records for the region under study
and another one containing the regional socio-economic levels
computed by the local National Statistical Institute throughhousehold surveys. This phase first computes a set of cell phone
usage behavioral patterns from call records. Next, it combines
both datasets to obtain a map that associates to each cellular
tower in the region under study a set of cell phone behavioral
variables and a socio-economic level. This map is used to train aclassification model that will output the socio-economic levels of
the areas not covered by the enumerators based on the cell phone
behavioral patterns of its citizens (classification phase). It has
to be noted that CenCell uses anonymized aggregated patterns

5

Session 8 // Social data collection
2

Cell phone networks are built using a set of base transceiver
station (BTS) towers that are responsible for communicating cell
phone devices within the network. Each BTS or cellular tower is
identified by the latitude and longitude of its geographical location. The area of coverage of a BTS can be approximated using
Voronoi tesselation. Call Detail Records (CDRs) are generated
whenever a cell phone connected to the network makes or receives
a phone call or uses a service (e.g., SMS, MMS). In the process,the BTS details are logged, which gives an indication of thegeographical position of the user at the time of the call. From all
the information contained in a CDR, CenCell only considers theencrypted originating number, the encrypted destination number,the time and date of the call, the duration of the call, and the BTS
that the cell phone was connected to when the call took place.
Using call detail records, CenCell computes three sets of variables
per subscriber so as to model cell phone usage: (1) consumption
variables; (2) social network variables and (3) mobility variables.
The consumption variables characterize the general cell phone
use statistics, measuring, among others, the number of input or
output calls, the duration of the calls or the expenses. The social
network variables compute measurements relative to the social

Census Data
CenCell uses the census maps collected by National Statistical
Institutes (NSI) to gather socio-economic information about the
population under study. NSIs carry out individual and household
surveys at a national level every five to ten years. These surveys
employ a large staff of enumerators that are responsible forinterviewing every household head within their assigned geographical area. The enumerators have been especially trained
to be able to gather all the required information in a proper
manner. Although in some cities in emerging economies thecensus information is collected with laptops, in general, paper
survey forms are still very common, which makes the collection
process even more expensive and time consuming. Given theprivate nature of the individual census information, NSIs only
make public average values over specific geographical areas.
These areas might represent states, cities, neighborhoods orgeographical units (GUs), the smallest geographical division,
which divides cities into small areas of up to a few square
kilometers (approximately blocks). The census variables gathered
by NSIs are usually divided into three groups: education variables,
demographic variables and goods’ ownership variables. With thisinformation NSIs compute the socio-economic level (SEL) of aregion as a weighted average of all the census variables. As
mentioned earlier, SEL values are typically represented as a setof discrete values typified through letters.

Merging Call Records with Census Data
The objective of the calibration phase is to build a socioeconomic classification model from cell phone records. To doso, we need to compute a training set that associates to each
BTS cell tower: (1) aggregated cell phone behavioral variables
for the citizens that live within the cell tower coverage area and(2) the corresponding socio-economic level (SEL) for that same
area. However, given that call records are gathered per BTS area
and that the census information is reported per geographicalunit (GU), the tool uses a three step protocol to merge thetwo sources of information [4]: (Step 1) Associate the residential
location of each subscriber to a BTS area; (Step 2) Compute theoverlapping between Voronoi diagrams and Geographic Units(GUs). This mapping allows us to merge census and BTS maps
so as to associate socio-economic levels to each BTS coveragearea; and finally, (Step 3) For each BTS, compute the aggregatedconsumption, social and mobility variables of all the subscribers
whose residential location is within that area. These three steps
produce a map that associates to each BTS a socio-economic level
as well as a set of variables (features) characterizing the averagecell phone usage for that area. Next, we explain the machinelearning techniques used in the calibration phase.

IV. C LASSIFICATION M ODEL G ENERATION
The generation of the Classification Model represents the last
step in the calibration phase. It builds a model that will allow
the classification phase to approximate the socio-economic level ofgeographical areas that have not been covered by the evaluators
to save budget. Specifically, it takes as input the (SEL, f eatures)
dataset for a specific region and identifies the best classification
technique for each socio-economic granularity. CenCell considers
four different SEL granularities: three, four, five and six ranges

63

5III. DATASETS AND I NFORMATION M ERGING
Call Detail Records

network of each subscriber. These variables include the input andoutput degree of the social network, the social distance between
contacts (diameter of the social network) or the strength of thecommunication ties. Finally, the mobility variables characterize
the geographic areas where a person typically spends most of
his/her work and leisure time as well as the spatio-temporalmobility patterns. In total, CenCell computes 69 consumption
variables, 192 social network variables and 18 mobility variables.

Session 8

of behavior so no individual information is used to build theclassification models.
During the calibration phase CenCell tests the classification
accuracy of a battery of supervised and unsupervised algorithms
and selects the one with the best results, which will be usedin the classification phase. Previous work explored the use of
supervised techniques (SVMs and Random Forests) to forecastsocio-economic levels from cell phone records [2]. However, giventhat CenCell computes the socio-economic levels (SELs) for eachcellular tower based on a weighted average of overlay maps,
the final values might be smoothed or blurred depending on
the information distribution in the original maps. In an attempt
to overcome this problem, CenCell also explores unsupervised
techniques to identify groups of cell phone behaviors without
prior knowledge of their socio-economic values. Sections IV
and V cover details about the different techniques explored by
CenCell and its results. Additionally CenCell’s calibration phase,computes classification models for different socio-economic level
granularities. Although the SEL is a continuous variable, it isoften times expressed as a discrete value through a letter (A,B, C, etc.). The granularity of the SELs i.e., the number of
SEL classes in which the continuous values are divided into,
varies a lot across studies. Some researchers differentiate threesocio-economic levels while others prefer to use a larger rangeof values in their analyses. To account for this need, CenCell
outputs the best predictive technique for each granularity value
in the calibration phase. As such, CenCell provides a knob thatallows researchers to select a specific granularity depending on
the classification error they are willing to accept. In Section V,
we delve more into results and implications of this approach.
Finally, it is important to highlight that in order to build accurateclassification models the tool needs that: (i) the area selected
for the calibration phase is representative of the different socioeconomic levels of the country and (ii) both call records andsocio-economic variables correspond to a similar period in time.
On the other hand, the classification phase uses the models
generated by the calibration phase to compute the socio-economic
level of the geographical areas in the region that were not coveredby the enumerators. This phase, which only requires access toanonymized aggregated calling records, can be executed as manytimes as needed and allows policy makers to compute affordable
census maps without the need to held household surveys across all
the region under study but rather in a few areas. Next, Sections
III and IV describe the calibration phase in detail.

5

Session 8 // Social data collection

Session 8

53(classes). For six SEL classes A covers range [100 − 83), B[83 − 66.4), C [66.4 − 49.8), D [49.8 − 33.2), E [33.2 − 16.6) andF [16.6−0]. Smaller granularities follow a similar distribution in
ranges. In terms of the features (cell phone variables), having alarge number might boost classification, but it can also generate
a lot of noise (the curse of high dimensional datasets). For thatreason, CenCell first identifies the significance of each feature andapplies classification techniques on the features ordered by theirrelevance. Specifically, it evaluates two different feature selection
techniques: maxrel and mRMR (as difference mRMR-MID or
quotient mRMR-MIQ). Once the features have been orderedaccording to their significance, CenCell tests supervised SVMs
as well as unsupervised EM Clustering. We selected SVMs since
they have been successfully used in similar classification problems[5]. On the other hand, unsupervised EM clustering was selectedamong all clustering unsupervised techniques, since populations
have been previously shown to follow Gaussian distributions interms of socio-economic variables [6]. CenCell evaluates eachcombination of technique and granularity and outputs the onethat has the best predictive power for each SEL granularity.
As a result, the tool provides policy makers with the possibilityof selecting a granularity and classification quality according totheir own interests.
This step first partitions the BTS dataset with the orderedfeatures and SELs for training and testing, containing 2/3 and 1/3
respectively. Using each supervised and unsupervised technique,
CenCell computes the classification rate for each SEL granularity,from three classes (A, B and C) to six (A, B, C, D, E and F ), andfor each subset of ordered features in n = {1, . . . , 279} producedby Maxrel, mRMR-MIQ and mRMR-MID. CenCell implements
the SVM using a Gaussian RBF kernel and identifying its two
parameters (C and γ) through a 5-fold cross-validation over the
training set for each combination of technique, granularity, andsubset of features. As for EM clustering, CenCell computes a
mixture Gaussian models for each socio-economic level, granularity and subset of features until the log-likelihood values are
maximized. During testing, each final cluster is labeled with thedominating socio-economic level.

V. E XPERIMENTAL E VALUATIONThe objective of this section is to evaluate the classification
power of CenCell to determine the socio-economic level of regionsthat are not covered by household surveys to save budget. Our
CDR dataset contains 6 months of cell phone calls, SMSs and
MMSs from over 500, 000 pre-paid and contract subscribers froma large city in an emerging economy in Latin America. The
city has a total of 920 BTS cellular towers and the subscribers
represent a 20% of the total population. On the other hand,the census information was acquired from the local NSI andcontained a total of 1200 GUs with their SEL expressed asa continuous value between 0 and 100. The city was selected
because it covered all ranges of SELs. Our final dataset consistsof 920 pairs (SEL, f eatures) that we divide into training (552pairs) and testing sets (368 pairs).
The classification accuracies for each pair of technique andsocio-economic granularity explored by CenCell are presented in
Figure 2. We observe that SVMs achieve classification rates of
up to 76% when differentiating three SEL classes and the top 38
ordered features selected by mRMR-MIQ. We also notice that as
we increase the granularity of the SEL, the classification accuracy
decreases reaching a value of 57% for six SEL classes and the
top 19 features. On the contrary, EM clustering achieves worseclassification rates than SVM for granularities three, four andfive. However, EM clustering yields better results when six socioeconomic levels are differentiated. It shows accuracies of 63%
compared to the 57% reached by SVMs, with 6 clusters and the
top 20 features. We hypothesize that as the socio-economic granularity increases, the map-overlay technique in (Step 2) generates
more blurred SEL levels thus making unsupervised techniques

64(a) SVM(b) EM Clustering
Fig. 2.

Accuracy for (a) SVM and (b) EM clustering with mRMR.

a more adequate approach. At the end of the calibration phase,
CenCell would select SVMs as classification tools for granularities
three, four and five; and would select EM clustering when six
socio-economic levels are differentiated. To understand better thenature of the classification errors, we analyzed the confusion
matrices for the best approaches selected by CenCell. These
confusion matrices revealed that when incorrectly predicted, theoutput SEL class tends to be adjacent to the correct one. Sucherrors reflect an implicit order between the SELs which limits
the impact of the errors on the computation of the maps.

R EFERENCES[1] N. Eagle. Behavioral inference across cultures: Using telephones asa cultural lens. IEEE Intelligent Systems, 23:4:62–64, 2008.
[2] V. Soto, V.Frias-Martinez, J. Virseda, and E. Frias-Martinez. Prediction of socioeconomic levels using cell phone records. In User
Modelling, Adapt. and Pers., UMAP, 2011.
[3] V. Frias-Martinez and J. Virseda. On the relationship between socioeconomic factors and cell phone usage. ICTD, Atlanta, USA, 2012.
[4] V. Frias-Martinez, J. Virseda, A.Rubio, and E. Frias. Towards large
scale technology impact analyses: Automatic residential localizationfrom mobile phone-call data. ICTD, 2010.
[5] Robert Burbidge and Bernard Buxton. An introduction to support
vector machines for data mining. Technical report, UCL, 2001.
[6] M. Yadollahi. Factors affecting family economic status. European
Journal of Scientific Research, 37:94–109, 2009.

Session

9Social structure65

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Session 9 // Social structure

Mobile communication in business networks: structure andleadership
1,*

1

1

2

Gautier Krings , Didier Baclin , Loic Jacobs van Merlen , Marcelo Lobato Pimenta , Felipe De Abreu Galli

2

1

Real Impact Analytics, 30 Grand Rue, 1660 Luxembourg, Luxembourg
Telefonica/Vivo, Av. Eng. Luís Carlos Berrini, 1376 - Brooklin, Itaim Bibi, São Paulo, 04571-000, Brazil*
gautier.krings@realimpactanalytics.com
2

Session 9

1

Mobile Call Data Records (CDRs) have been widely used for the last 15 years for the purpose of Social
Network Analysis (SNA). Typical questions are about the structural organization of these networks[1], the detection of leaders among the nodes [2] or the spreading of information [3]. In a majority of
the research published in this topic, the analysed CDRs are related to private accounts and theanalyses are mostly focused on social interactions such as friendships or families. Little research has
been conducted so far on the structure of such communication networks in corporate environments(see related work for studies of face-to-face communications [4], email networks [5] or in politicalenvironments [6]).
In business networks, we state the principle of leadership in a different way, which is bound to theway companies tend to work. We will highlight the differences between our B2B decision makers’
discovery method and the existing leadership algorithms. We use this method to score the entire
network of B2B customers. This allows the operator to prioritize some lines in order to offer todecision makers an optimal experience, which is especially relevant in emerging markets where thegrowth of the customer base has often outpaced the investments in the underlying network that isoften the cause of poor experience, call quality, slow data transfer speed.
We study a network built on two months of call data records of approximately six million businesscustomers from one large Brazilian operator. For each customer, the company ID is known allowing
us to segment the network into subnetworks corresponding to the internal communication inside thecompany. The initial communication network is hence splitted into 334,000 networks representing
each one company, with sizes ranging from 1 to 58,000 nodes.
The original CDRs consist of about 2.3 billions calls, SMS and MMS (in 2 months), which result into an
undirected weighted network where links are weighted by the number of calls, SMS and MMS thathave been exchanged by both numbers. It has often been pointed out that a preliminary filtering of
the network allows to remove fake calls and non-social interactions [7], however we decided not toapply such a filtering, given that calls between two employees of the same company is a priori
expected to be a meaningful interaction.
Previous research about networks in a corporate environment has been mostly focused on searching
information in the network structure about the company’s organization. Here, we go a step further
into the analysis by searching how to find to infer leadership from the network structure. Leadershipin social networks has been a long-studied topic and can be defined in various ways. In social
networks, leaders may be individuals being in the centre of dense parts of the network [2],individuals being referenced by others, or hubs instead, that are sources of useful references [8]. In

66

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Session 9 // Social structurebusiness networks, we state the principle of leadership in a different way, which is bound to the way
companies tend to work.
While communication is crucial for an organization to work correctly, it is observed that mostcompanies work as silos: a large fraction of the communication stays inside departments and only
few calls are passed across different departments. In such a configuration, managers and teamleaders are located in a central position of the network, and act as bridges of information acrossdepartments. We propose a centrality measure that integrates these hypotheses and works similarly
to Google’s PageRank measure. We first estimate the structure of departments inside each company
communication network using the Louvain method with standard modularity. The choice of using thestandard modularity instead of a scaled variant of this quality measure [9,10] is made on purpose;the standard modularity suffers of the so-called resolution limit [11] which makes the sizes of thecommunities dependent of the size of the network. While this limit is often seen as a problem, in ourcase we use it to our advantage: in small companies, we will detect small departments, while in large
companies, the Louvain method returns large communities, hence large departments.
Using the communities as approximation of the departmental structure of the company, whencalculate the leadership measure of each node using the following two assumptions:
Leaders communicate often with leaders of other departments
Leaders communicate with most of the departments of the company

Based on these assumptions, we calculate recursively the leadership measure ( ) of each node with

Session 9




1

()

| |

∑

(

( ))

where is the set of all communities in the network, is the community index of node , and ( )returns the node of community that has the strongest connection with node . In other words, theleadership of a node is the average of the leadership of his closest connection in each community
different of his own community. If a node does not communicate with one or several communities,
his leadership level is hence penalized in comparison to nodes that communicate to all othercommunities. We apply this computation recursively and normalize the leadership values at the endof each iteration such that the highest leader in each community has a leadership measure of 1. Afterthe convergence of the iterative process, we recover the leadership measure for all nodes of the
network.
We have tested this measure on the internal communication network of a part of the operator’s
employees, consisting of approximately 1,000 individuals for which the department and the positionin the company have been made available. As a first result, we observe that the detected
communities and the exact departments have a very significant overlap. Moreover, we used theleadership measure in conjunction with several other network variables such as the core number in adata mining model, and managed to predict correctly over 70% of the leaders in the company.

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Session 9 // Social structure

Session 9

1

Although the leadership measure isbased on a community detection
algorithm that has a non-deterministic
component, the obtained scores remain
consistent when the communities
returned by the detection method differ
one from another.
We score the entire network of B2B
customers, and observe that decision
makers are a useful source of
information about the structure of the
network. Their communication patterns
provide information on the company’s
way of working. On a commercial side,
Figure 1: network of the operator’s internal communications, colors we show how leaders act as drivers ofcorrespond to communities and overlap strongly with the actual
adoption for several products offered bycompany’s departments.
the operator: if the decision maker of acommunity is adopting a product, the members of his department are more likely to adopt thisproduct as well. Finally, decision makers help as well the operator to rank lines that are out of his
network based on the leadership score of his own customers.
References:[1] Blondel, V., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large
networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
[2] Blondel, V., De Kerchove, C., Huens, E., & Van Dooren, P. (2006). Social leaders in graphs. Lecture notes incontrol and information sciences, 341, 231.
[3] Tabourier, L., Stoica, A., & Peruani, F. (2012). How to detect causality effects on large dynamicalcommunication networks: a case study. In Communication Systems and Networks (COMSNETS), 2012 FourthInternational Conference on (pp. 1-7). IEEE.
[4] Olguín, D. O., Waber, B. N., Kim, T., Mohan, A., Ara, K., & Pentland, A. (2009). Sensible organizations:Technology and methodology for automatically measuring organizational behavior. Systems, Man, andCybernetics, Part B: Cybernetics, IEEE Transactions on, 39(1), 43-55.
[5] McCallum, A., Wang, X., & Corrada-Emmanuel, A. (2007). Topic and role discovery in social networks withexperiments on enron and academic email.Journal of Artificial Intelligence Research, 30(1), 249-272.
[6] Porter, M. A., Mucha, P. J., Newman, M. E., & Friend, A. J. (2007). Community structure in the united stateshouse of representatives. Physica A: Statistical Mechanics and its Applications, 386(1), 414-438.
[7] Onnela, J. P., Saramäki, J., Hyvönen, J., Szabó, G., De Menezes, M. A., Kaski, K., ... & Kertész, J. (2007).
Analysis of a large-scale weighted network of one-to-one human communication. New Journal of Physics, 9(6),179.
[8] Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment.Journal of the ACM(JACM), 46(5), 604-632.
[9] Lambiotte, R., Delvenne, J. C., & Barahona, M. (2008). Laplacian dynamics and multiscale modular structure
in networks. arXiv preprint arXiv:0812.1770.
[10] Traag, V. A., Van Dooren, P., & Nesterov, Y. (2011). Narrow scope for resolution-limit-free community
detection. Physical Review E, 84(1), 016114.
[11] Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the
National Academy of Sciences, 104(1), 36-41.

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Session 9 // Social structure

A Place-focused Model for Social Network Formation in
Cities
Chloë Brown, Anastasios Noulas, Cecilia Mascolo
Computer Laboratory
University of Cambridge
email: firstname.lastname@cl.cam.ac.uk
Vincent Blondel
Department of Mathematical Engineering
Université Catholique de Louvain
email: vincent.blondel@uclouvain.be

Introduction

Session 9

1

2

It is an intuitive idea that social relationships between people, arise out of meetings and shared activities
in common spaces. Scott Feld’s theory of the focused organization of social ties posits that friendships
form between individuals whose interactions are organized around extra-network foci, which can include
physical places. In the paper outlining this theory [Feld1981], Feld discusses the implications of this theory
for the structure of social networks, and shows how commonly observed structural properties could arise
from the formation of social ties in the way he describes.
Empirical investigation of Feld’s theory has traditionally been difficult and time-consuming, requiring
interviews with and observation of necessarily small groups of people, and these difficulties have meant thatit has been impossible to test on a large scale. However, the recent widespread adoption of location-based
online social services has provided us with a huge volume of data both about the structure of people’s social
networks, as described by social ties explicitly declared by users of these services, and about their activities and meetings at places in their local environment, thanks to the location-sharing dimension. We now
therefore have an unprecedented opportunity to investigate the role that places may play in the formation of
the structure of social networks on a scale not previously possible. Furthermore, the semantic information
about places available in location-based online social services allows us to investigate for the first time therelationship between the categories of places where people meet and the likelihood that those people arefriends. This is a great advantage offered by these services, where user position is becoming available when
they use the respective service similarly to cellular datasets, yet compared to the latter, the combination ofmultiple layers of data opens new avenues for addressing previously unanswered research questions.
In this work, we study a large dataset from Foursquare, the most popular location-based online social
network, which has over 35 million users as of January 2013, analyzing the social and spatial properties
of social networks in cities. We then use our observations to present a model for social network formation
based on Feld’s focused organization theory, and show that the model produces networks with the structuralproperties expected of social networks. Our work makes the following contributions:• We first define and analyze place-based social networks at the city scale, to answer the question:
what do intra-city social networks look like, and do they have common structural characteristics?
We show that the social networks in the Foursquare dataset have the structural properties observed
by sociologists studying real-world social networks, namely a power-law degree distribution, smallworld properties (high clustering and small diameter), and strong community structure. While the69Session 9 // Social structure

global properties of online and offline social networks have previously been analyzed, we believethat our work is the first to examine and compare the structures of place-based social networks withindifferent cities and to show these common structural properties.
• We then address the question: does this large location-based social dataset support Feld’s theoryof focused tie organization? Exploiting the combination of social information and specific semantic
information available in the Foursquare dataset, we are able to investigate for the first time the relationship between the category of a place where people meet and the probability of friendship. We
find that the type of a place where people meet has a strong influence on the likelihood that they arefriends, which provides support for Feld’s theory of focused organization at a scale not previously
possible.
• Inspired by Feld’s theory, we present a model for the formation of a social network in a city basedaround meetings at places, and show that this model is able to produce networks with the structuralproperties observed in real social networks. The fact that this model is able to reproduce empiricallyobserved social network features represents a computational validation of the focused organization
theory, and supports its suggested mechanisms for the formation of friendship between individuals.

Session 9

2

We believe that our work has intrinsic interest, as we investigate an area largely unexplored, namely, thatof the structural similarities between social networks at the city scale within different cities, and demonstrate that the networks in different cities show striking similarities. Furthermore, our model demonstrates
computationally that Feld’s theory of the focused organisation of social ties, with places as foci, results in
networks with the structural features commonly observed in social networks.
From a practical perspective, our observation that the type of a place where people meet strongly affects
the probability of friendship could be useful to online location-based social services such as Foursquare.
For example, one important application in location-based social networks is the recommendation to users ofvenues they might want to visit [Berjani and Strufe2011, Long, Jin, and Joshi2012, Noulas et al.2012]. Ourfinding that the type of place where people meet has a strong influence on friendship suggests that different
recommendations would be appropriate depending on the other users present.
This has potential applications in the development of smarter privacy controls in location-based onlinesocial networks: Page et al. found that people’s concerns about privacy in location-sharing services centeraround the desire to preserve one’s existing offline relationship boundaries [Page, Kobsa, and Knijnenburg2012],
and our observations suggest that these boundaries might be reflected in the types of places where friends
meet (for example, closer friends at homes and nightlife spots, less close acquaintances only at professional
venues or transport spots). Use of this information could enable services such as Foursquare to adjust thedefault audience of a check-in, for example, based on relationship semantics inferred using meeting places.

References[Berjani and Strufe2011] Berjani, B., and Strufe, T. 2011. A recommendation system for spots in location-based
online social networks. In Proc. of the 4th Workshop on Social Network Systems. ACM.
[Feld1981] Feld, S. L. 1981. The focused organization of social ties. American Journal of Sociology 86(5).
[Long, Jin, and Joshi2012] Long, X.; Jin, L.; and Joshi, J. 2012. Exploring trajectory-driven local geographic topics
in foursquare. In LBSN’12.
[Noulas et al.2012] Noulas, A.; Scellato, S.; Lathia, N.; and Mascolo, C. 2012. A random walk around the city: Newvenue recommendation in location-based social networks. In SocialCom ’12.
[Page, Kobsa, and Knijnenburg2012] Page, X.; Kobsa, A.; and Knijnenburg, B. 2012. Dont disturb my circles! boundary preservation is at the center of location-sharing concerns. In ICWSM ’12.

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Session 9 // Social structure

A Comparative Study of Decentralized Routing in Social Network Based on Mobile
Phone Data
Carlos Herrera, ∗ Christian M. Schneider, † Thomas Couronne,
Zbigniew Smoreda, Rosa M. Benito, ‡ and Marta C. González §
1,

1,

2

1

1,

2

1,

Department of Civil and Environmental Engineering,

Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America

2Sociology and Economics of Networks and Services department,

Orange Labs, 38 rue du Général Leclerc, 92794 Issy les Moulineaux, France

Permanent address:

Universidad Politécnica de Madrid, 28040

Madrid, Spain† Email: schnechr@mit.edu‡ Permanent address:

Grupo de Sistemas Complejos, and Depar-

tamento de Física y Mecánica, Escuela Técnica Superior de Ingenieros Agrónomos, Universidad Politécnica de Madrid, 28040
Madrid, Spain§ Email: martag@mit.edu

3∗ Email: carloshy@mit.edu

Session 9

In this work we focus on the study of social networks
extracted from mobile phone data. The data has beenltered in such a way that the nodes are a subset of themobile phone users and the links represent a social interaction that indicates an acquaintance or a friendship as
opposed to just an occasional phone call. This is possible
because our current data spans over a period of at least
six months in each of the countries we study (displayedin Fig. 1).
The main idea behind this work is based on the famous
small-world experiment performed by the social psychologist Stanley Milgram [1] in the 1960s. Milgram's experiment led to two striking discoveries, of which the existence of short paths was only the rst. The second wasthat people in society were collectively able to forward a
letter to a distant unknown target surprisingly fast withknowledge of only their own personal acquaintances (local information) and only the name, location and theprofession of the target.
Many interesting questions remain open in relation to
Milgram's, experiment, such as why should a social network contain such short paths and how people are able toselect among hundreds of acquaintances the correct person to form the next link in the chain. To answer thesequestions several works have been carried out, both empirically and mathematically. Dodds et al. [2] repeated
Milgram`s experiment at large scale with e-mails, providing conrmation that geographical position of the nodes
has a crucial role in the possibility of the network to be
searchable. On the other hand, Lieben-Nowell et al. [3]proved a simple geo-greedy algorithm is able to eciently
route a message between dierent cities using the social
network, with data from an online blog community whereusers declare the city where they live. Watts and Strogatz[4, 5] proposed a hierarchical network model that present
the small world eect and high clustering. Kleinberg [6]has proposed several analytical treatable network models

Figure 1. The social communities in the three studied countries and their capitols. While on the country level the communities divide dierent regions, within cities the geographical separation collapse and it reveal social groups.

in which actors are capable of nding short paths withhigh probability just by using local information.
The goal of this research is to understand how people areable to deliver a message within a small number of hops
from a source user to a target user whose location is given
in terms of his/her coordinates in a city of the country
under study. Therefore, we study dierent strategies forthe case of limited information (without global knowledge of the social network), just using local informationsuch as the identities and connectivity of a node's neighbors based on the structure of the real social networks.
This study is performed in dierent countries and both71

3

at the inter-city and at the intra-city levels demonstrating that decentralized search algorithm can work well onreal friendship networks on both levels. We use threedierent large data sets corresponding to six months ofanonymized mobile phone calls of three European countries (France, Portugal, and Spain) with over 7 billionof calls. The data sets include information of the mostused tower coordinates (France and Portugal), or billing
zip codes (Spain) for each of the users present in the calldata. From these data sets we construct the friendshipnetworks based on reciprocal communications taking into
account the user location.
First we show the existence of short paths with an av-

erage length of 7.75, 7.44, and 9.2 for France, Portugal
and Spain, respectively. We further simulate delivering amessage from a source user to a target user whose location is given in terms of his/her coordinates in a city of
the country using only information of the source's neighbors. We report the success of three dierent decentralized routing strategies based on: random information,
geographical information, and community information.
Finally, we connect the results with the underlying social network structure and conclude that both information Milgram provided (geography and profession) are
required to successfully route to an unknown person.

[1] Milgram, S., The small world problem. Psychology Today
1, 62-67 (1967).
[2] Dodds, P.S., Muhamad, R., Watts, D.J., An Experimental
Study of Search in Global Social Networks. Science 301,
827-829 (2003).
[3] Lieben-Nowell, D., Novak, J., Kumar, R., Raghavan,P., Tomkins, A., Geographic routing in social networks.
PNAS 102, 11623 (2005).

[4] Watts, D.J., Strogatz S.H., Collective Dynamics of Small
World Networks. Nature 393, 440 (1998)[5] Watts, D.J., Doods, P.S., Newman, M.E.J., Identity and
Search in Social Networks. Science 296, 1302-1305 (2002).
[6] Kleinberg, J., Complex networks and decentralized searchalgorithms. Proceedings of the International Congress ofMathematicians: Madrid, August 22-30, 2006: invited lectures, 1019-1044 (2006).

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4

Session 9 // Social structure

Migration and Ethnic Segregation: Evidence from
Estonia's Mobile Phone Logs

Joshua Blumenstock

Ott Toomet

University of Washington

Tartu University

joshblum@uw.edu

otoomet@gmail.com

DRAFT VERSION
February 2013∗

Abstract:

segregate along ethnic lines has important economic and social consequences, aecting employment andeducation decisions, processes of information creation and diusion, and several other aspects of society.
Yet, due in part to data constraints, little is known about the extent and dynamics of migrant integration

Session 9

What is the eect of migration on ethnic segregation in urban areas? The manner in which individuals

within and across ethnicities. We exploit a novel source of communications data that allows us to observe

tense ethnic relations. In addition to containing detailed information about inter- and intra-ethnic network
structure, we are able to observe the migration and movement of each individual using mobile positioning
data. We can thereby infer, for each individual, whether a migration takes place, the extent to which the
migrant communicates with coethnics and non-coethnics pre- and post-migration, and whether the migrantis physically proximate to coethnics and non-coethnics.

Our study contains three related sets of results:(i) Whether the average migrant integrates within or across ethnicities, (ii) How quickly integration withcoethnics and non-coethnics occurs for dierent types of migrants; and (iii) Aggregate eects of migration
and urbanization on city- and country-wide levels of ethnic segregation.

Keywords: Segregation, Migration, Homophily, Urbanization and Cities, Mobile Phones; Estonia.

∗ The authors are grateful for thoughtful comments from Rein Ahas, Matthew Jackson, Frederico Finan, Tyler McCormick,and to seminar participants at the University of Washington for helpful comments. We gratefully acknowledge nancial support
from CERGE-EI and the National Science Foundation. All errors are our own.

1

73

4

the ethnicity of hundreds of thousands of mobile phone subscribers in Estonia, a country with a history of4Session 9 // Social structure

1 Introduction
Ethnic segregation is a common phenomenon in many of the world's cities, and a prominent feature ofmost developing economies (Taeuber and Taeuber, 1965; Massey and Denton, 1993). The consequences ofsegregation are far-reaching, aecting investment in human capital, the structure of labor markets, levels ofviolence and corruption, inequality, and patterns of prejudice and discrimination (Easterly and Levine, 1997;
Miguel and Gugerty, 2005; Cutler and Glaeser, 2007; Bayard, Hellerstein, Neumark, and Troske, 1999).
Internal migration plays a critical role in both short- and long-term dynamics of segregration and integration. Migrants often choose to migrate to areas where their networks are stronger (Munshi, 2003), existing
residents often strive to keep out dissimilar immigrants (Schelling, 1971), and political and institutional forces
often prevent integration of migrants across ethnic lines (Yinger, 1986; Clark, 1986). However, due largely
to shortcomings in the data used in the analysis of migration and segregation, very little is known about
the extent to which migrants integrate into communities in the location of destination, and whether recent
migrants aect aggregate levels of segregation. The vast majority of empirical studies on segregation relyon census or household survey data that is sampled infrequently, and which is notoriously bad at tracking
domestic migration. These studies almost invariably focus on segregation at the place of residence, ignoring

Session 94

segregation in other aspects of life, such as place of work and leisure time (van Ham and Manley, 2010).

2 Context and Data
Estonia is an ethnically fractured society that provides an ideal context for studying patterns of ethnic
segregation through mobile phone datasets. Besides native Estonians, the country houses a large and relatively homogeneous Russian-speaking population. The relationship between these two ethnic groups is afragile one, with the tensions culminating in Bronze Soldier riots in 2007. In previous work, we have usedthe call data to show that ethnic Estonians and ethnic Russians remain highly segregated in major urban
areas (Toomet, Silm, Saluveer, Tammaru, and Ahas, 2012) and in their respective communication network(Toomet, van der Leij, and Rolfe, 2012).
A unique feature of the data at our disposal is the fact that we observe the language spoken by each
subscriber in the dataset.

In Estonia, a bilingual economy where native Estonians speak Estonian andnative Russians speak Russian, this allows us to very accurately infer the ethnicity of each subscriber. As
with other Call Detail Records used in recent research (cf.

Gonzalez et al.

2008, Song et al.

2010), weadditionally observe the complete communication records of each individual, as well as a long history of
passive positioning data, which indicates for each phone call and text message, the time and location of
the subscriber involved in the event.

The location is specied by the cell tower, which gives us a spatial

resolution ranging between many kilometers in rural areas to a few hundred meters in dense urban settings.

3 Measuring segregation and migration from mobile call data
Our approach allows us to measure several types of segregation simultaneously, over a period of multiple

1 For the long-run data, we rely on the call records to infer the location of each individual throughout

years.

1 Census and survey data typically only permit the measurement of segregation at the place of residence. However, segregation
is more complex, as important parts of our life are spent at work, school, and in places where we pursue various leisure activities.
These various types of segregation are correlated (Schnell and Yoav, 2001), as employment opportunities are aected by local
ethnic networks (Edin, Fredriksson, and Åslund, 2003), and by the fact that similar people tend to cluster into similar jobs
(Sørensen, 2004). Still, Åslund and Skans (2010) nd that, in Sweden, workplaces are less segregated than residences.

74

4

1.0

Session 9 // Social structure

0.0

0.2

homophily
0.4
0.6

0.8

EE homeRU home

0.0

0.2

0.4
0.6
group percentage

0.8

1.0

Figure 1: Relationship between the homophily at the home neighborhood as a function of the corresponding
ethnic composition (Toomet, Silm, Saluveer, Tammaru, and Ahas, 2012).

Group percentage is calculated

from 2000 census. Points represent average values for residents in the corresponding Tallinn municipality
districts. Circles represents ethnic Estonians; triangles ethnic Russians.

are close to each other. Formally, we measure copresence from the passive positioning data. The sameplace and same time must be operationalized to timeframes, where we specify what is the required spatial

Session 9

the day. This allows us to observe at which times of the day individuals of similar and dissimilar ethnicities

proximity (for instance, a city tract) and time interval (for instance, 1 hour). We dene copresence between∑k1(cjk ∈ Ci ), where 1(·) is the indicator function, cjk is timeframe ofcall k of individual j , and Ci is the set of all timeframes of individual i. Copresence can be understood as
the meeting potential between given two individuals and hence treated as a (proxy for a) social tie.

In addition to the positioning data, we observe the actual call graph, as determined by the calls madebetween individuals in the dataset. Knowing the ethnicity of the caller and recipient allows us to measure thesocial segregation in the call network, which we take as a proxy for underlying patterns of ethnic segregation.
We can further infer the strength of each network edge using the the volume and length of calls between eachdyad. Formally, homophily for individual i is dened as hi =si
si +di , where s denotes the measure of contacts

with the same-language individuals, and d that with the dierent-language ones. s and d are dened throughcopresence as

si =

∑
j∈1
j̸=i

1(λj = λi ) · pij

and

di =

∑
j∈1
j̸=i

1(λj ̸= λi ) · pij ,(1)

where λi denotes the preferred language of individual i. As we have shown in prior work, the homophily inthe location of residence follows the population percentage rather closely (see Figure 1), while at workplaceand during free-time the relationship is much weaker (Toomet, Silm, Saluveer, Tammaru, and Ahas, 2012).
Given a continuous sequence of locations for all individuals over several years, we can similarly identify the home and work (geographic) locations for cellphone users using the anchor-point methodologyof Ahas, Silm, Järv, Saluveer, and Tiru (2010).

This permits us to identify both short and long-distancemovers, and those who change their workplace while remaining settled in the same area. We are then able toclassify these moves as temporary or permanent migrations, and thus construct a binary indicator variable

Mit that indicates for each individual i whether he/she migrated at time t.
Using the above measure of homophily hit as an indicator of the extent to which individual i prefers to75

4individuals i and j , pij , as pij =4Session 9 // Social structureassociate with coethnics over non-coethnics at time t, and taking Mit to be a binary indicator of whether

i migrates at time t, a basic xed-eects model can be used to study the eect of the migration on i'spreference for coethnics.

hit = α1 +T∑

βs Mi(t−s) + µi + πt + ϵit

(2)

s=0

where µi is an individual-specic xed eect that accounts for the fact that each individual may have adierent preference for segregation at baseline, and πt is a time-specic xed eect to reduce bias caused bycommon trends across all individuals over time. The coecients on the βs thus indicate the extent to which
migrations in the past T periods (for s = 0,1,...,T ) lead to changes in observed homophily for the average
migrant. It is these estimated βs coecients that reveal whether migrants tend to be more segregated in theplace of destination or of origin.
Adding heterogeneous eects to Model 2 permits us to identify which types of migrants are more orless likely to be integrated into communities of coethnics and non-coethnics.

Letting Xi denote a vector

individual characteristics that vary between individuals but which are constant over time, we estimate

Session 94

hit = α2 +

T
∑
s=0

βs Mi(t−s) +

T
∑
s=0

γs (Mi(t−s) ∗ Xi ) + µi + πt + ϵit(3)

As before, the βs indicate the extent to which past migrations aect current homophily, and now the γsestimate the extent to which individuals of dierent Xi are more or less likely to be aected through the
migration.

As an example, if Xi includes a measure of the homophily of i prior to migration, then γs

indicates whether migrants who are more integrated across ethnic lines pre-migration are more or less likely
to integrate post-migration.

Figure 2: Inbreeding homophily, a measure of co-ethnic bias, as a function of the relative group size, forseveral hundred municipalities in Estonia. Red dots represent Russian speakers; black dots represent Estonian
speakers. Solid dots correspond to outgoing calls; hollow dots to incoming calls.

76

4

Session 9 // Social structure

References
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.
Bayard, K., J. Hellerstein, D. Neumark, and K. Troske (1999): Why are Racial and Ethnic Wage Gaps

Larger for Men than for Women? Exploring the Role of Segregation, Working Paper 6997, National Bureau of
Economic Research.
Clark, W. A. V. (1986): Residential Segregation in American Cities; A Review and Interpretation, Population
Research and Policy Review, 5, 95117.

Cutler, D. M., and E. L. Glaeser (2007): Social interactions and smoking, Working Paper 13477, NBER, 1050

Massachusetts Avenue, Cambridge, MA 02138, USA.
Easterly, W., and R. Levine (1997): Africa's Growth Tragedy: Policies and Ethnic Divisions, The Quarterly
Journal of Economics, 112(4), 12031250.

Edin, P.-A., P. Fredriksson, and O. Åslund (2003):

Ethnic Enclaves and the Economic Success of Immi-

grantsEvidence from a Natural Experiment, The Quarterly Journal of Economics, 118(1), 329357.

Harvard University Press, Cambridge, MA.
Miguel, E., and M. K. Gugerty (2005): Ethnic diversity, social sanctions, and public goods in Kenya, Journal

Session 9

Massey, D. S., and N. A. Denton (1993): American Apartheid: Segregation and the Making of the Underclass.

of Public Economics, 89(1112), 2325  2368.

4

Munshi, K. (2003): Networks in the Modern Economy: Mexican Migrants in the U. S. Labor Market, The Quarterly
Journal of Economics, 118(2), 549599.

Schelling, T. C. (1971): Dynamic models of segregation, Journal of Mathematical Sociology, 1, 143186.
Schnell, I., and B. Yoav (2001): The Sociospatial Isolation of Agents in Everyday Life Spaces as an Aspect ofSegregation, Annals of Association of American Geographers, 91(4), 622636.
Åslund, O., and O. N. Skans (2010):

Will I See You at Work?

Ethnic Workplace Segregation in Sweden,

1985-2002, Industrial and Labor Relations Review, 63(3), 471493.
Sørensen, J. B. (2004): The Organizational Demography of Racial Employment Segregation, American Journalof Sociology, 110(3), pp. 626671.

Taeuber, K. E., and A. F. Taeuber (1965): Negroes in cities: Residential segregation and neighborhood change.

Aldine Pub. Co., Chicago, IL.
Toomet, O., S. Silm, E. Saluveer, T. Tammaru, and R. Ahas (2012):

Where do Ethnic Groups Meet?

Copresence at Residence, Work, and Free-time, Tartu University.
Toomet, O., M. J. van der Leij, and M. Rolfe (2012): Social Networks and Labor Market Inequality between

Ethnicities and Races, Tinbergen Institute Discussion Papers 12-120/II, Tinbergen Institute.
van Ham, M., and D. Manley (2010): The eect of neighborhood housing tenure mix on labour market outcomes:

a longitudinal investigation of neighbourhood eects, Journal of Economic Geography, 10, 257282.
Yinger, J. (1986): Measuring Racial Discrimination with Fair Housing Audits: Caught in the Act., American
Economic Review, 76(5), 881893.

77

5

Session 9 // Social structure
Why does my phone always ring when I am about to make a call?
Jeppe Juul1 and Albert-László Barabási2,3,4

1

friends, defined as users with reciprocal calls, is centeredat lower values than can be explained by gender, age,or regional similarity. Furthermore, weighting the distance measure according to the frequency of reciprocal
calls shifts the distribution to even lower values (see Fig.
2). That is, close friends will have a tendency to makecalls at the same times during the week. This could indicate the existence of a new of type of communication
homophily. We explore the alternative hypothesis thatfriends establish mutual norms for phone usage and that
this can account for the alignment effect observed for thecall profiles of users who phone each other frequently.

