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

Book of Abstracts :: Posters

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

Organized bySponsored by2015

Contents
Poster Session 1 :: April 8
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Extrapolation in a Big Data Environment: Detect Home Locations in a Continuous Stream of
Location Data
Hendrik Wagenseil and Markus Ziegler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Classic meets Machine Learning
Vlad Ardelean and Nina Meinel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Social Networks, Ethnicity, and Political Accountability
Nicholas Eubank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Discovering dependence of tweet inter-arrival times
Balázs Gerencsér, Christophe Cloquet and Vincent Blondel . . . . . . . . . . . . . . . . . . . . . . . . . . 11
A Unified Model for Individual Spatial Temporal Mobility Patterns
Yingxiang Yang Yang and Marta Gonzalez. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Estimating the Wealth of an Individual Based on Individual Patterns of Phone Use
Joshua Blumenstock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Predicting commuting trips based on mobile phone data
Carlos Andre Reis Pinheiro, Alexandre Evsukoff, Bart Baesens, Nelson Ebecken and Moacyr Silva . 21
Real-time Event Detection Method using Location Information from Mobile Phone Handsets
Satoshi Nishiyama, Kento Kimura, Jun Nakajima and Shinji Kashima . . . . . . . . . . . . . . . . . . . . 25
Measuring global and regional influence of cites using geolocated tweets
Jose J. Ramasco, Maxime Lenormand, Bruno Gonçalves, and Antonia Tugores . . . . . . . . . . . . . 27
Empirical Evaluation of Disaggregated Trip Data Collected from Cellular Networks
Peter Widhalm and Michael Ulm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
I don’t have a photograph, but you can have my footprints
Christopher Riederer, Sebastian Zimmeck, Coralie Phanord, Augustin Chaintreau and Steven Bellovin 32
Longitudinal Human Mobility and Real-time Access to a National Density Surface of Retail
Outlets
Thomas Kirchner, Hong Gao, Andrew Anesetti-Rothermel, Heather Carlos and Brian House . . . . . 33
Bandicoot: a Python toolbox to extract behavioral indicators from mobile phone metadata
Yves-Alexandre de Montjoye, Luc Rocher and Alex ’Sandy’ Pentland . . . . . . . . . . . . . . . . . . . . 36
Detecting Train Commuters using CDRs and GIS information
Hiroki Ishizuka, Nao Kobayashi, Shigeki Muramatsu and Chihiro Ono. . . . . . . . . . . . . . . . . . . . . 39
Agent-Based Dynamic Pricing for Wireless Services
Dina Elreedy, Amir Atiya and Hatem Fayed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Measuring the predictability of interstate travel times using real time traffic monitoring data
for Boston
Morgan Frank, Serdar Colak, Jameson Toole and Marta Gonzalez . . . . . . . . . . . . . . . . . . . . . . 45
Analysis and Modeling of Mobile Data Traffic in Mexico City
Eduardo Mucelli Rezende Oliveira, Aline Carneiro Viana, Naveen Kolar Purushothama and Carlos
Sarraute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Inference of Users Demographic Attributes based on Homophily in Communication Networks
Jorge Brea, Javier Burroni and Carlos Sarraute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
The City Pulse of Buenos Aires
Carlos Sarraute, Carolina Lang, Nicolas Ponieman and Sebastian Anapolsky. . . . . . . . . . . . . . . 54
Detecting and understanding big events in big cities
Barbara Furletti, Lorenzo Gabrielli, Roberto Trasarti, Zbigniew Smoreda, Maarten Vanhoof andCezary Ziemlicki. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Vehicle-Relative Positioning System
Márton Hunyady, Gergely Lukács and András Oláh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Change Detection in Human Mobility Patterns from Successive OD Matrices
Julio Chaves, Moacyr Silva and Alexandre Evsukoff. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Inferring social status and rich club effects in enterprise communication networks
Yuxiao Dong, Jie Tang and Nitesh Chawla. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