0.06

random pair of people
friends
friends weighted by activity

0.05

Probability density

In large social structures, a tendency for individuals
to form links with people similar to themselves has been
observed. This is known as the homophily principle [1, 2].
Recently, there has been a lot of focus on the structuraland dynamic properties of social networks. The increased
availability of large sets of data on communication patterns has made it possible to study human interaction ona scale and at a level of detail not previously possible [3].
In particular, mobile phone call records have been used
to investigate human mobility, responses to emergencies,
and the formation of new social ties [4–6]. The numberof people an individual has links to and the strength ofthese links characterize the topology of a social network
and are important for understanding the formation ofcommunities and the spread of information through the
network [7, 8].
We studied the communication habits of 2.5 million
mobile phone users over a year. We used a large datasetof 3 billion calls to examine their call profiles, defined as
the fraction of outgoing calls made during each hour of
the week. By grouping users according to gender, age,and geographical region we were able to identify characteristic call profiles for different demographics. We introduced a distance measure, based on the area of nonoverlap between individual call profiles (see Fig. 1), andexamined its distribution for different groups of users.
Interestingly, we find that the distance distribution for

0.04
0.03
0.02
0.01
0
0.06

0.04

Profile 1
Profile 2
0.03

Distance

0.02

0.01

0.04
0.03
0.02
0.01
0

0

Mon

Tue

Wed

Thu

time

Fri

Sat

Sun

Figure 1: Mobile phone users can be characterized by theircall profiles, defined as the fraction of calls made during eachhour of the week. We introduce a distance measure between
users as the non-overlap between individual call profiles.

78

friends weighted by activity
friends
age 20−29
age 30−39
age 40−49
age 50−59
age 60−69
age above 70

0.05

Probability density

Call probability density

Session 9

5

University of Copenhagen, Niels Bohr Institute, Blegdamsvej 17,
Copenhagen, Denmark, Electronic address: jeppesj@nbi.dk
2Center for Complex Network Research, Northeastern University, Boston, USA
3
Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, USA
4
Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

0

0.5

1

1.5

2

Distance between call profiles

Figure 2: The distance between call profiles of mobile phoneusers with reciprocal calls (friends) is significantly smaller
than between random pairs of user (top) or users of the sameage (bottom), gender, or geographical area.

5

Session 9 // Social structure[1] M. McPherson, L. Smith-Lovin, and J. M. Cook, Annual
review of sociology pp. 415–444 (2001).
[2] G. Kossinets and D. J. Watts, American Journal of Sociology 115, 405 (2009).
[3] G. Miritello, E. Moro, R. Lara, R. Martı́nez-López,
J. Belchamber, S. G. Roberts, and R. I. Dunbar, Social
Networks (2013).
[4] D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.L. Barabasi, in Proceedings of the 17th ACM SIGKDD
international conference on Knowledge discovery and data
mining (ACM, 2011), pp. 1100–1108.
[5] J. P. Bagrow, D. Wang, and A.-L. Barabási, PloS one 6,
e17680 (2011).
[6] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, Nature 453, 779 (2008).
[7] G. Palla, A.-L. Barabasi, and T. Vicsek, Nature 446, 664(2007).
[8] J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, M. A.
De Menezes, K. Kaski, A.-L. Barabási, and J. Kertész,
New Journal of Physics 9, 179 (2007).

Session 9

5

79

80

Session10

Privacy81

1Session 10 // Privacy

Differentially Private Modeling of Human Mobility
at Metropolitan Scales
Darakhshan J. Mir

Ramón Cáceres

mir@cs.rutgers.edu∗

ramon@research.att.com

Rutgers University

1
Session 10

isaacman@cs.loyola.edu

Rebecca N. Wright

mrm@princeton.edu

rebecca.wright@rutgers.edu*

Models of human mobility have wide applicability in fields
such as infrastructure and resource planning, analysis of infectious disease dynamics, and ecology. The abundance of
spatio-temporal data from cellular telephone networks affords opportunities to construct such models. However, thisdata consists of individuals’ locations and cellular phone activities, thus raising privacy concerns that have prevented
the release and wider use of such models. In response to suchprivacy concerns, our work seeks to to adapt the WHERE [9]approach for modeling human mobility in metropolitan areas to be differentially private.
Differential privacy [4, 5] is a notion of privacy receiving increasing attention. It makes privacy a mathematical requirement on the results of interactions with data. It captures theintuition that an individual’s risk of being identified shouldbe almost the same whether or not they participate in a
database. This is a strong notion of privacy that makes noassumptions about the power or background knowledge of apotential adversary.
Starting with Call Detail Records (CDRs) from a cellular
telephone network that have gone through a straightforward
anonymization procedure, WHERE [9] produces synthetic
CDRs for a synthetic population. WHERE has been experimentally validated against billions of location samples
for hundreds of thousands of cell phones in the New Yorkand Los Angeles metropolitan areas. To ensure that the resulting synthetic CDR’s are provably private, we propose tomodify WHERE to be differentially private. The aim is toenable the creation and possible release of synthetic CDRs
that capture the mobility patterns of real metropolitan populations while preserving individual privacy.
To the best of our knowledge, the problem of making humanmobility models (based on sensitive spatio-temporal data)
differentially private has not been studied before. Differential privacy has been examined in other contexts of spatiotemporal data. Chen et al. [3] study the problem of publishing a differentially private version of the trajectory dataof commuters in Montreal. They then evaluate the utility of
published private data in terms of count queries and frequent
sequential pattern mining. WHERE does not directly model
the sequentiality of the spatio-temporal data at the level of
an individual. However, it would be interesting to compare
the two approaches. Similarly, Ho and Ruan [7] consider the
problem of location pattern mining with differential privacy
Partially supported by NSF award CCF-1018445.

82

Loyola University Maryland

Margaret Martonosi
Princeton University∗

Sibren Isaacman

AT&T Labs

Rutgers University

for spatial databases. Andrés et al. [1] introduce the notionof geo-indistinguishability in location-based systems, which
protects the exact location of a user while allowing releaseof information needed to gain access to a service.

1.

BACKGROUND

Consider a database D of m simplified CDRs containing n
distinct users. Each row of the database corresponds to avoice call or text message (hereafter both referred to interchangeably as calls) and includes the following attributes:
user-id

date

time

lat

long

Each user is indexed by a unique user ID in the set [n] ={1, 2, . . . n}. For each user, home and work locations are estimated according to earlier techniques [8]. We assume these
two locations are appended to each row of D.

1.1

WHERE

WHERE works with a CDR database D representing calls ina given metropolitan area that has been divided into smaller
geographic areas by imposing a rectangular grid of granularity d × d. To model the users in D, WHERE computes
six types of spatial and temporal distributions:Home and Work. For each grid cell, all users who havehome and work locations in that area are counted. Next, thenumber of home and work locations in that area is divided
by the total number of users in the database, thus giving aprobability for the home and work locations for each area ofinterest. To construct a synthetic user, WHERE creates acumulative distribution function (CDF) of the home or work
probability for every possible area, randomly picks a number
uniformly between 0 and 1, and selects the correspondingarea as the synthetic home or work.
CommuteDistance. For each grid cell, we compute a distribution on home-to-work commuting distances of people wholive in that cell. There are a total of d2 of these CommuteDistance distributions.
CallsPerDay. For each user in the CDR, we calculate themean (µ) and standard deviation (σ) for the number of calls
made per day. Now, a two-dimensional histogram is constructed for the various values of µ and σ. Each user’s µ1

Session 10 // Privacyand σ is rounded to the tenths place. A two-dimensional
matrix M is maintained, in which an appropriate cell indexed by a user’s µ and σ is incremented. Then, dividing
each cell by the total number of users gives a 2-D probability
distribution function (that is, the sum of all the cells adds to1) called CallsPerDay. We can traverse the matrix in either
row or column major order to construct a CDF which canbe easily sampled from and converted back into the µ and σthat index to the random cell. When determining how manycalls a synthetic user makes on a particular day, WHERE
samples a number uniformly at random between 0 and 1 andlocate the (rounded) µ and σ that this number corresponds
to in the CDF of CallsPerDay. A number sampled from anormal distribution determined by this µ and σ gives thenumber of calls that the user makes.

change in the function over all neighboring databases.

CallTime. For each user in the CDR database, WHERE
computes the distribution of calls over the day. However, using X-Means clustering, user call-behavior can be clusteredinto two classes [9]. Subsequently, using the CDR database,per-minute call probability distributions are computed separately for each user class. From this, a probability distribution CallTime, for each hour of the day, for each user classis constructed. Previous work [9] showed that while bothclasses exhibit diurnal patterns in the calling behavior, oneclass favors evening calls while the other favors afternoon.

We discuss modifications to the distributions described in
Section 1.1 to make them differentially private.

1.2

Differential Privacy

Differential privacy rests on the guarantee that an individual’s risk of being identified is almost the same whether or
not the individual participates in a database, even for individuals with unique or outlier behaviors. To formalize
this we need the notion of neighborhood [5] of two CDR
databases. We introduce two notions of neighborhood: calllevel and user-level.
Def. 1 (Neighbors). Two CDR databases D and D0 are call-level neighbors if |D ⊕ D0 | = 1. D
and D0 are user-level neighbors if for all CDRs, c ∈ D ⊕ D0 ,user-id (c) = k, for some k ∈ [n].
In other words, neighboring CDR databases D and D0 are
call-level neighbors if they differ in exactly one CDR. They
are user-level neighbors if they differ in the records of exactly
one user (who may have made many calls).
We will also make use of the concept of the global sensitivityof a function of the database [5]. This is the maximum

D,D

where D and D0 are (user or call-level) neighbors.
Known results show that differential privacy can be achievedby adding noise to the outcome of f that is proportional to
the global sensitivity of f [5].

2.

DIFFERENTIALLY PRIVATE WHERE

2.1

Probability Distributions

Home and Work . We need to compute a differentially private CDF for the Home and Work distributions. Let  be
the privacy level we want to achieve. (Values like 0.1 or 1 are
typically used in practice.) Let Lap(0, λ) denote a Laplacian
distribution with mean 0 and standard deviation λ. Assume
a row or column major ordering of the d2 grid cells.
Let CountNum(home, i) be a function that returns the number of distinct people in the database D with homes ingrid cell i in this ordering. Then, applying standard noise
adding techniques [5], using Algorithm 2.1 provides an differentially private approximationof the CDF. It has an√error proportional to d2 .
Algorithm 2.1: DPhomeCDF(D, )Count ← 02
for i ←
 1 to dCount ← Count +CountNum(home, i)+√do
Lap(0, 2 ).

CDF[i] ← Count .
return (CDF)

1

Hourly. For every hour, WHERE computes a distributionof calls made over every latitude-longitude area (grid cell)in the grid. There are 24 distributions over the metropolitan area, one for each hour. Each distribution reflects theprobability of people being at a specific location at a specifictime. It is not explicitly tied to either a user or a home orwork location.

GSf := max0 kf (D) − f (D0 )k1

Session 10

During the creation of a synthetic call by a synthetic user,
WHERE first determines the number of calls made per day
using CallsPerDay as described above. After assigning auser to one of the two user-classes, it uses the relevant CallTime distribution to synthesize call times.

Def. 2 ([5]). The global sensitivity of a function of adatabase D, f : D → R` is

The error can be improved by McSherry and Mahajan’s
method [11] to publish a CDF with error proportional to(log d2 )3/2 . Moreover, the noisy CDF does not correspond toa legitimate probability distribution since the noisy counts
are not necessarily non-decreasing. We can use Hay et al.’s
post-processing techniques [6] to “clean-up” this noise andcreate a legitimate CDF.
CommuteDistance. Assume a min and max for the commuting distance of people living in each grid cell. Let Range =max − min +1. Let CountCommute(i, dist) count the number of people living in grid cell i who commute a distance ofdist. Then Algorithm 2.2 provides an -differentially privatecomputation of the original CommuteDistance distribution.
For each grid cell, the CommuteDistance distribution canbe computed independent of the distribution for other cells.
Applying the parallel composition theorem [10], the d2 invocations of Algorithm 2.2, are still -differentially private.

83

1

Session 10 // Privacy

Algorithm 2.2: commuteCDF(D, cor, )Count ← 0
for i ←
 1 to Range
Count ← Count +CountCommute(cor, i)+√do2Lap(0,  ).

CDF[i] ← Count .
return (CDF)
CallsPerDay. To make the CDF of CallsPerDay differentially private, we assume that the mean number of calls per
day for any user falls in the range [µmin , µmax ]. Similarly
the standard deviation of the number of calls per day is in
the range [σmin σmax ]. Using this we have a fixed size twodimensional matrix M as described above√ and a noise matrix
N , such that ∀, i, j, N (i, j) = Lap(0, 2 ). However, after
every user’s µ and σ is used to increment the appropriate
matrix cell of M , M is replaced by M + N .
Now, we proceed as before with the modified M giving an
-differentially private CDF of CallsPerDay.

Session 10

1

CallTime. We cluster the users into one of the two classes
using −differentially private k-means clustering [2]. Subsequently, we compute a probability distribution CallTime,for each hour of the day, for each user class, separately byusing a variant of randomized-response [12].
The stochastic creation of synthetic calls by synthetic users
proceeds as before. WHERE first determines the numberof calls made per day using the private CallsPerDay as described above. After assigning a user to one of the two userclasses, it uses the relevant private CallTime distribution tosynthesize call times.
Hourly. For every hour, the global sensitivity of the querythat counts the number of calls made over all grid cell ismaxcalls . Add noise proportional to this quantity by adding
a noise matrix N , where√ maxcalls 2,
N (i, j) ∼ Lap 0,
yields an -differentially private approximation of the matrixof calls made over the entire metropolitan over an hour. Thiscan then yield a differentially private Hourly probability distribution. When a synthetic call is made using the CallTime
and CallsPerDay distributions, its location is determined tobe either home or work according to the probability of aperson being in the location at that time of day using the
Hourly distribution.

2.2

Summary and Evaluation Plan

In summary, the modified WHERE approach we have outlined above achieves differential privacy. This privacy may
come at some accuracy cost, however, because noise is introduced in each probability distribution. We plan to compare
the accuracy of the models produced by the differentiallyprivate and original versions of WHERE. The  parameter
offers us a “knob” by which to trade off privacy and accuracy.
Our evaluations will compare the differentially private distributions to their original counterparts, using several metrics.

84

As in [9], we will use Earth Mover’s Distance (EMD) to compare the two sets of distributions. In addition, other metrics
such as daily range can also be used. Overall, once evaluated for accuracy, our work can represent a significant step
towards making large-scale well-validated mobility models
provably private and therefore easier to distribute and build
from.

3.

REFERENCES[1] M. E. Andrés, N. E. Bordenabe, K. Chatzikokolakis,
and C. Palamidessi. Geo-indistinguishability:
Differential privacy for location-based systems. CoRR,
abs/1212.1984, 2012.
[2] A. Blum, C. Dwork, F. McSherry, and K. Nissim.
Practical privacy: the sulq framework. In Proceedingsof the twenty-fourth ACM
SIGMOD-SIGACT-SIGART symposium on Principles
of database systems, PODS ’05, pages 128–138, New
York, NY, USA, 2005. ACM.
[3] R. Chen, B. C. M. Fung, B. C. Desai, and N. M.
Sossou. Differentially private transit data publication:a case study on the montreal transportation system.
In KDD, pages 213–221, 2012.
[4] C. Dwork. Differential privacy. In ICALP ’06:
Proceedings of the 33rd International Colloquium on
Automata, Languages and Programming (2), pages
1–12, 2006.
[5] C. Dwork, F. Mcsherry, K. Nissim, and A. Smith.
Calibrating noise to sensitivity in private data
analysis. In TCC ’06: In Proceedings of the 3rd Theoryof Cryptography Conference, pages 265–284, 2006.
[6] M. Hay, V. Rastogi, G. Miklau, and D. Suciu.
Boosting the accuracy of differentially private
histograms through consistency. PVLDB,
3(1):1021–1032, 2010.
[7] S.-S. Ho and S. Ruan. Differential privacy for location
pattern mining. In Proceedings of the 4th ACM
SIGSPATIAL International Workshop on Security andPrivacy in GIS and LBS, SPRINGL ’11, pages 17–24,
New York, NY, USA, 2011. ACM.
[8] S. Isaacman, R. Becker, R. Cáceres, S. Kobourov,
M. Martonosi, J. Rowland, and A. Varshavsky.
Identifying important places in people’s lives from
cellular network data. In Proceedings of the 9thinternational conference on Pervasive computing,
Pervasive’11, pages 133–151, Berlin, Heidelberg, 2011.
Springer-Verlag.
[9] S. Isaacman, R. A. Becker, R. Cáceres, M. Martonosi,
J. Rowland, A. Varshavsky, and W. Willinger. Human
mobility modeling at metropolitan scales. In MobiSys,
pages 239–252, 2012.
[10] F. McSherry. Privacy integrated queries: an extensible
platform for privacy-preserving data analysis.
Commun. ACM, 53(9):89–97, Sept. 2010.
[11] F. McSherry and R. Mahajan. Differentially-private
network trace analysis. In SIGCOMM, pages 123–134,
2010.
[12] S. L. Warner. Randomized Response: A Survey
Technique for Eliminating Evasive Answer Bias.
Journal of the American Statistical Association,
60(309):63+, Mar. 1965.

2

Session 10 // Privacy

Unique in the Crowd: The privacy bounds of human mobility
Yves-Alexandre de Montjoye∗ 1,2 , César A. Hidalgo1,3,4 , Michel Verleysen2 & Vincent D. Blondel2,51 Massachusetts Institute of Technology, Media Lab, 20 Ames Street, Cambridge, MA 02139 USA
2 Université catholique de Louvain, Institute for Information and Communication Technologies, Electronics

and Applied Mathematics, Avenue Georges Lemaı̂tre 4, B-1348 Louvain-la-Neuve, Belgium
3 Harvard University, Center for International Development, 79 JFK Street, Cambridge, MA 02138, USA
4 Instituto de Sistemas Complejos de Valparaı́so, Paseo 21 de Mayo, Valparaı́so, Chile
5 Massachusetts Institute of Technology,

Laboratory for Information and Decision Systems, 77 Mas-

sachusetts Avenue, Cambridge, MA 02139, USA

find that human mobility traces are highly unique. In fact, in a dataset where the locationof an individual is specified hourly, and with a spatial resolution equal to that given by thedividuals. We coarsen the data spatially and temporally to find a formula for the uniquenessof human mobility traces given their resolution and the available outside information. This
formula shows that the uniqueness of mobility traces decays approximately as the 1/10 powerof their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual’s privacy and have important implications
for the design of frameworks and institutions dedicated to protect the privacy of individuals.
Despite its importance, privacy has mainly relied on informal protection mechanisms. For instance, tracking individuals’ movements has been historically difficult, making them de-facto pri∗Correspondence should be addressed to Yves-Alexandre de Montjoye (email: yva@mit.edu)

1

85

2

carrier’s antennas, four spatio-temporal points are enough to uniquely identify 95% of the in-

Session 10

We study fifteen months of human mobility data for one and a half million individuals and2

Session 10 // Privacy

A

B

C

AntennaPhone
activity

Figure 1: (A) Trace of an anonymized mobile phone user during a day. (B) The same user’s trace
as recorded in a mobility database. (C) The same individual’s trace when we lower the resolutionof our dataset through spatial and temporal aggregation.
vate. Modern information technologies such as the Internet and mobile phones, however, magnify
the uniqueness of individuals, further enhancing the traditional challenges to privacy. While in the

Session 10

2

past, mobility traces were only available to mobile phone carriers, the advent of smartphones andother means of data collection has made these broadly available. Mobility data is among the mostsensitive data currently being collected
A simply anonymized dataset does not contain name, home address, phone number or otherobvious identifier. Yet, if individual’s patterns are unique enough, outside information can be used
to link the data back to an individual. We study the unicity of mobility traces in a mobile phonedataset containing 15 months of mobility data for 1.5M people, a significant and representative
part of the population of a small European country. We show that four randomly chosen points areenough to uniquely characterize 95% of the users (E > .95), whereas two randomly chosen points
still uniquely characterize more than 50% of the users (E > .5). This shows that mobility traces
are highly unique, and can therefore be re-identified using little outside information.

2

86

2

Session 10 // Privacy

Nonetheless, E depends on the spatial and temporal resolution of the dataset. Here, we determine this dependence by lowering the resolution of our dataset through spatial and temporal
aggregation. We show that it is possible to find one formula to estimate the uniqueness of traces
given both, the spatial and temporal resolution of the data, and the number of points available
to an outside observer. We show that the uniqueness of a trace decreases as the power function
E = α − xβ , for decreases in both the spatial and temporal resolution (x), and for all consideredp = 4, 6, 8 and 10. The power-law dependency of E means that, on average, each time the spatialor temporal resolution of the traces is divided by two, their uniqueness decreases by a constantfactor ∼ (2)−β . This implies that privacy is increasingly hard to gain by lowering the resolution ofa dataset.
We also shows that E increases with p. The mitigating effect of p on E is mediated by the
implies that a few additional points might be all that is needed to identify an individual in a datasetwith a lower resolution. Because of the functional of E on p through the exponent β, mobility

Session 10

exponent β which decays linearly with p: β = 0.157 − 0.007 ∗ p. The dependence of β on p

2

datasets are likely to be re-identifiable using information on only a few outside locations.
We showed that the uniqueness of human mobility traces is high, thereby emphasizing the importance of the idiosyncrasy of human movements for individual privacy. This uniqueness meansthat little outside information is needed to re-identify the trace of a targeted individual even in asparse, large-scale, and coarse mobility dataset. These results should inform future thinking in thecollection, use, and protection of mobility data. Going forward, the importance of location data
will only increase and knowing the bounds of individual’s privacy will be crucial in the design ofboth future policies and information technologies.

3

87

88

Session

11

Information propagation89

1

Session 11 // Information propagation

The use of mobile phone call record data for malaria control andelimination strategic planning
Tatem A.J. 1,2*, Huang Z. 3,4, Kumar U. 3,5, Pindolia, D.3,4, Kandula, D.6 and Lourenco, C. 6,7
1. Department of Geography and Environment, University of Southampton, UK 2. Fogarty International Center, National
Institutes of Health, Bethesda, USA., 3. Department of Geography, University of Florida, Gainesville, USA., 4. Emerging
Pathogens Institute, University of Florida, Gainesville, USA., 5. Department of Computer Science, University of Florida,
Gainesville, USA., 6. Clinton Health Access Initiative, Boston, USA, 7. National Vector‐borne Diseases Control Programme,
Windhoek, Namibia.

Session 11

1

E‐mails: andy.tatem@gmail.com, seenhzj@gmail.com, udayan.kumar@gmail.com, dkandula@clintonhealthaccess.org, clourenco@clintonhealthaccess.org

Introduction
The control and elimination of an infectious
disease from a local area or country is often
complicated by human movement bringing
infections in from endemic areas. In the case ofmalaria, parasite importation to transmission‐
receptive areas has resulted in the resurgenceand reestablishment of the disease in previously
free areas, and represents a significant
challenge for the 36 countries and regions now
planning for elimination. The design of strategicplans for controlling, eliminating and preventing
malaria reestablishment should therefore
ideally account for human and parasite
movement patterns. Here we describe howanonimized mobile phone call data records canbe analysed to provide information to support
planning using the example of Namibia.
Data
Mobile phone call data records covering
October 2010 to September 2011 were
provided by the leading mobile phone service
provider in Namibia, MTC, who reported a 90%
market share. Daily locations were calculated
for anonymous subscribers using the location ofcalls and texts at one of 402 regions across thecountry, following methods outlined in othersimilar studies 1‐5. Movements between
locations were calculated by examining the90

temporal sequences of communications andassigning a movement to a new location and atime of this move when the region throughwhich the call/text was routed changed.
To characterize spatial variations inmalaria transmission intensity across thecountry, and the uncertainties that exist inthese measurements, 25%, 50% and 75%
quartile P. falciparum prevalence estimates in
the 2‐10 year age group for 2010 were obtained
6. Previously defined mathematical models 7
were used to adjust the malaria risk maps to the
15‐30 age group, to match that of the majorityof subscribers and travellers 8, 9. To adjust
estimated malaria risks to capture the strong
seasonality that occurs in Namibia, reported
outputs from the national malaria control
program surveillance system in 2011 wereobtained to provide quantitative risk
adjustments for low and high transmission
season months.
Movementsofinfections
were
calculated for two types of traveller, following
previous approaches 5, 10: (i) 'Returning
residents'; Residents of a location who visitedan endemic area then returned to their homelocation, potentially bringing an infection withthem, and (ii) 'Visitors'; Residents of an endemic
area who visited a new location and potentially
carried an infection with them.

1

Session 11 // Information propagation

Results
Human movements
Figure 1(a) shows that population movements
in Namibia follow patterns seen elsewhere 1, 3,
11, but with a deviation at around 400km from asmooth power law distribution, corresponding
to the distance apart of the two major regionsof high population density.
Figure 2. Mapped communities of human travel ‐ regions
mapped with the same colour are ones for whichmovements within them are stronger than movements
between surrounding communities.

1

Communities
Clusters of communities that are connected to
one another through high levels of human or
malaria parasite movement were mapped to aid
in the regional planning of interventions,
identifying how an intervention in one
community may impact imported case numbers
in surrounding communities. The identificationof distinct communities within the weighted
networks of human and malaria parasite
movements was undertaken using a modularity
optimization algorithm 12, with results forhuman travel shown in figure 2.

Session 11

Figure 1. (a) Plot of radius of gyration for all movements in
Namibia, with inset map showing movement totals
between regions over the Oct 2010‐Sept2011 period. Ratesof movement are coloured from yellow (lowest) to red(highest).

Sources and sinks
Targetting the largest exporter communities('sources') of infections is likely to have an
impact on the numbers of infections seen insurrounding areas that are net importers of
infections ('sinks') and be a better use of limited
resources. For each location, the estimatedtotal number of infections exported orimported annually, and by month, for visitors
and returning residents were summed, and thedifferences between these values calculated toassess whether locations were likely to besources or sinks of infections (figure 3).

Figure 3. Map of 'sources' (net exporters) and 'sinks' (netimporters) of malaria infections. Areas coloured red are
infection sources and those coloured blue are sinks. Thosecoloured green are neither sinks nor sources.

The likely regional effects of targeting control
efforts on the largest source and sink areas are91

1

Session 11 // Information propagation

shown in figure 4, with substantial differences
evident, due to differing levels of connectivity
through parasite movement.

threatening success in achieving elimination.
With mobile phone usage proliferation
continuing and data continually being collectedby network providers, a huge potential exists tomake operational use of such valuable data ininfectious disease control and elimination.
References(a)Session 11

1(b)
Figure 4. The effects of human and parasite mobility on
control targetting effectiveness. (a) The percentage
reduction in estimated imported case numbers throughreducing parasite exportation numbers to zero in the area
marked with a star, which is one of the major source
regionsin figure 3; (b) The percentage reduction inestimated imported case numbers through reducing
parasite exportation numbers to zero in the area marked
with a star, which is one of the major sink regions in figure
3.

Conclusions
As Namibia makes progress towards
elimination, imported cases will make up an
increasingly large proportion of total cases
seen, both in terms of cases imported into
Namibia from Angola, and cases seen in districts
close to elimination that are imported fromhigh transmission districts. Such imported cases
will become increasingly important in terms of92

1.
Gonzalez MC, Hidalgo CA, Barabasi AL.
Understanding individual human mobility patterns.
Nature. 2008 Jun 5;453(7196):779‐82.
2.
Tatem AJ, Qiu Y, Smith DL, Sabot O, Ali AS,
Moonen B. The use of mobile phone data for theestimation of the travel patterns and imported
Plasmodium falciparum rates among Zanzibar residents.
Malar Journal. 2009;8:287.
3.
Song C, Qu Z, Blumm N, Barabasi AL. Limits ofpredictability in human mobility. Science. 2010 Feb
19;327(5968):1018‐21.
4.
Bengtsson L, Lu X, Thorson A, Garfield R, vonSchreeb J. Improved response to disasters and outbreaks
by tracking population movements with mobile phone
network data: a post‐earthquake geospatial study in Haiti.
PLoS Med. 2011 Aug;8(8):e1001083.
5.
Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor
AM, Snow RW, et al. Quantifying the impact of humanmobility on malaria. Science. 2012;338:267‐270.
6.
Gething PW, Patil AP, Smith DL, Guerra CA,
Elyazar IR, Johnston GL, et al. A new world malaria map:
Plasmodium falciparum endemicity in 2010. Malar J.
2011;10:378.
7.
Smith DL, Guerra CA, Snow RW, Hay SI.
Standardizing estimates of the Plasmodium falciparum
parasite rate. Malaria Journal. 2007 Sep 25;6.
8.
Communications Regulatory Authority of
Namibia. Universal Service Baseline Study. Windhoek:
Communications Regulatory Authority of Namibia,; 2011.
9.
MEASURE DHS. Namibia Demographic and
Health Survey. Washington DC: USAID; 2006.
10.
Le Menach A, Tatem AJ, Cohen JM, Hay SI,
Randell H, Patil AP, et al. Travel risk, malaria importationand malaria transmission in Zanzibar. Sci Rep. 2011;1:93.
11.
Simini F, Gonzalez MC, Maritan A, Barabasi AL. A
universal model for mobility and migration patterns.
Nature. 2012 Apr 5;484(7392):96‐100.
12.
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre
E. Fast unfolding of communities in large networks. J Stat
Mech‐Theory E. 2008 Oct.

2

Session 11 // Information propagation

Time-varying networks and the weaknessof strong ties
Márton Karsai ∗†1 , Nicola Perra ∗2 , Alessandro Vespignani ∗‡§3

*Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston MA 02115 USA†Department of Biomedical Engineering and Computational Science, Aalto University, P.O. Box 12200, Finland‡Institute for Scientific Interchange Foundation, Turin 10133, Italy§Institute for Quantitative Social Sciences at Harvard University, Cambridge, MA, 02138
1
m.karsai@neu.edu, 2 n.perra@neu.edu, 3 a.vespignani@neu.edu

In this work we analyze a large scale mobile call dataset to investigate the temporal
evolution of the egocentric network of active individuals. We empirically observe a
simple quantitative statistical law characterizing the memory of agents and encode theobserved dynamics in a reinforcement process defining a generative computational networkmodel with time-varying connectivity patterns. This reinforced activity-driven networkmodel spontaneously generates the basic dynamic process for the differentiation between
strong and weak ties. The model is used to study the effect of time-varying heterogeneousinteractions on the spreading of information on social networks. We observe that thepresence of strong ties may severely inhibit the large scale spreading of information byconfining the process among agents with recurrent communication patterns. Our results
provide the counterintuitive evidence that strong ties may have a negative role in the
spreading of information across communities.

the aggregated mobile phone call network, and the reinforced activity-driven network. In panels (a), and (d) weplot the degree distributions. In panels (b), and (e) weplot the edge-weight distributions. Finally, in panels (c),and (f ) we plot the node-activity distributions.

the network dynamics. However, this framework ismemoryless, and it misses important features of realworld systems. In this work we propose to extend
the activity-driven modeling framework in order to

93

2

The characterization, and modeling of timevarying networks are still open, and active areas ofresearch [7]. A simplification of this framework has
been recently proposed by the activity-driven generative algorithm for time-varying networks [5]. Thisapproach defines the activity potential, a time invariant function characterizing the agents’ interactions,and constructs an activity-driven model capable ofencoding the instantaneous temporal description of

Session 11

In the last ten years the access to high resolution
datasets from mobile devices, communication, andpervasive technologies has propelled a wealth of developments in the analysis of social networks [1, 2, 3].
Particular efforts have been devoted to characterize
how their structure influences the critical behavior of dynamical processes evolving on top of them.
However, the large majority of the approaches put
forth to tackle this subject utilise a time-aggregatedrepresentation of the interactions and neglect theirtime-varying nature. Indeed, the concurrency, andtime ordering of interactions, even if the social network contains stable relationships, are crucial andmay have considerable effects [4, 5, 6].
Figure 1: Distributions of the characteristic measures of2

Session 11 // Information propagation

(a)

(b)

Session 11

2

Figure 2: Rumor spreading processes in (a) memoryless and (b) reinforced activity-driven networks of the same
parameters. Nodes colors assign the actual node states as ignorant (blue), spreader (red) and stifler (green) states
after the same number of evaluation steps. Node sizes, color, and width of edges represent the corresponding degrees
and weights.

explain the effect of the repetitive emergence of in- against results of data-driven simulations performed
teractions within one’s social circle. We perform a in the actual mobile call time-varying network.
thorough analysis of a large-scale mobile phone-calldataset, which contains the records of time-stamped
References
communication events of millions of individuals. We
show that recurrence interaction of connected individ- [1] Albert, R & Barabási, A. L. (2002) Rev. Mod.
uals can be explained by simple memory effects synPhys. 74, 47–97.
thesized by non-Markovian reinforcement processes.
The introduction of this mechanism in the activity- [2] Newman, M. (2010) Networks: An Introduction.
(Oxford University Press, USA), 1 edition.
driven model allows us capturing the evolution of
the egocentric network of each actor in the system,
recovering also its global dynamics. In particular, [3] Barrat, A, Barthélemy, M, & Vespignani, A.
(2008) Dynamical Processes on Complex Netit induces the proper weight heterogeneities reducworks. (Cambridge University Press), 1 edition.
ing the degree diversity of the emerging structure ingood agreement with the empirical observations as [4] Karsai, M., et.al. (2011) Phys. Rev. E 83,
it is shown in Fig.1.
025102(R).
Using this model we study the effect of repetitive time-varying interactions on a family of rumor [5] Perra, N, et.al. (2012) Sci. Rep. 2, 469.
spreading models [8]. We assume that the time scales [6] Perra, N, et.al. (2012) Phys. Rev. Lett. 109,of the contact patterns evolution, and the spreading
238701.
process are comparable. Interestingly, our findings
clearly show that memory in the interaction dynam- [7] Holme, P & Saramäki, J. (2012) Phys. Rep.
519, 97–125.
ics hamper the rumor spreading process. Strong ties
have an important role in the early cessation of the
spreading dynamics. They favor interactions with [8] Daley, D. J & Kendall, D. G. (1964) Nature
204, 1118.
agents already aware of the rumor and allow the rumor to diffuse only locally. This is illustrated in Fig.2
where the difference in the contagion level between
the memoryless and reinforced process spreading isapparent. This evidence points out that strong ties
may have an active role in weakening the spreading of
information by constraining the dynamical process inclumps of strongly connected social groups. We validate the basic assumption, and modeling framework

94

3

Session 11 // Information propagation

Limited communication capacity unveils strategies for human interaction
Giovanna Miritello,1, 2 Rubén Lara,2 Manuel Cebrian,3, 4 and Esteban Moro1, 5, ∗1

Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganés, Spain
2
Telefónica Research, 28050 Madrid, Spain
3
NICTA, Melbourne, Victoria 3010, Australia
4
Department of Computer Science & Engineering, University of California at San Diego, La Jolla, CA 92093, USA
5
Instituto de Ingenierı́a del Conocimiento, Universidad Autónoma de Madrid, 28049 Madrid, Spain

Several reasons have hampered the observation of tie
activation/deactivation dynamics in social networks atlarge scale: on the one hand, studies of diffusion basedon datasets from pre-electronic eras have safely assumed
that tie activation/deactivation is a much slower process
than interactions within a tie, and thus their dynamics
might be safely neglected [20–22]. However, the current
ability to communicate faster and further than ever accelerates tie dynamics in an unprecedented manner tothe point that tie activation/deactivation may rival intime with processes like information spreading. On theother hand, available data about how ties form or decay were restricted to egocentric, small social networks
and/or short periods of time which made it difficult toassess the universality of the results obtained and theirextension to other situations [6, 8, 9]. Finally, although insome online social networks there are explicit rules for theestablishment of social ties, in most cases activity is the
only way to assess the existence or not of the tie [23, 24].
Online social networks are plagued with this problem dueto the cheap cost of maintaining “friends” which are infact already deactivated relationships [25]. However, using activity as proxy for tie presence is a problem in mostcommunication channels like mobile phone calls, emails,
electronic social networks etc., since tie activity is very
bursty [26] and so far there is no clear method to discriminate those social ties that are already inactive fromlarge-inter even times within active relationships [27].
In this communication we study the formation anddecay of communication ties using a large (both crosssectional and longitudinal) database of of the anonymized
voice calls of about 20 million users that form 700 million communication ties. We use a novel method to determine the presence of a tie based on activity data (seefigure 1) and apply it to understand the dynamics offormation and destruction of ties. While previous work
has analyzed datasets over insufficient time-spans (thusconfounding tie dynamics with the bursty nature of ties)
and/or small cohorts (thus unable to obtain statistically95

3∗ Corresponding author emoro@math.uc3m.es

However, the incorrect assumption that time, attention
and cognition are elastic resources has blurred the studyof how individuals manage their social interactions overtime [17–19]. Understanding such social strategies is not
only of paramount importance to make progress in thecharacterization of human behavior, but also to improve
our current description of social networks as evolutionary
objects against the (aggregated) ever-growing or static
pictures of the social structure.

Session 11

Social connectivity is the key process that characterizes the structural properties of social networks and in turn processes such as navigation,
influence or information diffusion. An empiricallimitation of studies to date is the definition ofwhat constitutes an active tie that retains capacity to transfer information in the future. The
limited observational length of existing human interaction datasets, together with the bursty nature of dyadic communications lie at the core ofthis problem, hindering the discrimination of inactive ties from large inter-event times in active
ones. Here we develop a method for the detectionof tie activation/deactivation, and apply it to alarge longitudinal, cross-sectional communication
dataset (≈ 19 months, ≈ 20 million people). Contrary to the perception of ever-growing connectivity, we observe that individuals exhibit a finite
communication capacity, which limits the numberof ties they can maintain active. In particular wefind that men have an overall higher communication capacity than women and that this capacity
decrease gradually for both sexes over the lifespanof individuals (16-70 years). We are then able toseparate communication capacity from communication activity, revealing a diverse range of tie
activation patterns, from stable to exploratory.
We find that, in simulation, individuals exhibiting
exploratory strategies display longer time to receive information spreading in the network thoseindividuals with stable strategies. Our principled
method to determine the communication capacityof an individual allows us to quantify how strategies for human interaction shape the dynamical
evolution of social networks.
Many different forces govern the evolution of social
relationships making them far from random. In recent
years, the understanding of what mechanisms control the
dynamics of activating or deactivating social ties have uncovered forces ranging from geography to structural positions in the social network (e.g. preferential attachment,
triadic closure), to homophily [2]. These finding are pervasive in empirical analyses across cultures, communication technologies and interaction environments [3–6, 10–
15].

3

Session 11 // Information propagation
Links tipo (a): 15%
Links tipo (b): 19%
Links tipo (c): 24%
Links tipo (d): 42%
Links tipo (e) : 3.5%

2

G. Miritello, R. Lara,
M. Cebrián & E. Moro
6 months
7 monthsij3

6 months

tij
(a)
(b)
(c)
(d)

t
0

⌦

(e)

T

Session 11

3

S2: (Color
Schematic view ofSchematicthe differentof tieformation/decay
FIG. 1. Figure
Detectionof tie online)
activation/deactivation
viewsituationsof the timeintervalsconsidered inand
our thedatabase andinterplay
betweentie communication andpatterns
and tie formation/decay
for a given observation
the different
situationsof tie theactivation/deactivation
the interplay
between the tie communication
patterns and tie activation/deactivation
for aofgiven
observation
time windowΩ of lines
lengthrefers
T = 7tomonths(shadowedarea).
Eachline refers to atime window
lengthT (shadowedarea). Eacha different
tie while
eachverticaldifferent segment
tie while eachverticalsegment indicateseventa communication
betweeni ↔ intereventj and δtij istime
the ininter-event
indicates
a communication
between i $event
j and
tij is thethe i $ jtime in thei ↔ j time series. Our method is based on the observation of tie activity in a time window before/after Ω: if tie activity is
time series.
observed in the 6 months before Ω then it is considered an old tie [cases (a) and (d)]; on the other hand, if activity is observedin the 6 months after Ω we will assume that the tie persists [cases (b) and (d)]. In any other case, we will consider that the tie
is activated and/or deactivated in Ω [cases (a), (b) and (c)].

for the link. As was shown in [3, ?] the pdf for inter event times depends mostly on the averageinter-event time tij , i.e. P( tij ) = P( tij / tij ) where P(x) is a universal function. Thus, we could
rewrite the previous expression as• A principled methodology to determine the comsignificant results to discriminate different communicaZmunication capacity of an individual enables ustion strategies), our method and extensive dataset allow
1 ⌧ij)=
P( tij /to tij)d tij(2) strateij | tijstrategies
characterize
individual communication
us to determine, for the first time, theP(⌧social⌧gies: while some individuals are social keepers (theywhich result from the limited communication capacityijof 0maintaina clusterized
statictnetwork
around them)
individuals. However,We have also
someheterogeneous
analysis of how
linksmadeare veryinsothe sense thatthey havevery different
ij . Or equivother seem to undergo a social exploring strategy
cial strategies depend on sex and age of the individuals
alently, they very different weights w = T/ t weight. Suppose that ⇧( tij ) is the distribution(in which many ties are formed and destroyed inand about how the strategies are correlated withij the (dy-ij
average inter-event
times
across links
that each nodechooses her links activities from thattime).
namical)oftopologicalstructure of
the network
and andon thedistribution
of
t
.
Then
the
probability
toobserveoneofherlinks at time ⌧ is given by:dependence of the access toij information with the strategy
Zdisplayed.
• Finally we use computer simulations to investigate
Our contribution shows a number of important
thetpossibility
that social explorers haveP(⌧) = resultsd tij ⇧( tij )P(⌧|(3)a competij )which we summarize as follows:
itive advantage towards information advantage becauseof theirof
fastandisdistanttietheformation
theagrowing
function oftotheobservedtemconnectivity as
a function
time
given by
ccf of dynam• WeThus,
develop
new methodology
investigate
ics.
Counterintuitively,
we
found
that
social keepP(⌧).
poral
networks, an issue which is convoluted withZt
ers receive information before social explorers do.
the way social networks are observed and modeled,
The answer to this paradox is that, despite
k (t) = k (1) P(⌧)d⌧(4) having aand which has been recently been pointedi out as ia
0large variability in the social structure around a soproblem in the field of social networks [28].
explorer,the⇧(amountof heavy
time allocatedwhere ki (1) is the total connectivity of node i. Note thatcial
since
P(x) andtij ) aretailed, to theircontacts
islowenoughtoproducecountervailing• Contrary
to isconventional
whichthen P(⌧)
heavy tailedwisdom
too andin
thus
the ksociali (t) can show an apparent non-trivial time dependence
effectsandhinderinformation
diffusion.
connectivity
is an areever-growing
quantity,
we have time.
even if all links
open during
the observation
found Let’s
that doinstantaneous
bounded:an example:connectivity
assume thatisthe
distribution ofinter-event
is given
byprovides
the exponenThis
result is times
importantas itconclusive evihumans manage their interactions
so that the num- t/ t
tial pdf P( t| t) = e/ t and also that the pdf forthe foraverageinter-event
time isthean exponendencethedivergence
betweenstaticand dynamic
ber of open connections is kept constant throughcharacterizations
human
interaction.
Fine-grained,
lontime.
gitudinal and cross-sectional data as the one presented in
Activity localization in online social networks
this study are then needed to fully understand processes• We find that men have an overall higher communisuch as navigation, influence and information diffusion as
cation capacity than women and that this capacity
they happen concurrently and possibly entangled to thedecrease gradually for both sexes over the lifespan
unfolding of social strategies in time.
of individuals (16-70years).

96

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Session 11 // Information propagation
3

97

3

[15] Rand, D.G., Arbesman, S., Christakis, N.A. (2012) Dynamic social networks promote cooperation in experiments with humans, Proc Natl. Acad Sci U S A 108:
19193-19198.
[16] Krings, G. Karsai, M., Bernharsson, S., Blondel, V.D.,
Saramäki, J. Effects of time window size and placementon the structure of aggregated networks, EPJ Data Science, 2012, Volume 1, Number 1, 4. [3–6, 10–15].
[17] Wu, F., Huberman, B.A. (2007) Novelty and collective
attention, Proc Natl Acad Sci U S A, 104,17599-17601.
[18] Hodas, N.O., Lerman, K. (2012) How visibility and divided attention constrain social contagion, arXiv preprint
arXiv:1205.2736.
[19] Backstrom, L., Bakshy, E., Kleinberg, J., Lento, T.M.,
Rosenn, I. (2011) Center of attention: How facebook
users allocate attention across friends, Proc. 5th International Conference on Weblogs and Social Media.
[20] Hagerstrand, T. (1968) Innovation diffusion as a spatial
process, Chicago, USA: Univ. Chicago Press.
[21] Rogers, E.M. (1995) Diffusion of innovations, Simon andSchuster.
[22] Christakis, N.A. and Fowler, J.H. (2007) The spread ofobesity in a large social network over 32 years, he New
England journal of medicine, 357(4):370-379.
[23] Huberman B.A., Romero D.M. & Wu F. (2009) Social
networks that matter: Twitter under microscope. First
Monday 14: 1.
[24] Grabowicz, P.A., Ramasco, J.J., Moro, E., Pujol, J.M. &Eguı́luz,V.M. (2011) Social features of online networks:
the strength of weak ties in online social media, PLoS
ONE, 7(11), e29358.
[25] Sibona, C., Walczak, S. (2011) Unfriending on Facebook:
Friend request and online/offline behavior analysis, System Sciences (HICSS), 44th Hawaii International Conference on, pp.1-10.
[26] Barabási, A.-L., (2005). The origin of bursts and heavy
tails in human dynamics, Nature Scientific Reports, 435,
207-211.
[27] Song, C., Wang, D. and Barabasi, A.-L., (2012). Joint
Scaling Theory of Human Dynamics and Network Science. arxiv preprint, arXiv:1209.1411v1.
[28] Krings, G. Karsai, M., Bernharsson, S., Blondel, V.D.,
Saramäki, J. Effects of time window size and placementon the structure of aggregated networks, EPJ Data Science, 2012, Volume 1, Number 1, 4.

Session 11[*] emoro@math.uc3m.es[2] Rivera, M.T., Soderstrom, S.B. and Uzzi, B., (2010) Dynamics of Dyads in Social Networks: Assortative, Relational, and Proximity Mechanisms. Annual Review ofSociology, 36(1), pp.91-115.
[3] R.S. Burt, (2000) Deactivation functions, Social Networks 22, 1-28.
[4] Martin, J. L. and Yeung, K.-T. (2006) Persistence of
close personal ties over a 12-year period. Social Networks
28:331-362.
[5] Hidalgo, C. & Rodriguez-Sickert, C., (2008) The dynamics of a mobile phone network. Physica A, 387(12), pp.
3017-3024.
[6] Raeder, T., Lizardo, Chawla N.V., Hachen, D. (2011)
Predictors of short-term deactivation of cell phone contacts in a large scale communication network. Social Networks Volume 33, Issue 4, Pages 245-257.
[7] J. Saramäki et al. (2012) The persistence of social signatures in human communication, arXiv:1204.5602.
[8] N. Eagle, A. Pentland, (2006) Reality mining: sensing
complex social systems, Personal and Ubiquitous Computing 10 255?268.
[9] C. Cattuto, W. van den Broeck, A. Barrat, V. Colizza,
J.-F. Pinton, A. Vespignani, (2010) Dynamics of personto-person interactions from distributed RFID sensor networks, PLoS One 5 e11596.
[10] Kossinets, G., Watts, D. J. (2009) Origins of homophily
in an evolving social network. American Journal of Sociology, 115(2):405-450.
[11] Crandall, D., Cosley, D., Huttenlocher,D., Kleinberg, J.
and Suri, S., (2008) Feedback effects between similarity
and social influence in online communities, in KDD’08:
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. New
York, NY, USA: ACM, pp. 160-168.
[12] Romero, D.M., Meeder,B., Barash,V., Kleinberg, J.,(2011) Maintaining Ties on Social Media Sites: The
Competing Effect of Balance, Exchange and Betweenness, Proceedings of ICWSM 2011: the fifth AAAI conference on Weblogs and Social Media, Barcelona, Spain.
[13] Wang, J., Suri, S., Watts, D.J.(2012) Cooperation andassortativity with dynamic partner updating, Proc Natl
Acad Sci U S A 109: 14363-14368.
[14] Apicella, C. L., Marlowe, F.W., Fowler, J.H., Christakis,
N.A. (2012) Social networks and cooperation in huntergatherers, Nature 481: 497-501.

4

Session 11 // Information propagation

Is Social Influence Always Positive? Evidence from a Large Mobile
Network
Rodrigo Belo∗, Pedro Ferreira†Carnegie Mellon University

Session 11

4

1

Introduction

and McKendrick, 1927; Bass, 1969; Granovetter, 1978;
Watts and Dodds, 2007). Common to all these models
is the characteristic that, under the right conditions,a small number of initial adopters may lead to a large
number of adoptions.
However, these models are also consistent withother phenomena, such as heterogeneity in the propensity to adopt, homophily, correlated unobservables, or
simultaneity. These phenomena pose significant challenges to the identification of social influence in observational data (Shalizi and Thomas, 2011). Commonly used identification strategies include the definition of structural models (e.g., Ma et al., 2009), the useof instrumental variables (e.g., Tucker, 2008), propensity score matching (e.g., Aral et al., 2009), and more
recently, randomization (e.g., Anagnostopoulos et al.,2008; La Fond and Neville, 2010).
Randomization techniques consist of generating
pseudo-samples based on the original sample by selectively permuting the values of some variables amongobservations (Noreen, 1989), allowing for the estimation of empirical distributions of a parameter of interest
under the null hypothesis of no influence (e.g., Anagnostopoulos et al., 2008; La Fond and Neville, 2010).
We apply these methods to assess the magnitude ofpeer influence in the adoption of products and services
in a mobile network setting.

The design of a new product often involves incorporating viral features that increase the likelihood that
adopters influence their peers to adopt the product aswell (e.g., Aral and Walker, 2011). Network effects
are a specific class of viral features that are frequently
present in networked industries and more so in mobile networks, where pairwise communication still represents the major form of communication. We analyze
a subset of products deployed by a large European mobile carrier and look at their characteristics in terms ofviral features and network externalities. We develop amodel to help identify the incentives in the adoption ofthese products.
We use randomization methods (e.g., Anagnostopoulos et al., 2008; La Fond and Neville, 2010; Beloand Ferreira, 2012) to identify social influence fromobservational data and find that network externalities
contribute to an increase in the adoption rate of someproducts. For other products their contribution is negative.
These results have important management and policy implications. Social influence is not always positive and depends on the viral features of the products
considered. Therefore, it is important to carefully design products that exhibit the correct characteristics
for adoption to occur and spread as expected.

2

3

Identifying social influence

Social influence can be defined as the degree by which
an action from an individual changes the behavior ofsomeone else. For the purpose of this paper we look at
how the adoption of a given promotion influences peers
to adopt the same promotion. Social influence plays animportant role in many diffusion models (e.g., Kermack

We develop a model for how social influence can affectthe adoption rate of products that exhibit network externalities. We focus on the specific case of free-calls
within the network and look at the adoption incentivesof two slightly different products.
By default with this carrier people pay for all thecalls they make. Received calls are free. Consider now
a product that offers free calls among members of a

∗ Rodrigo Belo, CMU, rbelo@cmu.edu.

† Pedro Ferreira, CMU, pedrof@cmu.edu.

198Incentives to adopt with network externalities

4

Session 11 // Information propagation

4

network for a flat rate fee.
Consider a family of products k that allow callingfor free. Type I promotions allow calling for free subscribers that also adopt the same product. Type II
promotions allow calling for free any subscriber in thesame network. Subscribers can choose either of these
options. Both of them require subscribers to pay a
fixed fee.
We assume each user has a fixed number of friends.
Two subscribers are friends they exchange at least 3
calls in the same calendar month. Subscribers derive utility from calling each friend, uij , independentlyof how much she talks to other friends. We assume
quadratic pairwise utility for the calls between user iand user j, cij :Data and Methods

5

Results

We identify social influence on seven different products, one of them corresponding to a type I product— with free calls only to same-product adopters, while
the other six correspond to type II products — freecalls for all users in the network. Figure 1 depicts
the results for the type I product and for one of the
4btype II products (all other type II products are simifk
dNk i ≥
aplar). Table 1 shows the total number of adopters forwhere dNk i represents the number of friends of i each of these products, the coefficient obtained fromthat will not adopt.
the original data, the average coefficient obtained fromThus, apparently somewhat similar products can randomization and respective standard deviation, andgenerate different adoption incentives.
our estimate of social influence.
All the empirical distributions have positive mean
and are statistically different from zero, meaning that
confounding factors, such as homophily are at play.
2

99

4

where dAk i represents the number of friends of ithat will eventually adopt product k. User i will adopt
if there is a minimum number of friends that will also
adopt product k.
The incentives to adopt type II products are quite
different. In this case, adoption occurs as long as there
are at least a minimum number of friends that will not
adopt:

Session 11

We use an 11-month anonymized panel of data comprising of detailed information about all subscribers ina large mobile European network provider. The data
include detailed call and SMS data records, pricing
plans, and adoption of products and promotions.
The data are comprised of detailed information
about every call and SMS originated and received byroughly 4 million subscribers during the period of analysis. These details include, among others, origin, destination, location, start time and duration of a call.
On an average day subscribers generate about 4 million calls and exchange 40 million SMSs. Additionally, the data contain information about subscribers’
pricing plans and supplementary services. At a given
moment in time each subscriber is associated with oneXX[a(cij + cji ) − b(cij + cji )2 − pcij ] pricing plan, and possibly several supplementary seruij =
ui =
j∈Fi
j∈Fi
vices. Supplementary services are à la carte add-on
services that subscribers can acquire, such as a pack ofwhere p represents the price per call. If user i adopts 1000 SMSs at a discounted rate, free calls on the weektype I promotion, she will pay a fixed fee, fk , and not ends for a given period of time, or simply voice-mailpay for calls:
activation. We currently limit our analysis to a sample of 10,000 randomly selected subscribers and theirX
direct neighbors.
2
ui |Ak =[a(cij + cji ) − b(cij + cji ) ] − fk
We apply the shuffle test described by Anagnosj∈Fi
topoulos et al. (2008) and use randomized versions of
The user will choose to adopt product k if the utility the data to infer an empirical distribution for the nullderived from adopting is higher than the utility derived scenario of no influence. We then compare the coeffrom not adopting, i.e., if ui |Ak ≥ ui . Thus, i will ficient obtained from the original data with the null
empirical distribution. This procedure has been shown
adopt k iff:
to yield a lower bound for the magnitude of social in4b
fluence and is described in detail in Belo and FerreirafkdAk i ≥
ap
(2012).

4

Session 11 // Information propagation

Type I ProductFrequency

75

Prod.
Type

Adp.

Orig.
Coeff.

Emp.
Dist

Marg.
Eff.

Extra
Adopt.

I

1126.0019***(4.5e-04)

8.4e-04(2.1e-04)

1.0e-3***148 (13%)

II (1)

534-33 (-6%)357-7.0e-4***-22 (-6%)

II (3)

341-8.0e-4***-29 (-9%)

II (4)299

II (5)205

II (6)

110.0048(1.7e-04).0036(1.6e-04).0037(2.0e-04).0032(1.6e-04).0018(1.4e-04).0011(2.1e-04)-8.0e-4***

II (2).004***(4.4e-04).0029***(4.0e-04).0028***(5.0e-04).0032***(4.7e-04).0015***(4.0e-04).0015***(4.1e-04)50-6.0e-5-2.5e-4**-6 (-3%)-3.6e-4

25

Table 1: Influence estimates for type I and type II
products.
x

00.00000.00050.00100.0015cient obtained using the original data is .0019, outside
the 95% confidence interval of the empirical distribution (see Figure 1), we conclude that social influence
plays a role in the diffusion of this product. Its total effect corresponds to 13% of the total observed adoption.
In the case of the first type II product, peer influence
reduces observed adoption in 6%.

0.0020

Empirical Distribution

Type II Product100

6Session 11

Conclusion and Future Work50

We show that peer influence can be either positive or
negative in the adoption of products with network externalities. Previous literature on network externalities shows that the incentive to adopt increases with
the number of friends that adopt. In this paper weshow that network externalities can also trade-off withword-of-mouth, resulting in a decrease in the incentive
to adopt as the number of friends that adopt increases.
The direction of such incentives is partly determinedby the design of the product.
This research is work in progress and we plan to
improve on it by separating the network externalities
incentives from the effect of word-of-mouth in the adoption of type II products. We intend to do this bylooking at users with a relatively large number of nonadopter friends and test whether there is a positive total effect from social influence. In such a case the positive effect would have to come from word-of-mouth.
We also plan to analyze under which conditions type I
and type II products are profitable for the carrier, andto find the optimal fees for such products dependingon the network topology.

4

Frequency

75

25

0

x

0.00320.00360.00400.00440.0048Empirical Distribution

Figure 1: Type I and Type II Product coefficients over
1,000 adoption date shuffles. The ‘×’ mark represents
the coefficient obtained from the original data. Dashedlines represent 95% confidence intervals.
Despite the existence of these effects, our estimates arealigned with the model outlined in section 3: social influence is positive for the type I product and negative
for most type II products. For instance, in the case of
the first type I product, the average coefficient obtained
from randomization is 0.00084. Given that the coeffi3

100

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Session 11 // Information propagation

References

W. Kermack and A. McKendrick. A contribution to
the mathematical theory of epidemics. Proceedings
A. Anagnostopoulos, R. Kumar, and M. Mahdian.
of the Royal Society of London. Series A, 115(772):
Influence and correlation in social networks. In
700–721, 1927.
KDD ’08: Proceeding of the 14th ACM SIGKDD
international conference on Knowledge discovery T. La Fond and J. Neville. Randomization tests for
distinguishing social influence and homophily effects.
and data mining, pages 7–15, New York, NY,
In Proceedings of the 19th international conference
USA, 2008. ACM. ISBN 978-1-60558-193-4. doi:on World wide web, pages 601–610. ACM, 2010.
http://doi.acm.org/10.1145/1401890.1401897.
S. Aral and D. Walker. Creating social contagion L. Ma, R. Krishnan, and A. Montgomery. Homophilyor Influence? An Empirical Analysis of Purchasethrough viral product design: A randomized trial
within a Social Network, 2009.
of peer influence in networks. Management Science,57(9):1623–1639, 2011.
E. Noreen. Computer Intensive Methods for TestS. Aral, L. Muchnik, and A. Sundararajan. Distining Hypothesis- An Introduction. JOHN WILEY &guishing influence-based contagion from homophilySONS, (229), 1989.
driven diffusion in dynamic networks. Proceedingsof the National Academy of Sciences, 106(51):21544, C. Shalizi and A. Thomas. Homophily and contagion
are generically confounded in observational social
2009.
network studies. Sociological Methods & Research,
F. Bass. A New Product Growth for Model Consumer
40(2):211–239, 2011.
Durables. Management Science, 15(5):215, 1969.
C. Tucker. Identifying formal and informal influence
R. Belo and P. Ferreira. Using Randomization Methin technology adoption with network externalities.
ods to Identify Social Influence in Mobile Networks.
Management Science, 54(12):2024, 2008.
The Fourth IEEE International Conference on Social
D. Watts and P. Dodds. Influentials, networks, andComputing, SocialCom 2012, 2012.
public opinion formation. Journal of Consumer ReM. Granovetter. Threshold models of collective behavsearch, 34(4):441–458, 2007.
ior. American journal of sociology, pages 1420–1443,
1978.

Session 11

4

4

101

102

Poster

1

Mobility103

1

Poster 1 // Mobility

Human Mobility and Predictability enriched by Social
Phenomena Information
Nicolas B. Ponieman1,2 , Alejo Salles2 , and Carlos Sarraute11Grandata Labs, Argentina{nico,charles}@grandata.com
2
Physics Dept., Universidad de Buenos Aires, Argentina
alejo@df.uba.ar

1

Introduction

0.65

Predictability for each time slot
Outgoing calls
Incoming calls
All calls(I+O)

Correct prediction probability

The information collected by mobile phone opera0.60
tors can be considered as the most detailed infor0.55
mation on human mobility accross a large part of0.50
the population [1]. The study of the dynamics of
0.45
human mobility using the collected geolocations ofusers, and applying it to predict future users’ loca0.40
tions, has been an active field of research in recent
0.35
years [2, 3].
0.30
In this work, we study the extent to which social
phenomena are reflected in mobile phone data, fo0.25
cusing in particular in the cases of urban commute
0.200
20
40
60
80
100 120 140 160 180
and major sports events. We illustrate how theseTime Slot
events are reflected in the data, and show how information about the events can be used to improve Figure 1: Users’ location predictability by time slot.
predictability in a simple model for a mobile phone Blue: Outgoing calls. Red: Incoming calls. Green:user’s location.
All calls.

Poster 1

1

2

Mobile Data Source

Our data source is anonymized traffic information from a mobile operator in Argentina, focusing
mostly in the Buenos Aires metropolitan area, overa period of 5 months. We use Call Detail Records(CDR) including time of the call, users involved,
direction of the call (incoming/outgoing), the antenna used in the communication, and its position.
The raw data logs contain around 50 million calls
per day. CDRs are an attractive source of location
information since they are collected for all active
cellular users (about 40 million users in Argentina),
and creating additional uses of CDR data incur little marginal cost.

3

Mobility Modelslots per week. Since humans tend to have very
predictable mobility patterns [1, 4, 5], this simple
model turns out to give a good predictability baseline, achieving an average of around 35% correct
predictions for a period of 2 weeks, training with 15
weeks of data, including peaks of above 50% predictability. This model was used as a baseline in [6],with which our results agree. In Figure 1 we show
the average predictability for all time slots.
It is important to notice that it is possible toimprove the accuracy of this simple model by clustering antennas, instead of defining each antenna as
a location.

4

Urban Commute

The phenomenon of commuting is prevalent in large
metropolitan areas (often provoking upsetting trafTo predict a user’s position, we use a simple model fic jams and incidents), and naturally appears inbased on previous most frequent locations. In or- mobile phone data. For instance, in [7] the authors
der to compute these locations, we split the week in study commute distances in Los Angeles and New
time slots, one for each hour, totalizing 7 ∗ 24 = 168 York areas. Mobile data can lead to quantification

104

1

Poster 1 // Mobility(a) 6 a.m.

(b) 8 a.m.

(c) 10 a.m.

(d) 5 p.m.

(e) 7 p.m.

(f) 8 p.m.

Figure 2: Commute to Buenos Aires city from the surrounding areas on a weekday, for different hours.
Red color corresponds to a higher number of calls, whereas blue corresponds to an intermediate numberof calls and light blue to a smaller one.

5

Sports Events

1 Greater resolution versions of these maps, as well as
additional figures, are available in the Labs section at www.
grandata.com.

So far, our results allow us to understand (andquantify) social events through the analysis of mobile phone data. This understanding can be in turnused to improve the mobility model. Social relations among individuals have been used to improvepredictability in mobility models before, as in [6],where social links learned from the mobile phonerecords are used to this end. Here, instead of peer
to peer links learned from the mobile data, we show
how an external data source can be used to improvethe model.
We illustrate this effect using as proof of concept the case study of soccer matches. By taking
the soccer fixture, we tag users as “Boca Juniors
fans” if they make calls using antennas around thestadium and time slot where Boca plays for threeconsecutive matches (which include both home andaway matches). Using this tagging, we can dramatically improve predictability for this group of Boca
fans, even predicting positions that had never been
visited by a user before. The predictability of themodel for these users considering the fixture datarises for the matches to 38% – which doubles the
19% accuracy achieved by our previous model forthe same set. Moreover, the initial model is only
able to make predictions in 63% of events in thegiven set (as a consequence of a lack of informationfrom the training set data), whereas the sociallyenriched model tries to predict 100% of the events
during match days, which make the previous results

105

1

As in the urban commute case, we study humanmobility in sports events as seen through mobile
phone data. In Figure 3, we show how assistants toa Boca Juniors soccer match converge to the stadium in the hours prior to the game, and disperse
outwards1 .
Note that postselecting the users attending theevent necessarily produces the effect of having nocalls outside the chosen area during the match, however, the convergence pattern observed is markedly
different from the one seen for the same time slotof the week on a day with no match.

Improving Predictability with External Data

Poster 1of this phenomenon in terms of useful quantities,
which are much harder to measure directly. We include a series of call patterns illustrating the Buenos
Aires commute in Figure 21 .
From the data, we can estimate the radius of thecommute (the average distance traveled by commuters). Considering the two most frequently used
antennas as the important places for each user
(home and work, see [8]), we get an average commute radius of 7.8 km (as a comparison, the diameter of the city is about 14 km, and the diameter ofthe considered metropolitan area is 90 km).

1

Poster 1 // Mobility(a) 5 hours before(b) 1 hour before(c) 1 hour after(d) 3 hours after

Figure 3: Convergence to Boca Juniors stadium on hours prior to a soccer match, and dispersal after
its end. Red color corresponds to a higher number of calls, whereas blue corresponds to an intermediate
number of calls and light blue to a smaller one.

Poster 1

1even more significant.
human mobility and social interactions. Nokia
Mobile Data Challenge Workshop, 2012.
In order to understand these results, we illustratewith an example where the enriched model outperforms the simple model: the simple model would [3] Xin Lu, Linus Bengtsson, and Petter Holme.
Predictability of population displacement after
rarely predict a user’s location on a different city,
the 2010 haiti earthquake. Proceedings of thewhereas the enriched model would do so if the user
National Academy of Sciences, 109(29):11576–
is a Boca fan, and Boca has an away match in that
11581, 2012.
city.
[4] Marta C Gonzalez, Cesar A Hidalgo, andAlbert-Laszlo Barabasi.
Understanding in6 Conclusion
dividual human mobility patterns. Nature,453(7196):779–782, 2008.
We illustrated how social phenomena can be studied through the lens of mobile phone data, which [5] Shan Jiang, Joseph Ferreira, and Marta C
can be used to quantify different aspects of these
González. Clustering daily patterns of humanphenomena with great practicity. Furthermore, we
activities in the city. Data Mining and Knowlshowed how including external information aboutedge Discovery, pages 1–33, 2012.
these phenomena can improve the predictability ofhuman mobility models.
[6] Eunjoon Cho, Seth A Myers, and Jure Leskovec.
Although we showed this in a specific case as a
Friendship and mobility: user movement in
proof of concept experiment, we note that this prolocation-based social networks.
In ACM
cedure can be extended to other settings, not reSIGKDD, pages 1082–1090. ACM, 2011.
stricted to sports but including cultural events, vacation patterns and so on (see [3] for a specially rel- [7] Sibren Isaacman, Richard Becker, RamónCáceres,
Stephen Kobourov,
Margaretevant application). The tagging obtained is useful
Martonosi,James
Rowland,andAlexanon its own and is of great value for mobile phonederVarshavsky.
Identifying
importantplacesoperators. The big challenge in this line of workinpeople’s
lives
fromcellularnetworkdata.
is to manage to include external data sources in a
Pervasive
Computing,pages
133–151,
2011.
systematic way.
Lastly, the tag-based predictions can be taken to [8] Balázs Cs Csáji, Arnaud Browet, VA Traag,
the community level. Defining, for instance, theJean-Charles Delvenne, Etienne Huens, Paul
“Boca Juniors fans” community, we can predict
Van Dooren, Zbigniew Smoreda, and Vincent D
that if some users of this community make or reBlondel. Exploring the mobility of mobile phone
ceive calls in a certain location, other users in theusers. Physica A: Statistical Mechanics and its
community will do it as well.
Applications, 2012.

References[1] Chaoming Song, Zehui Qu, Nicholas Blumm,
and Albert-László Barabási. Limits of predictability in human mobility.
Science,327(5968):1018–1021, 2010.
[2] Manlio De Domenico, Antonio Lima, and Mirco
Musolesi. Interdependence and predictability of

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Pisa Tourism Fluxes Observatory: deriving mobility
indicators from GSM calls habits
Barbara Furletti, Lorenzo Gabrielli, Salvatore Rinzivillo, Chiara Renso
KDDLAB - ISTI CNR, Pisa, Italyname.surname@isti.cnr.it

Abstract
The necessity to improve the management of the resources,
urged many local governments to adhere to European initiatives in the context of competitiveness and sustainability, for creating the right balance between the welfare of
tourists, the needs of the natural and cultural environment
and the development and competitiveness of destinations
and businesses. For many Italian Municipalities, this requirements become concrete with the establishment of a
tourism monitoring systems that aims at survey these phenomenon through the analysis of heterogeneous data ranging from information of the territory, energy consumption,use of the land, and linked data (arrival and departure fromthe airport, bus, hotels etc). We describe the permanent
observatory of touristic fluxes we realized in the town of
Pisa where the standard indicators have been extended withan indicator of people presence extracted from mobile GSM
call data and other exploratory analyses made by using themobile phone data.we developed a method to partition theusers into residents, commuters, in transit and visitors starting from a spatio-temporal profile inferred from people call
habits.

<Caller ID, ID Cell Start, Start Time, ID Cell End,Duration>

where: Caller ID is the anonymous identifier of the caller,
ID Cell Start and ID Cell End are the identifiers of the cell
where the call starts and ends respectively, Start Time isthe date and time when the call starts, and Duration is thecall duration.

Poster 1

2

Extending the TFO with GSM-based analysis
Art cities attract many tourists and visitors and these incoming flows concur to consume the cities resources bothnatural (energy, water, air, . . . ) and of services (parkings,
public transportations, garbage collectors, . . . ). For thesereasons some conflicts with residents and visitors can arise in
the use of the limited resources. It is an hard task and a duty
for the local administrations to guarantee general welfare forboth citizen and visitors and the Sustainable Tourism Planning is more and more implemented in order to preserve
local resources. In general, not only the tourism but also
commuters flows cause criticism in the typical dynamics ofa city (traffic increasing, public transportation congestion,
pollution).
The Tourism Fluxes Observatory (TFO) carried out in cooperation with the Municipality of Pisa, aims at studying
the fluxes of tourist visiting the town in order to evaluate
the overall quality of the reception system on the territory.
This is supposed to be a means of support for the Administrations and the private operators in assessing the overall
quality of the reception system planning.
The data used in a TFO come from heterogeneous sources
and represent observations of several socio-economic and environmental phenomena. Some of the indicators included in
the TFO are directly or indirectly influenced by the presence of tourists on the territory like the number of tourist

buses that stop at a parking, the consumption of water andenergy, or the presence at the hotels.
The TFO we realized in Pisa enriches the standard studies of
tourists flows integrating and using unconventional data like
GSM and Social Media data (e.g. Flickr, Facebook, Twitterdata). The use of these data supply a different view of thephenomena related to behaviors or cultural interests of theindividuals (e.g., the call habits or the shared photos of thevisited places).
It is known from the literature the ability of these new data
to give quali-quantitative estimations of several phenomena
as for example the traffic flows [1], the city dynamics, andan estimation of people presence at the points of interest [2].
Furthermore the use of GSM and Social Media data is particularly interesting because they overcome the problems ofthe massive data collection as for example the high costs forsurveys and the limitation related to the availability of bothhuge amount and up-to-date data.
Besides the quantitative indicators extracted from the data
provided by the local administrations and operators in the
tourism sector, we extended the TFO with a set of analysis
conduced on a GSM dataset. This dataset consists of Call
Data Records (CDR) collected in the urban area of Pisa of
about 232.200 users with a national mobile phone contract(no roaming users are included in the dataset). These users
have been observed from January 9th to February 8th 2012,and generated around 7.8 million of tracks.
A CDR records is recorder for each call fo the user and has
the following information:

Taking inspiration from the literature, we used CDRs toestimate the presence of people in the city and to identify
which areas are particularly popular and attractive. By
studying the calls density, we are able to answer to some
non-trivial questions about people habit: How many. . . ?
(the “volume” of people in the area of interest), Where. . . ?
(the origin and destination of people), and When. . . ? (thetemporal profile).
Figure 1, shows the density of people presence in the areaof Pisa. The thick lines delimit the main districts, while thepolygons are the Voronoi tessellation of the GSM coverage.
The density is shown by using the colors in the map: the

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higher density are red. The red cells labeled with (1) contain“Piazza dei Miracoli” (the square with the leaning Tower)
and the Hospital; the red cell labeled with (2) contains theGalilei International Airport, and the cell labeled with (3)contains the National Research Council (CNR). These threeareas actually attract many from tourist/visitors and workers (both resident and commuters). Starting from this anal-

Figure 3: Call distribution over the time, in the cell that
contains Ponte di Mezzo.

Figure 1: Density map in the city center of Pisa computedwith the CDRs.

2ysis, giving just an idea on how people are distributed on
the territory, it is possible to focus on a particular area
(GSM cell) in order to analyse the origin and destination of
the individuals. Considering the cell numbered as (4), that
contains “Ponte di mezzo” (the Middle Bridge), we identified where people go after having traversing that area, andwhere people come from. Figure 2 shows the origin and destination cells involved in the movements to and from thecell (4): the thickness of the arrows identify the flow volumes, and the direction of the movements. The presence inFigure 2: Origin and destination map of people seen in thecell that contains Ponte di Mezzo.

(Left) Cells of Origin;

Poster 1(Right) Cells of the Destinations.

each area can be further investigated by time. In particular, grouping the calls per hours we can plot the chart that
represents how the presence change over the day. Figure 3
shows the variation of the presences in the cell number (4).
Each separate group is a different day of the week startingfrom Monday. The presence, during each day, have the typical form with the two peaks early in the morning and in
the middle of the afternoon i.e., the hours in which people
move to reach the work/school places and when they leave.
As introduced above, an important aspect when we try tomonitor the territory is to understand what kind of people
are moving on. Very often the local Administrations do nothave exact estimations of tourist flows, but only partial datacollected from the tourist offices or census statistics. In the
TFO of Pisa we implemented a method for people profiling using the CDR [3]. We identify, with a certain degree

108

of approximation, which calls may correspond to predefinedusers categories among residents, commuters, in transit andtourists. Starting from these profiles we extract and indicator of presence that enrich the set of variables of the TFO.
Identifying tourists/visitors is essential to study how the city
is receiving people from outside and how their movements
are affecting the city. Again, being able to combine themobility of resident population with the temporary population (like commuters, visitors or people in transit) may givea measure of the sustainability of the incoming population
with respect to resident one. The population on a territory
consumes resources like water, air and produces negative effects on the surroundings, like garbage, pollution, noise. In
the cases where these resources are limited, the incoming
tourist population may break the sustainable equilibriumof such resources. Thus, the ratio between residents andincoming people should be monitored in order to prevent
critical situations.
The profiling methodology uses an inductive machine learning step based on the SOM [4] starting from a spatio-temporaluser profiles extracted from people call habits.
Starting from the set of calls made by each user in the time
window, a Space Constrained Temporal Profile of each user
is reconstructed. According to the definition given in [3], aTemporal Profile is a vector of call statistics according toa given temporal discretization, and the Space constrained
Temporal Profile is a Temporal Profile where only the calls
performed in the cells contained within the a certain area,
are considered.
Since our aim is to study the mobility of residents and visitors in the area of Pisa, from the whole network we first selected the cells overlapping the urban area of the city (Figure
4-Left). The urban center of the city and its corresponding
cells are highlighted in pink, while the larger gray area corresponds to the administrative territory of the city. The time
projection is built by performing two temporal operations(Figure 4-Right):
1) the aggregation of the days in weekday and weekend slots;2) the splitting of each slot in time bands representing 3 interesting time windows during the day:t1 = [00:00:00 - 07:59:59], Early in the morning when people
are usually still at home;t2 = [08:00:00 - 18:59:59], Middle day when people are out
for work/school or other activities;
t3 = [19:00:00 - 23:59:59], Late in the evening and night
when people are back to home.

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Poster 1 // Mobility

Starting from the calls along the days, the presences overFigure 4:(Left) GSM Cell coverage in the area of Pisa;(Right) Reconstruction of the Temporal Profile for the users
in Pisa.

t1, t2 ,t3 are computed and then aggregated over the weekdays and the weekend summing up all of them. The result
is a sort of compact representation of the user’s behaviors
measured by his calls.
The dataset is then processed by using the SOM algorithm
in order to extract the typical global profiles. A SOM is a
type of neural network based on unsupervised learning that
produces a one/two-dimensional representation of the input
space using a neighbourhood function to preserve the topological properties of the input space [4]. In our case, theSOM output is a set of nodes representing groups of users
with similar temporal profiles. The SOM tends to highlightthe results, we are able to extend the cathegories with new
profiles such as for example the “Occasional visitors” and“The night visitors”. While the former are essentially people
that come to Pisa only few times along a month, the latter
visit Pisa almost regularly but only the night (maybe for thenightlife in the pubs).
The population rates have been integrated in the TFO as aparticular socio-demographic indicator.
The profiling methods through the SOM, permits also tofurther discovery particular situation or events that can enrich the global description of a city dynamics. Analyzing
the different profiles we can discover special cases that canhide interesting information, as the one shown in Figure 6(Left). This profile groups a huge amount of people that appear in Pisa only in a unique slot. This slot corresponds tothe weekdays from Monday 23rd to Friday 27th 2012. Whenplotting the daily time distributions of the calls, we discovered a peak of calls during Friday 27th around 4 p.m. as
shown in Figure 6 (Right). Actually this day some minutes
before 4 p.m. a strong earthquake affected the Tuscany andPisa. In this case the GSM data are a good proxy to capture
extraordinary events.

Figure 6:(Left) Particular profile and the corresponding
daily time calls distribution.

[1] H. Bar-Gera. Evaluation of a cellular phone-based
system for measurements of traffic speeds and travel
times: A case study from israel. Transportation
Research Part C, pages 380–391, 2007.
[2] F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, andC. Ratti. Real-time urban monitoring using cell phones:
A case study in rome. IEEE Transactions on Intelligent
Transportation Systems, 12:141–151, 2011.
[3] B. Furletti, L. Gabrielli, C. Renso, and S. Rinzivillo.
Identifying users profiles from mobile calls habits. In In
the Prooc. of UrbComp’12, 2012.
[4] T. Kohonen. Self-Organizing Maps. Springer Series inInformation Sciences. Springer, 2001.

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similar and compatible presence profile of longer stay people, allowing to separate commuters and residents, by exploiting the calling habits of these users in particular during
the weekends. In particular, as shown in Figure 5, on thebottom left corner (identified with blue points) there are thetemporal profiles corresponding commuter-like pattern withhigh frequency during the workdays and a smaller activity
during weekends. On the upper right, there are instead the
profiles describing residents with high presence during thewhole time windows. In the central part (identified withyellow points) there are the profiles corresponding to shortvisits of the city.
Counting the instances in each group, we estimates the percentage of residents, commuters and visitors as 20%, 9%,
and 45%, respectively. The 26% of the individuals are unclussified at first, because they do not match with any predefined profiles. Carrying out a more accurate analysis of

Poster 1

Figure 5: SOM Result: The user profiles.

Conclusions
In this abstract we sketched some features of the Tourist
Fluxes Observatory we have set up in collaboration with theMunicipality of Pisa. Apart from integrating the basic standard data like hotel and parking presence, airports arrivals
and car rentals, the main innovative point of this Observatory is the ability to analyse GSM data. We have developed
mobility analysis including density based presence of people
during time, to more sophisticated analysis to infer the user
profile among residents, commuter and tourists.
References

3

Poster 1 // Mobility

Hierarchical Exploration of Human Mobility Regularities
Zhenhui Jessie Li
Penn State University, University Park, PA
jessieli@ist.psu.edu

ABSTRACTThe spatial and temporal regularities are the most fundamental patterns in human movements. In this work, we
address the problem of analyzing human regularity in a hierarchical way. Our intuition is that, a person may show
different temporal patterns with respect to locations at different spatial granularities. When looking at the raw trajectory of a person, there could be several cities/towns thathe/she usually goes to. And the person could have yearly
regularity visiting some of these cities. And we could further zoom into those cities and find certain specific locations
that this person frequently visits, such as home, office or local grocery stores. Taking these locations as references, we
may further observe the daily and weekly patterns.
We propose to use reference spots, which are the frequent
locations, to observe human movements. The challenge lies
in to how to effectively detect reference spots at different
spatial granularities. To solve this, we first calculate thedensity map over all locations and iteratively breaks the region into reference spots. The reference spots form a hierarchy and the temporal patterns in a 24-hour window and7-day window will reveal the mobility regularities respect
to a reference spot. The experiments are carried on Nokia
dataset, mainly using the GPS data. We will show somecase studies and all the results are put online.

Poster 1

3

1.

INTRODUCTION

With the advanced position technology, massive amountsof object movement data have been collected. Human movement data collected from mobile phones is a particular interesting moving object data. The mobility patterns minedfrom human movement are useful for urban planning, traffic
forecasting, and the spread of biological and mobile viruses.
An important analysis on human movement data is tofind the locations that he/she frequently visits and show the
temporal regularity w.r.t. these locations. Intuitive examples include daily behaviors in-and-out of home and weekly
visits to local grocery store.
There are several interesting studies showing the potentialof using mobile or positioning technologies to study the human mobility regularity [2, 1, 6]. In our recent work [4], we
study the problem of detecting periods in movement data.
Our idea is to find dense regions, namely reference spots,
and examine the in-and-out pattern w.r.t. reference spots.
Periods can be detected using Fourier transform and autocorrelation on the binary in-and-out sequences.
In this work, we will study human movement regularity
in a hierarchical structure of spatial locations. For example,

110

if we take a town as one big reference spot, we could findthat the person has yearly regularity visiting the town. If
we take a person’s office as one small reference spot, theweekly regularity visiting the office could be revealed. Reference spots could be a state, a city, a downtown region, ora building. By looking at reference spots at different spatial granularity, we gain better insights into human mobility
from various aspects.
The reference spots in hierarchy are data-dependent andshould be detected automatically. Our previous work [4,5] proposes to use kernel-based method to detect reference
spots. The method is limited to detect only one-level reference spots. Now the challenge lies in how to detect thehierarchical structure of the reference spots. We propose toa top-down iterative way to detect reference spots. In eachlevel, we first calculate the density map over all the locations. If there are more than two peaks on the density map,
we will find the lowest density threshold to break them into
separate reference spots. Then, we further look into eachreference spot and iteratively do the same for the locations
in every reference spot. Since we know human usually follow
daily or weekly calendar behavior, for each reference spot,
we will examine the regularity of the times visit this reference spot in a 24-hour window, 7-day window and all-time
window. The skewed time distribution will reveal the human
movement regularities.

2.

METHODLet D = {(loc1 , time1 ), (loc2 , time2 ), . . . (locn , timen )} be
the original movement database for a moving object, whereloci is a spatial point represented as a pair (loci .x, loci .y).
A reference spot is a dense area that is frequently visitedin the movement. It is important to find reference spots at
different spatial granularities. In this section, we will firstdescribe how to calculate the density map and then discuss
our method to detect reference spots in a hierarchical way.

2.1

Kernel-based Density Map Calculation

Intuitively, reference spots are those dense regions containing more points than the other regions. While computing the density for each location in a continuous spaceis computationally expensive, we discretize the space intoa regular w × h grid and compute the density for each cell.
The grid size is determined by the desired resolution to view
the spatial data.
To estimate the density of each cell, we adapt a popular kernel method [7], which is designed for the purpose offinding home ranges of animals. If an animal has frequent

3

Poster 1 // Mobility

activities at one place, this place will have higher probability to be its home. This actually aligns very well with ourdefinition of reference spots.
For each grid cell c, the density is estimated using thebivariate normal density kernel,f (c) =n|c − loci |2
1 X 1
exp(−
),
nγ 2 i=1 2π
2γ 2where |c − loci | is the distance between cell c and locationloci . In addition, γ is a smoothing parameter which is determined by the following heuristic method [7],Figure 1: Raw trajectory of a person.

111
γ = (σx2 + σy2 ) 2 n− 6 ,2where σx and σy are the standard deviations of the whole
sequence LOC in its x and y-coordinates, respectively. The
time complexity for this method is O(w · h · n).

2.2

Hierarchical Detection of Reference Spots

After obtaining the density values, a reference spot can be
defined by a contour line on the map. A contour line joins
the cells of equal density. We use contour line to define theboundary of a reference spot. Any point within the reference
spot has higher density value than that of the boundary. So
the reference spot is essentially an area with high density.
When we set density threshold p equal to 0, all the grids
will be in one single big contour. If we gradually increasep, the size of the contour will shrink. If there is only one
peak on the density grids, the contour with density p will
eventually shrinks to empty as p decreases. If there is morethan one peak on the density grids, there will be multiple
contours with density p when p decreases to certain value.
Therefore, we can increase p from 0 to the maximal density
value over all the grids, the first time when we get morethan one contours with density p, we take these contours as
the reference spots at the current level and further look into
each contour separately.

2.3

Summarization of Periodic Behaviors

A CASE STUDY

We analyze a user trajectory in the Nokia dataset [3]. The
raw trajectory of this user is plotted on Google Earth as

4.

CONCLUSION AND DISCUSSIONSIn this work, we present the method of analyzing human
movement in a hierarchical way. We propose using reference spots at different spatial granularities to observe human movement. The discovery of human movements can berevealed from the temporal regularities in reference spots.
It will also be interesting to study the regularity in the
trajectory paths. This would be more challenging since thefrequent trajectory paths are more difficult to be detected
than reference spots.