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24. Home and Work Estimation from Mobile Phone Data: Improving Accuracy and Privacy throughSpatial Aggregation
Bradley Sturt, Jameson Toole, Serdar Çolak and Marta Gonzalez. . . . . . . . . . . . . . . . . . . . . . . 66
25. Studying Human Behavior through the Lens of Mobile Phones during Floods
Alfredo J. Morales, David Pastor-Escuredo, Yolanda Torres, Vanessa Frias-Martinez, Enrique FriasMartinez, Nuria Oliver, Alex Rutherford, Tomaz Logar, Rene Clausen Nielsen, Olivia De Backer andMiguel Angel Luengo-Oroz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
26. Earthquakes, Hurricanes and Mobile Communication Patterns in the New York Metro Area:
Collective Behavior during Extreme Events
Christopher Small, Richard Becker, Ramón Cáceres and Simon Urbanek. . . . . . . . . . . . . . . . . . 72
27. Application of Floating Phone Data (FPD) in Germany
Moritz von Mörer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
28. Evolving classification based on CDR-derived behavior patterns
Michal Mucha, Dominik Filipiak and Agata Filipowska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
29. Routing messages through mobile phone network in dense cities
Floran Berthaud, Yannick Léo, Carlos Sarraute, Anthony Busson and Eric Fleury. . . . . . . . . . . . . 79
30. Topological Properties and Temporal Dynamics of Place Networks in Urban Environments
Anastasios Noulas, Blake Shaw, Renaud Lambiotte and Cecilia Mascolo. . . . . . . . . . . . . . . . . . 81
31. The Effect of Geographical Proximity on Mobile Communication
Hyungtae Kim and Tony Jebara. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
32. How mobile positioning data can contribute to urban geography: measuring ethnic segregation in daily activity spaces in Estonia
Rein Ahas, Siiri Silm and Erki Saluveer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
33. Protecting the Privacy of Location Data using the openPDS/SafeAnswers Framework
Fatima Makki, Wassim El-Hajj, Abbas El-Hakim and Yves-Alexandre de Montjoye . . . . . . . . . . . 91
34. Characterizing preferences and returns in human mobility
Hugo Barbosa Filho, Fernando B. De Lima Neto, Alexandre Evsukoff and Ronaldo Menezes . . . . . 95
35. Utilizing Origin Destination Information obtained from Mobile Phones in Equilibriated Path
ChoiceSerdar Colak and Marta Gonzalez. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
36. Land use classification using call detail records
Kaushalya Madhawa, Sriganesh Lokanathan, Danaja Maldeniya and Rohan Samarajiva. . . . . . . . . 104
37. Impact of Indoor-Outdoor Context on Crowdsourcing based Mobile Coverage Analysis
Mahesh Marina, Valentin Radu and Konstantinos Balampekos. . . . . . . . . . . . . . . . . . . . . . . . . 107

Poster Session 2 :: April 9
1.
2.
3.

4.
5.

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7.

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Anomaly detection in mobile phone data ? Exploratory analysis using Self-Organizing Maps
Veena Mendiratta, Vijay Gurbani and Chitra Phadke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Mobile Phone Data as a Means of Studying Activity Space Segregation at Scale
Robert Manduca, Bradley Sturt and Marta Gonzalez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Gender-based Characterization of Communication Behavior
Riyadh Alnasser, Faisal Aleissa, Fahad Alhasoun, Abdullah Almaatouq, Anas Alfaris and Marta Gonzalez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Dynamics of social and spatial segregation using mobile phone metadata
Johannes Bjelland, Bjørn-Atle Reme and Pål Sundsøy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom
Defection and Loyalty
Muhammad Raza Khan, Joshua Manoj, Anikate Singh and Joshua Blumenstock . . . . . . . . . . . . 119
Places and Mobility: the Influence of Attractions on People Movement
Fahad Alhasoun, May Alhazzani, Faisal Aleissa, Riyadh Alnasser and Marta Gonzalez . . . . . . . . . 122
Classification of Human Population in Hospitals Using Mobile Phone Data
Guanning Dong, Xianfeng Song and Xinhai Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

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Campaign Optimization through Mobility Network Analysis
Yaniv Altshuler, Erez Shmueli, Guy Zyskind, Oren Lederman, Nuria Oliver and Alex Pentland . . . . 127

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Poster Session 1 :: April 84Extrapolation in a Big Data Environment:
Detect Home Locations in a Continuous Stream of Location Data
Dr. Hendrik Wagenseil, GfK Geomarketing, Nuremberg, Germany
Markus Ziegler, GfK Marketing & Data Sciences, Nuremberg, Germany
Framework and Objective
In recent years, a lot of effort has been put into making use of mobile phone data in the field of Marketing in order to solve business questions. Therefore, GfK, one of the biggest market research companies in
the world, has created Location Insights as an independent field of research and, hence, partnered withmajor carriers in several countries in order to capitalize the spati-temporal content that is inherent in
mobile network data.