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3

3.

shown in Figure 1. Each green pin is one recorded locationof this user. The density map over all the locations is shownas Figure 2.
When we reach to node 1 at level 3, the density map isshown as Figure 3(a). We see from Figure 3(b) that theperson is more likely to visit the region on weekends withskewed time distribution in 7-day time window. When wego to level 4, it breaks into two reference spots. And thetime distributions of these two reference spots are showing
very different characteristics. The reference spot as shownin Figure 3(c) showing strong weekly regularity. The person
has high probability to be in this reference spot on weekends
and sometimes showing up in this spot at nights. However,for the other reference spot as shown in Figure 3(e), the time
distribution in a 7-day window is more even and the person
is observed in that spot mostly during daytime as seen in
the 24-hour window. Therefore, we gain insights into themobility pattern of this person in level 4 than level 3. Only
when we treat these two reference spots separately, we willobserve this person is likely to be in Spot-Level-4-Node-1 on
weekends, which indicates this could be the region that thisperson is usually spending his spare time. And Spot-Level4-Node-2 is likely to be a working place that this person
usually shows up at daytime.

Poster 1

Given a reference spot, we have a set of locations fall inthis spot: {(loci1 , timei1 ), (loci2 , timei2 ), . . . (locim , timeim )}.
Since human usually follow either daily periodicity or weekly
periodicity, we examine the time distributions of the reported locations in this reference spot in a 24-hour window
and 7-day window. For example, in the 24-hour window,
if the frequency of some particular hours are significantly
higher than other hours, it means this person has high daily
regularity visiting this reference spot.
To examine the time distributions, we could look at thefrequencies per hour or per day. However, it is worth notingthat the original time distribution in the data collection is
not even. As we found in Nokia dataset, there are very few
locations reported from late night to early morning. For
example, if there are 100 locations reported at 1 a.m. in
the whole movement history, 80 out 100 are found to fall
into at one specific reference spot. Then the probability
that the person is at this reference spot is 0.8. Probabilities
normalize the the biased collection of the raw data.

Figure 2: Density map of the trajectory.

3

Poster 1 // Mobility(a) Density-Level-3-Node-1(b) Time-Level-3-Node-1(c) Spot-Level-4-Node-1(d) Time-Level-4-Node-1(e) Spot-Level-4-Node-2(f) Time-Level-4-Node-2

Poster 1

3

Figure 3: A Case Study.
In our next step, we want to integrate this method into
our online demo system, MoveMine1 . Currently, MoveMine
is providing data mining functions to analyze animal movements. We will implement this method to enable our systemanalyze human movements regularity in a hierarchical way.

5.

REFERENCES[1] Nathan Eagle and Alex Pentland. Eigenbehaviors:
identifying structure in routine. In Behavioral Ecology
and Sociobiology, pages 1057–1066, 2009.
[2] Marta C. González, Cesar A. Hidalgo R., andAlbert-László Barabási. Understanding individual
human mobility patterns. Nature, 453:779–782, 2008.
[3] Juha K. Laurila, Daniel Gatica-Perez, Imad Aad, Jan
Blom, Olivier Bornet, Trinh-Minh-Tri Do, Olivier
Dousse, Julien Eberle, and Markus Miettinen. The
mobile data challenge: Big data for mobile computing
research. In Mobile Data Challenge by Nokia Workshop
1

dm.cs.uiuc.edu/movemine112

in conjunction with Int. Conf.. on Pervasive
Computing, June 2012.
[4] Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays, andPeter Nye. Mining periodic behaviors for moving
objects. In ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD’10),
pages 1099–1108, 2010.
[5] Zhenhui Li, Jingjing Wang, and Jiawei Han. Mining
periodicity for sparse and incomplete event data. In
Proc. of 2012 ACM SIGKDD Int. Conf. on Knowledge
Discovery and Data Mining (KDD’12), Beijing, China,
Aug. 2012.
[6] Chaoming Song, Zehui Qu, Nicholas Blumm, andAlbert-László Barabási. Limits of predictability inhuman mobility. Science, 327:1018–1021, 2010.
[7] B. J. Worton. Kernel methods for estimating theutilization distribution in home-range studies. In
Ecology, volume 70, 1989.

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Poster 1 // Mobility

Mining User Mobility Features for Next Place Prediction in
Location-based Services
Anastasios Noulas, Salvatore Scellato, Neal Lathia, Cecilia Mascolo
Computer Laboratory, University of Cambridge
email: firstname.lastname@cl.cam.ac.uk

1

Introduction

Poster 1

Understanding human mobility has been a long-standing subject in academic research due to themultitude of potential applications. Those range from the better grasp of human behavior andmigration patterns, to the evolution of epidemics and spread of disease, or the understanding of
the mechanisms that shape social networks. Besides, studying human movement and geographic
activity is increasingly a focal point of research in computer sciences. The rise of the Mobile Web
and the provision of Internet scale applications and services to millions of smartphone users isbringing geography and location to the spotlight; knowing how people move and choose to visit
specific places in a city can benefit a plethora of applications, including mobile web browsing, local
search and content discovery. Up until recently most attempts towards the analysis and modelingof human mobility were relying on mining spatio-temporal datasets sourced from GPS sensors [3],WiFi logs or Cellular Data [2]. These data sources describe with fine grained geographic andtemporal granularity user movements, where each data point is pair of longitudinal coordinates
coupled with a timestamp that informs us where a user is at a particular time. The existence of
these datasets has led to the development of numerous statistical frameworks that aim to predict
the whereabouts of users, the characterization of spaces based on local human activity or thedetection of significant locations in urban environments [1].
Nonetheless, with the introduction and increasing popularity of location-based services, theopportunity to study human movement in a qualitatively different setting is provided. Mobile
applications such as Foursquare, where users check in broadcasting their visits to places, allow
us not only to know the geographic coordinates of a user at a given time, but also the exact
places they may go. As with cellular data, the position of the user is becoming known when they
explicitly use the service, yet the multiple layers of data offered open new avenues for addressing
previously unanswered research questions. A library, a cinema or an airport terminal are only
a few examples amongst the millions of places which are accessible through these services. The
knowledge about the specific places users visit, which goes beyond plain geographic coordinates,
can be exploited as an additional dimension to describe human mobility. Insights about the type
and time of users’ visits can greatly improve the development of recommendation systems. For
instance, advertisers who want to push offers to users would greatly benefit from knowing the next
location a user is going to visit, so to offer the right coupon or the right recommendation in atimely manner. However there are many challenges involved in the prediction of the next visited
location, such as user preferences, place properties as well as spatio-temporal conditions. In thiswork we address this research question by mining user data generated on a popular location-based
service and studying the predictive power that different dimensions of the data offer. We formalize
the Next Check-in Problem, where we aim to predict the exact place a user will visit next given
historical data and the current location.
The challenge posed in this context is to rank all the potential target places in the prediction

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scenario, which could easily contain thousands of candidates, so that the actual place visited next
by the user is ranked as high as possible. This represents a highly imbalanced prediction scenario,
where a single correct instance has to be found (the place a user is going to) amongst thousands ofcandidate instances. We study what factors may drive user behavior by analyzing a large dataset
sourced from the most popular location-based social network, Foursquare. We have collected
approximately 35 million user check-ins over a period of 5 months in 2010, taking place over a setof five million geo-tagged venues. We focus our prediction problem over 33 cities, the most active
in our dataset in terms of check-in number, treating each city as a different prediction scenario.
Our contributions can be summarized as follows:• We define a set of prediction features that exploit different information dimensions about
users’ movements: those include information tailored specifically to an individual user, suchas historical visits or social ties, and features extracted by mining global knowledge about thesystem such the popularity of places, their geographic distance and user transitions between
them. Moreover, we employ a set of features that leverage explicitly temporal information
about users’ movements. We assess the predictability of individual features and we discoverthat the most effective features are those which leverage the popularity of target venues anduser preferences.
• We combine the predictive power of individual features in a supervised learning framework.
By training two supervised regressors, a simple linear model and M5 model trees, on pastuser movements, we demonstrate how a supervised approach can significantly outperform
single features in the prediction of future user movements, indicating that user behavior inlocation-based services is driven by multiple factors who may act synchronously. Notably,
M5 Model Trees rank constantly one in two user check-ins in the top 50 predicted venues.
• We study the performance of features and classifiers over time, finding that prediction performance is higher over lunchtime and weekdays. In all cases, a strong temporal periodicity
is apparent in the prediction task, but features based on the geographic distance amongst
places are achieving higher scores at nighttime, unlike other features. This shows how thefactors driving human mobility can vary over time and highlights the importance adding
spatio-temporal context to the prediction task.

Poster 1

4

We envision a number of applications for which our work may prove beneficial, including mobile
recommendations and content delivery for mobile Web users. In particular, the decomposition ofusers’ movements into a set of distinct features is central to our approach and evaluation strategy,
as one of the principal goals of this work is to understand how mobile users are driven by different
factors in their choice of places. Insights on this process can be offered both for research scientists
in human mobility and urban planning and for mobile application developers. In the following
sections, we begin by analyzing the Foursquare check-in dataset. Subsequently we define twelve
mobility prediction features and evaluate them individually and in a supervised learning framework.
We close with remarks on related work and conclusions.

References[1] D. Ashbrook and T. Starner. Using GPS to Learn Significant Locations and Predict Movement AcrossMultiple Users. Journal of Personal and Ubiquitous Computing, 7(5):275–286, October 2003.
[2] M. C. González, C. A. Hidalgo, and A.-L. Barabasi. Understanding individual human mobility patterns.
Nature, 453(7196):779–782, 2008.
[3] V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. Collaborative location and activity recommendations
with GPS history data. In Proceedings of WWW ’10, 2010.

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Extracting People’s Stays from Cellular Network Data
Mori Kurokawa,1 Takafumi Watanabe,1 Shigeki Muramatsu,1Hiroshi Kanasugi,2 Yoshihide Sekimoto,2 and Ryosuke Shibasaki2
1

KDDI R&D Laboratories Inc., Saitama, Japan
2
The University of Tokyo, Chiba, Japan
mo-kurokawa@kddilabs.jp, tk-watanabe@kddilabs.jp, mura@kddilabs.jp,
yok@csis.u-tokyo.ac.jp, sekimoto@csis.u-tokyo.ac.jp, shiba@csis.u-tokyo.ac.jp
Abstract— Behavior analysis of mobile phone users has increased
in importance for mobile phone carriers in accordance with mobile
phone traffic increases. Call detail records (CDRs) known as
cellular network data are an important data source in the inspectionof human behavior. We propose a novel method of segmenting
CDR time sequence into staying and moving via Mean-shift to findstay time intervals from CDRs. We also show accuracy throughexperiments using CDRs and web diaries obtained from theexperimental survey conducted for 25 days with 162 examinees.

17 km

Keywords-Call detail records, segmentation, extracting stays

I.

INTRODUCTION

17 km
Figure 1. An example of candidate stay locations
We propose a novel method of segmenting CDR time
sequence and extracting people’s stays, which is robust withregard to the spatiotemporal sparseness of CDRs. We apply
Mean-shift to location data on time segments to findcandidate stay time intervals and locations, and then apply
Mean-shift again to cluster candidate stay time intervals andlocations. The proposed method is assessed by CDRs andweb diaries obtained from an experimental survey conducted
for 25 days with 162 examinees.
The remainder of this paper is as follows. In section II,
we explain the proposed method. In section IV, we present
the results of the experiment. In section IV, we describe
related studies based on CDRs. Finally, in section V, weconclude this paper and suggest further directions forresearch.

5

II.

Poster 1

Mobile phone traffic has been increasing due to thewidespread use of smartphones and smartphone applications.
Mobile phone carriers have found it more important tocontinuously survey actual human behavior of using mobile
phones in order to plan resource deployment and capacity.
Call detail records (CDRs) known as cellular network
data are an important data source in the inspection of humanbehavior. CDRs are recorded on cellular network equipment,
mainly to detect and resolve problems with the equipment.
Each record is generated through a call, sending of a text
message, or browsing the Internet via a mobile phone, andthe record contains a timestamp and location related toconnected base stations.
Global positioning systems (GPS) are an alternative wayof acquiring locations. Although embedding GPS
functionality into mobile phones has become common,
issues of power consumption and indoor positioning remain.
Furthermore, some applications using GPS functionality
send additional traffic to cellular networks.
We utilize CDRs to survey actual human behavior. The
difficulty of analyzing CDRs lies in its spatiotemporal
resolution. The temporal resolution of CDRs differs for each
person according to the mobile phone communication pattern.
Additionally, the spatial resolution of CDRs is lower thanthat of existing positioning devices, such as GPS, becausesuch devices depend on the coverage area of base stations.
In this paper, we focus especially on segmenting CDRtime sequence into staying and moving because we believe
there is a significant difference in mobile phone usage
between the behaviors.

PROPOSED METHOD

In this section, we describe our method of extracting
stays. Our method consists of two steps: extracting candidate
stays and clustering. Figure 1 shows an example of candidate
stay locations for one examinee (represented by whitemarkers) and the density estimated using Kernel density
estimation via Gaussian Kernel with a bandwidth equal to 1km.
A. Extracting candidate stays
Here, we describe how to extract candidate stay timeintervals and locations from a personal location history {p(t):

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Poster 1 // Mobilityt = t0,…,tn}, where p(t) represents a two-dimensional spatial
point at time t in CDRs.
We assume that the connected base station locations are
distributed around the stay locations while staying. In otherwords, the mode of spatial distribution of base station
locations is supposed to be close to the stay location. Thus,
we try to seek the mode of spatial distribution of connectedbase stations in each time interval.
We apply a sliding time window to extract stay timeintervals because we do not know the stay time intervals apriori. We employed a sliding time window with width T andshift S, where the first time segment is [t0, t0+T), the second
time segment is [t0 + S, t0 + T + S), and so on.
We apply Mean-shift [1] to each time segment which
includes no less than N points. Mean-shift is a popular modeseeking method, and each iteration calculates the meanm(p(t)) of nearby points of p(t) within the window
determined by the window function K(.), shifts p(t) to m(p(t)),
and then proceeds to the next iteration.
∑∑

Poster 1

5

As for the window function K(.), we use a rectangular
window defined as follows: K(x) = 1 if ||x|| < th1, and 0
otherwise. The threshold parameter th1 corresponds to a
bandwidth of the density estimation. Convergence is checkedby evaluating the difference in the mean points ||m(p(t)) p(t)|| < th2.
Then, we determine candidate stay time intervals as a setof time segments in which the resulting mean points areconcentrated within the range of a circle with a radius of th2.
We then determine candidate stay locations as the resulting
mean points within each candidate stay time interval.
B. Clustering
Here, we describe how to cluster extracted candidate staylocations and time intervals.
1) Clustering stay locations
Extracted candidate stay locations are noisy due to few
observed points within each time segment, and thus we apply
Mean-shift with the same parameters as used in extracting
candidates again to cluster stay locations. We determine staylocations by the resulting mean points.
2) Clustering stay time intervals
We determine stay time intervals as a series of time
segments that meet the following conditions.
a)
The stay location in the first time segment and theone in the last time segment are equal.
b)
The stay location in the intermediate time segment
does not belong to the stay locations other than the staylocation determined in a) above.
III.

B. Experimental Method and Evaluation Metrics
We compared parameter settings of our method. We
compared settings of the sliding time window when th1 = 4km, th2 = 1 km, and N = 4. Additionally, we compared thethreshold parameter th1 when (T, S) = (40, 10), th2 = 1 km,
and N = 4.

We also compared our method with the following
baseline method, which was based on [2], where staylocations were determined by timestamps and locations
under the following conditions.
a)There are two or more CDR records within 24 h.
b)
Sequentially recorded positions of the base stations
are within 4 km.
c)
Duration of sequential positions under condition b)
is less than 20 minutes.
We employed F-measure of stay time intervals for an
evaluation metric. We calculated the F-measure for eachexaminee.
Let Tw be a set of estimated stay time intervals of eachexaminee and let Tc be a set of correct stay time intervals thateach examinee registered in the web diary; precision andrecall are defined as follows and F-measure is the harmonic
mean. We eliminated time intervals about which theexaminee did not register activities from time intervals forevaluation.
||
Pecision| |RecallEXPERIMENTS

In this section, we explain the experimental methods andresults with practical CDRs and web diaries from anexperimental survey.

116

A. Experimental Data
From November 28, 2011, to December 22, 2011, we
conducted an experimental survey with 184 examinees toobtain activity data that included CDRs, GPS logs, actual
activity data from a web diary and personal attributes via aquestionnaire. Examinees consented to the privacy policy
and terms of the experiment. The average number ofaccumulated CDRs in a day was 454.9 for each examinee.
That is, one telecommunication event occurred every 3
minutes. The Android application for GPS logging acquired
positions every 5 minutes and sent logs to the web server
every 15 minutes.
We prepared a web diary system for examinees toregister actual activities and to collect ground truth data. In
order to facilitate the entry of activities, the web diary system
automatically suggested some candidate stay locations thatthe system preliminarily estimated based on GPS logs. The
average number of registered stay locations was 4.6 per day.
Among 184 examinees, there were 162 examinees whose
CDRs and activity states were both available. For theexperiments described below, we used the available resultsof these 162 examinees.

F‐measure

|

|
| |

2 · Precision · Recall
Precision Recall

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Poster 1 // Mobility

Figure 2. Comparison of width of sliding time window
C. Experimental Results
Figure 2 and Figure 3 show box-plots of precision, recalland F-measure of stay time intervals. The results shows that
our methods exceeded the baseline except the case of a width
equal to 40 minutes in Figure 2 and the case of a thresholdparameter th1 equal to 2 km in Figure 3.
With regard to settings of the sliding time window,
Figure 2 showed that a width equal to 20 minutes was thebest. In that case, the F-measure of stay time intervals was
93.7% on median. The precision increased and the recall
declined with a longer width. The width of the sliding time
has the effect of increasing the sensitivity to switching
between staying and moving and increasing false positive
stays, when short. The obtained data contained a relatively
high number of points per unit time and thus the sliding time
window with a width equal to 20 minutes showed sufficientaccuracy.
The threshold parameter of Mean-shift th1 has the effectof decreasing false negative stays and increasing false
positive stays, when large. Figure 3 showed that th1 equal to
5 km was the best. In that case, the F-measure of stay timeintervals was 93.8% on median, which is nearly the same asin the case of th1 equal to 4 km.

V.

In this study, we attempted to estimate stay time intervals
using CDRs. The proposed methods are assessed by CDRs
and web diaries obtained by an experimental survey.
According to the results, the best case was the case with awidth of the sliding time window equal to 20 minutes andwith a threshold parameter of Mean-shift equal to 5 km, andin that case, the F-measure of stay time intervals was 93.8%
on median.
In the future, we would like to conduct detailed
experiments to tune our method. We would also like to try toestimate more detailed human behavior, such as stayobjectives and transportation modes.
REFERENCES
[1]
[2]

[3]

[4]

RELATED WORK
[5]

[6]

[7]

[8]

Y. Cheng, “Mean Shift, Mode Seeking, and Clustering”, IEEE Trans.
Pattern Anal. Mach. Intell, Vol. 17, No. 8, pp. 790-799, 1995
J. Liu, O. Wolfson, H. Yin, “Extracting Semantic Location from
Outdoor Positioning Systems”, Proc. of the 7th International
Conference on Mobile Data Management (MDM 2006), pp. 73, 2006
D. Ashbrook and T. Starner, “Using GPS to learn significant
locations and predict movement across multiple users”, Personal andUbiquitous Computing, Vol. 7, pp. 275-286, 2003
C. Zhou, D. Frankowski, P. Ludford, S. Shekhar, L. Terveen,“Discovering personally meaningful places: An interactive clustering
approach”, ACM Trans. Inf. Syst, Vol. 25, No. 3, 2007
P. Nurmi, and S. Bhattacharya, “Identifying meaningful places: The
non-parametric way”, Proc. of the 6th International Conference on
Pervasive Computing, pp.111-127, 2008.
S. Isaacman, R. Becker, R. Caceres, S. G. Kobourov, M. Martonosi, J.
Rowland, and A. Varshavsky, “Identifying Important Places inPeople's Lives from Cellular Network Data”, Proc. of the 9thInternational Conference on Pervasive Computing, pp. 133-151, 2011.
J. H. Kang, W. Welbourne, B. Stewart, G. Borriello, “Extracting
Places from Traces of Locations”, Mobile Computing andCommunications Review, Vol. 9, No. 3, pp. 58-68, 2005
M. A. Bayir, M. Demirbas, and N. Eagle, “Mobility profiler: A
framework for discovering mobility profiles of cell phone users”,
Proc. of the International Conference on Pervasive and Mobile
Computing, Vol. 6, No. 4, pp. 435-454, 2010

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5

While there are numerous studies related to mobility
estimation using GPS logs, some studies are applicable to
CDR-based estimation [2][3][4][5]. On the other hand, by
regarding CDRs as footprints of personal mobility, somestudies attempted to extract significant locations [6][7][8].
Among them, [6] uses Leader algorithm which is a locationbased clustering algorithm. The others apply time-based
clustering: [7] clusters points based on distance between
temporally adjacent points and filters small clusters where
little time was spent, and [8] clusters points by setting athreshold for switching counts of cell towers.

CONCLUSION AND FURTHER STUDY

Poster 1

IV.

Figure 3. Comparison of threshold parameter of Mean-shift

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Poster 1 // Mobility

Building Energy-Efficient Spatio-Temporal Mobility Profiles for Mobile Users
Kuldeep Yadav, Vinayak Naik, Amarjeet Singh
Indraprastha Institute of Information Technology, New Delhi, India
Email : {kuldeep,naik,amarjeet}@iiitd.ac.in
Abstract—Fine grained mobility information of mobile phoneusers are required for many context-aware application. Mostof research in this space uses location interfaces such as GPS
and WiFi which results in high power consumption and also
have limited availability. GPS and WiFi is not available on
many low-cost phones (popularly called as feature phones).
Also, GPS does not work indoors and WiFi infrastructure is
not widespread especially in developing countries.
In this paper, we propose a framework to find different
places visited by a person solely using GSM (Cell ID) data
and then use them to build spatio-temporal mobility profiles
for the users. Also, proposed framework incorporates data fromother location data sources (i.e. WiFi) to improve the accuracyof only Cell ID-based clustering. We have done comprehensive
evaluation of proposed algorithms on two real-world datasets
i.e. self collected dataset (16 users, 4 weeks) and Nokia MDC
dataset (45 users, 50 weeks) and found it very accurate.

Poster 1

6

I. I NTRODUCTION AND M OTIVATION
A mobility profile for a user consists of all the places
visited by her with accurate arrival and departure time information for the respective places. An accurate mobility profile
can enable many context aware applications such as locationbased notifications, targeted advertisements, content-sharing
decisions [4], pollution impact report and many others.
Mobile Phones have various kind of location interfaces such
as Global Positioning System (GPS), WiFi [3], Cell IDbased (GSM information) [2] etc which can be used forhigh resolution location tracking of users. Each of theseinterfaces differ in terms of accuracy, availability, and power
consumption. Most phones in developing countries are feature phones which have limited capabilities, e.g. they lacksensors such as Global Positioning System (GPS) and WiFi.
Due to limited capabilities, feature phones are not able to use
context aware applications which uses mobility profiles. For
smart phones too, building mobility profile using GPS and
WiFi requires continuous tracking of location which drains
the phone battery very quickly. Friedman et al [6] found thatscanning modes of WiFi and Bluetooth consume significant
power and quickly drain the battery in continuous location
sensing applications.
Researchers have worked on using GPS and WiFi data
to find places visited by the user automatically and thenbuilding accurate spatio-temporal mobility profile. Bayir et
al [2] proposed a framework which discover places using
Cell ID data in reality mining dataset but it take help of
manually tagged Cell IDs for clustering. There is a lack of118

an algorithm/framework that can discover places visited by auser using Cell ID data without human intervention/tagging.
In this paper, we propose a framework to build mobility
profiles of users using energy-efficient and widely available
location sensing frameworks i.e. GSM (Cell ID) and WiFi
(if available). Our clustering algorithm automatically learn
places visited by a user solely using Cell ID data. Evaluations on our self collected dataset 1 (Location : India, 16
users, 4 weeks of duration) showed that, framework correctly
learn places with nearly 80% accuracy when comparedto places learnt using WiFi data. Further, we develop an
algorithm which uses an initial training of WiFi data to learn
places using Cell ID data for some days and later use Cell ID
data only. With the help of WiFi training, accuracy improves
further i.e. 87% if 8 days of training is provided. Further,
we have applied same framework on a publicly available
dataset which has data of 38 users for 40 week collected
in Switzerland. In Nokia MDC datase, proposed framework
were able to find places with an accuracy of 86.06% without
any training.
II. U SER M OBILITY P ROFILING F RAMEWORK
A place is defined as a location, where the user stays for asignificant amount of time , e.g., “Home” and “Workplace”.
For building mobility profile, one of first step and challenging task is to find different places that a person visited using
raw location data and then use this place information to findarrival and departure time specific to those places. In thissection, we will present algorithms to find places using GSM
information and combination of GSM and WiFi. Also, wewill use “clusters” and “places” interchangeably from hereon.
A. Cell ID (GSM) based Mobility Profiling :Finding distinct places using only GSM information has
several challenges. Previous work [2] has shown that even if
a user stays at the same place, the Cell ID may change due
to various reasons such as network load, small time signal
fading, and inter-network (2G to 3G or vice versa) handoff.
This change in Cell ID at the same place is called as an“oscillating effect”.
Assuming that {C1 ,C2 ,C3 ,....Ck } are the distinct timeordered Cell IDs observed in a day (Step 1 of Figure 1),
we build an undirected graph, called as movement graph,
1 We will be releasing our dataset publicly very soon.

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Poster 1 // Mobility

Algorithm 1: Pseudocode of Graph Clustering Algorithm
Algorithm: Graph-based Cell Clustering Algorithm
Input: Movement Graph G(V, E) where V is set of vertices and E
is the set of edges
Output: Set of Cell ID Clusters CG
2 begin3
Rank all the edges in E into decreasing order of their weight;
4
CG = φ ;5
while (∀ek ∈ E) AND w(ek ) ≥ η do
6
if vi ∈ CGj where vi ∈ ek , i ∈ (1, 2), CGj ∈ CG then7
CGj = CGj ∪ vk 1 ∪ vk 2 ;8else
9Create new cluster CGn = vk 1 ∪ vk 2 and add it toCG ;1

Figure 1: A snapshot of different steps in mobility profilingframework
G(V, E) where ∀i∈{1,k} Ci ∈ V and there exist an edgee(Ci , Cj ) between Ci and Cj , if both of the following
conditions are satisfied:
1) Ci and Cj are contiguous in time ordered cell records
2) Time difference between start time of Cj and end timeof Ci is less than α.

11
12
13
14
15
16
17
18
19
20

while (∀vj ∈ CGk ) where CGk ∈ CG do
if (degree(vj ) ≥ η 0 ) then
CGk = CGk ∪ neighbors(vj ) ;CG0 = φ ;while (∀CGi ∈ CG) do
isExist = f alse ;while (∀CGj ∈ CG0 ) do
if (CGi ∩ CGj ) 6= φ thenCGi = CGi ∪ CGj ; isExist = true; break ;if ¬(isExist) then
Add CGi to CG0 ;23

while (∀vi ∈ V ) do
if (vi ∈/ ∃CGk ) where CGk ∈ CG0 thenCreate new cluster cn = vi and add it to CG0 ;24

return CG0 ;21
22

B. WiFi Trained Cell ID Clustering (WTCA)

C. Evaluation

GCA inherently assumes that different places in a user’s
profile will have non-overlapping sets of Cell IDs. As a

Cellular network assigns a group of cell base station ina location area with the same identifier, known as Location

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6

result, for a person visiting distinct places that are in close
proximity, e.g. a student staying in a dorm that is close to theacademic building, GCA will merge the two different places
if a common Cell ID is observed at each of the two places.
This merging effect of GCA is also observed in our collected
data as some of our users also live in campus residence. For
instance, if an user saw Cell IDs {a,b,c,d} at P1 and Cell IDs{d,e,f,g} at place P2 . Though, Cell ID d remains overlapping
across places P1 and P2 .
While such geographically close places may have overlapping Cell IDs, it is unlikely that they will have overlapping
WiFi APs. Further, not all Cell IDs will overlap acrossthe two distinct places. In the above example, we can take
into account other Cell IDs such as a and e to distinguishbetween different places. We use these two insights to extend
GCA by training it with WiFi based Cell ID clustering.
For training purpose, we take help of WiFi mobility profile
to determine corresponding Cell ID clusters, accounting for
arrival and departure time at a place. As user is likely to visit
same places again, a few days of training is sufficient to learn
conflicting Cell IDs and GCA use that input while clustering
Cell IDs. The detailed description of this algorithm is in [5].

Poster 1

As an example in Figure 1, C1 and C2 occurred contiguously and t2 -t2 ≤α, so there will be an edge between
C1 and C2 in the corresponding movement graph of theuser. Multiple edges between Ci and Cj are merged intoa single edge with weight equal to the number of edges
between Ci and Cj . α ensures that an edge occurs only
across neighboring (in time) Cell IDs and other cell records,
that may be neighboring but with a high time difference
between them due to reasons such as switching off of thephone, unavailability of the network, and loss of location
updates, are pruned. An example of movement graph createdfrom user X’s data is shown in step 2 of Figure 1.
As seen in step 3 of Figure 1, even same place Cell
IDs (i.e.{C1 ,C2 ,C3 ,C4 }) have many fluctuations among
themselves (i.e. oscillating effect) which is modeled asedge weight in the movement graph. To cluster Cell IDs,
accounting for the oscillating effect, into different places
visited by the user, we propose a three phase algorithm
as described in Algorithm 1. Graph Clustering Algorithm
takes movement graph as an input and produces Cell ID
clusters as an output, where each cluster will represent a
different place and can be used to build mobility profile(step 4 in Figure 1). The detailed description of algorithm
can be found in [5]. Graph Clustering Algorithm also takes
two parameters into account i.e. one of them is oscillation
parameter η, which measures the number of fluctuations
between a pair of Cell IDs in a day and η 0 , which measures
the number of transitions from a Cell ID to any other Cell
IDs. We will empirically derive the good value of η and η 0
in evaluation section.

10

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Poster 1

6

Poster 1 // Mobility

Area Code (LAC). One of the basic way to cluster Cell IDs
are by considering LAC information which is called as LACbased Clustering Algorithm [5], which will be used here
for comparison purpose. Using WiFi mobility profiles, weassociate Cell IDs with actual physical places which is used
as ground truth for evaluation of Cell ID based clustering
approaches, GCA and LCA. For instance, by using WiFi
mobility profiles, we discover that user stayed at a place Pi
from 9 : 00 AM to 5 : 00 PM, the Cell IDs which are seenin this time duration will form a cluster.
Using WiFi mobility profile, we find equivalent Cell ID
clusters (say CW ) from one day’s Cell ID data. For GCA,
we empirically found η and η 0 to be equal to 3 and used
it for performing all experiments related to GCA. For every
day, we define Cell ID clusters made using WiFi (groundtruth), GCA and LCA as CW , CG and CL respectively. We
further define a pair-wise comparison metrics, called Correct
Pair for our evaluation. A Cell ID pair (i.e. Ci and Cj ) is
counted as Correct Pair, if their occurrence within the sameor across different clusters in CW is reflected accordingly
in the Cell ID based approach evaluated.
Correspondingly, to evaluate GCA, we first calculate the% of Correct Pairs, out of total pairs, of Cell IDs across
CW and CG, and then take an average across differentdays to compute the final accuracy of GCA. Similar process
is followed w.r.t CW and CL and accuracy of LCA iscomputed. As shown in Figure 2, GCA produces 80.29%
correct pairs (on an average across all users and all days)
while LCA produces 70.82% correct pairs (on an average).
Errors in GCA occurred since it mistakenly merged places
that were geographically close (as discussed earlier). Ourevaluations on Nokia MDC dataset found that GCA produces 86.06% correct pairs as compared to ground truth
which was generated using GPS stay points.
With 8 days of traning, WTCA either equals or improves% of correct pairs across all users as compared to GCA
or LCA. On an average across all users and days, WTCA
produces 87.30% correct pairs as compared to GCA which
produces 80.29% correct pairs. WTCA improves upon theoverall accuracy of clustering, when compared to GCA,
since it can split merged places and put them into different
clusters, using the training data.
III. D ISCUSSION AND F UTURE W ORK
Due to large heterogeneity among mobile devices andavailable location interfaces on them, there is lack of ageneric method to build mobility profiles. Also, current location interfaces are power hungry. We proposed a framework
which uses location interfaces such as GSM to build mobility
profile. There are two main advantages of using GSM based
interface for mobility profiling, (1) It consume very lessenergy as compared to current alternatives (WiFi, GSM),(2) It is available on all programmable mobile devices
and can work in smartphones as well as feature phones.

120

Figure 2: Comparison of GCA,WTCA, and LCA w.r.t. groundtruth in our self collected dataset. On an average, WTCA produce more correct pairs (87.30%) than GCA (80.29%) and LCA(70.82%)

Our evaluations on two big diverse datasets confirmed that
proposed framework can work in real-world while giving
good accuracy.
We are working to create a cloud service of proposed
framework so that mobile application developers can utilize
these algorithms to build user’s mobility profiles, some ofthem are following:1) LifeMap: Logging and visualization of all the places
that a user visits with low energy. It can be further extended with location-based reminders, meetingscheduling, logging encounters with other users.
2) MobiShare : It is a system designed to support
opportunistic content search and sharing with limited
Internet (2G) connection. It uses proposed framework
to predict encounters between a pair of users [5].
3) Unity : A social collaborative content downloading
application which predicts the time at which a groupof friends will be co-located.
Finally, we believe that the work described in this paper
is an interesting direction and in future, we envision mobile applications/systems using our energy-efficient mobility
profiling algorithms to deliver context based information tomobile users.
R EFERENCES[1] Barabi A., Understanding individual human mobility patterns,” Nature 453, 779-782.
[2] Bayir M.A. et al, Discovering spatiotemporal mobility profilesof cellphone users, WOWMOM 2009.
[3] Vu et al, Jyotish: Constructive approach for context predictions of people movement from joint Wifi/Bluetooth trace,
PerCom 2011.
[4] Talipov et al, Content Sharing Over Smartphone-based Delaytolerant Networks, IEEE TMC,2012.
[5] Yadav et al. MobiShare: Cloud-enabled Opprtunistic Content
Sharing among Mobile Peers, IIITD-TR-2012-009.
[6] Friedman et al, On Power and Throughput Tradeoffs of WiFi
and Bluetooth in Smartphones, INFOCOM 2011.

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Poster 1 // Mobility

Properties of the Positioning Error of Cell Phone
Trajectories
Michael Ulm, Peter Widhalm
Austrian Institute of Technology, Mobility Department
michael.ulm@ait.ac.at, peter.widhalm@ait.ac.at

Abstract
Cell phone data has become a major source for the scientific community to analyze humanmobility behaviour. A typical task in this context is to estimate motion trajectories based onsequences of antenna locations. In this paper we examine the question: given that a mobile
device is logged in to an antenna with known position and characteristics, what can be said
about the probability distribution of the position of the device?
We examine three datasets of trajectory data, where the location was determined using a
GPS logger. Based on this data, we give estimates on the distribution of the locations of theusers given that they are logged in to one antenna. Finally, we evaluate different strategies
for estimating user location from the antenna position.

Figure 1. GPS Positions of mobile devices, logged in to Antenna 52024

Poster 1

7

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Privacy in Computational Social Science: An overview
Riccardo Pietri
Technical University ofDenmarks110913@student.dtu.dk

Arkadiusz Stopczynski
Technical University ofDenmarkarks@dtu.dk

ABSTRACTIn recent years the amount of information collected about human beings has increased dramatically. Users store their datain online social networks or collect it for self-tracking purposes; our environments sense and record us with embedded
RFIDs, WiFi access points, traffic monitoring. Concurrently,
we are seeing an increase in dedicated Computational Social
Science (CSS) studies, where researchers collect data on human behavior with unprecedented resolution and scale, providing important insights into human nature. The more powerful the data collection (bitrate, number of users, duration,etc) and analysis, the more important privacy becomes.

Poster 1

8

One serious threat to CSS as a field is a ‘privacy catastrophe’, where the participants’ rights or data are dramaticallycompromised as a result of malevolent parties or gross misunderstandings between researchers and experiment participants. Such a ‘catastrophe’ carries the potential lead to a lossof public confidence and hence a decreased ability to carryout future experiments and research. In order to avoid such anegative scenario, we urge a renewed focus on privacy, users’rights, and data security.
Here, we argue that the current state-of-the-art on these privacy related issues can be improved significantly. For example, study purposes are often not made explicit, ‘informed
consent’ is difficult to define in many cases, security and sharing protocols are only partially disclosed, etc. Below we provide a survey of the work related to privacy issues in CSS
studies. In particular, focus on topics of informed consent,
anonymization, and data security. We also include our reflections on the key problems and provide some recommendations for future work.
POLICY AND INFORMED CONSENTFor CSS studies informed consent consists of an agreement
between researchers and the data producer (user, participant)by which the latter agrees to understand the procedures applied to his data (collection, transmission, storing, sharing,
and analysis). Users need to comprehend through informed
consent which information will be collected, who will haveaccess to them, what is the incentive, and for which purposes
the data is used [1].
We begin by noting a scarcity of available examples and bestpractices for informed consent in the literature; the majority of the reviewed studies do not mention any consent procedures [2]–[7]. While this does not necessarily imply that

122

Sune LehmannTechnical University ofDenmarksljo@dtu.dk

experiment participants did not consent to the data collection procedures, it is difficult to produce comparisons andcreate useful models applicable for future studies. In cases
when the procedure for achieving informed consent was reported, the agreement was carried out using forms, similar
to http://green-way.cs.illinois.edu/GreenGPS_files/
ConsentForm.pdf, containing users’ rights [8]–[10]. However, simply stating all the information does not guaranteethat informed consent is implemented sufficiently: years of
EULAs and other lengthy legal agreements show that mostindividuals tend to blindly accept form that appear before
them and to unconditionally trust the validity of the default
settings which are perceived as authoritative [11]. Such an
all-or-nothing approach does not allow the user to select subsets of the permissions, making it only possible to either participate in the study fully or not at all [12].
One improvement would be to allow users to gradually grant
permission over time. The efficacy of this approach is notclear: some studies have shown that users understand the issues about security and privacy more clearly, when individual
requests are presented gradually [13]; others argue that too
many warnings distract users [12], [14]. The literature contains only few analyses of whether the consenting participantsare, in fact, informed. Evaluating how people understand theirprivacy conditions can be done by conducting feedback sessions throughout the duration of the experiment [8]. If wewish to increase the focus on participants’ rights, approaches
such as this should be the norm, not the exception. One option
is for participants to be more closely involved in shaping theprivacy policies. This view gains support from studies showing that people do not realize smartphone sensing capabilities
nor the consequences of privacy decisions [15], [16]. Additionally, we suggest to carefully consider special cases where
the participants may not have the competence or authority to
fully understand the privacy aspects [17], [18].
Since so little is understood about the precise nature of conclusions that may be drawn from highly detailed data collection, we need to constantly work to improve informed consent
as our understanding continues to grow. We recommend thatthe paradigm should move from a one-time static agreement
to dynamic consent management [19]. Furthermore, the concerns related to privacy are context-specific and vary acrossdifferent cultures [20]. The need for a way to let the users
easily understand and specify which kinds of data they would
like to share and under what conditions was foreseen in 2002
for the Internet purposes by the W3C group, where the aim
was to define a Platform for Privacy Preferences (P3P) (suspended in 2006), in 2003 by Kagal et al. [21], and also in82005 by Friedman et al. [1], all shaping dynamic models for
informed consent. Recent studies such as [22] have worked todesign machine learning algorithms that automatically infer
policies based on user similarities. These frameworks can beseen as a mixture of recommendation systems and collaborative policy tools where default privacy settings are suggested
to the user and then modified over time.
ANONYMIZATION

The datasets created in CSS studies often contain highly sensitive information about the users. Their privacy needs tobe protected either for the purpose of disclosing the data to
public scrutiny [23]–[25] or to guarantee that users can not
abuse the the provided services [6], [26], [27]. This canbe achieved by various anonymization techniques, where the
Personal Identifiable Information (PII) is removed from thedata. Making data anonymous (or de-identified) decreases thedata utility by reducing resolution or introducing noise [28].

DATA SECURITY

The security of the collected data, although necessary for ensuring privacy goals, is rarely discussed in the studies, with
only the most obvious issues addressed [2], [9], [10], [35].
Because it is easier to deploy for small-scale experiments,
the centralized architecture has been the preferred solution in
the surveyed frameworks [2]–[5], [8], [10], [26]. If the main
server is subject of denial-of-service attacks, it can not guarantee the availability of the service [30]. This might result inthe smartphones having to retain a more information locallywith consequential privacy risks. More importantly, however,single server can compromise all user data in an instant.
A complementary approach is to deploy a decentralized architecture where algorithms can run with inputs coming fromdifferent nodes. One possible way to achieve this is to upload data from the mobile device, not to a single server, but
onto personal datasets [36], like a personal home computer, or
cloud-based virtual machines, lowering users’ concern aboutsystems that centralize data. On one hand, users would feel—and possibly be—more in control of their personal data, having this electronic aliases. On the other hand, part of the security of the data would inevitably rely on the user.
Given the amount of sensitive information present on mobile devices, it is our recommendation that social science researchers should team up with engineers to develop robust
portable applications in order to avoid possible privacy violations [37] due to viruses and malware. Some of the studied
frameworks reduce the time that the sensed raw information
is kept on the phone. For example, in [2] the data records
are discarded once the classification task has been performed.
Since most of the sensing applications use an opportunist way
of uploading the data to the servers they might still store quite
a lot of data temporarily on external memory [4]. This introduces a security threat if the device does not procure an encrypted file-system by default. A possible way to tackle thisproblem is employing frameworks like Funf, an open-source
sensing platform for Android devices developed in [3] andalso used in the SensibleDTU project. Funf provides the developers with a reliable storing system that encrypts the files
before moving them to special archives on the SD card. Thenan automatic process uploads what archived keeping a temporary (encrypted) backup. This layer of defense also contrasts

123

8

Another frequent method employed for anonymization is ensuring k-anonymity [29] for a published database. This technique ensures that is not possible to distinguish a particular user from at least k − 1 people in the same dataset.
AnonySense - a general framework for opportunistic task reports [30] - and the LDCC platform both create k-anonymous
different-sized tiles to preserve users’ location privacy, outputting a geographic region containing at least k − 1 people
instead of single user’s location. Nevertheless, later studies
have shown how this property is not well suited as a privacy
metric [31]: first, Machanavajjhala et al. tryed to solve kanonymity weakneses with a different privacy notion called
l-diversity [32]; then, Li et al. proposed a third metric, tcloseness, arguing the necessity and the efficacy of l-diversity[33]. Although these two techniques seem to overcome mostof the previous limitations, they have not been deployed inany practical framework to date.

A more complex option is to employ homomorphic encryption, an advanced cryptographic technique that allows an entity to perform some operations on a ciphertext which corresponds to others on the plaintext. It allows users to sendanonymous information (the ciphertext) to a central serverwhich can compute meaningful operations. The results aresent back to the users who can finally decrypt and obtain theplaintext. In VPriv (a privacy-aware toll system) a central
server first collects anonymous tickets produced when cars
exit the highways; then by homomorphic transformations it
computes the total amount that each driver has to pay at theend of the month [26]. Another similar example is HICCUPS
which is a health system that keeps patient records encrypted,
but at the same time gives doctors access to aggregated information through homomorphic encryptions [34].

Poster 1

The most common practice in the data anonymization field
is to one-way hash all the PII such as MAC addresses, network identifiers, logs, names, etc. This breaks the direct
link between a user in given dataset to other, possibly public
datasets (e.g. Facebook profile). There are two main methods
to achieve this. The first - used in the LDCC study (a genericdata collector framework developed for the Lausanne Data
Collection Campaign [8])—is to upload raw data from thesmartphone to an intermediate proxy server where algorithms
hash the collected information. Once anonymized, the data
can be transferred to a second server which researcher haveaccess to. We argue that a less vulnerable option is to hash thedata directly on the smartphones and then upload the result
the final server for being analyzed. This alternative design has
been selected for many MIT studies [3]–[5] and for the SensibleDTU project (http://www.sensible.dtu.dk/). In principle, hashing does not reduce the quality of the data (provided
that it is consistent within the dataset), but it makes easier
to control what data is collected about the user and where itcomes from. However, it does not guarantee that users cannot
be identified in the dataset. Finally, some types of raw data like audio samples - can be obfuscated directly on the phone
without losing the usability before uploading [8].

Poster 1 // Mobility

8Poster 1 // Mobility

unintended disclosures of information if the smartphone getsstolen/is lost. In this case the last resort is to provide a remote
access to delete the data off the phone.

19. P. Shabajee. Informed consent on the semantic web-issues forinteraction and interface designers. In 3rd International Semantic Web
User Interaction Workshop.¡ http://swui. semanticweb.
org/swui06/papers/Shabajee/Shabajee. pdf¿(retrieved 15.11. 10), 2006.

REFERENCES

20. M. Li, K. Sampigethaya, L. Huang, and R. Poovendran. Swing & swap:
user-centric approaches towards maximizing location privacy. In
Proceedings of the 5th ACM workshop on Privacy in electronic society,pages 19–28. ACM, 2006.

1. B. Friedman, P. Lin, and J.K. Miller. Informed consent by design.
Security and Usability, pages 495–521, 2005.
2. E. Miluzzo, N.D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi,
S.B. Eisenman, X. Zheng, and A.T. Campbell. Sensing meets mobile
social networks: the design, implementation and evaluation of thecenceme application. In Proceedings of the 6th ACM conference on
Embedded network sensor systems, pages 337–350. ACM, 2008.
3. N. Aharony, W. Pan, C. Ip, I. Khayal, and A. Pentland. Social fmri:
Investigating and shaping social mechanisms in the real world.
Pervasive and Mobile Computing, 2011.
4. A. Madan, K. Farrahi, D. Gatica-Perez, and A. Pentland. Pervasive
sensing to model political opinions in face-to-face networks. Pervasive
Computing, pages 214–231, 2011.
5. A. Madan, S.T. Moturu, D. Lazer, and A.S. Pentland. Social sensing:obesity, unhealthy eating and exercise in face-to-face networks. In
Wireless Health 2010, pages 104–110. ACM, 2010.
6. P. Ypodimatopoulos and A. Lippman. ’follow me’: a web-based,location-sharing architecture for large, indoor environments. In
Proceedings of the 19th international conference on World wide web,pages 1375–1378. ACM, 2010.
7. N. Eagle, A.S. Pentland, and D. Lazer. Inferring friendship network
structure by using mobile phone data. Proceedings of the National
Academy of Sciences, 106(36):15274–15278, 2009.
8. N. Kiukkonen, J. Blom, O. Dousse, D. Gatica-Perez, and J. Laurila.
Towards rich mobile phone datasets: Lausanne data collection
campaign. Proc. ICPS, Berlin, 2010.
9. R.K. Ganti, N. Pham, H. Ahmadi, S. Nangia, and T.F. Abdelzaher.
Greengps: A participatory sensing fuel-efficient maps application. In
Proceedings of the 8th international conference on Mobile systems,
applications, and services, pages 151–164. ACM, 2010.

10. D. Kotz, S. Avancha, and A. Baxi. A privacy framework for mobile
health and home-care systems. In Proceedings of the first ACM
workshop on Security and privacy in medical and home-care systems,
pages 1–12. ACM, 2009.

Poster 1

8

11. R. Böhme and S. Köpsell. Trained to accept?: A field experiment on
consent dialogs. In Proceedings of the 28th international conference on
Human factors in computing systems, pages 2403–2406. ACM, 2010.
12. A.P. Felt, K. Greenwood, and D. Wagner. The effectiveness ofapplication permissions. In Proc. of the USENIX Conference on Web
Application Development, 2011.
13. S. Egelman, A.P. Felt, and D. Wagner. Choice architecture andsmartphone privacy: Theres a price for that. In Workshop on the
Economics of Information Security (WEIS), 2012.
14. A. Kapadia, T. Henderson, J. Fielding, and D. Kotz. Virtual walls:
Protecting digital privacy in pervasive environments. Pervasive
Computing, pages 162–179, 2007.

21. L. Kagal, T. Finin, and A. Joshi. A policy based approach to security
for the semantic web. The Semantic Web-ISWC 2003, pages 402–418,
2003.
22. E. Toch, N.M. Sadeh, and J. Hong. Generating default privacy policies
for online social networks. In Proceedings of the 28th of theinternational conference extended abstracts on Human factors incomputing systems, pages 4243–4248. ACM, 2010.
23. A. Narayanan and V. Shmatikov. Robust de-anonymization of large
sparse datasets. In Security and Privacy, 2008. SP 2008. IEEE
Symposium on, pages 111–125. IEEE, 2008.
24. M. Barbaro, T. Zeller, and S. Hansell. A face is exposed for aol
searcher no. 4417749. New York Times, 9(2008):8For, 2006.
25. L. Sweeney. Simple demographics often identify people uniquely.
Health (San Francisco), pages 1–34, 2000.
26. R.A. Popa, H. Balakrishnan, and A. Blumberg. Vpriv: protecting
privacy in location-based vehicular services. In Proceedings of the 18th
conference on USENIX security symposium, pages 335–350. USENIX
Association, 2009.
27. B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu,
E. Shih, H. Balakrishnan, and S. Madden. Cartel: a distributed mobile
sensor computing system. In Proceedings of the 4th international
conference on Embedded networked sensor systems, pages 125–138.
ACM, 2006.
28. T. Li and N. Li. On the tradeoff between privacy and utility in data
publishing. In Proceedings of the 15th ACM SIGKDD international
conference on Knowledge discovery and data mining, pages 517–526.
ACM, 2009.
29. L. Sweeney. k-anonymity: A model for protecting privacy.
International Journal of Uncertainty, Fuzziness and Knowledge-Based
Systems, 10(05):557–570, 2002.
30. C. Cornelius, A. Kapadia, D. Kotz, D. Peebles, M. Shin, and
N. Triandopoulos. Anonysense: privacy-aware people-centric sensing.
In Proceedings of the 6th international conference on Mobile systems,
applications, and services, pages 211–224. ACM, 2008.
31. R. Shokri, G. Theodorakopoulos, J. Le Boudec, and J. Hubaux.
Quantifying location privacy. In Security and Privacy (SP), 2011 IEEE
Symposium on, pages 247–262. IEEE, 2011.
32. A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam.
l-diversity: Privacy beyond k-anonymity. ACM Transactions on
Knowledge Discovery from Data (TKDD), 1(1):3, 2007.
33. N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond
k-anonymity and l-diversity. In Data Engineering, 2007. ICDE 2007.
IEEE 23rd International Conference on, pages 106–115. IEEE, 2007.

15. P. Klasnja, S. Consolvo, T. Choudhury, R. Beckwith, and J. Hightower.
Exploring privacy concerns about personal sensing. Pervasive
Computing, pages 176–183, 2009.

34. A.D. Molina, M. Salajegheh, and K. Fu. Hiccups: health information
collaborative collection using privacy and security. In Proceedings ofthe first ACM workshop on Security and privacy in medical andhome-care systems, pages 21–30. ACM, 2009.

16. A.P. Felt, E. Ha, S. Egelman, A. Haney, E. Chin, and D. Wagner.
Android permissions: User attention, comprehension, and behavior. In
Proceedings of the Eighth Symposium on Usable Privacy and Security,
page 3. ACM, 2012.

35. E. Miluzzo, C.T. Cornelius, A. Ramaswamy, T. Choudhury, Z. Liu, andA.T. Campbell. Darwin phones: the evolution of sensing and inference
on mobile phones. In Proceedings of the 8th international conference
on Mobile systems, applications, and services, pages 5–20. ACM, 2010.

17. M.K. Underwood, L.H. Rosen, D. More, S.E. Ehrenreich, and J.K.
Gentsch. The blackberry project: Capturing the content of adolescents’text messaging. Developmental psychology, 48(2):295, 2012.

36. J.I. Hong and J.A. Landay. An architecture for privacy-sensitive
ubiquitous computing. In Proceedings of the 2nd international
conference on Mobile systems, applications, and services, pages
177–189. ACM, 2004.

18. S. Vosoughi, M.S. Goodwin, B. Washabaugh, and D. Roy. A portableaudio/video recorder for longitudinal study of child development. In
Proceedings of the 14th ACM international conference on Multimodal
interaction, pages 193–200. ACM, 2012.

124

37. Y. Altshuler, N. Aharony, Y. Elovici, A. Pentland, and M. Cebrian.
Stealing reality: when criminals become data scientists (or vice versa).
Security and Privacy in Social Networks, pages 133–151, 2011.

9

Poster 1 // Mobility

Participant Behavior in P HONE L AB
Anandatirtha Nandugudi, Anudipa Maiti, Fatih Bulut, Sonali Batra, Taeyeon Ki
Geoffrey Challen, Murat Demirbas, Steven Y. Ko, Tevfik Kosar, and Chunming Qiao
Department of Computer Science and Engineering
University at Buffalo, The State University of New York
team@phone-lab.org
1 Introduction

Affiliation
Freshman
Sophomore
JuniorSeniorThis abstract examines the behavior of the participantsin P HONE L AB, a public smartphone testbed being developed at SUNY Buffalo. Currently consisting of 191
participants using Nexus S 4G smartphones, P HONE L AB aims to provide a combination of unique featuresdesirable for smartphone experimentation. This abstract
briefly introduces P HONE L AB and presents some of theearly results of a usage measurement study conductedwith 115 participants.
1.1

64
33
1
1

Masters
PhD
Faculty/Staff
None

5
53
29
5

GenderFemale

51

Male

140

30–34
35–39
40–49
50–59
60+

15
6
13
7
1

Age
Under 18
18–19
20–21
22–24
25–29

P HONE L AB Overview

P HONE L AB is designed to provide the following features
necessary for smartphone research—open access, scale,power, realism, locality, and relevance:

12
74
12
22
29

Table 1: Demographic breakdown of 191 P HONE L AB participants.
Date ranges are inclusive.

of new experiments and install the experimental applications directly from the Play Store. On the other hand,
P HONE L AB platform-level experiments are distributed
through the P HONE L AB control software that runs oneach participant’s phone; this control software is capableof updating platform components, e.g., libraries and kernel modules. To the best of our knowledge, P HONE L AB
is the only testbed that provides all the above features
together.
1.2

P HONE L AB Demographics

1

125

9

Currently, P HONE L AB consists of 191 participants.
Roughly half of our participants are first- and secondyear undergraduates, a quarter PhD students, and a fifth
faculty, staff and other professionals. However, males
greatly outnumber females, and the young outnumber the
middle-aged and older, both unrepresentative features wewill try and rectify in the future years. For management
reasons we limited participation to people with a SUNY
Buffalo affiliation except for several exceptions: a local
reporter, a technology writer, and an international rockstar. Table 1 summarizes our demographics.

P HONE L AB application-level experiments are distributed through the Play Store; participants are notified

Poster 1• Open Access: After the initial approval process, P HONE L AB allows any researcher to deploy
their research prototype on the participants’ smartphones.
• Scale: By 2014, P HONE L AB will grow to 700 participants already incentivized and recruited to participate in experiments; participants of P HONE L AB
receive discounted voice, data, and messaging.
• Power: By utilizing the Android open-source
smartphone platform,
P HONE L AB
allowsapplication-level experiments as well as platformlevel, i.e., the OS kernel, middleware, and libraries.
• Realism: Participants use the phones as their primary device.
• Locality: Most participants live in Buffalo near
SUNY campuses, enabling research requiring
device-to-device interaction.
• Relevance: P HONE L AB allows researchers to stop
relying on out-of-date datasets. Instead, new data
can be collected in the most appropriate way for theexperiment.

9

Poster 1 // Mobility

Travel3G
Wifi

Fig. 1: 3G to Wifi transition locations. The map indicates that there are several common areas where network hand-offs occur.

100%% of Total

80

Display (45.7%)
Idle Data (19.3%)
Active CPU (11.2%)
Idle Wifi (9.4%)
Idle CPU (6.3%)
Sleep (4.2%)
Phone (3.1%)
Active Data (0.4%)
GPS (0.2%)
Bluetooth (0.0%)

60
40
20
TestbedFig. 2: Power usage by component. The large bar at left shows an aggregated breakdown for all participants. The participant bars are scaled
against the participant with the most energy usage.

Poster 1

92

Participant Behavior

We have conducted a usage measurement study with 115
participants over 21 days. For this purpose, we havedeveloped a measurement application that collects all
salient features of smartphone usage: networking, mobility, power consumption, and application usage.
This section presents some of the early results of thisstudy. We show the network transition behavior between
3G and WiFi first and the battery and charging behavior
next.
2.1

Mobile Network Transitions

Mobile devices like smartphones move through a complex network environment. Providing the illusion of
seamless connectivity requires negotiating hand-offs
both between Wifi access points and between Wifi and
3G radios. We were interested in observing hand-offs
between 3G (provided by Sprint, P HONE L AB’s operational partner) and Wifi and found many in the dataset
collected by our usage experiment. Since the Android ConnectivityService frequently switches
network interfaces for exploration purposes, we have defined a transition as two one-minute or longer sessions
on different interfaces separated by less than one minute.

126

We further limit ourselves to cases where we received alocation update during the transition.
Figure 1 plots the location of transitions that occurredon or near SUNY North Campus. We notice that manycluster in expected locations: near the entrance and exitsof buildings where participants are likely to be movingfrom campus Wifi to 3G.
2.2

Energy Breakdown

A single-day component-by-component breakdown isshown in Figure 2. Our results are similar to those reported by a previous smaller-scale study [4], and indicate that mobile data (labeled as “Idle data” and “Activedata” depending on the state), the screen, and CPU usage
are the main sources of smartphone power consumption.
The per-participant bars also show a great deal of variation, with differences in both the amount and the breakdown of energy consumed by each participant.
One supposedly power-hungry component that hasless of an impact than we had expected is the GPS.
This is particularly surprising given the large amount oflocation-monitoring work motivated by GPS power consumption. One of several factors may be at work. First,
the Android platform estimates the GPS chipset current

9Poster 1 // Mobility

consumption at 50 mA. This number is used by the standard “Fuel Gauge” battery monitor and by our calculations. However, it is lower than the data sheet for the
Broadcom 4751 GPS receiver [1] and may represent a
best-case average. Still, even if the GPS current consumption is off by as much as a factor of five, it does not
represent a significant contribution. Other hypotheses
are that Android network location is providing location
with sufficient accuracy for many applications, eliminating the need for GPS, or participants and applications
may simply be conscious of GPS power consumption
and taking steps to control it.

Participants

Charge Level
Opportunistic Needed
Opportunistic Unneeded
Habitual

2.3 Opportunistic Charging
One way that users work around the battery limitationsof their smartphone devices is by finding new times andplaces to charge their phones: plugging in at their desk
at work, in the car during their commute, or at home before a long night out. We refer to these charging sessions as opportunistic to distinguish them from habitualnightly charging. Assuming that many smartphone users
encounter plug points throughout the day, engaging inopportunistic charging becomes an additional sign of energy awareness, and understanding opportunistic charging becomes necessary to improving energy management
on mobile devices. Others have analyzed this behavior
before [2, 3] and our goal is to examine the battery charging behavior of P HONE L AB partipants.

5
10
15 20
Time (hours after midnight)

Fig. 3: Patterns of opportunistic charging. Many users perform opportunistic charging multiple times during the day.

3

Conclusions

This abstract introduced P HONE L AB, a new large-scale
programmable smartphone testbed operated by SUNY
Buffalo and presented the participant behavior in termsof network transitions, energy, and charging.

References[1] Broadcom BCM4751 Integrated Monolithic GPS Receiver.
http://www.broadcom.com/products/GPS/
GPS-Silicon-Solutions/BCM4751.
[2] N. Banerjee, A. Rahmati, M. D. Corner, S. Rollins, and L. Zhong.
Users and Batteries: Interactions and Adaptive Energy Management in Mobile Systems. In UbiComp, 2007.
[3] A. Rahmati, A. Qian, and L. Zhong. Understanding HumanBattery Interaction on Mobile Phones. In MobileHCI, 2007.
[4] A. Shye, B. Scholbrock, and G. Memik. Into the Wild: Studying Real User Activity Patterns to Guide Power Optimizations for
Mobile Architectures. In MICRO, 2009.

127

9

Opportunistic charging may be a response to an anticipated need for more smartphone battery power: thestudent who plugs her smartphone in for a brief charge
before a night out. Our data also allowed us to examine how many of these opportunistic charging sessions
were necessary to bridge the gap to the next full charge.
We found that 24 of the 96 (25%) of the opportunistic
charges we observed were necessary. We believe that
this indicates that participants have responded to theirsmartphones’ battery limitations by engaging in conservative charging behavior, grabbing power whenever possible even if they do not anticipate needing it later.

0

Poster 1

Figure 3 shows that many users engage in opportunistic charging. We define a charging session as opportunistic if is long enough to not be spurious (over 10 minutes) but does not bring the battery to a fully-charged
state, indicating that the user disconnected the device
before charging could finish. For a representative day
during our experiment, of the 245 charging sessions weobserved that day, 96 (39%) were opportunistic by thisdefinition. 50 of 95 active participants engaged in opportunistic charging at some point during our experiment an
average of once per day.

10

Poster 1 // Mobility

Exploring the Use of Urban Greenspace
through Cellular Network Activity
Ramón Cáceres† , James Rowland† , Christopher Small‡ , Simon Urbanek††

AT&T Labs‡

Columbia University

Poster 1

10{ramon, jrr, urbanek}@research.att.com

Knowing the extent to which people make use of urban greenspace is central to our understanding of urban
ecology. While the type and location of greenspace inurban areas is well documented, we lack accurate, quantitative measures of when and where people occupy it.
Cellular telephone networks can provide a wealth of
information about the use of urban greenspace. Mobile
phones are carried by a large portion of the population
and are used throughout the day. A measure of how
many phones are active in which geographic areas canthus serve as a proxy for human density in those areas.
In this work, we use anonymous records of cellular
network activity to quantify the spatiotemporal patterns of human density within a major US metropolitan
area. More specifically, we use counts of voice calls andtext messages handled by cellular antennas as a measureof how many people are in the geographic areas coveredby those antennas. Because of the close-knit spacing ofantennas in urban areas, variations in these counts canshed light on the use of individual green spaces.
We aim to characterize how the density of network
activity changes over time, and how these density patterns relate to greenspace and microclimate. By aggregating activity into density maps at different timesof day, week, and season, we hope to enhance our understanding of when people occupy different types ofgreenspace. This paper presents the vision and someearly results of this effort.

1.

Finally, we sum the voice calls and text messages toarrive at a single measure of cellular network activity
that we term call volume. Similarly, we use call density
to denote call volume per geographic area, and treat
call density as a proxy for human density.
Our current dataset spans the six months between
February 1 and July 31, 2011. It contains one record
per minute for more than 12,000 sectors, yielding morethan 3 billion call-volume samples. We are currently
gathering data for a full year.
We have been careful to preserve privacy throughout
this work. In particular, this study uses only anonymous and aggregate data. There is no personally identifying information in the data records described above.

2.

TESSELLATIONOur cellular network data gives us estimates of human activity levels, but we need a way to assign that
activity to geographic areas. Voronoi tessellation has
been used to associate spatial regions with cellular towers [4, 12]. However, basing the tessellation only on
tower locations results in coarse regions, and therefore
coarse assignments of activity to geographic areas.
We have developed an algorithm that performs a finergrained tessellation by making use of antenna directionsin addition to tower locations. Figure 1 illustrates theresult of our refined tessellation based on sector azimuths, with added edges drawn as dashed lines. Making use of the azimuth information clearly improves the
granularity. For the New York metropolitan area, themedian area of a Voronoi region resulting from the extended tessellation is roughly one quarter of the medianarea resulting from the regular tessellation.

DATASETWe have gathered from a major US communications
service provider a dataset of anonymous cellular network activity in the New York metro area. We identified
the set of ZIP codes within 50 miles of downtown Manhattan, then obtained a list of cellular antennas thatwere active in those ZIP codes during our study. We
grouped into a sector the set of antennas that resideon the same cellular tower and that point in the same
compass direction, or azimuth. For each of those sectors
and for each minute of each day, we gathered counts of
how many new voice calls and how many text messages
were handled by the antennas in that sector.

3.

EOF ANALYSIS

In this study, we seek to quantify the spatiotemporal
patterns of call volumes in order to infer the spatiotemporal distribution of people in the New York metro
area. We can then analyze these patterns in the context of the spatial distribution of greenspace and temporal variations in weather conditions. We determine thegreenspace distribution using vegetation maps derived
1

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csmall@columbia.edu

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Poster 1 // Mobility

so that EOFs represent temporal patterns and PCs represent spatial weights. We consider daily, weekly, andseasonal trends that result from deterministic processes
such as commuting, as well as higher-frequency day-today variability presumably related to ambient environmental conditions and isolated transient events. Additional details of the approach are given by [11].
Our EOF analysis is mathematically related to themethods used in [2] and [7] to analyze cellular network
data. However, we use the EOFs to identify and remove the dominant temporal periodicities in the data—
thereby revealing any non-periodic spatial patterns related to greenspace and temporal patterns related toweather. In addition, we are experimenting with the
combined use of EOF analyses and linear mixture models as described by [11].

Figure 1: Voronoi tessellation for a sample 60km2 area. Red crosses denote cellular tower
locations. Taking into account antennas andtheir directions produces finer-grained estimatesof coverage areas.

4.

2

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10

We are continuing to refine our analysis approach andapply it to our data. However, we can already see relevant patterns of human behavior emerge from preliminary analysis of selected subsets of the data.
As an example of a seasonal change in human activity
around greenspace, we compared Saturday-afternoon
call density in central Manhattan between February 12
and July 9, 2011. We summed the per-minute call volumes between 2pm and 3pm for each sector within thisarea, then normalized by the sector area to produce
density maps for each date. Figure 2 shows these twomaps and their difference, along with a satellite image
that highlights greenspace in the same area. From winter to summer, we find that density increases in thegreenspace of southern Central Park, and decreases in
the residential areas of the Upper East and West Sides.
Our analysis strategy for the complete New York metroarea is based on the identification of spatiotemporal
regularities and anomalies. We will use EOF analysis
to quantify the spatial form of the dominant daily andweekly cycles associated with commuter migration, as
well as any seasonal components that emerge in the loworder dimensions. Once identified, we will remove thesecomponents by inverse transformation of the remaining
dimensions to produce a spatiotemporal representationof any anomalies that are distinct from the dominantperiodicities. We can then directly compare the spatial
components of these anomalies to maps of greenspace
and thermal microclimate. We can likewise compare thetemporal components of the anomalies to time series of
air temperature, precipitation, and humidity, in order toquantify whatever relationships may exist between theresidual call volumes and the spatiotemporal variations
in microclimate and ambient environmental conditions.
Please see our longer paper [1] for additional details.

Poster 1

from visible and infrared satellite imagery [9, 10]. We
capture weather variations using data from a regional
network of weather stations.
One objective of this coanalysis is to quantify the relationship(s) between outdoor ambient environmental
conditions and the spatiotemporal distribution of people within the urban area. A primary challenge in thisanalysis is to distinguish between indoor and outdoor
activity. A related challenge is to distinguish between
regular patterns of activity (e.g. the dominant daily
and weekly cycles) and the variations in these patterns
that may be related to environmental conditions (e.g.
indoors on cold days, outdoors on temperate days).
We will approach both of these challenges by mapping deviations from regular patterns as anomalies intime and space. We will accomplish this mapping using
Empirical Orthogonal Function (EOF) analysis, a tool
commonly used to quantify spatiotemporal patterns inmeteorology and oceanography [13]. EOF analysis is aform of Principal Component (PC) analysis.
We will treat the call volume data as instantaneous
spatial snapshots of call volumes, then analyse the spatiotemporal patterns in these time series of call volume maps. Our approach is similar to how PC analysis is used to reduce the dimensionality of multispectral imagery in remote sensing applications (e.g., [3, 5,8]). Because variables in high-dimensional data are often correlated, PC transforms provide an efficient lowdimensional projection of the uncorrelated componentsof the data. The same property applies to temporaldimensions.
Generally, EOFs are spatial patterns intended to represent spatially continuous modes of variability of physical processes, while the PCs are the weights representing
the temporal contribution of the corresponding spatial
patterns [6, 13]. In this study, we reverse the convention

EARLY RESULTS

10

Poster 1 // Mobility(a) Density: Feb 12, 2011, 2-3pm(b) Density: July 9, 2011, 2-3pm(c) Density change: July minus Feb(d) Landsat 5: July 7, 2011, 11am

Figure 2: Spatiotemporal change in Saturday afternoon call density for central Manhattan. From
winter to summer, call density increases in the greenspace of Central Park, but decreases in residential
areas on the Upper East and West Sides. The visible-infrared satellite image shows parks and othergreenspace as shades of green.

Poster 1

10

5.

REFERENCES

Amsterdam, 1988.
[7] J. Reades, F. Calabrese, and C. Ratti.
Eigenplaces: Analysing cities using the space-time
structure of the mobile phone network.
Environment and Planning B: Planning and
Design, 36:824–836, 2009.
[8] A. Singh and A. Harrison. Standardized principal
components. International Journal of Remote
Sensing, 6:883–896, 1985.
[9] C. Small. Estimation of urban vegetation
abundance by spectral mixture analysis.
International Journal of Remote Sensing,
22:1305–1334, 2001.
[10] C. Small. High spatial resolution spectral mixture
analysis of urban reflectance. Remote Sensing ofEnvironment, 88:170–186, 2003.
[11] C. Small. Spatio-temporal dimensionality andcharacterization of multitemporal imagery. To
appear in Remote Sensing of Environment, 2012.
[12] V. Soto and E. Frias-Martinez. Robust land use
characterization of urban landscapes using cell
phone data. In Workshop on Pervasive Urban
Applications, 2011.
[13] H. von Storch and F. Zwiers. Statistical Analysis
in Climate Research. Cambridge University Press,
Cambridge, UK, 1999.

[1] R. Cáceres, J. Rowland, C. Small, and
S. Urbanek. Exploring the use of urban
greenspace through cellular network activity. In
2nd Workshop on Pervasive Urban Applications(PURBA), 2012.
[2] F. Calabrese, F. Pereira, G. DiLorenzo, L. Liu,and C. Ratti. The geography of taste: analyzingcell-phone mobility and social events. In
International Conference on Pervasive
Computing, 2010.
[3] A. Green, M. Berman, P. Switzer, and M. Craig.
A transformation for ordering mutispectral datain terms of image quality with implications fornoise removal. IEEE Transactions on Geoscienceand Remote Sensing, 26:65–74, 1988.
[4] T. Horanont and R. Shibasaki. An
implementation of mobile sensing for large-scaleurban monitoring. In Workshop on Urban,
Community, and Social Applications of Networked
Sensing Systems, 2008.
[5] J. Lee, A. Woodyatt, and M. Berman.
Enhancement of high spectral resolution remotesensing data by a noise-adjusted principal
components tranform. IEEE Transactions on
Geoscience and Remote Sensing, 28:295–304,
1990.
[6] R. Preisendorffer. Principal component analysis inmeteorology and oceanography. Elsevier,
3

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Poster 1 // Mobility

Citizen as a Sensor for the SmartCity: The Barcelona
Urban Mobility Use-caseDaniel Villatoro, Marc Pous, Jetzabel Serna, Francesco Ronzano and Marc Torrent-Moreno
Fundació Barcelona Digital Centre Tecnològic (BDigital)
Barcelona, Spain{dvillatoro,mpous,jserna,fronzano,mtorrent}@bdigital.org
ABSTRACT
Recently, and with the penetration of the smart city paradigm,
urban mobility is becoming an active research area. Moreover, the explosion of mobile technology enables the transformation of citizens into active and passive sensors increasing the amount of information of our scope of study: thecity. The usage of citizens as sensors results in a low-cost
infrastructureless framework that not only profits from themobile facet of humans but also from their common sense labelling contextual information such as anomalous situations
or venue categories. These aspects are treated in two different scenarios specific for the city of Barcelona: one thatallows us to obtain information about the state of the public
transportation network and the other that detects clustersof activity within the urban environment.

1. INTRODUCTIONURBAN MACRO VISION THROUGH SOCIAL NETWORKS

Because of its popularity, geo-positioning capabilities andpenetration of its mobile app1 , we opted to use Twitter as
the first source of information. This social sensor provides us
with information (in the form of geolocalized tweets sharedthrough users mobile devices) to observe and detect alterations of several parameters such as areas of activity, temporal patterns or mobility routes, which define the Urban
Chronotype of the city. In order to obtain another dimension of information, we enrich the information obtained fromTwitter with information from Foursquare. This combination results in a fully crowdsourced sensor where the metainformation provided through “tweets” is augmented withthat of “check-ins”.
The processes of tweet-collection, foursquare cross-referencing
and analysis presented certain technical barriers that havebeen solved with the development of our Urban Sensing Platform. This platform ensures the acquisition in near real-timeof all the tweets, without the requirement of any private
partnership with Twitter as commonly done by other researchers, allowing an agile deployment in any system. The
obtained geopositioned tweets are enriched with Foursquare
1
According to an study released by Semiocast in January
2013, 74% of the tweets are generated using a mobile device.

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11

0

2.

Poster 1

Researchers from different scientific areas have worried
about the dynamics of urban environments, specifically focusing of how humans transit the city [2, 3, 1].
Dedicated sensors have been installed in cities to capturesome data and try to understand the behaviour of such complex system, determined by the aggregation of the individual
decisions.
Moreover, in recent years with the explosion of mobile
technologies citizens have been given the capability to continuously share information anytime anywhere. This ubiquitous and continuous sensing capability combined with thehumans’ “common sense” transforms any device-holder ina potential proactive intelligent sensor, which extends theclassical continuous-sensing sensors installed in a certain location with an specific scope.
In this extended abstract we propose two complementary
ways to exploit the human sensing capabilities in order tounderstand their location in the urban environments, andtheir mobility patterns within it (where do citizens go andhow they go there). Namely, the macro vision obtained
through the analysis of proactive citizen-sensed information
retrieved from social media sites (in this paper Twitter) allows us to understand a wide range of aspects of the city
(because of the free text accompanied with GPS metain-

formation) although generated by a specific segment of the
population. Consequently this macro vision provides us withintuitions of the mobility patterns of the city (captured withconsecutive geopositioned tweets). In order to obtain deeper
insights and representative information arises the necessityof a dedicated sensor able to acquire more accurate and representative information on this matter. This link with themicro vision led us to develop Commutio, a participatory
sensing mobile app targetted for commuters. Commutio
data will provide us with real-time access to the load of
the public transportation network. This information is essential (1) to have a complete overview of the public transpotration system dynamics, (2) recommend alternative andmore efficient trips trips for individuals, (3) and provide realtime information of unexpected incidents and alternatives
for afected commuters.
In this contribution we describe the barriers encounteredwhen deploying our solution with the goal of understanding
urban processes such as the identification of transitory areasof activity and mobility patterns in the public transportation
network (PTN).

11

Poster 1 // Mobility

4000
3500

All
GeolocatedNumber of Users

3000
2500
2000
1500
1000
500
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Day(a) User Distribution: During the MWC2012 we detectedthat the number of Twitter users providing the specific locations of their posted tweets was significantly incremented.
80
LocalsTourists
Unknown

70

Users %

60
50
40
30
20
10
0
0 1 2

3 4 5 6 7

8 9 10 11 12 13 14 15 16 17 18 19 20 21
Day

Poster 1

11(b) User Origins: The distribution of the origins of the users
changed along the three weeks of the experiments, changing
the number of tourists with respect to the locals.

(c) Tourist Behaviour with Foursquare Augmented
Info: We can quantify that during the MWC2012 (Week
1) certain venue categories activity are significantly altered,specifically those that relate directly to such type of event.
Figure 1: Participatory Sensing during MWC2012
through Twitter and Foursquare.

132

data, obtaining a distribution of venue categories. Moreover, it is important to understand the relationship of our
citizen-based sensors with the city to better understand thesamples they generate. For such task, our platform has beenenriched with a semantic-intelligence module that contextualize the geographical origin of users, obtained from their
Twitter profile. Categorizing users as locals or foreigners
with respect to the city of study allows for a detailed behavioural pattern analysis differentiating their urban habits.
We performed an experiment during the expected although
exceptional situation of the celebration of the Mobile World
Congress (MWC) in Barcelona (whose main results are presented in Fig. 1). Our knowledge about this specific event
allowed us to compare the average urban chronotype of thecity (in the normal state without external influences), withthat obtained during the event, allowing us to observe different behavioural patterns within the city. The empiricalresults obtained have given us the possibility to mine andcompare the behavioural patterns of the city in its normal
state and during the event: the number of sensed users increases during the event as it can be seen in Fig. 1(a); on
the other han, we can infer that these users attending theevent are tourists as Fig. 1(b) shows; and finally, Fig. 1(c)shows how the types of venues change accordingly to thetype of event in the city, affecting mainly proffesional, traveland food venues. Substantial differences have been observedin the areas of activity of the city after applying geospatial
data mining techniques (geo-clustering), thus providing afirst proof of validity of our urban social sensing approach.
For a more descriptive explanation of this experiment andfurther experimental results we refer the reader to [4].

3.

URBAN MICRO VISION USING CITIZENSAS SENSORS

However, profiting from the information proactively sharedby the citizens in social media applications only gives us access to a restricted subset of the potential information that
represents the Urban Chronotype of the city: a partial understanding of the location of the users can be made remaining still unclear how people get to those places. This is where
the macro vision of the social sensed connects with the micronecessity of undertanding how people transit the city, specifically, what the transit dynamics of the public transportation
network are? Unfortunately, the transportation company’s
existing infrastructure cannot provide specific real-time information about the number and location of users within
the network: in cities such Barcelona it is only controlled
the access stations of travellers, but not the exits neither
the load of the vehicles or the transportation lines. Our vision assumes that citizens in the network (through the usageof their mobile devices) can provide with this information ina crowdsourced way. However social media applications (asthe previously used) do not provide the incentives for users
to continuously share their position within the network.
For such reasons, and to obtain more accurate real-time
information about the near real-time state of the PTN, wehave created an ad-hoc sensor in the form of a app (shownin Fig.2) that profits from the sensing capabilities of travellers and, while open, continuously share the citizen geoposition (latitude, longitude and timestamp) with our centralized server. However, we faced two problems: why will users
open an app that sacrifices part of their battery life? and11

Poster 1 // Mobility
how can we obtain enough critical mass of users to makethis crowdsourced sensor work? To solve both questions wehave created an upper layer using gamification techniques.
Thanks to a competition-based real-time trivia-like game,
we incentivate our users to play with our app: the game
constitutes an individual incentive to open the app, and thereal-time competition with other users and friends ensuresthe virality and the long-term usage of the app.
With the real-time data gathered from the commuters ofBarcelona we are able to understand the load of the PTN,
obtaining also a commuter behavioural profile in the system
in various situations (e.g. demonstrations, or unexpected
incidents). Our modelization using a dynamic network data
structure allows us to apply graph theory algorithms to optimize the behaviour of the system in several aspects andeventually build a route recommender system. Amongst thechallenges we have faced in this project, it is interesting toemphasize how the lack of GPS-signal in underground transportations have been solved using a Case-Based Reasoning
algorithm as shown in Fig. 3.

Figure 2: Commutio: Through this mobile application we are able to track Barcelona’s public transportation network travellers in real-time.

4.

CONCLUSIONS

These two specific applications have given us different but
complementing visions of the urban flows: (1) a macro vision, captured from the proactively shared social media contents, allowing us to understand the location of a specific segment of the population; and a micro vision, with dedicatedapps sensing a specific subject such as the PTN, allowing us
to obtain complete information of how citizens transit thecity.
Nowadays, the concept of smart city appears with a topdown vision where citizens receive the services implemented
by the city and the government. As we proposed, the proper
usage of the collected information would provide a more
complete picture (micro and macro) with near real-time information about the target city allowing for a significant
improvement of the urban performance, with no specific investment on hardware infrastructure.

Acknowledgements

5.

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11

Figure 3: One Metro-commuter Trace: The red
icons in the map are the position provided by theclient application to the server and the red lines are
the connections between two consecutive geolocations. These positions belong to a user that was
travelling in the metro line specified with the blueline. We can easily notice how divergent are the acquired data from that of the real user location. Withthe usage of the adequately trained Case-Based Reasoning algorithm, our framework will be able to associate the captured GPS positions to the stations
or edges associated to each of them.

REFERENCES[1] C. F. Daganzo. Fundamentals of Transportation andTraffic Operations. Elsevier Science, 1997.
[2] K. Lynch. The Image of the City. The MIT Press, June
1960.
[3] C. E. Mandl. Evaluation and optimization of urban
public transportation networks. European Journal ofOperational Research, 5(6):396–404, December 1980.
[4] D. Villatoro, J. Serna, V. Rodrı́guez, andM. Torrent-Moreno. The tweetbeat of the city:
Microblogging used for discovering behavioural patterns
during the mwc2012. In J. Nin and D. Villatoro,
editors, Citizen in Sensor Networks, volume 7685 ofLecture Notes in Computer Science, pages 43–56.
Springer Berlin Heidelberg, 2013.

Poster 1

This work has been completed with the support of ACC1Ó,the Catalan Agency to promote applied research and innovation; and by the Spanish Centre for Development of Industrial Technology under the INNPRONTA program, projectIPT-20111006, “CIUDAD2020”.

12

Poster 1 // Mobility

Trace-based Analysis to Identify Popular Locations Visited in
Mobile Ad hoc Networks

Aarti Munjal

Department of Biostatistics andInformatics,
Colorado School of Public Health
Aurora, CO 80045
Aarti.munjal@ucdenver.edu

Thyago Mota

Tracy Camp

Department of ElectricalEngineering andComputer Science, Colorado Schoolof Mines
Golden, CO 80401

Department of ElectricalEngineering andComputer Science, Colorado Schoolof Mines
Golden, CO 80401

tmota@mines.edu

tcamp@mines.edu

In this work, we propose to extend our analysis done in [CITE1] and explore theimpact of different input values used in the analysis methods. Specifically, we plan
to analyze the Nokia MDC dataset and investigate the following research questions:
1. Song et al. in [CITE2] analyzed a large dataset of human movement traces
and concluded that while moving, humans revisit a location based on theprobability given by Equation 1 in [CITE1]. We note that the values ofparameters a and b (Equation 1 in [CITE1]) obtained from the Nokia MDC’12
dataset are different than the values of a and b obtained in [CITE2]. In
particular, a and b values extracted from Nokia dataset are 0.98 and 0.0008,
respectively, while a and b obtained from [CITE2] are 0.6 and 0.21,
respectively. We note that these values are based on the dataset as well as
the definition used for a “previously visited” location. In [CITE2], the authors
track the cell phone towers a user is connected to during his movement.
Thus, if a user reconnects to a previously visited cell phone tower, it isrecorded as a visit to a “previously visited” location. In [CITE1], however, we
define X as a “previously visited” location if the user is within 5 meters of alocation Y previously visited by him. In other words, the scale in our work in

Poster 1

12

Research shows that human movement patterns are predictable to some extent[CITE2]. Finding patterns in human mobility is of interest for several reasons, e.g.,knowledge of contacts made by humans carrying mobile devices can be used in
making efficient routing decisions. The movement trace collected from a real
scenario reveals several interesting statistical features present in human mobility.
We note, however, that the extracted statistical patterns are based on the dataset
used for analysis. As a result, observations made from one dataset may not beapplicable to another scenario. Our recent work towards understanding humanmobility can be found in [CITE1, CITE3]. Our submission titled “Exploring Social
Interactions via Multi-Modal Learning” presents results obtained from the analysis of
the Nokia Mobile Data Challenge (MDC) dataset. As part of Nokia MDC’12, we werereleased a dataset for 38 participants. (See [CITE1] for a detailed description of ouranalysis of the Nokia MDC’12 dataset.)

134

12

Poster 1 // Mobility[CITE1] is much more precise than in [CITE2]. In this work, we plan to extend
our analysis by defining a “previously visited” location similar to [CITE2]. We
then propose to compare our results with the results obtained in [CITE2] tovalidate the results in [CITE2] and our methodology in [CITE1].
2. In [CITE1], we analyzed the MDC’12 dataset for 38 users to explore social
interactions among the users. (See [CITE1] for details.) During analysis, wefound that users that belong to the same social group have a strong
correlation between their visited locations. In other words, we found asignificant overlap between the locations visited by users that showed strongsocial ties. In this work, we are interested in investigating the type of social
interaction between a pair of nodes. In the MDC'12 dataset, for example, user
94 visits 1.75% of the locations visited by user 23; however, user 23 visits
67.44% of the locations visited by user 94. With this observation, we may
conclude that user 94 does not move as often as user 23. For example,
perhaps user 94 is a bank teller and user 23 is visiting the bank as acustomer; in other words, perhaps user 94 only has a few locations that are
visited (e.g., bank, store, home) and user 23 visits two of them (e.g., bank andstore). Similarly, user 2 contacts user 75 more frequently than contacts user
51 and user 68. Thus, based on the contact information (e.g., frequency ofcontacts, duration of contacts, etc.) and/or the amount of overlap between
the locations visited by the users, we are interested in exploring the type ofsocial interactions that may exist between a pair of users.
3. We plan to simulate the Nokia MDC scenario on our mobility model called
SMOOTH [CITE3] and then compare the movement traces generated by the
scenario simulated on SMOOTH with the real (dataset) traces for theirseveral statistical features listed in [CITE3].