Poster Session 1 :: April 8

2015

In this context, GfK receives anonymized tri-laterated location data derived from mobile phone data on
individual level, and is fully responsible for the entire data management, data processing and reporting.
One of the key methodological questions is how to extrapolate from this data to the full population of acountry in order to serve specific business needs, e.g. estimating how many visitors were at a given location. Such information is widely regarded as extremely valuable to a variety of clients, e.g. retailers, OOH
media agencies, transportation agencies or urban planning. One of the key requirements for any extrapolation approach is a proper estimation of the place of residence of the devices.
Commercial projects using mobile phone data delivered by a carrier usually face specific challenges. Firstand foremost, the continuous stream of location data poses a major challenge for data transfer, storage
and processing. It is important to install approaches which allow for the processing of millions of device
data in a manageable amount of time. Additionally, it is in the best interest of both, operator and commercial partner, to make sure that all privacy legislation is strictly adhered to even if this leads to shorter
device lifetimes or a smaller sample to work with. Losing the trust of any device owner or the public canresult in major turmoil for future business relationships. Moreover, the value of the results is heavily dependent on the accuracy of the data. Therefore, working with the data always means taking into the account the fact that location data is affected by measurement error. Finally, a proper extrapolation approach is closely linked to overcoming biases in the data. This contains aspects like differing market
shares of an operator across country and customer segments or the influence of network technologies
on the amount of locations created. All these aspects set the framework for the analytical processes ofanalyzing the data. Hence, dealing with location data in such a setting requires algorithms which are efficient, easy to implement in a big data environment, and easy to adjust to the boundaries set by the data. The estimation of the home location is the first step of the journey to extrapolating results of device
movements. It allows for the linkage of population information and device counts and, as a result, paves
the way for the implementation of an extrapolation factor per device.

Methodology
In order to estimate the home location for all devices, GfK has developed a two-step Bayesian estimation
process which allows accounting for uncertainty in the data while linking them to information about theuniverse. In accordance with Isaacman (2011) the estimation of the home location is based on sightings

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occurring between 7pm and 7am only.
The first step aims at accounting for measurement error in the data. This is achieved by distributing all
relevant locations of a single device to a 100 x 100 meter grid depending on the uncertainty level of every record. As a result, relevant areas on the map can be identified which could serve as potential homeareas.
In the second step, coherent clusters are determined with the most likely being chosen for further calculations. Linking the grid information with census information, each grid cell can be assigned a population
density value which in turn is used as the prior information in the Bayesian approach. The posterior is theprobability of a given cell being the actual home location. Aggregating these cell probabilities allows linking back to the census units and determining the respective home probabilities.

Poster Session 1 :: April 8

2015Conclusion
The proposed two step approach allows to link location data of a mobile device with any kind of census
information while accounting for uncertainty in the location measures. Our talk will outline the main
challenges of doing research with location data derived from mobile network from a commercial point ofview, describe the core ingredients of our algorithm and present some validation results based on a large
sample of devices demonstrating the feasibility of the approach over time.

Sightings from 7pm to 7am:

Allocation of a probability to each affected tractp =0.31

p =0.12

p =0.6
3

Figure 1: Estimation of probabilities for the home location of one device

References
Isaacman, Sibren; Becker, Richard; C´aceres, Ram´on; Kobourov, Stephen; Martonosi, Margaret; Rowland, James; Varshavsky, Alexander (2011): Identifying important places in people’s lives from cellular
network data. Pervasive Computing, p. 133–151

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“Classic meets Machine Learning”
Our world is increasingly online: mobile devices such as tablets or smartphones have become thenatural way to be online 24/7, our entry point to a myriad of services, shops, entertainment andinformation available whenever we need them.
Within different projects, cooperation with mobile carriers allows GfK Marketing and Data Science toanalyze data of mobile devices. The challenges in measuring information in mobile context are
manifold. Most of these challenges come from technical and / or legal limitations; others are simply
due to the sheer size. In some projects mobile data focuses on internet traffic information left in acarrier´s cellular network. The large scale of the data enables to derive app and browsing behavior
as well as the long tail and spatial information. The opportunities this presents brands, advertisersand marketers is huge, and to truly maximise them, it’s essential to understand how mobile devices
are used, and especially who is using them and when. In particular we are interested incharacterizing those populations by socio-demographic profiles, by consumer-related attitudes andbehaviors, which is within carriers CRM information partly or not available. Being able to enrichthose data would add enormous value to the data.

Poster Session 1 :: April 8

Dr. Vlad Ardelean, Dr. Nina Meinel, GfK Marketing & Data Sciences

There are different data source and methods, which might be used to enrich those mobile data andpredict individual characteristics. More specifically, an individual enrichment by using a survey orpanel data could be implemented. Hence, we worked on approaches to enrich those mobile datawith further information by using different integration methods. A short overview is given in following
graphic.
Simple techniques

Focus on aggregates• Random Draws• Mean Imputation

Classical statisticaltechniques

Focus on individuals

Machine learningtechniques

Focus on individuals• Predictive Mean Matching• Lasso Regression• Support Vector Machines• Random Forest• Decision Trees

The paper examines these techniques and their prediction performance for specific set of variables.
The comparison is done by an internal validation and therefore a GfK recruited opt-in panel of thesize of about 3,000 respondents is used. The cooperating carrier provided a month of individual
mobile data.
The presentation will demonstrate the process of investigating different approaches by enriching ahuge data set and summarizes findings, experiences and gives a recommendation on how to enrichmobile data. The presentation will focus on a limited set of variables showing findings and results ona few selected examples.