Poster 1[CITE1] A. Munjal, T. Mota, and T. Camp. Exploring Social Interactions via Multi- Modal
Learning. Proceedings of Mobile Data Challenge by Nokia Workshop, 2012.
[CITE2] C. Song, T. Koren, P. Wang, and .- . araba si. Modelling the scaling properties ofhuman mobility. Nature Physics, pages 818–823, 2010.
simple way to model human

12[CITE3] A. Munjal, T. Camp, and W. C. Navidi, “SMOOTH:
walks,” Proceedings of MSWiM, pp.351-360, 2011.

135

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Poster2

Network137

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Poster 2 // Network

1

Simulation of Epidemic Spread using Cell-Phone
Call Data: H1N1 Case Study
Enrique Frı́as-Martı́nez ‡ , Graham Williamson # , Vanessa Frı́as-Martı́nez ‡ ,‡#Telefónica Research, Madrid – Spain

School of Computer Science , University College Dublin – Ireland{efm,graham,vanessa}@tid.es

Abstract—Ubiquitous computing technologies enable capturing
large amounts of human behavioral data. The digital footprints
computed from these datasets provide information for the study ofsocial dynamics, including social networks and mobility patterns,
key elements for the effective modeling of virus spreading.
Traditional epidemiologic models do not consider individual
information and hence have limited ability to capture the inherent
complexity of the disease spreading process. In this paper we
propose an agent-based system that uses social interactions andindividual mobility patterns extracted from call detail records
to accurately model virus spreading. The proposed approach isapplied to study the 2009 H1N1 outbreak in Mexico.

Poster 2

14

I.

I NTRODUCTION

Traditional epidemiological approaches base their solutions
on using differential equations that divide the population into
subgroups based on socio-economic and demographic characteristics. Although these models fail to capture the complexity
and individuality of human behavior, they have been extremely
successful in guiding and designing public health policies. The
recent adoption of agent-based modeling (ABM) approaches has
allowed to capture individual human behavior and its inherent
fuzziness by representing every person as a software agent.
The adoption of ubiquitous computing technologies by verylarge portions of the population (e.g. GPS devices, ubiquitous
cellular networks or geolocated services) has enabled capturing
large scale human behavioral data. These datasets contain information that is critical to accurately model the spread of avirus, such as human mobility patterns or the social network
characteristics of each individual
In this paper, we propose an ABM system designed to simulate
virus spreading using agents that are characterized by their individual mobility patterns and social networks as extracted fromcell phone records. We carry out simulations with data collectedduring the 2009 Mexican H1N1 outbreak and measure the impactthat government calls had on the mobility of individuals and thesubsequent effect on the spread of the H1N1 virus. An extended
description of our system and its evaluation using the 2009 H1N1
outbreak can be found in [1].
We have used call detail records(CDR) to compute: (1) amobility user model and (2) a social user model that identifies
each agent’s social network. This approach of capturing andmodeling agent behavior from CDRs sets our work apart fromothers because: (1) we model agents from real individual dataand not from census or surveys; and (2) we capture behavioral
adaptations to the spread of the disease.

138

II.

ABM OF V IRUS S PREADING USING CDR S

We propose an ABM architecture with two main components:(1) a set of agents that are modeled using the information
contained in call detail records; and (2) a discrete event simulator(DES) that simulates the virus propagation over time based on
the agents’ models.

Agent Generation
We define the behavior of each agent with three models: (1) amobility model extracted from CDR data; (2) a social networkmodel computed from CDR data; and (3) a disease model thatcharacterizes the progression of the disease through its various
states in each agent.
The mobility model provides the position (at the BTS level)
where the agent is at each moment in time. This model is usedby the event simulation process to predict the location of eachagent at each simulation step. We propose a mobility model that
divides each day into a set S of i non-overlapping equal-length
time slots. The mobility model of agent n, Mn , is defined as:
Mn = {Mnwday , Mnwend } ={{Mnwday,0 , .., Mnwday,i }, {Mnwend,0 , .., Mnwend,i }}} ∀j ∈ B, . . . , pwday,i,j
Mnwday,i = {pwday,i,0nnwend,i,0
wend,i} ∀j ∈ B, . . . , pwend,i,j
= {pn
Mn
n

∀i ∈ S(1)
where B is the number of BTS towers that give coverage
to a geographic area; and pwday,i,j
and pwend,i,jdenote thennprobability that agent n may be found at BTS j in timeslot iduring a weekday or weekend, respectively. Given a CDR dataset,the mobility model is built by associating with each time slot ithe set of BTSs where each person has been observed during
weekdays or weekends during the period of time under study.
Note that each individual might be assigned to more than one
BTS in a specific time slot i. In this case, the event simulator
assigns the position of the tower with the highest probability,i.e., the BTS that the individual has used the most over the
training period. Since people tend to show monotonic behaviors,
an average person typically has very few BTS towers in his/hermobility model.
We compute the social network of an agent as the set ofindividuals with whom there was at least one reciprocal contact
during the time period under study:
Sn = {Snwday , Snwend } =Snwday = {list of reciprocal contacts in wdays}
Snwend = {list of reciprocal contacts in wends}

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Poster 2 // Network2

where Snwday is the social network during the weekdays andSnwends the social network during the weekends. Given the social
networks of an agent, we assume that the probability of being
physically close to another agent will be higher if that otheragent is part of its social network. To model physical proximity
within a BTS coverage area we define two probabilities: (1) p1
is the probability that two agents that are in the same BTS at
the same time of the simulation and are part of the same social
network are physically close enough for the virus to be possiblytransmitted; and (2) p2 the probability that two agents that arein the same BTS and are not in the same social network at thesame moment in time are physically close for the virus to bepossibly transmitted.
The disease model captures the progression of the disease ineach agent. We follow a similar approach to that of Barret et al.
[2] and define a disease model that is composed of two parts: thebetween hosts transmission model and the within host progression
model. In Figure 1 we observe that the between hosts transmission
model happens at a probability pi and represents the probability
that an agent goes from Susceptible to Exposed. The within hostmodel represents the evolution from Exposed to Infective in agiven period of time ǫ, and from Infected to Removed in periodof time β.

measure to reduce the transmission of the disease. The actions
implemented by the Mexican government to control the H1N1
flu outbreak of April 2009 constitute an illustrative example. The
actions consisted in three stages: (a) a medical alert issued on
Thursday, April 16th, which was triggered by the diagnosis of
the first H1N1 flu cases; followed by (b) the closing of schools anduniversities, enacted from Monday April 27th through Thursday,
April 30th; and (c) the suspension of all non essential activities,
implemented from Friday, May 1st to Tuesday, May 5th.
PeriodprefluDate Range
1/1 – 16/4

alert

17/4 – 26/4

closed

27/4 – 31/4

shutdown
reopened

1/5 – 5/5
6/5 – 31/5

Description
Period before any H1N1 case has been discovered. Agents will move largely unaffected
and showing their usual mobility patterns.
April 16th - Diagnosis of H1N1 cases andmedical alert triggered the following day.
People may be reacting to the news andmodify their usual mobility patterns.
Schools and Universities closed. Normal behavior disrupted as people change their usual
mobility patterns.
Closure of all non-essential activities.
Restrictions lifted.

TABLE I.

T IME PERIODS OF STUDY.

Experimental Setting

Fig. 1. Disease Model composed of Between hosts and Within hosts models.

Discrete Event Simulator

E XPERIMENTS : T HE CASE OF H1N1 IN M EXICO

We collected CDRs from January 1st to May 31st of 2009 of
one of the most affected Mexican cities. The dataset contains 1
billion CDRs and 2.4 million unique cell phone numbers. Each cell
phone number is associated with one agent and we compute themobility, social and disease models for both the baseline and theintervention scenarios. The mobility models are computed with agranularity of one hour. Following Song et al. [3], we only consider
the agents that (1) are assigned to at least two BTSs; (2) havea minimum average calling rate of 0.25 calls/hour; and (3) haveat least 20% of the hourly time slots filled. These requirements
narrow down the final number of agents to 25, 000.
We also build the social network models for the baseline andthe intervention scenarios. As part of these models, we needed todefine values for the contact probabilities p1 and p2 . In order tocompute their values, we make use of the work by Cruz-Pacheco
et al. [4], where the authors examined the effect of the government
intervention measures on the epidemic spread using SIR. Details
can be found in [1]. Our search determined that the best values
were p1 = 0.9 and p2 = 0.1.

139

14

III.

In case of a pandemic, the World Health Organization (WHO)recommends authoritative bodies to consider the suspension ofactivities in educational, government and business units as a

Generation of Agents

Poster 2

The Discrete Event Simulator (DES) simulates the evolution of
the epidemic spreading for a set of agents over a specific periodof time. To bootstrap the epidemic spreading, we assume that an
initial agent is Infected and starts the transmission. Specifically,
the DES does the following consecutive tasks: (1) It identifies
the geographical area (BTS) where each agent is located using
the mobility model; (2) it identifies the geographical areas where
there is, at least, one Infective agent; (3) for each Infective agent,
it takes all the Susceptible agents of his social network that arelocated in the same geographical area (BTS coverage) and applies
probability p1 that they will be physically close for the virus
to be transmitted; (4) for each Infective agent and the rest of
Susceptible agents included in its geographical area (not part ofits social network), it applies the probability p2 that they will bephysically close for the virus to be transmitted; (5) for the set of
agents physically close obtained from steps (3) and (4), it applies
the between hosts transmission probability to go from Susceptible
to Exposed; (6) for the agents that are already in the Exposed or
Infective state of the disease model, it applies the corresponding
progression; and at last (7) it removes from the simulation allagents that have reached the Removed state.

In order to examine the impact of government restrictions
we evaluate changes in the mobility and disease models in fivechronological periods. Table I presents the timeline under study.
We generate agents (with corresponding mobility and social
models) for each of these time periods. In order to measure
behavioral changes, we define two scenarios: a baseline scenario
and an intervention scenario. The baseline scenario is built using
the mobility and social models obtained during the pre-flu period,
when individuals show normal – not affected by medical alerts –mobility behavior. The intervention scenario considers the models
that are built with data from the alert, closed, shutdown andreopened periods. In this case, depending on the moment of thesimulation, the DES will jump from one set of models to thenext. The evaluation is done by comparing the results obtained byboth scenarios. Due to the inherent randomness of the spreading
process we run each scenario 10 times and average the results.

14

Poster 2 // Network

3

To build each agent’s disease model, we use the parameters reported in the literature related to the H1N1 outbreak: R0 = 1.75
(Estimated Reproduction number), ǫ = 26.4−1 hours (Expected
duration latent period), β = 60−1 hours, (Expected duration
infectious period) and ρ = 34−1 hours (Expected time before
infecting another agent). We initialize our simulations with one
infected agent on April 17th (the first day a case was detected)[4] and run the simulation for 30 days.

Analysis of the Results
In this Section, we compare the results of the intervention
scenario with the baseline scenario from a mobility perspective
and from a disease model perspective.
Agent Mobility: In order to measure the changes in mobility
due to government mandates, we computed for each scenario thepercentage of agents that moved from one BTS coverage area
to another one at each step of the simulation (1 step = 1 hour).
Figure 2 shows the results.
Both the baseline and the intervention plots show similar
cyclical changes. However, there are a number of importantdifferences. There is a significant decrease in mobility on April
27th , precisely when the alert period finishes and the close period
starts. This decrease in mobility continues until the beginning of
the shutdown period. On May 1st and throughout the shutdown
period, there is an even larger decrease in mobility (< 30%) thatlasts until all restrictions are lifted on May 6th . To sum up, during
the intervention scenario there is a reduction in the mobility of
the agents of 10% during the alert period and of up to 30%
during the closing and shutdown periods, when compared to thebaseline. These differences in the agents’ mobility disappear once
the reopen period starts (from May 6th onwards).

managed to reduce the number of infected agents when comparedto the baseline scenario. The largest difference between both
sigmoid functions takes place during the peak of the epidemic,
with approximately a 10% less of susceptible agents in theintervention scenario.
Figure 4 shows the percentage of infected agents during thesimulation for both scenarios. We observe that the peak of theepidemic in the intervention scenario happens later in time thanin the baseline, and has a smaller absolute value. The reduction
in mobility and the closure of public buildings delayed the peakof the epidemic by 40 hours. Also, in our simulations, the total
number of infected agents was reduced by 10% in the peak of
the epidemic in the intervention scenario when compared to thebaseline scenario. These results are in agreement with the ones
reported in [4].

Fig. 3. Fraction of susceptible agents in the population over time. These
curves are an average of all simulation runs.

Fig. 4. Fraction of infected agents over time. These curves are an averageof all simulation runs.

R EFERENCES

Poster 2

14

Fig. 2. Percentage of agents that move between BTSs for the intervention
and baseline scenarios. The temporal granularity is 1 hour.

Disease Transmission: In this section we study the evolutionof the disease focusing on the number of susceptible and infectedagents in the intervention and baseline simulations.
Figure 3 displays the percentage of the population that is in
the susceptible stage of the disease model for a specific date andtime. Results are shown for both the intervention and the baseline
scenarios. We observe that at the beginning of the simulation
all agents are susceptible of being infected. As time passes, theevolution of susceptible agents is described by a sigmoid function.
The number of susceptible agents decreases faster in the baseline
scenario, i.e. the number of infected agents grows faster thanin the intervention scenario. This result supports the hypothesisthat the government measures taken during the intervention
scenario had an impact on the agents’ mobility patterns and hence

140

[1]

[2]

[3]

[4]

E. Frias-Martinez, G. Williamson, and V. Frias-Martinez. An agentbased model of epidemic spread using human mobility and social
network information. In Privacy, security, risk and trust (passat), 2011
ieee third international conference on and 2011 ieee third international
conference on social computing (socialcom), pages 57–64. IEEE, 2011.
Christopher L. Barrett, Keith R. Bisset, Stephen G. Eubank, Xizhou
Feng, and Madhav V. Marathe. EpiSimdemics: an efficient algorithm
for simulating the spread of infectious disease over large realisticsocial networks. In SC’08: Proceedings of the 2008 ACM/IEEE
conference on Supercomputing, 2008.
Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László
Barabási. Limits of predictability in human mobility. Science,
327(5968):1018–21, 2010.
G Cruz-Pacheco, L Duran, L Esteva, A A Minzoni, M LópezCervantes, P Panayotaros, A Ahued Ortega, and I Villaseñor Ruı́z.
Modelling of the influenza A(H1N1)V outbreak in Mexico City,
April-May 2009, with control sanitary measures. Eurosurveillance,
14(26), 2009.

15

Poster 2 // Network

Network-behavior Dynamics in a Medium Size Mobile Phone Network
Cheng Wang

David S. Hachen

University of California, Irvine

Unviersity of Notre Dame

Department of Public Health

Department of Sociology

chengw5@uci.edu

dhachen@nd.edu

Abstract
The stochastic actor-based model (SABM) has been widely used to explore the coevolution of friendship networks and behavior dynamics simultaneously in recent
years. This study analyzes data from the Netsense project, a longitudinal survey andsmartphone data collection of 196 college students over 4 semesters since 2011. And wefind that selection effects play an important and consistent role in creating peer clusters
with similar political tastes in a dynamic context, but friends were not found to influencepolitical tastes, net of other sociodemographic, network, or family factors. In case ofobesity contagion both selection and influence effect are detected: friendships are more
likely to be found among those with similar body mass index (BMI) and friendships also
drive those involving in them to have similar BMIs. We also study the network-behavior

Poster 2

dynamics of academic achievement, smoking behavior, drinking behavior, depressive
feeling, and religious preferences.

15

141

16

Poster 2 // Network

On Information Propagation in Mobile Call Networks

Kirill Dyagilev
Shie Mannor
EE department, Technion, Haifa, Israel
Elad Yom-Tov
Microsoft Research, Herzliya, Israel
In the last decade the accessibility of large-scale social
interaction data has led to an explosion of research in
the field of social networks (Jackson, 2008).
In particular, a substantial body of work has focusedon telecommunication data, e.g., (Eagle et al., 2009;
Hill et al., 2006; Nanavati et al., 2006; Richter et al.,2010). A common approach in this research is to rely
on a static call graph constructed from aggregates ofcalls given in Call Data Records (CDRs), which
record, among other details, the calling and callednumbers of the subscribers. This graph contains nodes
that represent different subscribers and edges that connect subscribers that have social relation. The existence of a meaningful social connection between users
A and B is assumed if the total interaction of calls between these two subscribers over some period of time
is significant. The volume of calls can be measured,e.g., by the total number of calls over a designated period, the total duration of calls, or a fraction of calls
made by user A to user B out of calls made by user
A. The edges in the call graph can be directed andundirected depending on the context. It was shown in
Eagle et al. (2009) that this graph accurately approximates the actual social network.

Poster 2

16

However, this approach disregards the information encapsulated in the dynamics of the interactions. In contrast, we focus on the actual sequences of informationpassing events.
We propose a method to identify sequences of callsthat are likely to be related to the same topic or propagate the same information. It is evident that without knowledge of the actual content of these calls, thistask is impossible. Therefore we heuristically confine
our search to a specific mode of information diffusion
in which once the information is received, it is eithertransferred to somebody else during a relatively shortperiod of time or not transferred to anyone. We refer to this mode of information propagation as Rapid
Propagation of Information (RPI). As an illustration of RPI, one can consider the following two se-142

kirilld@tx.technion.ac.il
shie@ee.technion.ac.ileladyt@microsoft.com

quences of information diffusion calls:
Scenario 1. The sequence begins with Alice calling
Bob. Once their conversation is over, Bob immediately calls Clare. Shortly after the end of the second
call, Clare dials Daniel and so on. Assuming that thischain of calls is long enough and that time intervals
between consecutive calls are short, this sequence represents a rare event. In this scenario, calls seem totrigger other calls, hence could be related to the same
topic.
Scenario 2. In this scenario, Alice makes calls to allof her friends within a relatively short period of time.
Our measurements show that such a “burst” of calls is
a very unusual subscriber behavior that might suggestthat Alice transfers the same piece of information to
all of her friends, e.g., an invitation to a party.
We design an algorithm for identification of RPIs in
the call data records and apply this method to data
sets DS-1 and DS-2 from two providers from different parts of the world. Data set DS-1 contained all
calls logged by an operator in a country with population of over 7 million people in a period of 35 days.
The data contains more than 600 million calls involving approximately 3 million distinct subscribers fromthe analyzed operator and over 5 million subscribers
belonging to other service providers. Data set DS-2
contained calls in a city with a population of over 2
million people in 24 days out of period. The data contains more than 50 million calls involving 5.4 million
distinct subscribers, out which approximately 2.1 million belong to the analyzed operator.
We study the properties of RPIs found in these data
sets and compare them. In particular, we consider
the properties of information flow trees, namely,
the paths in which information propagates from one
subscriber to another within RPI. We show that the
typical topologies of these trees are the same in bothdata sets (see Figure 1), indicating that these topologies represent universal modes of information propagation. However, the fractions of RPIs belonging to each16

Poster 2 // Network
On Information Propagation in Mobile Call Networks

(1)

(2)

(3)

(4)

Figure 1. Typical topologies of information flow trees. Arrows point from parent node to child node.

of these topologies differs between two data sets. We
conjecture that this difference can be attributed to thedifference in the type of clients (pre-paid vs. post-paidcustomers, private vs. business clients). We further
show that a significant fraction of RPIs have a single
subscriber propagating the information to majority ofother users. We refer to these users as “disseminationleaders” and conjecture that they play a significant
role in diffusion of information in the mobile phone
network.
We develop two generative models of RPIs that havea relatively small number of parameters. The firstmodel generates sequences of calls that yield an RPI.
The second model describes the emergence of different topologies of information flow trees. We show thatthese models accurately predict features of RPIs foundin the real-world data. The simplicity and locality ofthese models allows them to be a computationally simple way to generate synthetic call data records that
capture the RPI phenomenon.
We justify our heuristic definition of RPI by providing
evidence that some of the RPIs propagate geo-spatial
information. In particular, we show that appearance
in the same RPI increases the chances that two subscribers will visit the same geographical location during the same day.

The method proposed by Richter et al. (2010) is called
Group-First Churn Prediction (GFCP) and proceedsin the following manner. It employs an informationtheory based measure to quantify the strength of thesocial connection between pairs of subscribers. By
keeping only the strongest connection, this algorithm
identifies closely-knit groups of subscribers and theleader of each group. GFCP then establishes a churnscore for each group using a novel group Key Performance Indicators. It finally assigns individual churnscore to each subscriber based on the corresponding
group churn score and subscriber’s personal characteristics.
We propose a new churn prediction method that relies
on subscribers’ behaviors in RPIs. We thus introduce
the first algorithm for churn prediction that is basedon subscribers’ dynamic, rather than static, social behavior. Our algorithm proceeds through the following
steps. It receives call data records (CDRs) of all calls
made over an input period of several days, along

143

16

The mainstream approach to churn prediction (e.g.,
Coussement & Van den Poel (2008); Datta et al.
(2000)) considers each customer separately. Essentially, each subscriber is characterized by a numerous
features based on their socio-economic characteristics
and call behavior. These features are then used bysome learning regression algorithm to calculate subscriber’s likelihood to churn.

Dasgupta et al. (2008) introduced the Spreading Activation algorithm (SPA). Its underlying assumption is that recent churners are known and they arelikely to affect the churning decisions of their social
neighborhood. The network of subscribers is thenmodeled as a weighted directed call graph. Next, adiffusion process is used to model the flow of churnpropensity from recent churners to their social environment. Specifically, each node in the call graph that
corresponds to a recent churner is assigned an initial
weight which propagates to the call graph network according to a decaying diffusion process. Once diffusion
process converges, each subscriber in the call graph hassome associated weight corresponding to the amountof churn propensity that has reached him. The individual churn scores, namely, the likelihood of a user
to churn, are then derived directly from these weights.

Poster 2

Finally, we consider the problem of churn prediction, namely, identification of subscribers that areconsidering transferring their business to a competing
mobile phone service provider. Since it is usually significantly cheaper to retain an existing subscriber thanto acquire a new one, churn prediction has become acentral business intelligence application for telecommunication operators (Fildes & Kumar, 2002).

One drawback of this approach is fairly obvious: it
relies on the assumption that the decision to churn ismade by each user individually and is not affected bya subscriber’s social circle. However, it is well-known
that there are social aspects to churn. E.g., Nitzan& Libai (2010) considered a social network of mobile
phone subscribers and showed that having a neighborthat churned increases the chances of churn by 80%.
Thus, it seems that leveraging social relations may leadto churn prediction systems with better performance.
While commercial solutions have started exploring thisdirection, to the best of our knowledge the only published work directly in this context is the diffusionbased algorithm introduced in Dasgupta et al. (2008)
and machine-learning based algorithm in Richter et al.
(2010).

16

Poster 2 // Network
On Information Propagation in Mobile Call Networks

with identities of all users that churned during thisperiod. Our goal is to accurately identify users thatwill churn in the prediction period that follows the
input period.
As the first step, we identify RPIs in the input period data. We then characterize all subscribers thatbelong to the analyzed carrier with attributes of their
behavior in RPIs. E.g., we count the number of RPIs
the user participated in, whether the user had a central location in one or more information flow tree, thenumber of churners that participated in the same RPIs
etc.
As the second step, we use a pre-trained regressionclassification tree (Duda et al., 2001) to map between the values of these features of the user andthe churn score. This churn score indicates the likelihood of a user to churn during the prediction period.
The regression-classification tree mentioned above istrained using historical data. Namely, we designate
input and prediction periods in data from the past.
We use CDRs from the input period to extract subscribers’ features and use the 0 − 1 churn signal observed during the prediction period as the desired output of the trained tree. The regression-classification
tree is trained once and used in all future predictions.

Poster 2

16

We compared the performance of our algorithm (RPICP) to the performance of the SPA algorithm thatassumes knowledge of similar information. We showthat the RPI-CP algorithm outperforms the SPA algorithm on both data sets. In particular, we take the
top 1% of subscribers, as ranked most likely to churnby each algorithm, and we check the actual number ofchurners in each set. In DS-1, the RPI-CP algorithm
finds five times more actual churners than the SPA
algorithm. In DS-2, the RPI-CP algorithm finds 1.5
times more actual churners than the SPA algorithm.
Further work of immediate interest includes leveraging
geographical information to get additional insights on
the social structure of mobile call networks. We also
note that the actual content of calls was unavailable tous, therefore our research was solely based on temporal features of calls. However, there exist other social
media, e.g. Twitter, in which both temporal and content data are available. The extension of our approachto these media can provide us with additional insights
to dynamics of RPI.

Acknowledgments
The full version of this paper can be found in Dyagilev
et al. (to appear). Kirill Dyagilev and Elad Yom-Tov
were also with IBM Haifa Research Lab, Haifa, Israel
during part of this research.

144

References
Coussement, K. and Van den Poel, D. Churn prediction in subscription services: An applicationof support vector machines while comparing two
parameter-selection techniques. Expert systems withapplications, 34(1):313–327, 2008. ISSN 0957-4174.
Dasgupta, K., Singh, R., Viswanathan, B.,
Chakraborty, D., Mukherjea, S., Nanavati, A.A.,
and Joshi, A. Social ties and their relevance tochurn in mobile telecom networks. In EDBT’08,
2008.
Datta, P., Masand, B., Mani, DR, and Li, B. Automated cellular modeling and prediction on a large
scale. Artificial Intelligence Review, 14(6):485–502,
2000. ISSN 0269-2821.
Duda, R.O., Hart, P.E., and Stork, D.G. Pattern classification. Wiley New York, 2001.
Dyagilev, K., Mannor, S., and Yom-Tov, E. On Information Propagation in Mobile Call Networks. Social
Network Analysis and Mining, to appear.
Eagle, N., Pentland, A., and Lazer, D. Inferring social
network structure using mobile phone data. PNAS,
106(36), 2009.
Fildes, R. and Kumar, V. Telecommunications demand forecasting–a review. International Journal of
Forecasting, 18(4):489–522, 2002. ISSN 0169-2070.
Hill, S., Provost, F., and Volinsky, C. Network-based
marketing: Identifying likely adopters via consumer
networks. Statistical Science, pp. 256–276, 2006.
Jackson, M.O. Social and Economic Networks. Princeton University Press, Princeton and Oxford, 2008.
Nanavati, A.A., Gurumurthy, S., Das, G.,
Chakraborty, D., Dasgupta, K., Mukherjea,
S., and Joshi, A. On the structural propertiesof massive telecom call graphs: findings andimplications. In ICIKM’06, 2006.
Nitzan, I. and Libai, B. Social effects on customer retention. Marketing Science Institute, working paper
10-107, 2010.
Richter, Y., Yom-Tov, E., and Slonim, N. Predicting
customer churn in mobile networks through analysis
of social groups. In ICDM’10, 2010.

17

Poster 2 // Network
Using telephone network data to study the effects of socio demographics on ego
network structure
byBinh Phan1, Kenth Engø-Monsen2, and Øystein D. Fjeldstad1

Abstract
The effects of socio demographics on ego network structure have captured much research attention
in the 1980s and 1990s (such as, Beggs, Haines and Hurlbert (1996), Marsden (1987)). The network
data in these studies was collected using interviews (survey). Extant research has shown issues
associated with such a network data. Specifically, people face memory constraints which makethem report much less partners than they actually have (Marsden, 1993, 2003), and are biased
toward recent events, physically proximate and close partners (Eagle, Pentland, & Lazer, 2009).
With the support of technology, recent social network studies use automatically recorded
communication networks, such as an email correspondence network (Ebel, Mielsch, & Bornholdt,
2002; Guimera, Danon, Díaz-Guilera, Giralt, & Arenas, 2003), and a telephone network (Eagle etal., 2009; Lambiotte et al., 2008; Onnela et al., 2007), as reliable proxies for real social networks.
The use of these networks partly overcomes the issues associated with the survey network data.
Among recorded communication networks, the telephone network, including fixed-line call, mobile
phone call and SMS networks, is considered to be a better proxy for the real social network(Hidalgo & Rodriguez-Sickert, 2008; Onnela et al., 2007; Pool & Kochen, 1978) because themobile phone call service has been used as one of the main communication mediums in societies
over decades (Kwan, 2007), and has the high penetration rate, almost 100 percent in developed
countries and more than 90 percent in fast-growing countries.3
In this study, we examine the effects of socio demographics with a sample of 509 anonymous
mobile users which was randomly collected in Norway in 2010. We use the telephone network as aconsists of 12529 nodes and 17737 ties. We found that (i) age has a U-shaped effect on ego network
clustering and an inverse U-shaped effect on ego network size, (ii) geographic centrality has a17negative effect on ego network clustering but no effect on ego network size.

Poster 2

proxy for the social network, and Ordinary Least Squared (OLS) estimation. This network data

Our findings, although generally consistent with prior research using survey data, nuances priorfindings. The effects of age and geographic location have been examined in the studies of Marsden
1

Department of Strategy and Logistics, BI Norwegian Business School, 0484 Oslo, Norway. Emails:
tbinhphan@yahoo.com and oystein.fjeldstad@bi.no2
Telenor Group, Research and Future Studies, Snarøyveien 30, N-1331 Fornebu, Norway. Email: Kenth.EngoMonsen@telenor.com
3
According to International Telecommunication Union (ITU):http://www.itu.int/ITU-D/ict/facts/2011/index.html

145

Poster 2 // Network

17(1987) and Beggs et al. (1996). These studies used the 1985 General Social Survey (GSS)’s
network data in the US. In this data, interviewees were asked to give the names of five or more
people whom they discusses “important matters” with and provide the information about theconnections among partners. In this data, the average size and clustering of ego networks are 3.01
and 0.61 respectively (Marsden, 1987). In our data, the average size and clustering of ego networks
are 23.48 and 0.37 respectively. Marsden (1987) found that age has a positive effect on ego network
clustering and has a inverse U-shaped effect on ego network size. Marsden (1987)’s finding
suggests that at early ages, people have small and sparse ego networks. This is probably because in
the data, nearly a quarter of respondents report to have ego networks of size 0 or 1 (Marsden, 1987).
Such a small size is not adequate to construct ego network clustering. In our data, all ego networks
have a size of at least 5 alters, which is adequate to capture the true ego network clustering(Marsden, 1993). With a different type of data, our findings extend the findings of Marsden (1987)
by showing that at early ages, people have small and clustered ego networks.
Further, our findings and Beggs et al. (1996)’s findings are consistent on a negative effect ofgeographic centrality on ego network clustering. Beggs et al. (1996) found a significantly positiveeffect of geographic centrality on ego network size while we find no support. This difference isconceivably due to the different time points of data collection and the different types of network
data used. Networking is costly (Burt, 1992). People face a physical constraint on networking,especially with geographically distant partners. The cost of networking and the physical constraints
limit the number of ties that people can maintain (Burt, 1992: 17). Communication technologies
reduce the cost of networking and the physical constraint (Wellman, Haase, Witte, & Hampton,2001). Beggs et al. (1996) used the GSS data collected in 1985, when the gap of the use ofcommunication technologies was significant between rural and urban areas. The use ofcommunication services, especially mobile services, in rural areas has been recently dramatically17

growing. This gap in developed countries remains, but has been significantly mitigated over time
(LaRose, Gregg, Strover, Straubhaar, & Carpenter, 2007). The significant mitigation in the gap

Poster 2

would reduce the difference in ego network size between urban and rural people. The use of thetelephone network in this study is able to capture the effect of this gap mitigation. Moreover, theindividual participation to social and non-economic organizations and associations where people
can establish and maintain non-work ties has been shown to decline over the past 40 years (Putnam,2010). This decline is especially high in urban areas where people face a large pressure of money
and work (Gellis, Kim, & Hwang, 2004). This high decline in urban areas significantly reduces thenumber of non-work ties in the ego networks of people in these areas. People in rural areas with aless pressure of money and work can remain a high participation to these non-economic
146

17

Poster 2 // Networkorganizations and associations, and therefore maintain a significant number of non-work ties. The
combination of these two reasons may explain why a positive effect was found to be significant
with the data collected in 1985, but is non-significant with the data collected in 2010.
In summary, our findings imply that the use of the telephone network data as a proxy for the social
network in the research on the antecedents of social network structure may extend the findings ofprior studies using the survey network data, and is also able to capture the effects of communication
technologies on people’s networking and network structure. We call for future research to use thetelephone network data to reexamine the origins and effects of social network structure.
References:

17

147

Poster 2

Beggs, J. J., Haines, V. A., & Hurlbert, J. S. 1996. Revisiting the Rural-Urban Contrast: Personal Networks
in Nonmetropolitan and Metropolitan Settings1. Rural Sociology, 61(2): 306-325.
Burt, R. S. 1992. Structural Holes: Cambridge: MA: Harvard University Press.
Eagle, N., Pentland, A., & Lazer, D. 2009. Inferring Friendship Network Structure by Using Mobile Phone
Data. Proceedings of the National Academy of Sciences of the United States of America, 106(36): 1527415278.
Ebel, H., Mielsch, L.-I., & Bornholdt, S. 2002. Scale-Free Topology of E-Mail Networks. Physical Review E,
66(3): 035103.
Gellis, Z., Kim, J., & Hwang, S. 2004. New York State Case Manager Survey: Urban and Rural Differencesin Job Activities, Job Stress, and Job Satisfaction. The Journal of Behavioral Health Services & Research,
31(4): 430-440.
Guimera, R., Danon, L., Díaz-Guilera, A., Giralt, F., & Arenas, A. 2003. Self-Similar Community Structure
in a Network of Human Interactions. Physical Review E, 68(6): 065103.
Hidalgo, C. A. & Rodriguez-Sickert, C. 2008. The Dynamics of a Mobile Phone Network. Physica A:
Statistical Mechanics and its Applications, 387(12): 3017-3024.
Kwan, M.-P. 2007. Mobile Communications, Social Networks, and Urban Travel: Hypertext as a New
Metaphor for Conceptualizing Spatial Interaction*. The Professional Geographer, 59(4): 434-446.
Lambiotte, R., Blondel, V. D., de Kerchove, C., Huens, E., Prieur, C., Smoreda, Z., & Van Dooren, P. 2008.
Geographical Dispersal of Mobile Communication Networks. Physica A: Statistical Mechanics and its
Applications, 387(21): 5317-5325.
LaRose, R., Gregg, J. L., Strover, S., Straubhaar, J., & Carpenter, S. 2007. Closing the Rural Broadband Gap:
Promoting Adoption of the Internet in Rural America. Telecommunications Policy, 31(6–7): 359-373.
Marsden, P. V. 1987. Core Discussion Networks of Americans. American Sociological Review, 52(1): 122131.
Marsden, P. V. 1993. The Reliability of Network Density and Composition Measures. Social Networks,
15(4): 399-421.
Marsden, P. V. 2003. Interviewer Effects in Measuring Network Size Using a Single Name Generator.
Social Networks, 25(1): 1-16.
Onnela, J.-P., Saramaki, J., Hyvonen, J., Szabo, G., de Menezes, M. A., Kaski, K., Barabasi, A.-L., &Kertesz, J. 2007. Analysis of a Large-Scale Weighted Network of One-to-One Human Communication. New
Journal of Physics, 9: 27.
Pool, I. d. S. & Kochen, M. 1978. Contacts and Influence. Social Networks, 1(1): 5-51.
Putnam, R. D. 2010. Bowling Alone: The Collapse and Revival of American Community. New York:
Simon & Schuster Inc.
Wellman, B., Haase, A. Q., Witte, J., & Hampton, K. 2001. Does the Internet Increase, Decrease, orSupplement Social Capital?: Social Networks, Participation, and Community Commitment. American
Behavioral Scientist, 45(3): 436-455.

18

Poster 2 // Network

Understanding Social Influence Using Combined Network Analysis and Machine Learning Models
Dhaval Adjodah, Alex ‘Sandy’ Pentland
dhaval@mit.edu, sandy@media.mit.edu
MIT Media Lab, Cambridge, MA
Abstract:
In this paper, the drivers of social influence in a social network during a phone-tracked intervention
will be investigated. The funf dataset1, which comprises detailed high-frequency data gathered from 25
mobile phone-based signals from 130 people over a period of 15 months, will be used to test the
hypothesis that people who are closer to each other have a greater ability to influence each other.
Various metrics of closeness will be investigated such as self-reported friendships (collected throughsurveys), call logs and Bluetooth co-location signals. A graph of all participants is then created using
such metrics as edge weights, and the network constraint2 of each pair of participants is calculated as ameasure of not only the direct friendship (or number of calls or Bluetooth co-locations) between two
participants but also the indirect friendships through intermediate connections that form closed triads
with both the participants being assessed. To measure influence, the results of the live funf intervention
will be used where behavior change of each participant to be more physically active was rewarded - thereward being calculated live. There were three variants of the reward structure: one where eachparticipant was rewarded for her own behavior change without seeing that of anybody else (thecontrol), one where each participant was paired up with two 'buddies' whose behavior change she could
see live but she was still rewarded based on her own behavior, and one where each participant who waspaired with two others was paid based on their behavior change that she could see live. As a metric forsocial influence, it will be considered how the change in slope and average physical activity levels of
one person follows the change in slope and average physical activity levels of the buddy who saw her

18

data and/or was rewarded based on her performance. Finally, a linear regression model that uses thevarious types the network constraints (self-reported friendship, call logs, Bluetooth) will be created toPoster 2

predict the behavior change of one participant based on her closeness with her buddy. In addition to
explaining and demonstrating the causes of social influence with unprecedented detail, this paper will
also briefly discuss the policy implications of this technology such as privacy, moral hazard, misuseand effectiveness in the long term.

1 Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A., & Nadav Aharony, Wei Pan, Cory Ip, Inas Khayal, A. P. (2011).
Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing, 7(6),643–659. doi:10.1016/j.pmcj.2011.09.004
2Burt, Ronald S. 1992. Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University
Press.

148

19

Poster 2 // Network

Accelerating internet growth in Asia
using viral spreadingPål Sundsøy1a, Johannes Bjelland1b, Geoffrey Canright1c, Kenth Engø-Monsen1d, Asif M.Iqbal1e, David Lazer23f
1

Telenor ASA, Research & Future Studies, 1360 Fornebu, Norway
Harvard University, John F. Kennedy School of Government, Cambridge, MA 02138
3
NorthEastern University, Lazer Lab, Boston MA 02115
2

E-mails: apal-roe.sundsoy@telenor.com, bjohannes.bjelland@telenor.com, cgeoffrey.canright@telenor.com , dkenth.engo-monsen@telenor.com,
easifmiqbal@outlook.com, fdavid_lazer@harvard.edu
For many in Asia, the mobile phone is their only gateway to
the Web. The penetration of internet in these countries
is nevertheless very small, causing a large digital discrepancy
between more and less developed countries - the socalled digital divide. Increased mobile internet adoption may
help to bridge this gap.
In this talk we examine how viral campaigns can boost themobile internet adoption in one developing Asian country. By
sending a unique offer code to 70 000 people, forwardable tofriends, we are able to observe how the offer spreads from
customer to customer. The spreading can be observed bycoupling adoption data with call data records, which canprovide a good proxy for the social network. By counting both

direct and indirect hits we observe a strong adoption rate of
53%. On average we find that each person recruits 8.3 othercustomers, while the most extreme people recruit over
350 others.
We also introduce molecular targeting - where we aim toreach socially connected pairs of people. Our results indicatethat individuals in these ‘molecules’ adopt more often together
than expected from the single-individual hit rate - leading us
to believe that the neighbours boost each other’s awareness ofmobile internet, due to the so called “up-in-air effect”. Early
results also indicate that this approach increases the overall
spreading as measured by the indirect hitrates.
.

Poster 2

Fig 2 The extreme case where one single customer (ingreen) recruits 360 other customers. The missing links
indicate that the offer is also spread via other channels(which our call data does not pick up)

19

Fig 1 The social network among the mobile internet
adopters 5 days after campaign launch. 45% of theconnected adopters can be found in the largest connectedcomponent. The connections are based on voice + SMS
communication.

Fig 3 Molecular targeting: A component where two
customers (hexagons) are targeted with an offer. Theyboth adopt, and also recruit 12 other customers.149

20

Poster 2 // Network

Determinants of Subscriber Churn in Wireless Networks:
The Role of Peer Influence∗[Extended Abstract]
Qiwei Han*†
qiweih@cmu.edu*

Department of Engineering and Public Policy
Carnegie Mellon University
Pittsburgh, PA 15213
United States

Pedro Ferreira*‡
pedrof@cmu.edu‡
Heinz College
Carnegie Mellon University
Pittsburgh, PA 15213
United States

ABSTRACT
Subscriber churn is a top challenge for wireless carriers. Understanding the determinants of churn is key for carriers
to identify potential churners and apply effective retention
strategies to reduce subscriber loss. In this paper, we apply generalized propensity score matching to separate peer
influence from other confounding factors that might affectchurn. Our empirical analysis, developed over a large scale
wireless network, confirms that peer influence plays a rolein churn. The estimated marginal influence of having a firstfriend churn is roughly 3%. While the marginal effect offriends’ churn decreases significantly as more friends do so,
contagious churn is still a significant part of the story beyond
high churn rates in the mobile industry.

1. INTRODUCTION

Poster 2

20

In today’s competitive wireless industry, subscriber churnis considered to be the “biggest issue for all wireless carriers” [7]. Preserving the existing subscriber base is of crucial
importance for carriers to ensure their profitability. Understanding determinants of churn becomes fundamental for
carriers so that they can identify potential churners andapply appropriate retention strategies to reduce subscriber
loss. However, the complex nature of churn poses significant
challenges to carriers that pursue effective churn management solutions to deal with all kinds of churn problems. As
a consequence, most carriers focus only on retaining theirmost valuable subscribers.
Advances in studying the effect of social influence on subscriber churn in wireless networks have received much attention in recent times. [5] found that the likelihood ofchurn increases with the number of friends who have already churned. [6] also confirmed that the “word-of-mouth”
effect has a positive impact on subscriber’s churn. However, work that identifies contagious churn on a causal sense
and separates it from confounding effects such as homophily
still lacks. Correlation in the behavior among people whoshare social ties can be explained by both peer influence andtheir inherent similarities [10]. Therefore, misattribution of∗Support for this research was provided by the Fundaçãopara a Cie̊ncia e a Tecnologia (Portuguese Foundation for
Science and Technology) through the Carnegie Mellon Portugal Program under Grant No. SFRH/BD/51153/2010.

150†
Instituto Superior Técnico
Universidade Técnica de Lisboa
Lisbon, 1049-001
Portugal

homophily to contagion, or-and vice versa, needs to be carefully thought from an empirical point of view.
Numerous studies on identification of peer influence in othernetworked context have been proposed (e.g. [1, 4, 13]). [2]used dynamic propensity score matching (PSM) to estimate
the effect of contagion in the adoption of an online service byanalyzing a community of instant messenging users. Theirfindings suggest that homophily accounted for much of the
adoption previously perceived as peer influence. However,[2] dichotomize the treatment due to the binary nature oftreatment regime. The applicability of PSM therefore isconfined, as effects of different numbers of adopter friends
are overlapped. To overcome this problem, in this paper,
we apply a generalized propensity score matching (GPSM)
method to separate peer influence from homophily. We perform our empirical analysis on a massive dataset from amajor European wireless carrier (hereafter refer to as EURMO). We have call detail records (CDR) and tariff plan
information from EURMO. The GPSM method allows us
to estimate the magnitude of the contagion effect given different numbers of friends who churn. This can provide us
with more information on the marginal effect of peer influence and thus help us better understand the role of peer
influence on churn.

2.

DATA

The EURMO dataset includes CDRs for roughly 4 million
prepaid subscribers between August 2008 and June 2009.
For each call we know the caller and the callee, the durationand time of the call. For each SMS we know the sender andreceiver and the time of the SMS. Subscribers are identified
by their anonymized phone number. For each subscriber,
we know their tariff plan at all times. Understanding subscriber churn for prepaid consumers is quite different frompostpaid subscribers. First, we have little demographic information on prepaid subscribers. Second, the usage pattern of prepaid subscribers is more irregular than that of
postpaid subscribers. Third, prepaid subscribers churn byceasing usage whereas postpaid subscribers explicitly informthe carrier when they want to do so. After consulting withthe carrier, we use its definition of churn and thus assume
that a prepaid subscriber churns if she does not place a callor sends a SMS for three consecutive months.

20

Poster 2 // Network
Variable
Description
Time Invariant Individual
PLAN ID
The ID for the tariff plan
Time Variant Individual#CALL
Number of calls made or received per day
AIRTIME
Duration of calls made or received (in min)
#NEIGHBOR
Number of friends#SMS
Number of SMS sent or received
LIFETIME
Duration since subscription to carrier (in month)
RCO
Ratio of calls to other networks

MD C

SD C

MD NC

SD NC

1.09

1.15

1.35

1.95

0.24
0.22
10
0.012
3.67
0.2

2.29
4.68
46.37
4.51
12.08
0.31

1.41
1.59
64
0.30
15.37
0.15

3.16
7.32
104.40
20.14
18.58
0.23

Table 1: List of covariates extracted from EURMO, MD is median and SD is standard deviation, C stands for churner andNC stands for non-churner
We use a random sample of 10,000 subscribers together with
their 690,000 friends (430,000 in the same network). Two
subscribers are called friends if at least they exchange one
call in the same calendar month. We observe network dynamics: every month new subscribers join EURMO, existing
subscribers leave EURMO and subscribers call and/or text
different friends. Therefore, we aggregate time-varying individual subscriber usage and time-invariant characteristics
at the monthly level (Table 1). Over the eleven months in
our period of analysis, the 10,000 subscribers in our sample
placed 6.5 million calls. 2,282 of them left EURMO, whichamounts to an average monthly churn rate of 2.07%.
We find that the subscribers that churn have much less usage and fewer friends than the subscribers who do not bothin terms of number of calls and airtime. Moreover, we also
observe that both subscribers who churn and do not havemuch more usage within the network. This is sensible because calls across carriers cost more as carriers pass on to
subscribers part of the interconnection charges. We also findthat the conditional churn probability decreases with thesubscriber’s lifetime with the carrier. One possible explanation can be that subscribers become loyalty to carriers overtime. We also note that subscribers exhibit a significantly
higher churning rate during the first three months they sign
up with the carrier. This implies that carriers should pay
particularly attention to these subscribers who just join the
network and thus build up good customer relationships withthem to keep them in the firm, as they become more valuablewith time.

3. METHODOLOGYGPSM requires weak unconfoundedness: Y (t) ⊥⊥ T |X. In
our case, though the number of friends who churn at time
p−1 is not randomly assigned, we observed all variables that
can affect both the subscriber’s churn at time p and the likelihood of receiving treatment (justification of this assumption is discussed in the next section). We estimate the conditional distribution of the number of friends who churn giventhese covariates to estimate the generalized propensity score(GPS) for each subscriber, Ri (we assume that the logarithmof the number of friends who churn is normally distributed).
We also investigate the balancing property for our covariates adjusted by GPS by testing whether the mean of one of
the four treatment levels was different from the mean of theother three treatment levels. We generally observe moderate evidence against the balancing properties according to a
two-sided t-test.
We denote the dose response function as a set of potential
outcomes given the treatment level t: {Yip (t)t∈T } where T is
the set of potential treatment values. Then the conditional
expectation of churn is a function of number of friends whochurn T and of the GPS R:
β(t, r) = E[Yip (t)|r(t, Xip ) = r] = E[Yi |Tip−1 = t, Ri = r]
We use a polynomial approximation of order two to regress
the subscribers’ churn Yi on the number of churned friends
Ti , and the GPS Ri .
Yi = α0 + α1 Ti + α2 Ti2 + α3 Ri + α4 Ri2 + α5 Ti Ri
Therefore, the effect of peer influence on churn is the average
conditional expectation over GPS at a particular number offriends who churn:

Poster 2µ(t) = E[β(t, r(t, Xi ))]Taking derivatives, we can easily obtain the marginal effectassociated with one more friend churn on the subscriber’s
churn for different levels of treatment.

4.

RESULTS AND DISCUSSIONSFor each month we evaluate the dose response function separately (we use the Stata package provided by [3]). We use

151

20

Propensity Score Matching (PSM) is a widely used method
to evaluate the causal treatment effect from observational
data in various empirical research fields [11], in particularwhen the assignment of a binary treatment is not random
and counterfactual outcomes are unavailable. With PSM
units from a treated group are matched to those in a control
group using a propensity score. Differences in the behavior of these pairs of units measure the effect of the treatment. [8] extended and proposed this framework to allow
for continuous levels of treatment. Formally, consider a setof N subscribers and let i denote a single subscriber. Let
P = {1, . . . p} represent a set of time periods. We observea vector of pre-treatment covariates Xip as shown in Table
1 at each time period. We define the treatment at each period for each subscriber as her exposure to a certain numberof friends who churned in the last time period τip−1 . Veryfew subscribers in our sample have more than 3 friends whochurn. Therefore, we decided to use four levels of treatment: T ∈ {0, 1, 2, 3}, to indicate whether 0, 1, 2 or 3 ormore friends churn, respectively. The outcome of interest iswhether subscriber i churns in time period p: Yip ∈ {0, 1}.

20

Poster 2 // Network

Treatment Effect Function

0

.04
.02
ï.02

0

.06
.04
.02

E[churn_0810(t)].08

E[churn_0810(t+1)]ïE[churn_0810(t)]

.1

.06

Dose Response Function012Treatment level
Dose Response3
Low bound

Upper boundConfidence Bounds at .95 % level
Dose response function = Probability of a positive outcome
Regression command = logit

012Treatment level
Treatment Effect3
Low bound

Upper boundConfidence Bounds at .95 % level
Dose response function = Probability of a positive outcom
Regression command = logit

Figure 1: Dose response function and treatment effect function in October 2008

Poster 2

20

bootstrapping to calculate the asymptotic standard errors
and confidence intervals. Figure 1 shows the average dose
response and treatment effect for October 2008 (estimatesfor other months exhibit similar characteristics). The figureon the left shows that the effect on subscribers churn increases with the number of friends who churn. For example,
the probability of churn for subscribers who have two friends
who churned in September 2008 is 4% higher than that for
subscribers who had no friends who churned in September
2008. The figure on the right shows that the marginal influence (the effect of having one more friend who churns)
decreases as more friends churn. For example, having one
friend who churns compared to having no friends who do sowill lead to an increase of 3% in the probability of churn but
having two or more friends who churn compared to having
one friend who churns will increase the probably of churnby only 1%. Our results confirm the positive effect of peer
influence on churn. When we remove the selection bias dueto the homophily, we still observe contagious churn.
We notice the argument made by [12] that the plausibilityof the unconfoundedness assumption remains unidentifiablefrom observational data. As long as there are systematic differences in unobserved covariates, we cannot safely conclude
that the unconfoundedness assumption holds. Therefore, we
acknolwedge that our results may still be biased. As future
work, we will perform sensitivity analysis to check the robustness of our results. One possible way to do so is torelax the unconfoundedness assumption, introduce an artificial unobserved variable and reestimate the dose response
function to check whether the presence of unobserved heterogeneity may significantly change our results [9].

5.

REFERENCES[1] Anagnostopoulos, A., Kumar, R., and Mahdian,
M. Influence and correlation in social networks. In
Proceedings of the 14th ACM SIGKDD international
conference on Knowledge discovery and data mining(2008), KDD ’08, ACM, pp. 7–15.
[2] Aral, S., Muchnik, L., and Sundarajan.
Distinguishing influence-based contagion from152

homophily-driven diffusion in dynamic networks.
Proceedings of National Academy of Science 106, 51(2009), 21544–21549.
[3] Bia, M., and Mattei, A. A stata package for theestimation of the dose-response function throughadjustment for the generalized propensity score. The
Stata Journal 8, 3 (2008), 354–373.
[4] Bramoullé, Y., Djebbari, H., and Fortin, B.
Identification of peer effects through social networks.
Journal of Econometrics 150, 1 (2009), 41–55.
[5] Dasgupta, K., Singh, R., Viswanathan, B.,
Chakraborty, D., Mukherjea, S., Nanavati,
A. A., and Joshi, A. Social ties and their relevanceto churn in mobile telecom networks. In Proceedings ofthe 11th international conference on Extending
database technology: Advances in database technology(2008), EDBT ’08, ACM, pp. 668–677.
[6] Dierkes, T., Bichler, M., and Krishnan, R.
Estimating the effect of word of mouth on churn andcross-buying in the mobile phone market with markov
logic networks. Decision Support Systems 51, 3 (2011),
361–371.
[7] FCC. Annual report and analysis of competitivemarket conditions with respect to commercial mobile
services. WT Docket 08-27, Federal Communication
Commission, 2009.
[8] Hirano, K., and Imbens, G. W. The Propensity
Score with Continuous Treatments. John Wiley &Sons, Ltd, 2005, pp. 73–84.
[9] Ichino, A., Mealli, F., and Nannicini, T. From
temporary help jobs to permanent employment; what
can we learn from matching estimators and their
sensitivity? Journal of Applied Econometrics 23(2008), 305–327.
[10] McPherson, M., Smith-Lovin, L., and Cook, J.
Birds of a feather: Homophily in social networks.
Annual Review of Sociology 27 (2001), 415–444.
[11] Rosenbaum, P., and Rubin, R. The central role ofthe propensity score in observational studies for causal
effects. Biometrika 70, 1 (1983), 41–55.
[12] Shalizi, C., and Thomas, A. Homophily andcontagion are generically confounded in observationalsocial network studies. Sociological Methods andResearch 40, 2 (2011), 211–239.
[13] Steglich, C., Snijders, T., and Pearson, M.
Dynamic networks and behavior: Separating selection
from influence. Sociological Methodology 40, 1 (2010),
329–393.

21

Poster 2 // Network

Understanding Quota Dynamics in Wireless Networks
Matthew Andrews

Glenn Bruns
Mustafa K. DoğruHyoseop Lee
Bell Labs, Alcatel-Lucent
Email: {matthew.andrews,glenn.bruns,mustafa.dogru,hs.lee}@alcatel-lucent.com
II. DATA A NALYSIS
We consider CDRs from a population of prepaid wirelessusers that make both voice calls and SMSs. The data set spans
6 weeks and corresponds to about 1.6 million subscribers.
However, some of the subscribers have complex plans withvarious types of “free” calls that are bundled in with themonthly subscription. For simplicity, we focus on users whoseplan has a simple monetary quota. For these users, the data
set contains about 60 million outgoing calls and 150 million
SMSs. Each voice call or SMS decrements the balance by apredetermined amount. In the remainder of this section we
discuss how call duration, inter-call time and inter-SMS timevary as a function of remaining balance.
Fig. 1 (top) shows how call duration varies with current
user balance. Clearly, call duration decreases as the balance
gets low. The spikes around the balances 1,000, 1,700, and
2,000 are closely related to the most frequent top-up amounts.
One could conclude that the first call after top-up typicallylasts longer than any other calls.
There are different price rates for a single call depending
on subscribers’ plans. Among them, the price rates 1.0 and
0.5 monetary units per second appear most frequently in thedata. (We use the term “monetary unit” so as not to hint
at the source of data.) Fig. 1 (middle) depicts the behaviorof subscribers under different price rates. The longer calldurations of subscribers with the lower price rate is obvious.
Note however that the half-price rate does not give a twofold increase in duration, but extends call duration by about
50%. Nevertheless, this behavior suggests some degree of pricesensitivity among the users.
Users’ different responses to remaining balances is observed in Fig. 1 (bottom). In order to differentiate responses,
each user’s call duration is modeled as a power function ofremaining balance. The exponent is estimated from each individual’s call history, and then used as a classifier. The figure
only includes the individuals whose history of call durations
can successfully yield parameters of the model. Group number
1, 2, and 3 stands for the group having approximately zero,
positive, and negative exponent, respectively. Each point for agroup in the figure is the average of all call durations for thatgroup. Note that all three groups behave similarly when thebalance is low, particularly below 1,000. However, the usage
patterns show different trends as the balance increases. In
particular, Group 2 continues responding to increasing balanceby having longer calls. This demonstrates there are different
responses to remaining balance within the user population andthis suggests different price sensitivities for different users.
Fig. 2 shows the time between consecutive outgoing-calls(blue) and outgoing-SMSs (red) as a function of current user
balance. Sharply increasing inter-call time indicates the users’
reluctance for topping up. For the inter-SMS time, since theraw data is noisy, we applied a smoothing filter based on

153

21

H. Lee’s work was supported by the Seoul Metropolitan Government R&BD
Program WR080951. The authors would like to thank J. Borger, A. Groverand K. Nithi for valuable conversations.

CDRs for mobile data users in order to perform such a study.

Poster 2

I. I NTRODUCTION
Wireless service providers would like to learn more about
how users respond to prices and the utility users derivefrom various network transactions. This kind of information
is valuable in developing models of user behavior and indesigning pricing schemes, especially dynamic pricing plans[2],[3].
However, obtaining such information is difficult, because
in today’s wireless networks users rarely make a separate
payment for each network transaction (e.g., voice call, SMS, ordata download), nor are transactions offered at multiple pricepoints. Instead, users pay for a quota, which can take twoforms. With a prepaid plan, the user pays for a certain quantityof voice or data service, which can then be consumed over aflexible time frame. Alternatively, with a capped plan, the user
pays a flat fee for a fixed voice or data quota, and any unused
quota at the end of the month perishes.
The idea we explore here is to see how much of what wewould like to know about users can be obtained via a studyof quota dynamics: the change in user behavior as a functionof quota balance and/or time to the end of the quota period.
For example, in a prepaid voice plan, how does a user’s callfrequency change with decreasing balance? Or, in a capped
data plan, how much of a user’s quota is typically wasted ineach period?
Understanding the dynamics of such quota use can (i)provide insights into user price sensitivity, (ii) enable us todevelop models of user behavior that can be fed into a variety
of pricing models (like the two-sided pricing model in [1]),and (iii) shed light on the underlying utility that end users
apply to different network transactions.
Our work on quota dynamics has two parts. First (Section II), we have performed data analysis on call detail records(CDRs) from an operator that services prepaid voice and SMS
users. This analysis shows that user behavior changes markedly
as the remaining quota gets low. Second, we have developed
user models to describe the observed behavior. In the firstmodel (Section III-A), there is an underlying set of “potential”
network transactions. The probability that the user actually
performs a transaction depends on the current balance as wellas the time to the end of the quota period. In the second model(Section III-B), we aim to explain this type of behavior via autility maximization framework where the user has different
utilities for different types of transactions and her goal is tomaximize the total utility received over the quota period.
We believe that, although our existing CDR data set is forvoice and SMS users only, an equivalent analysis could be
carried out for wireless data users. Moreover, that setting islikely to give richer behavior due to the greater heterogeneityof content. We are in discussions with an operator to obtain

21

Poster 2 // Network

300

60

250

50

200

40

1.9
1.8

150
100

Inter-SMS Time (Hour)Inter-Call Time (Hour)

Time (Second)

1.7
1.6
1.5

30

1.4

20

1.3
50
00

10
500

1000

1500
Balance

2000

300

2500

3000

Price rate=1.0
Price rate=0.5250Time (Second)

200
150
100
50
00

500

1000

1500
Balance

2000

2500

300

3000

Group1
Group2
Group3250

Time (Second)

200
150
100
50

Poster 2

21

00

1000

2000

Balance

3000

4000

5000

Fig. 1: Call durations (top), call durations at different rates(middle), and call durations of different user groups (bottom)
the Bartlett window function with window size 51. The interSMS time decreases as current user balance decreases. Thisis exactly the opposite of the trend for the inter-call time andindicates that users consider an SMS as a cheap substitute toa call when their balance is low. (For the plans we consider,
the cost of an SMS is about the same as a one or two-second
call.) Overall, the behavior observed in Fig. 2 provides further
empirical guidance in developing models of user behavior
responding to the current balance.
III. U SER M ODELINGIn this section we present models that could be used to
explain user behavior under prepaid and capped pricing plans.
A main difference between these two kinds of plans is that,

154

00

1.2
1000

2000

Balance

3000

4000

1.1
5000

Fig. 2: Inter-call time (blue) and inter-SMS time (red)in a capped plan, wireless usage is a perishable good: what is
not used at the end of the billing period is lost. Thus, one way
to try to learn about users’ price sensitivities is to examine
how much of their quota is typically “wasted”. On the other
hand, the two kinds of plans both have a notion of balance.
In the prepaid case, balance is the monetary balance in a user
account; in the capped case, balance is the amount of service
remaining for the billing period. In both cases we might expect
usage to decline as the balance decreases.
A. Modeling Usage Directly
We begin describing the model for the prepaid pricing
case, and then discuss how it can be extended to cappedplans for voice and data. Let us consider a user who feels theneed/urge to make calls that arrive following a Poisson processwith rate λ ≥ 0 per day. Each need is either satisfied withprobability p(q), which is a function of the remaining balanceq, or not satisfied with probability 1-p(q). We assume p(q) is
nondecreasing in q and the probability becomes 1 beyond athreshold: p(q) = 1 for q ≥ q0 . In other words, the user does
not take the balance into account and satisfies all calling needs
provided that the balance is high enough. It can be shown thatthe expected inter-call time for a given q, denoted by τ̄ (q),is the inverse of λp(q). If the urge/need is satisfied, the callduration is X(q), which is a non-negative random variable witha known distribution.
Depending on the service (voice/data, prepaid/capped),p(q) may take different forms for q < q0 . Next, we analyze thedata from Section II to estimate the parameters of the model
described above. Fig. 2 gives the inter-call times as a functionof q, which we use to estimate p(q). More explicitly,τ̄ (q) =1 1
λ p(q)

is utilized to estimate the probabilities and the arrival rate.
A log-log plot suggests a power law between the probabilityof acceptance and the remaining balance. Thus, the particularform of p(q) for the prepaid call data isaq b
if q < q0
Pr{accept|q} = p(q) =1
if q ≥ q0

where a, b, and q0 are parameters to be estimated. We fit thedata with nonlinear least squares method that yields a = 0.097,
b = 0.296, q0 = 2656, and λ = 7.142. Also, we can use the21

Poster 2 // Network

data for Fig. 1 (top) to fit a model for call duration X(q),which we omit due to space restrictions.
The model described here can be extended to model usage
in capped voice or data services. Now, the probability ofsatisfying a need/urge to use the service is a function of theremaining quota q and the remaining time in the billing cyclet. We expect p(q, t) to increase as q increases and decrease
as t increases. For capped data services, we can enrich themodel by incorporating how the usage pattern of different
applications (web surfing, video, e-mail, etc.) change in thecourse of a billing cycle. One way to accomplish this with ourmodel is to assume two arrival streams for the need/urge toaccess the network: λ1 for low bandwidth applications likee-mail and λ2 for high bandwidth applications like video.
The probability to satisfy the need/urge can be considerably
different for each stream within the billing cycle. Moreover, if
satisfied, each need would consume highly different amounts
from the remaining quota, X1 (r, t) and X2 (r, t) for low andhigh bandwidth applications, respectively.

Fig. 3: Data consumption under uncertain utility (top), and by
an “underestimating” user (bottom)
allowing users to be mistaken about the actual distribution overparameter c. For example, Fig. 3 (bottom) shows the pattern ofuser consumption when an approximately normal distribution
is used to guide decision making, but a distribution skewed
towards higher values of c is used to determine the actual cvalues encountered by the user. In other words, in this scenario
the user underestimates the utility she will obtain from data
at a future point within the quota period and therefor uses up
too much of her quota earlier in the period.
In our utility-maximizing model, a rational user will consume all available service in every billing period. This iscontrary to everyday experience. We are investigating extensions to our model to account for wasted quota, including
negative utility (sometimes the work to use a service outweighs
the derived benefit) and partially-observed state (users don’t
always know their balance or the date at which the quota periodends).
R EFERENCES
Matthew Andrews, Ulas Ozen, Martin I. Reiman, and Qiong Wang.
Economic models of sponsored content in wireless networks with uncertain demand. In Proc. of 2nd IEEE Workshop on Smart Data Pricing(SDP2013), 2013.
[2] C.A. Gizelis and D.D. Vergados. A survey of pricing schemes inwireless networks. IEEE Communications Surveys Tutorials, 13(1):126–
145, 2011.
[3] S. Sen, C. Joe-Wong, S. Ha, and M. Chiang. A survey of broadband
data pricing: Past proposals, current plans, and future trends. to appear
in ACM Computing Surveys, 2013.

Poster 2

[1]

155

21

B. Modeling Usage Via Utility
So far we have assumed that the need to access the network
is an exogenous process. We now discuss how this need can be
explained and modeled by the actions of a utility-maximizing
user. Our second model focuses on usage under capped plans.
The intuition behind the model is that users know the utility
they will derive from a service at the current time, but uncertain
about this utility in the future, since they do not know withcertainty which content they might want to access. A user’s
pattern of consumption over the billing cycle follows from theuser attempting to maximize the total expected utility for thebilling cycle in the face of this uncertainty.
We formalize the model as a Markov Decision Process(MDP). Recall that an MDP consists of states, actions, a statetransition function, and a reward function. Here, a state consistsof quota balance q, a number i of points remaining in the quota
period, and a parameter c of utility function uc (x) = 1 − e−cx .
An action a (with a ≤ q) is the amount of voice or data
consumed by a user at a state. If i > 0 (so that the quota
period is not finished), the probability of of a transition froma state (q, i, c) to a state (q − a, i − 1, c0 ) on action a is simply
the probability Pr(c0 ) that the utility function parameter takes
value c0 , based on a discrete prob. mass function. In otherwords, an amount a is consumed of the balance, the next time
period is reached, and the utility function of the next time
period is established. The reward with an action a is the utilityuc (a), regardless of source or destination state.
In this framework, the problem we solve is to compute auser strategy that will maximize the expected utility over thequota period. We do this by solving a set of Bellman equations,working backward from the end of the quota period.
We have run simulations to see how patterns of user
consumption are affected by the probability distribution ofutility function parameter c. We observed that the average
consumption pattern does not depend on the distribution for c.
In every distribution we have tested, the average consumption
at each point in the quota period is approximately equal. Fig. 3(top) shows simulated consumption over 30 quota periods, fora uniform distribution over five possible values of c.
Thus, by varying the distribution over c, we were unable
to generate user behaviors similar to Fig. 1. However, such
patterns of consumption can be generated by our model by22

Poster 2 // Network

Quantitative Analysis of Community Detection
Methods for Longitudinal Mobile Data
Syed Agha Muhammad and Kristof Van Laerhoven
Embedded Sensing Systems
Technische Universität Darmstadt
Germany{muhammad,kristof}@ess.tu-darmstadt.de
Abstract—Mobile phones are now equipped with an increasingly large number of built-in sensors, that can be utilized
to collect long-term socio-temporal data of social interactions.
Moreover, the data from different built-in sensors can be combined to predict social interactions. In this paper, we perform
quantitative analysis of 6 famous community detection algorithms
to uncover the community structure from the mobile data. We
use Bluetooth, WLAN, GPS, and contact data for analysis, where
each modality is modelled as an undirected weighted graph.
We evaluate community detection algorithms across 6 intermodality pairs, and use well-know partition evaluation features
to measure clustering similarity between the pairs. We compare
the performance of different methods based on the delivered
partitions.

Poster 2

22

I.

I NTRODUCTION

The problem of finding community structures within acomplex network is familiar in social sciences, and different
community detection methods have been introduced to accomplish this task. However, the methods developed till now
are not acknowledged to be fully reliable, due to the lack ofshared definition of the term community and partition. Mostly
community detection algorithms are analysed on benchmark
datasets [2]. To our knowledge, there has been no work
done on the evaluation of community detection methods onreal networks data. Community structures in a real world
environment can change abruptly, and benchmark datasets lackthe skewness found in real networks. Generally, real networks
lack reference community structures to evaluate algorithms.
To collect the complete ground truth from a large numberof participants is an inefficient and impractical solution. A
possible alternative is to utilize different built-in sensors within
mobile phones. Combining the data from different sensors canprovide points to the possible structures, and the possibility ofmore accurate prediction.
In this paper, we are interested in quantitative analysis ofcommunity detection methods on the Nokia Mobile Dataset[1]. The provided data does not have complete reference
structure of the community. We utilize Bluetooth, WLAN,
GPS, and contact data of the participants. We have anonymized
Bluetooth MAC address and mobile number of the participants.
Our goal is to model graphs for different modalities, apply
community detection methods and determine the partition similarity of different modality combinations. We utilize certain
attributes from each modality to build their graphs. Later on,
we test them with community detection methods. We evaluate
the partition results across 6 different pairs (Bluetooth and

156

GPS, Bluetooth and contact, Bluetooth and WLAN, GPS andcontact, GPS and WLAN, contact and WLAN). We compare
the partition results from every pair to measure clustering
similarity. Figure 1 shows the overview of our approach. For
the detailed discussion of creating graphs for each modality,
we refer the reader to our previous work [3].
II.

R ESULTS AND D ISCUSSIONSThe plots of Figure 2 and 3 illustrate the experimental
results for the pair of modalities and the community detection
methods. In the figures, each column represent the community
detection method and each row represent the combination ofmodalities. BT and CT in the figures represent Bluetooth andcontact. To measure the similarity between modalities, wecreated different pairs of modalities and later tested clustering
evaluation features against them. The blue bars represents computed Rand indices, green bars represents the Jaccard indices,
red bars represents the distance measures, and small blankspaces between the measures are given for the convenienceof the readers. We performed tests on the weighted and unweighted graphs to evaluate the performance of the community
detection methods under different conditions. We found thatthe weighted graphs have better community structures andindices values are higher than the unweighted graphs. The
communities detected by dynamic algorithms have followed
the power law distribution, one or two large communities alongwith many small communities were detected. Modularity optimization techniques have performed well for some modalities.
Similarly, edge betweenness and infomap have detected eithera single big outlier or a big group with many isolated nodes for
all modalities. We will discuss every method and the effect ofadding and removing weights upon the communities detected.
Edge betweenness has a poor overall performance for bothweighted and unweighted graphs. For some modalities a single
super community, and for some single big outlier along withmany isolated nodes were detected. The plots for the weighted
and unweighted graphs shows Rand and Jaccard indices havefluctuated values. The higher values come from the resultof comparing two super communities with many common
nodes, and lower values represent cases of comparing supercommunities with not too many common nodes. Similarly, itshows individual big bars along many small ones for distancemeasure. Normally, edge betweenness produces fair results fora sparse graph or graphs with an average degree of 16, but inour case the graphs are more dense with the minimum average
degree of 15 for single modality, and up to 29 for contact

22

Poster 2 // Network

Raw Data
with Attributes for Graph
Modeling
Raw data

Undirected Weighted andUnweighted Graphs forEvery Modality

Community
Detection Methods

Methodology
Bluetoothand
WLAN data

GPS data

Contact
data

Partition Similarity
Evaluation using
Partition Measures andCommunity Stability

Detecting Communities
from Weighted
and Unweighted Graphs

Blueooth and WLAN
Common Bluetooth and WLAN Bluetooth and WLAN
devices appearing toparticipants
GPS
Find distance between 2
distance points sharing sametime space

GPS

Contact
Mutual contacts shared
amongst participantContact●●

Divisive Algorithm●
Edge betweenness

Bluetooth and WLAN

Modularity Optimization
Technique●

Fast Greedy
Louvain Method
Statistical Inference
Technique●
Infomap●

Dynamic Algorithms

●
●

●
●

Spinglass
Walktrap

GPS

Contact

Inter-modality
Partition Evaluation
and Community
Stability
Partition Measure
Rand Index
Jaccard Index
Distance Measure
Inter-modality Pairs
Bluetooth and GPS
GPS and contact
GPS and WLAN
Bluetooth and contact
Bluetooth and WLAN
Contact and WLAN•

Fig. 1: Overview of our method. After preprocessing the raw data, a graph is generated for each modality that is used as an
input for community detection methods to detect the community structures. The community structures results of inter-modality
pairs are evaluated by partitioning measures to find the clustering similarity.

modality. Additionally, to study the effect of the numberof clusters on the grouping formed and indices, we varied
the number of clusters. The results showed no significant
improvements. For the unweighted graphs, the results are no
more different than for the weighted graph. The detectedgroups were of bigger size along with many individual isolated
communities except for the contacts, where only a single biggroup was detected.

Walktrap has fair results for weighted and unweighted
graphs. The Rand indices for combinations, such as GPS/BT,
GPS/WLAN, and WLAN/BT, are 0.61, 0.62, 0.75 respectively.
The distance measure for those combinations were around44%. The detected groups for WLAN, BT, GPS were almost
the same; however, there was a single big community forcontacts, and that resulted in comparatively lower indicesvalues for those combinations. For the unweighted graph, forcontacts and WLAN super communities with many isolated
nodes were identified. The overall performance for weighted
graphs were better as compare to unweighted graphs. The
length of random walks to perform can be a decisive factor
for the performance of this method. The variation of the length
showed that for larger steps single big communities with many
isolated nodes were detected and similarly for smaller valueof steps a single big community was detected.
Spinglass has performed efficiently for the unweighted
and weighted graph. The Rand, Jaccard indices, and distancemeasure for both weighted and unweighted graphs are thealmost same. We change some parameters of the algorithm
to find its effects on the results. We found for the lower spinstates, bigger communities were detected. By increasing the

157

22

The Louvain method has performed better on weighted
graphs. The detected community structures followed the powerlaw, except for contact modality. For contact modality, a single
super group was detected. It has higher Rand and Jaccard index
of 0.68, and 0.66 for the combination of ’GPS and WLAN’,
similarly distance measure shows many groups with similarity
around 0.40. The Rand indices for the remaining combinations
ranges between 0.50 and 0.68. For distance measure, detectedgroups have the similarity around 0.30. For the unweighted
graph, the detected groups were slightly bigger in size. The
average values for Rand and Jaccard indices have come down
for the combinations. The only exception can be found for the

Infomap has produced weak results for weighted andunweighted graphs. For the weighted graph, lots of small
groups were detected, Rand and Jaccard indices values werevery low. The detected groups have very small similarity. For
the unweighted graph, for every combination a single big
community was detected and this fact be easily observed in
fig 3 .

Poster 2

The Fast greedy method has produced heterogeneousgroups for some modalities, with some small and a single
or two large communities for modalities. For the contacts
modality; however, a single super community was detected.
The most probable reason could be due to a well known
resolution limit, that is biasness of the modularity towards
the bigger communities, which often yield a poor values ofthe modularity maxima. The rand indices for the weighted
graph are between 0.47 and 0.57. For the combinations of
BT/WLAN, the Rand index is slightly higher, and distancemeasure shows many similar clusters.

case of ‘GPS and BT‘, where the Rand index value is 0.65.

22

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

Spinglass

Walktrap

InfoMapLM

FastGreedy

EB

Poster 2 // Network

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

GPS and BT

GPS and CT

GPS and WLAN

BT and CT

BT and WLAN

CT and WLAN

Spinglass

Walktrap

InfoMapLM

FastGreedy

EB

Fig. 2: Community detection methods and the applied modality combinations for weighted graphs.

Poster 2

22

Fig. 3: Community detection methods and the applied modality combinations for unweighted graphs.

spin states, the size of the groups tends to be smaller. Until acertain point there was a changing behavior in the formationof the clusters, but then the detected groups were the same andthe only difference was in orders. The maximum value of therand, and Jaccard indices were found for BT/WLAN modality
with 0.68 and 0.65, and similarly the distance measure also
represents matches amongst the groups. We tested graphs
against the negative implementation, which assigns negativevalues to the edges. The computation time was slightly higher.
For our particular case, negative edge implementation had apoor performance and for most of the modalities single bigger
communities were produced. We also verified the effect of
updating the spins of the vertices in parallel on the results, andwe found that it effected the results by predicting single super

158

communities. For the unweighted graph, we found similar
results for the Rand, Jaccard indices, and distance measure.
The communities detected for the unweighted graphs wereslightly bigger than the weighted graphs.
In conclusion, our evaluation shows that spinglass has
produced better results for mobile data. It has performed
equally better for weighted and unweighted graphs. This fact
is further cemented by the higher values of Rand, Jaccard
indices and a consistent similarity found by distance measure.
Walktrap has also performed good; however, the number ofrandom walks are a crucial factor for the clustering. Thenat the third and fourth place comes Louvain and fast greedy
methods. These methods have performed for some modalities,
but they have also produced outliers for some modalities.

22

Poster 2 // Network

Infomap and edge betweenness performed poor as compared tothe other methods. Pairwise combinations of GPS, Bluetooth,and WLAN data provide points about reference structures.
For the combination of modalities, we found that GPS/BT,
WLAN/GPS, BT/ WLAN have the best results. Mostly features
have given a higher value for the pairs of these modalities.
Table I features some key results from our observations. It
represents results only from weighted graphs.

Spinglass
Walktrap
Louvain Method
Fast Greedy

GPS/BT
RI
JI
0.63
0.608
0.61
0.59
0.55
0.535
0.47
0.42

WLAN/GPS
RI
JI
0.64
0.62
0.62
0.605
0.68
0.66
0.48
0.46

BT/WLAN
RI
JI
0.68
0.65
0.75
0.72
0.59
0.56
0.51
0.49

TABLE I: Key results derived from the results (JI, and RI
stands for Jaccard and Rand Index).

R EFERENCES
[1]
[2]
[3]

N. Kiukkonen, Blom J., O. Dousse, Daniel Gatica-Perez, and Laurila J.
Towards rich mobile phone datasets: Lausanne data collection campaign.
In Proc. ACM Int. Conf. on Pervasive Services (ICPS,’,’,’), Berlin., 7.
Andrea Lancichinetti and Santo Fortunato. Community detection algorithms: a comparative analysis. September 2010.
Syed Agha Muhammad and Kristof Van Laerhoven. Discovery of usergroups within mobile data. In Nokia Mobile data challenge workshop,
june 2012.

Poster 2

22

159

23

Poster 2 // Network

Calling Patterns in Human Communication Dynamics
Zhi-Qiang Jiang,1, ∗ Wen-Jie Xie,1 Ming-Xia Li,1 Boris
Podobnik,2, 3 Wei-Xing Zhou,1, † and H. Eugene Stanley2, ‡1

School of Business, School of Science,and Research Center for Econophysics,
East China University of Science and Technology, Shanghai 200237, China
2Center for Polymer Studies and Department of Physics,
Boston University, Boston, Massachusetts 02215, USA

3

Zagreb School of Economics and Management, 10000 Zagreb, Croatia

Abstract
Modern technologies not only provide a variety of communication modes, e.g., texting, cellphone
conversation, and online instant messaging, but they also provide detailed electronic traces of thesecommunications between individuals. These electronic traces indicate that the interactions occur intemporal bursts. Here, we study the inter-call durations of the 100,000 most-active cellphone users
of a Chinese mobile phone operator. We confirm that the inter-call durations follow a power-law
distribution with an exponential cutoff at the population level but find differences when focusing
on individual users. We apply statistical tests at the individual level and find that the inter-calldurations follow a power-law distribution for only 3460 individuals (3.46%). The inter-call durations
for the majority (73.34%) follow a Weibull distribution. We quantify individual users using threemeasures: out-degree, percentage of outgoing calls, and communication diversity. We find that thecellphone users with a power-law duration distribution fall into three anomalous clusters: robot-

23

based callers, telecom frauds, and telephone sales. This information is of interest to both academics
and practitioners, mobile telecom operator in particular. In contrast, the individual users with a

Poster 2

Weibull duration distribution form the fourth cluster of ordinary cellphone users. We also discover
more information about the calling patterns of these four clusters, e.g., the probability that a user
will call the cr -th most contact and the probability distribution of burst sizes. Our findings may
enable a more detailed analysis of the huge body of data contained in the logs of massive users.

∗

zqjiang@ecust.edu.cn†

wxzhou@ecust.edu.cn‡

hes@bu.edu

160

24

Poster 2 // Network

Evolution of Communities with Focus on Stability
Carlos Sarraute
Grandata Labs
Buenos Aires, Argentinacharles@grandata.com

1

Introduction

date and time of the call or sms, and duration in
the case of calls.
For each month T , we construct a social graph
GT =< NT , ET >. This graph is based on the aggregation of the traffic of several months, more concretely GT depends on the traffic of three months:
T , T − 1 and T − 2. The raw aggregation of thecalls and messages gives a first graph with around
92 M (million) nodes and 565 M edges (on a typical month). The voice communications contribute
413 M edges and the messages contribute 296 M
edges to this graph.
We then perform a symmetrization of the graph,keeping only the edges (A, B) whenever there arecommunications from A to B and from B to A.
This new graph has around 56 M nodes and 133 M(undirected) edges, and represents stronger social
interactions between nodes. Additionally we filter
nodes with high degree (i.e. degree greater than200) since we are interested in the communications
between people (and not call centers or platform
numbers).

3

Dynamic Louvain Method

161

24

Our first experiment to detect evolving communities was to run the original Louvain algorithm [4] on
the graphs at time T and T + 1, and compare thetwo partitions, method that resulted very unstable.
Our second experiment was to run the Louvain algorithm modified by Aynaud and Guillaume [5] toobtain a more stable evolution. As we show in Section 4 the results were still unsatisfying in terms ofstability.
In our use case (e.g. telecom analysts performing actions on the communities), the stability ofthe partition is our main concern. With this goalin mind, we propose two modifications to the Louvain method, that give the partition at the previous
time step a sort of “momentum”, and make it moresuitable to track communities in dynamic graphs.
2 Data Sources
Before describing them, we introduce some notaOur raw data source is anonymized traffic informa- tions. As stated in the previous section, we contion from a mobile operator. The analyzed informa- sider snapshots of the social graph constructed attion ranges from January 2012 to January 2013, and discrete time steps (in our case every month). Let
contains for each communication the origin, target, GT =< NT , ET > be a graph that has already

Poster 2

The detection of communities is an important tool
used to analyze the social graph of mobile phoneusers. Within each community, customers are susceptible of attracting new ones, retaining old ones
and/or accepting new products or services throughthe leverage of mutual influences [1]. The communities of users are smaller units, easier to grasp, andallow for example the computation of role analysis – based on the centrality of an actor within his
community.
The problem of finding communities in static
graphs has been widely studied (see [2] for a survey).
However, from the point of view of a telecom analyst, to be really useful, the detected communities
must evolve as the social graph of communications
changes over time – for example, in order to perform marketing actions on communities and track
the results of those actions over time. Additionally the behaviors of communities of users over time
can be used to predict future activity that interests
the telecom operators, such as subscriber churn or
handset adoption [3]. Similary group evolution can
provide insights for designing strategies, such as theearly warning of group churn.
Stability is a crucial issue: the analysis performed
on a given community will be lost, if the analyst
cannot keep track of this community in the following time steps. This is the particular use case that
we tackle in this paper: tracking the evolution ofcommunities in dynamic scenarios with focus on
stability.
We propose two modifications to a widely usedstatic community detection algorithm. We then describe experiments to study the stability and quality of the resulting partitions on real-world social
networks, represented by monthly call graphs formillions of subscribers.

Gervasio Calderon
Grandata Labs
Buenos Aires, Argentina
gerva@grandata.com

24

Poster 2

24

Poster 2 // Network

been analyzed and partitioned in communities. LetΓ =< C1 , . . . , CR > be such partition in R communities. Given a new graph GT +1 =< NT +1 , ET +1 >our objective is to find a partition of GT +1 which isstable respect to Γ.
The first idea is to have a set of fixed nodes F.
Let R = NT ∩NT +1 be the set of nodes that remainfrom time T to T + 1. The set F is a subset of R,
whose nodes are assigned to the community they
had at time T . In other words, noting γ the functionthat assigns a community to each node, we require:γT +1 (x) = γT (x) ∀x ∈ F.
We experimented with different distributions of
the fixed nodes, ranging from no fixed nodes (F =∅) to all the remaining nodes (F = R). For theexperimental results, we used a parameter p that
represents the probability that a node belongs to F
(i.e. |F| = p · |R|).
The second idea is to add a probability q of “preferential attachment” to pre-existing communities.
With probability q, the new nodes will prefer to attach to a community existing at time T instead ofattaching to a community formed at time T +1. Wegive the details below.
The Louvain Method [4] is a hierarchical greedy
algorithm, composed of two phases. During phase
1, nodes are considered one by one, and each one
is placed in the neighboring community (including
its own community) that maximizes the modularity gain. This phase is repeated until no node is
moved (that is when the decomposition reaches alocal maximum). Then phase 2 consists in building
the graph between the communities obtained duringphase 1. Then the algorithm starts phase 1 again
with the new graph, in the next hierarchical level ofexecution, and continues until the modularity does
not improve anymore.
We construct a set P ⊆ NT +1 such that |P| =q · |NT +1 |. For every node x, we consider its neighbors that belong to a community existing at time
T , that is the set A(x) = {z ∈ NT +1 | (x, z) ∈ET +1 ∧ γT +1 (z) ∈ ΓT }. During phase 1 of the first
iteration of the algorithm (i.e. during the first hierarchical level of execution), the inner loop is modified. For all node x ∈ NT +1 , if x ∈ P and A(x) 6= ∅then place x in the community of A(x) which maximizes the modularity gain (whereas if A(x) = ∅proceed as usual).

Figure 1: Mutual Information as a function of p andq (expressed as percentages).

Figure 2: Matching communities.

Γ =< C1 , . . . , CR >; and a partition of GT +1 using our dynamic version of the Louvain Method,with different values of the parameters p and q. Let
Γ0 =< C10 , . . . , CS0 > be the partition of GT +1 . We
are interested in comparing Γ and Γ0 in terms of stability and quality of the partition. To this end, we
measure: (i) the mutual information between thetwo partitions; (ii) the number of matching communities (i.e. such that the proportion of nodes incommon is greater than a parameter r); (iii) the
final modularity of Γ0 (as defined in [6]).
The number of matching communities is computed as follows: for each community Cj0 ∈ Γ0 , weevaluate whether there is a community Ci ∈ Γ suchthat |Ci ∩Cj0 | > r·|Ci | and |Ci ∩Cj0 | > r·|Cj0 |, where r
is a fixed parameter verifying r > 0.50 (for instance
we used r = 0.51). In that case, we say that Cj0
4 Experimental Results
matches Ci . The matching communities are of particular interest, because Cj0 can be considered as the
In our experiments, we computed the social graphevolution of Ci (although the community may have(constructed as described in Section 2). Since wegrown or shrank) and can be individually followed
are interested in the real-world application of ourby a human analyst.
method, we preferred to evaluate it on real data.
The mutual information for two partitions ofGiven two months T and T + 1, we calculated acommunities (see [7, 3] for definitions1 ) is computed
partition in communities of GT using the Louvain
1 Since nodes can change between time T and T + 1, we
Method (with the modification of [5]) that we note

162

24

Poster 2 // Network

5

Figure 3: Modularity.
as:0

M I(Γ, Γ ) =R X
SX
i=1 j=1

P (Ci , Cj0 ) log

P (Ci , Cj0 ).
P (Ci ) · P (Cj0 )andFutureThe detection of evolving communities is a subject
that still requires further study from the scientificcommunity. We propose here a practical approach
for a particular version of this problem where thefocus is on stability. The introduction of fixed nodes(with probability p) increases significantly the stability of successive partitions, at the cost of a slightdecrease in the final modularity of each partition.
As future steps of this research, we plan to: (i)study the evolution of communities with finer grain,using smaller time steps; (ii) evaluate the proposed
method on publicly available datasets, to facilitate
the comparison of our results; (iii) refine the matching criteria, and consider additional events in theevolution of dynamic communities (such as birth,
death, merging, splitting, expansion and contraction [3]).

References[1] Bin Wu, Qi Ye, Shengqi Yang, and Bai Wang.
Group CRM: a new telecom CRM frameworkfrom social network perspective. In CNIKM’09,
pages 3–10. ACM, 2009.
[2] Santo Fortunato.
Community detection ingraphs. Physics Reports, 486(3):75–174, 2010.
[3] Derek Greene, Dónal Doyle, and Pádraig Cunningham. Tracking the evolution of communities
in dynamic social networks. In ASONAM’10,
pages 176–183. IEEE, 2010.
[4] Vincent Blondel, Jean-Loup Guillaume, Renaud
Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of
Statistical Mechanics: Theory and Experiment,
2008(10):P10008, 2008.
[5] Thomas Aynaud and Jean-Loup Guillaume.
Static community detection algorithms forevolving networks. In WiOpt’10, pages 513–519.
IEEE, 2010.

[7] Daniel J Fenn, Mason A Porter, Peter J Mucha,
Mark McDonald, Stacy Williams, Neil F Johnson, and Nick S Jones. Dynamical clusteringof exchange rates. Exchange Organizational Behavior Teaching Journal, 2012.

only consider the intersection NT ∩ NT +1 for the mutual
information computation.

163

24

[6] Mark EJ Newman and Michelle Girvan. Finding and evaluating community structure in networks. Physical review E, 69(2):026113, 2004.

Poster 2

To analyze the effect of p and q, we made thoseparameters vary from 0 to 1. The baseline, for p = 0
and q = 0, corresponds to the Louvain Method withthe modifications of [5].
Fig. 1 shows the effect on the mutual information between the two partitions. We can clearlyobserve that the mutual information increases as pincreases, and reaches its maximal values at p =100%. The effect of varying q is not so clear, since
it produces fluctuations of the mutual information
without a marked tendency.
Fig. 2 shows the number of matching communities (according to our criterion). In this graph wesee that the number of matching communities increases dramatically when p approaches 100%. The
effect of varying q is again not clearly marked, although the increase of q produces higher matchingcommunities for smaller values of p.
Fig. 3 shows the effect on the modularity of the
new partition. We can observe that the modularity
decreases slightly as p increases for small values ofq. For greater values of q (closer to 100%), varying
p produces fluctuations with a decreasing tendency.
As a conclusion, we can see that increasing theprobability p of fixed nodes has a clear effect on
increasing the mutual information between the twopartitions, and the number of matching communities. The trade-off with quality is good, since thedecrease in modularity is relatively low.
On the other side, increasing the probability qof preferential attachment to pre-existing communities has not a clear effect on mutual informationor matching communities. It does not seem advisable to use this second modification for generating
evolving communities.

Conclusion
StepsPoster 2 // Network	  Clustering	
  of	
  smartphones	
  ownership	
  in	
  developing	
  countries	  Rich	
  Ling	  Johannes	
  Bjelland	  IT	
  University	
  of	
  Copenhagen	
  Telenor	
  rili@itu.dk	
  Johannes.Bjelland@telenor.com	
  
	
  
	
  
Geoff	  Canright	  	
  Kenth	  Engø-­‐Monsen	
  Telenor	
  Telenor	  geoffrey.canright@telenor.com	
  kenth.engo-­‐monsen@telenor.com	
  
	
  
Pål	
  Roe	  Sundsøy	
  Telenor	
  Pal-­‐Roe.Sundsoy@telenor.com	
  Poster 2

25

We	
  are	
  seeing	
  the	
  development	
  of	
  a	
  large	
  number	
  of	
  applications	
  for	
  smart	  phones	
  that	  focus	  on	  the	
  needs	
  of	
  people	  in	
  developing	
  countries	
  (Alam,	  Khanam,	
  &	
  Khan,	
  2010).	
  These	  developments	
  promise	
  to	
  provide	
  users	
  with	  a	
  wide	  spectrum	
  of	
  services	
  and	
  functionality	
  ranging	
  from	  m-­‐health,	
  to	
  agricultural	
  information,	
  the	
  ability	
  to	
  report	
  problems	
  and	  the	  ability	  to	
  participate	
  in	
  political	
  life	  (Kulkarni	
  &	
  Agrawal,	
  2008).	
  A	
  critical	
  issue	  in	
  this	  situation	
  is,	
  however,	
  limited	
  access	
  to	
  smart-­‐phones	
  that	  can	
  use	  these	  applications.	
  It	
  is	
  
not	
  clear	
  as	
  to	
  whether	
  people	  in	
  developing	
  countries	
  have	  access	
  to	
  these	
  devices.	
  	
  
In	
  this	
  paper	  we	
  examine	
  the	
  degree	  to	
  which	
  smart	  phones	
  are	
  used	  in	
  a	
  developing	
  country	
  (Bangladesh)	
  among	  strong-­‐tie	
  clusters.	
  The	
  experience	
  of	
  adoption	
  in	
  developed	
  countries	
  shows	
  that	  there	  are	
  strong	  network	
  effects	
  when	  considering	
  the	  diffusion	
  of	
  smart	
  phones	
  (Sundsøy	
  et	
  al.,	
  2011).	
  	
  Analysis	
  shows	
  that	  in	
  developing	
  countries,	
  there	  are	  few	
  smart	  phones.	
  In	
  the	
  Scandinavian	  countries	
  the	
  adoption	
  of	
  smart	  phones	
  (here	  defined	  as	
  having	
  an	
  open	  OS	
  and	
  using	  GPRS),	
  has	
  reached	
  approximately	
  60%	  of	
  users.	
  By	
  contrast,	
  in	
  the	
  poorer	
  countries	
  of	
  southern	
  Asia	  there	  are	
  only	  about	
  3%	
  of	
  the	
  users	
  who	
  
have	
  a	
  smart	  phone	  (Telenor,	  2012).	
  This	  has	
  implications	  in	
  relation	
  to	
  the	
  functionality	
  of	
  
apps	
  that	  are,	
  in	
  some	  cases,	
  the	
  threshold	
  for	
  use	  of	
  m-­‐health	
  services,	  m-­‐agriculture	
  and	  m-­‐inclusion.	
  	
  
In	
  this	
  paper	  we	
  examine	
  the	
  degree	  to	
  which	  the	
  existing	
  users	
  of	
  smartphones	  in	
  Bangladesh	
  are	
  clustered.	
  To	
  do	
  this	  we	
  will	  examine	
  the	
  adoption	
  of	
  smart	  phones	
  among	
  100	
  000	
  users	  in	
  Bangladesh	
  to	
  determine	
  the	
  current	
  adoption	
  rates.	
  Further	
  we	
  will	  examine	  the	
  adoption	
  of	
  smart	  phones	  for	  the	  10	  top	
  links	
  for	
  each	  of	
  these	
  users.	
  Our	  hypothesis	
  is	
  that	  smart	  phone	
  users	
  cluster	
  with	  other	  smart	  phone	
  users	
  As	
  we	
  move	  down	
  the	
  scale	  from	  smart	  phones	
  to	
  feature	
  phones	  and	  to	
  basic	
  phones	
  that	  there	
  will	
  be	
  
164

25

25

Poster 2 // Network

decreased	
  clustering.	
  We	
  will	  examine	
  this	  clustering	
  in	
  the	
  light	
  of	
  subscription	
  type,	  urban/rural	
  location	  and	
  intensity	
  of	
  use.	
  	
  
This	
  analysis	
  will	  provide	  a	
  baseline	
  from	  which	  to	
  examine	
  the	  diffusion	
  of	
  this	  technology.	
  
Also	
  it	
  will	  help	
  us	
  to	
  understand	
  the	
  potential	
  for	
  app	
  adoption	  and	
  use.	
  	  	  Alam,	
  M.,	
  Khanam,	
  T.,	  &	
  Khan,	
  R.	
  (2010).	
  Assessing	
  the	
  scope	
  for	
  use	  of	
  mobile	
  based	  solution	
  to	
  improve	
  maternal	
  and	
  child	
  health	
  in	
  Bangladesh:	
  A	
  case	  study.	
  In	
  Proceedings	
  of	
  the	
  4th	
  ACM/IEEE	  International	
  Conference	
  on	
  Information	  and	
  Communication	
  Technologies	
  and	  Development	
  (p.	
  3).	
  Retrieved	
  from	  http://dl.acm.org/citation.cfm?id=2370755	
  Kulkarni,	
  S.,	  &	
  Agrawal,	
  P.	
  (2008).	
  Smartphone	
  driven	
  healthcare	
  system	
  for	
  rural	
  communities	
  in	
  developing	
  countries.	
  In	
  Proceedings	
  of	
  the	
  2nd	  International	
  Workshop	  on	
  Systems	
  and	  Networking	  Support	
  for	
  Health	
  Care	  and	  Assisted	
  Living	
  Environments	
  (pp.	
  8:1–8:3).	
  New	
  York,	
  NY,	
  USA:	
  ACM.	
  doi:10.1145/1515747.1515758	
  Sundsøy,	  P.,	
  Bjelland,	
  J.,	  Canright,	
  G.,	  Engø-­‐Monsen,	
  K.,	  &	
  Ling,	
  R.	
  (2011).	
  Comparing	
  and	  visualizing	
  the	
  social	
  spreading	
  of	
  products	
  on	
  a	
  large	
  social	
  network.	
  In	
  T.	
  Ozyer	  (Ed.),	
  The	  Influence	
  of	
  Technology	
  on	
  Social	
  Network	
  Analysis	
  and	
  Mining.	
  Springer.	
  Telenor.	
  (2012).	
  Handset	
  adoption	
  (Internal	
  material).	
  Fornebu:	
  Telenor.	
  	
  Poster 2

25

165

26

Poster 2 // Network

Mobile phones and digital generations in EU5 countries: a comparison of the 1996 and2009 survey data.
Leopoldina Fortunati1 and Sakari Taipale2
1

Department of Human Sciences, University of Udine, Italy (fortunati.deluca@tin.it)
2

Department of Social Sciences and Philosophy, University of Jyväskylä, Finland(sakari.taipale@jyu.fi)

Abstract
Mobile phone traffic data sets provide much-needed information on actual usage of mobile
phones, such as the type, volume, place and time of usage. However, compared withtraditional survey studies, traffic data involves only a small amount of information on users’socio-economic background (e.g. they do not say anything about users’ education, marital
status, occupation, income, family type or place of residence) and in some cases information
on users, for instance their age, may be unreliable as Ling, Bertel and Sundsøy (2012) haveshown. Furthermore, traffic data typically contains no motivational and attitudinal material. It
is against this backcloth, that we will build our study on the analysis of two consecutive
telephone surveys funded by Telecom Italia. These surveys, based on the same questionnaire(the questionnaire was slightly updated for the second survey) were carried out in Italy,
France, the United Kingdom, Germany and Spain (EU5 countries) in 1996 (N=6,609) and2009 (N=7,255). What makes these data sets rich is that they contain much cross-national
information and allow a comparison between 1996 and 2009 in EU5 countries.

26

In this paper we will investigate, with a special focus on mobile phones, if it makes sense totalk about digital generations in EU5 countries. Several studies have already questioned
whether the difference between digital native and digital immigrant generations, originallyproposed by Prensky (2001), is justified (e.g. Herold 2012). It has been shown that bothgroups are internally incoherent, and other factors (e.g. breadth of use, experience, gender,
education) have expressed in some cases more predictive power than age/generation (Selwyn
2004; Hargittai 2010; Helsper 2010; Helsper & Eynon 2010). In addition, it has been
proposed that a so-called second generation of digital natives (born after 1990) could be
separated from the first generation of natives (born in the 1980s) owing to their greater
immersion in the social media (Helsper & Eynon 2010; Fortunati 2011). The abovementioned studies have typically dealt with single countries and have been premised oncross-sectional data sets.

Poster 2

This study aims to see, firstly, whether the first generation of digital natives was the mosttechnologically equipped generation in 1996 and did its relative position sustain until 2009.
Secondly, we investigate if the first and second generation of digital natives differ from eachother as regards to the use of digital technologies, especially mobile phones. We will use bothbivariate statistics and Multiple Regression Analysis to analyse the data sets.
With regard to the first aim, our preliminary results show that the youngest respondents werethe most equipped with mobile phones (and personal computers) in 1996, but no longer in2009. Instead, it seems that the youth and young adults, who belong to the first generation ofdigital natives – and who also were the first group to adopt these devices in 1996 –
maintained their position in 2009. With regard to the second aim, our data shows that there
were actually no substantial differences between digital natives and immigrant adults in

166

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Poster 2 // Network

relation to the made/received mobile (and fixed) phone calls in 1996. However, it appears
that in 2009 the first generation of natives made and received more mobile phone calls andsend more SMS than the second generation of natives.
References
Fortunati L. (2011) General Native Generations and the New Media. In F. Colombo and L.
Fortunati (Eds.) Broadband Society and Generational Changes. Berlin: Peter Lang,
201–220.
Hargittai, E. (2010) Digital Na(t)ives? Variation in Internet Skills and Uses among Membersof the “Net Generation”, Sociological Inquiry, 80(1), 92–113.
Helsper, E. J. & Eynon, R. (2010) Digital Natives: Where is the Evidence?, British
Educational Research Journal, 36(3), 503–520
Helsper, E. J. (2010) Gendered Internet Use across Generations and Life Stages,
Communication Research, 37(3), 352–374
Herold D. (2012) Digital Natives: Discourses of Exclusion in an Inclusive Society. In E.
Loos, L. Haddon and E. Mante-Meijer (Eds.) Generational Use of New Media. London:
Ashgate, 71–88.
Ling, R. Bertel, T. F. & Sundsøy, P. R. (2012) The socio-demographics of texting: An
analysis of traffic data. New Media and Society, 14(2), 281–298.
Prensky, M., 2001. Digital Natives, Digital Immigrants. On the Horizon MCB University
Press, 95. Available from:http://pirate.shu.edu/~deyrupma/digital%20immigrants,%20part%20I.pdf [Accessed 20
December 2009]
Selwyn, N., 2004. The Information Aged: A Qualitative Study of Older Adults’ Use ofInformation and Communications Technology. Journal of Aging Studies, 18(4), 369384.

Poster 2

26

167

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Poster 2 // Network

Protecting Cellular Networks from Attacks
Launched Via Mobile Applications
E. Suissa, Y. Elovici, B. Chizi
Department of Information Systems Engineering Ben-Gurion University of the Negev P.O.B.
653, Beer-Sheva, 84105. Israel
Deutsche Telekom Laboratories at Ben-Gurion University Beer-Sheva, Israel{einatsui, elovici, chiziba}@post.bgu.ac.il
Malicious mobile applications pose a serious
risk tocellularnetworks
becausecompromised mobile devices operating as acollaborated group can lead to denial ofservice attacks against parts of the cellular
network infrastructure.
In this study we propose mitigating attacks
against the cellular network by loadbalancing the signaling traffic between cells.
The proposed method was evaluated using
two datasets: User's movement data set(containing 195,691 users and 04,,244,6
movement records), and an Antenna's
location and orientation data set (containing
24,462 antenna locations from the Verizon
wireless provider). The evaluation results
demonstrate the feasibility of such attacks
even after load balancing the signaling load
between the cells. The evaluation also
demonstrates how changing dynamically thecell’s location can prevent overloading the
network when there is high signaling traffic
in parts of the network. This mitigation
method is used to create a mechanism,
which allows for the natural immunity of
the network, prevents the denial of service
on parts of the cellular network andtherefore increases the network’s resistanceto signaling attacks launched via malicious
mobile applications.

Poster 2

27

Mobile devices permit the downloading ofmany apps from various repositories, andtherefore make the task of spreading new andpossibly malicious applications extremely easy[3]. While a single compromised device
threatens a single user, a large number of
infected devices become a threat to the cellular
network operator infrastructure.
Malicious and ill programmed mobile
applications can cause serious damage to thecellular networks by performing many kinds ofattacks as described in [2]. In fact, any
application that runs on many mobile devices
may cause DOS (denial of service) to users in
the cellular network.
The GSM networks contain many cell sites,
which sometimes called a "cell towers", Base

168

Transceiver Stations (BTS), or "base stations".
A base station is a physical structure that holds
radio antennas, and a sector refers to a
direction from a given cell tower.
When a mobile device attempt to establish
a connection with the network (i.e., initiate aservice), the base station assigns Standalone
Dedicated Control Channels (SDCCH), overwhich the signaling with the HLR (Home
Location Register) itself is performed. This
resource is shared between all devices in anarea [6].
In each cell in the cellular network, many
devicesareabletocommunicate
simultaneously with the base station. Each cell
has a fixed maximum service range of 35
kilometers. As the user moves with his/hermobile device, different cells connect him/herto the cellular network; hence the same device
can overload different base stations while theuser is moving to another location.
Researchers have already suggested
different protections mechanisms against
denial of service attacks on the cellular
network at the device level, where each device"decides" on its own, the level of the signaling
allowed without taking into consideration thethreat coming from other nearby mobile
devices (which seriously impacts the overallrisk level on the close cellular infrastructure)and the movement of other users. We argue
that a collaborative approach aimed at loadbalancing the signaling traffic between thecells will reduce overload and prevent
blocking legitimate users from getting service
much more effectively than limiting thesignaling at the device level.
We took several assumptions in our work.
One of them is that each mobile device
equipped with a malicious application causes
the same damage to the cellular network.
Moreover, we assume that the accumulated
damage of all the infected mobile devices is
the sum of each one of them in a given timeslot (i.e. the diminishing marginal proceeds
law is not taken into consideration in ourmodel). We also assume no dependency
between users, which means that the only way

27

Poster 2 // Network

Figure 1- Input and output of the method

Each value in the available service matrix
is a function of the distance between theantenna si and the device di (i.e. the value will
be higher as di is closer si). The malware
existence matrix describes which of thedevices are infected by malware (1 forinfected). The proposed target matrix indicates
which base station si gives service to eachdevice di.
Based on optimization techniques we foundfor each device the closest base station
possible, under the constraints regarding
limitations of the cellular network, without
overloading base stations. The optimization
allows us to balance the load automatically,using mathematical tools. It also provides us
the ability to make a sensitivity analysis and tounderstand the relationships between input andoutput variables in the model (i.e. how manycells required to deal with the given load, howeach cell loads the network etc). We calculated
the percentages of overloaded base stations foreach percentage of users running the malicious
applications, and the percentages of legitimate
users affected by the DOS attack for eachpercentage of users running the malicious
applications. The evaluation results based on
mobile phone dataset are summarized in
Figure 2 and Figure 3 respectively:

27

In order to assess the feasibility andpotential damage of the attacks caused bymobile applications on the cellular network
infrastructure, we used a mathematicaloptimization technique. This gives us precise
mathematical formulations for the practical
performance optimization task of loadbalancing, and the ability to create sensitivity
analysis automatically. We introduce thefollowing method, which is formalized as aninteger linear program (d - device, s – BTS):

Poster 2

users can get a malicious application is bydownloading it from the market independently;
therefore users do not infect each other by
Bluetooth or MMS messages, etc. Our lastassumption is that users can get service fromeach base station in the radius of 35km without
taking into consideration the height of the basestations and other factors, which may affect theworking range of the cell site – the range
within which mobile devices can connect to it
reliably.
In our study, according to [7], we firstbalanced the signaling traffic between thecells, i.e. we found the optimum base stationfor each mobile device at a specific time in a
specific location. Then, we assessed thefeasibility and potential damage of the attacks
caused by mobile applications on the cellular
network infrastructure. At last, we suggested
how to mitigate the risks caused by mobile
applications on the cellular network
infrastructure by changing part of the cell'slocations, i.e. attack oppression is performed
based on data on users’ movement and the
spread of the malicious mobile application.
The mitigation method provided in this study
is very useful and prevents DOS attacks on thecellular network infrastructure and assures thatusers will receive service in any place at any
time.
Most of the signaling services provided by
the cellular network can be delivered overeither the CCH (Common Control Channel) or
the DCH (Data Channel). One of the mostcritical wireless bottlenecks is the Standalone
Dedicated Control Channels (SDCCH). If
available, the base station assigns an SDCCH,
over which the signaling with the HLR itself is
performed. By repeatedly triggering radio
channel allocations and revocations tocomplete the data transfer a DOS attack may
occur.
Sectors in a cellular network typicallyallocate 8 or 12 SDCCHs. The hold time ofthis channel is 2.7 seconds (as modeled byprevious researches), so 0.37 users hold thischannel every second. Each base station has 3
sectors, for each sector 12 SDCCH channels
and each one of these channels is occupied by
0.37 users every second. Therefore the numberof signaling messages per second over whichan attack may be distributed in each basestation is calculated as follows:

Figure 2- Percentage of overload base stations for agiven percentage of users running the malicious
applications.

169

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Poster 2 // Network

number on each arrow presents the number ofantennas moving in that path.

Figure 3 - Percentages of legitimate users under DOS
attack for a given percentages of users running themalicious applications.

After evaluating the potential damage ofsuch attacks, we would like to ensure that eachuser will get service at any place and any time,and in order to do so we need to add antennas
in specific places and in accordance to thetiming of attacks.
If we add antennas to each base station
under attack, we will use an unnecessaryamount of resources instead of simply moving
antennas from one base station to another basestation at a different time, according to theusers' movement. To evaluate the exact
number of cellular antennas required tomitigate such attacks, we used an algorithm,
which takes as input the time of the attack,base station's locations and number of antennas
to add to each overloaded base station. The
results are described in Figure 4 (no time tomove antenna from S2 to S3).

Figure 5 – Output of the mobility algorithm. It
determines the amount of cellular antennas to add andtheir paths.

The outcome of this research will provide
advanced tools and methodology, which willallow us to evaluate the loads caused bymobile applications on the cellular network.
These results will include a demonstration of
possible denial of service attacks against thecellular network infrastructure for a given
movement data and antennas' locations andtheir potential damage. This can be represented
by heat maps of signaling traffic loads on themobile infrastructure.
The findings of this work can be used incases of abnormal signaling traffic to blockattacks and improve the quality of user data
communications. Moreover, the outcome ofthis research can be formulated as a set ofactionable insights, which can be taken into
account on to the everyday activity of any
cellular provider.
REFERENCES[1] Wang, P., González, M. C., Hidalgo, C. A., &Poster 2

27

Figure 4 – Base stations under attack Graph, theconnectivity of this graph determine whether it ispossible to transfer antennas to other base stations
according to the time and distance.

Our method was applied on our mobile
phone dataset and returns the minimum
number of cellular antennas that we need to
add in order to immunize the network andprevent it from being overloaded, and theirpaths (movement of antennas from one
location to another). Figure 5 present a small
example of the algorithm result, where eachbase station needs amount of added antennas(described in parentheses) to deal with theoverload in the specific time mentioned. The

170

Barabási, A. L. (2009). Understanding the spreading
patterns of mobile phone viruses. Science, 324(5930),
1071-1076.
[2] Mulliner, C., Liebergeld, S., Lange, M., & Seifert, J.
P. (2012, June). Taming Mr Hayes: Mitigating
signaling based attacks on smartphones. In
Dependable Systems and Networks (DSN), 2012 42nd
Annual IEEE/IFIP International Conference on (pp.
1-12). IEEE.‫‏‬[3] Hypponen, M. (2006). Malware goes mobile.
Scientific American, 295(5), 70-77.‫‏‬[4] Pollini, G. P., Meier-Hellstern, K. S., & Goodman, D.
J. (1995). Signaling traffic volume generated bymobile and personal communications.
Communications Magazine, IEEE, 33(6), 60-65.‫‏‬[5] Traynor, P., Lin, M., Ongtang, M., Rao, V., Jaeger, T.,
McDaniel, P., & La Porta, T. (2009, November). On
cellular botnets: Measuring the impact of malicious
devices on a cellular network core. In Proceedings ofthe 16th ACM conference on Computer andcommunications security (pp. 223-234). ACM.‫‏‬[6] Rappaport, T. S. (1996). Wireless communications:principles and practice. IEEE press.‫‏‬[7] Mathar, R., & Niessen, T. (2000). Optimum
positioning of base stations for cellular radio
networks. Wireless Networks, 6(6), 421-428.‫‏‬

28

Poster 2 // Network

Customer churn prediction: Integrationof sociometric theory of cliques into a
diffusion model
Uroš Droftinaa , Andrej Koširb
A Telekom Slovenije, d.d., Slovenia,
B Faculty of Electrical Engineering, University of Ljubljana, Slovenia
A uros.droftina@telekom.si, B andrej.kosir@ldos.fe.uni-lj.si

Abstract
This paper presents current state-of-the-art diffusion model used in churn prediction modelling and faces withsome of its drawbacks. A more sophisticated approach to determining initial energy of users is proposed, basedon direct connections between users with focus on maximal sociometric cliques. The proposed approach isexperimentally evaluated on real world data from Slovenian mobile service provider. Results indicate that
substantially improved performance is achieved using proposed approach for initial energy determination in
SPA model compared to the basic model.

I.

Introduction

II.

Methods

171

28

Presented SPA algorithm is an effective approachto determining potential churners influenced byrecent churners that are strongly connected tothem. However certain issues arise after a detailed study of SPA algorithm. One of the issues
we address and try to solve in this paper is thatall seed churners are assigned the same energy
on the beginning. It is known that users havevery different influence in their correspondingsocial neighbourhoods [4] which means that starting energy should be different among users. An
old saying says that it is better to have a few true

Poster 2

The area of churn prediction modelling has received much attention in the last two decades,
especially in the telco market. The first known
approach in this area uses machine learning
through data mining techniques [5]. A prediction model is constructed based on previous user
behaviour and used to predict events, such as
churn. An exhaustive overview of these techniques is presented in [7]. The problem of thisapproach is that it only considers individual user
attributes. It is believed that user decisions suchas churn are commonly influenced by other connected users.
With the evolving of social networks in the
recent years considering social ties has provento be very promising in churn prediction. Some
researchers in this area recommended and evaluated the usage of diffusion models for the purpose of modelling influence spread and consequently spread of churn. Dasgupta et al. [3]proposed a spreading activation-based technique(further addressed as "SPA" algorithm) that predicts potential churners based on their corresponding social network, including information
on users that already churned. The idea is thatusers, who recently churned, influenced with
their decisions the other users in their social
neighbourhood. Underlying algorithm assigns

all recent known churner the same initial positive
non-zero energy (e.g. 1) while all other users startwith zero energy. An energy spreading techniqueis then initialized where in each iteration active
nodes (with non-zero energy) transfer a portionof their energy to their neighbours, relative to thestrength of connections between nodes. Overall
amount of energy stays the same throughout thewhole process. Iterating process continues until
a stable state is achieved. Afterwards a simple
threshold-based technique is used where a threshold T is fixed and nodes with energy greater than
T are labelled as potential churners while otherare labelled as non-churners.

28

Poster 2 // Network

friends than lots of fake friends. In that aspect wehypothesise that users with fewer connections are
more influential than users with many connections. This is already partially implemented inoriginal SPA diffusion model at transfers of seednode energy to their connections, where smaller
number of connected nodes means more transferred energy per user, but our results show that
influence transfer ratio between less and moreconnected users should be greater.

Poster 2

28

Users churn because of different reasons. Reasons can be basically divided into two categories,
local reasons (e.g. non-optimal price, poor coverage) and social reasons (influence from otherusers in social network). We believe that if user’s
reasons to churn are social, these users will also
have greater influence on other users in theirneighbourhood as opposed to users that churned
due to local reasons. This is our second hypothesis.
Our third hypothesis says that even if users
have many connections, they can also have a few
very good friends that have considerably greater
influence on each another than on other users
outside their small groups. These groups canbe determined by searching maximal cliques [1]on a corresponding social network graph. In thearea of graph theory, cliques are such subsets ofundirected graphs that every two nodes in thesubset are connected by an edge. Furter maximal clique is a clique that cannot be extended
by including another adjacent node. In a social
network of people where connections represent
acquaintances, a clique is a group of people who
all know each other. Social science theory statesthat people in the same clique have greater influence on each other than on or from connections
outside cliques [2]. In this paper we proposea more sophisticated approach to determining
initial energy of users, based on their direct connections with focus on maximal cliques. The goalof this paper is to introduce and evaluate the improvement of SPA model using the applicationof social science clique theory for initial energy
determination.
Our proposed model assigns all users threedifferent types of contribution to initial energy where each contribution is given as a pair(energy, weight):

172

1. self contribution
2. clique contribution
3. out-of-clique contribution
Self contribution is the same energy contribution as in original SPA model, i.e. if a user recently churned, we assign him a self-contributionenergy Esel f = 1, else Esel f = 0. Weight of a self
contribution is determined as 1.
Clique contribution is an energy contribution from all maximal cliques a user is a partof. Due to simplicity reasons we further address
maximal cliques only as cliques. Energy contribution of each user in a clique is determined byequation (1)Ecl n = 2

nc
−1
nu

(1)

where nc is the number of churners in a cliqueand nu in the number of all users in a clique. Eachclique assigns a separate contribution to the energy of a user. Weight of a clique contribution
is usually greater than 1 (e.g. proportional tonumber of users in a clique).
Out-of-clique contribution is an energy contribution of all users that are not in any clique.
Out-of-clique energy contribution is also calculated by equation (1), where a user with its directly connected nodes is considered instead ofa clique. We determine the value of weight ofan out-of-clique contribution as 1. Users that are
members of at least one clique do not have an
out-of-clique contribution.
Ecl n in equation (1) can take values on interval[−1, 1] where value -1 describes a clique without
churners and value 1 describes a clique with all
churners. As a consequence a negative influenceis also introduced, which symbolically spreads
influence against churn decision.
When all the contributions are assigned, an
initial energy value is calculated as a scalar product of energy- and weight-vector (2)E0 = e · w(2)where e is an energy vector e =[esel f , e1 , e2 , ..., en ] and w is a weight vector w =[wsel f , w1 , w2 , ..., wn ]. The sign of initial energy
symbolically determines positive and negative
churn influence.

28

Poster 2 // Network

0.25

Optimal F−measure

original SPA
clique−based SPA
0.2
0.15
0.1
0.05
0

0

20

40
60
80
Number of iterations

100

120

Figure 1: F-measure values in each iteration of SPA

III. Results

Our preliminary results clearly show a significant
improved in churn prediction compared to basic
SPA diffusion model. An inspection of original
SPA model trend line in figure 1 shows that highest prediction value is already achieved after first
iteration. This indicates that users are consider-[1] Alba, R.D.: A graph-theoretic definition of a sociometric clique. The Journal of Mathematical Sociology 3(1),
113–126 (1973)[2] van den Berg, Y.H., Cillessen, A.H.: Computerized sociometric and peer assessment an empirical and practicalevaluation. International Journal of Behavioral Development 37(1), 68–76 (2013)[3] Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty,D., Mukherjea, S., Nanavati, A.A., Joshi, A.: Social ties
and their relevance to churn in mobile telecom networks.
In: Proceedings of the 11th international conference on
Extending database technology Advances in databasetechnology - EDBT ’08, pp. 697–711 (2008)[4] Kempe, D., Kleinberg, J.: Influential nodes in a diffusion
model for social networks. Automata, Languages and(2005)[5] Mozer, M.C., Wolniewicz, R., Grimes, D.B., Johnson, E.,
Kaushansky, H.: Predicting subscriber dissatisfaction andimproving retention in the wireless telecommunicationsindustry. IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council 11(3), 690–6(2000)[6] Östergård, P.R.: A fast algorithm for the maximum clique
problem. Discrete Applied Mathematics 120(1-3), 197–207(2002)

28

IV. Discussion

References

Poster 2

In this study, we compare the classification performance of SPA model using original initial energy determination as in [3], and in this paper
proposed algorithm. We evaluate the results using standard evaluation method, F-measure (orF-score).
For our experiment we used the data from thelargest Slovenian mobile service provider withmore than one million users. Due to the computational complexity of the problem only a subsetof over 5000 connected users was used in our experiment. A social graph was constructed froma one month period call detail records (CDR)
where nodes presented users and connections
presented communication between users. Connections were weighted by sum of successful
calls and sent short text messages where only
connections with both-way communication were
included (i.e. user A at least once called or sent
an SMS to user B, and vice versa).
Evaluation of both approaches is presented infigure 1. Figure presents how optimal F-measurevalue changes with the number of SPA iterations
applied.

ably more influenced by their directly connected
users rather than other users that are not in theirneighbourhood. Iterating original SPA diffusion
model until convergence is therefore not optimal.
Prediction evaluation of our approach is already relatively high when using initial values
as prediction scores. After running first few iterations of SPA algorithm using our initial values
prediction result also slightly improves. We believe that improvement of our algorithm can bemade where the best prediction accuracy will
be achieved before applying a process of energy
spreading.
In computer science, finding cliques in agraph is known to be NP-hard problem. Therefore even by avoiding an iterative process of SPA
algorithm completely, clique discovery still remains computationally most expensive part ofalgorithm. Using advanced fast algorithms forfinding cliques in social graphs addresses thisissue [6].

[7] Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens,
B.: New insights into churn prediction in the telecommunication sector: A profit driven data mining approach.
European Journal of Operational Research 218(1), 211–
229 (2012)

173

29

Poster 2 // Network

Characterizing social network spatio-temporal structure
through Cell Phone Records
Luis G. Moyano, Enrique Frias-Martinez,
Telefónica Research, Madrid, Spain
moyano,efm@tid.es

Oscar R. Moll Thomae
MIT, Boston, MA, USArimoll@mit.edu

Abstract
The connection between human mobility and social networks has gained muchattention lately, but research in this area is limited due to lack of adequate data that
describes both mobility and social interactions. In this work, we characterize spatiotemporal features of social networks taken from a comprehensive, national-wide datasetof cell phone records. Our results show a non-trivial dependence between social network structure and the spatial distribution of its elements (Fig 1., left panel). Moreover,
we describe and quantify precisely the probability of parties in a call to be at a given
distance. Our results show that this probability is well described by the framework ofgravity models, but with different decaying rates at urban and interurban scales (Fig.
1, right panel). Finally, we discuss how the structural information gained may be used
to estimate a class of unknown features of the network.

101
10-1

100
10-1

P(dijs)

Pr

10-2
10-3

10-2

10-4
10-5

10-3

10-6
10-7 0
10

101

wi

102

103

100

101

dijs

102

Poster 2

29

Figure 1: Left panel: Probability distribution of the mean number of calls n̄i given thatki ≥ 45 (open dots) and ki ≤ 3 (solid dots). Right panel: Probability distribution of the
distance di js (in km) associated to a call. The region approximately between 1 km and 10
km may be described by a power-law decay with exponent α = −0.77, while the region
from 10 km onward shows a faster decay consistent with an exponent α = −1.5.

174

30

Poster 2 // Network

Ethnic segregation in daily spatial mobility: Call Detail Record based study in Estonia
Rein Ahas1,2, Siiri Silm1, Erki Saluveer3
1

Department of Geography, University of Tartu, 46 Vanemuise St., Tartu 51014, Estonia,
rein.ahas@ut.ee; siiri.silm@ut.ee

2

Department of Geography, Ghent University, Krijgslaan 281, S8, B9000 Gent - Belgium

3

Positium LBS, Õpetaja 9, Tartu, Estonia, erki.saluveer@positium.ee

Abstract
Ethnic segregation remains a topical issue, with globalisation leading to increasing migration
flows and ethnic tensions escalating in a number of regions. The need to examine the entire
scope of activities of individuals in segregation studies is often emphasised by scientists andpolicy makers. We used mobile phone Call Detail Records (CDR) for one year to compare thespatial mobility of native Estonians and the Russian-speaking minority in Estonia andinternational travel. The results show that ethnicity has a significant influence on the spatial
mobility of individuals in Estonia. The biggest differences between the two population groups
occur in Estonia outside the respondents' home city of Tallinn where the Russian minority
were found to visit 45% fewer districts than Estonians. For international travel, the Russianspeaking minority visit fewer countries and have a 3.6 times higher probability of visiting
former Soviet Union countries than Estonians. Our results show that ethnic segregation hasless effect on everyday spatial mobility and a greater influence on the choices made regarding
long-distance travel.

Poster 2

30

175

	
  
Reality	
  Commons:	
  Rich	  Datasets	
  for	
  Mobile	
  Network	  Science	  	
  Human	  Dynamics	
  Lab,	
  MIT	
  http://realitycommons.media.mit.edu	
  
	
  
	
  
Cell	
   phones	
   have	
   become	   an	
   important	
   platform	
   for	   the	   understanding	
   of	
   social	  dynamics	
   and	   influence,	
   because	
   of	
   their	
   pervasiveness,	
   sensing	
   capabilities,	
   and	  computational	
  power.	
  Many	  applications	
  have	  emerged	
  in	
  recent	
  years	
  in	
  mobile	
  health,	  mobile	  banking,	
  location	
  based	
  services,	
  media	  democracy,	
  and	
  social	
  movements.	
  With	  these	
   new	
   capabilities,	
   we	
   can	
   potentially	
   be	
   able	   to	
   identify	
   exact	
   points	
   and	   times	
   of	
  infection	
   for	   diseases,	
   determine	
   who	   most	   influences	
   us	
   to	
   gain	
   weight	
   or	
   become	  healthier,	
   know	
   exactly	
   how	
   information	
   flows	
   among	   employees	
   and	   productivity	
  emerges	
  in	
  our	
  work	  spaces,	
  and	
  understand	
  how	
  rumors	
  spread.	
  	
  There	
   remain,	
   however,	
   significant	
   challenges	
   to	
   making	
   mobile	   phones	
   the	   essential	
  tool	
  for	  conducting	
  social	
  science	
  research	
  and	
  also	  supporting	
  mobile	
  commerce	
  with	  a	
  solid	
  social	
  science	
  foundation.	
  Perhaps	
  the	
  greatest	
  challenge	
  is	
  the	
  lack	  of	
  data	  in	
  the	  public	  domain,	
  data	  large	
  and	
  extensive	  enough	  to	
  capture	
  the	  disparate	
  facets	
  of	
  human	  behavior	
  and	
  interactions.	
  Another	  major	
  challenge	
  lies	  in	
  the	
  interdisciplinary	
  nature	
  of	
  conducting	
   social	
   science	
   research	
   with	   mobile	   phones.	
   Software	
   engineers	
   need	   to	  work	
  collaboratively	
  alongside	
  social	
  scientists	
  and	
  data	  miners	
  in	
  various	
  fields.	
  
	
  
In	
   an	
   attempt	
   to	
   address	
   these	   challenges,	
   we	
   release	
   several	
   mobile	
   data	   sets	
   here	   in	
  "Reality	
   Commons"	
   that	
   contain	
   the	   dynamics	
   of	   several	
   communities	
   of	
   about	   100	  people	  each.	
  We	
  invite	  researchers	
  to	
  propose	
  and	
  submit	
  their	
  own	
  applications	
  of	
  the	  data	
   to	
  demonstrate	
   the	  scientific	
   and	   business	
   values	
   of	
   these	
  data	   sets,	
  suggest	
  how	  to	
  meaningfully	
  extend	  these	
  experiments	
  to	
  larger	
  populations,	
  and	
  develop	
  the	
  math	  that	  fits	
  agent-­‐based	
  models	  or	
  systems	
  dynamics	
  models	
  to	
  larger	
  populations.	
  	
   Data,	  code,	  and	
  documentation	
  can	  be	
  found	  at	
  	
  http://realitycommons.media.mit.edu	
  	
  
	
  
These	
   data	   sets	
   were	
   collected	
   with	   tools	
   developed	   in	
   the	   MIT	
   Human	
   Dynamics	
   Lab	  and	
   are	   now	
   available	
   as	
   open	
   source	
   projects	
   (see	   the	   funf	   open-­‐source	
   sensing	  platform	
  for	  Android	
  phones,http://funf.media.mit.edu)	
  or	
  at	
  cost	  (e.g.,	
  the	
  sociometric	  badges	  for	
  sensing	  organizational	
  behavior,	
  see	  http://sociometricsolutions.com	
  )	
  
	
  
	
  

Poster 2

31

Poster 2 // Network176

31