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Social Networks, Ethnicity, & Political Accountability
Extended Abstract for NetMob 2015
Nicholas Eubank˚
January 10, 2015
Network structure impacts political outcomesby shaping the ability of citizens to interact, organize, and share information with one another.
This paper uses a novel source of data – sixmonths worth of detailed and geocoded telecommunications data from a cell phone provider inZambia – to test the relationship between social network properties and the capacity of citizens to engage in social sanctioning, collective
action, and to share information about incumbent politicians. This is accomplished by first
relating each of these dynamics to different social network structures – transitivity, community fragmentation, and rates of information diffusion – and second, by separately measuring
the properties of the social networks of residentsof each of Zambia’s 150 National Assembly electoral districts and more than 1,000 Local Council electoral districts. In addition, data is used
to examine the relationship between co-ethnicity
and social network proximity, testing the hypothesis that ethnic fragmentation is correlatedwith poor development outcomes because ethnically fragmented communities suffer from fragmented social networks.
While it is widely believed in Economics andPolitical Science that networks affect political outcomes, to date, we have very little empirical evidence on what these inter-citizen networks look
like, and how these networks correlate with political outcomes of interest. This paper advances thisliterature using cell-phone meta-data from morethan 9 million Zambians to directly measure thestructure of social networks across the entire country and relate these structures to social, economic,
and political outcomes of interest.

Systematic data on inter-citizen interactions has
historically been difficult to come by, especially indeveloping countries. As a result, existing work has
been forced to proxy for inter-citizen networks, either by measuring institutional affiliations [2, 17] orby assuming individuals with similar demographic
characteristics (like ethnicity) also belong to common networks [6, 9, 13, 14]. Although reliance onthese proxies in the past is understandable, neither
proxy captures the complexity of social relations.
Institutional affiliations neglect the informal ways
in which people might be connected, and ethnicity
measures neglect heterogeneity in connectedness
within groups. Moreover, these proxies also failto distinguish between the many distinct properties
that fall under the general label of “connectedness”– like the average network distance between individuals, or the degree to which people share common friends. While related, these types of properties are both empirically and theoretically distinct,
and may relate differently to different social phenomena.
This paper begins to fill this gap using a novel
source of data – namely, six months of detailed
telecommunications data on 9 million subscribers
from a cell phone provider in Zambia.1 This data –which is comprised of almost 2 billion telecommunication transactions – is used to test three mechanisms by which networks might shape the ability of citizens to: (1) socially sanction free-riders,(2) organize large groups for political action, and(3) share information about politician activities. In
each case, this paper attempts to tie variation in established social phenomena – like the effectivenessof social sanctioning – to observable, measurable
properties of social networks – like the degree towhich individuals share mutual friends.
˚
PhD Candidate in Political Economy, Stanford Graduate
The first phenomenon examined is social sancSchool of Business. nickeubank@stanford.edu. The author isdeeply indebted to Real Impact and in particular Chief Data
Scientist Gautier Krings for extensive support and assistancewith this project.

Poster Session 1 :: April 8

2015

1
The population of Zambia in 2010 was approximately
12.5 million, suggesting excellent data coverage.

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ing information diffusion in the actual networks ofZambians in different electoral districts, and thenexamines how simulated diffusion rates correlatewith levels of political knowledge among voters.
This investigation has the potential to not only
improve our understanding of the importance ofpatterns of inter-citizen interaction, but also directly investigate a proposed explanation for thenegative correlation between ethno-linguistic fractionalization (ELF) and development outcomes. In
recent years, studies of voting behavior [5, 11],
field studies of social sanctioning [13], and even lab
studies of ethnic preferences [9, 8] have all pointed
to the possibility that (1) co-ethnicity (belonging
to the same ethnicity) may actually be a proxy forsocial network proximity, and (2) that the reason
ELF and poor development outcomes are correlated may be that ethnically homogenous communities have integrated social networks in which citizens are better able to monitor and (when necessary) socially sanction one another. This explanation would have the potential to rationalize a diverse set of findings, including the fact that votersin African elections can be better characterized asvoting for co-ethnic individuals than co-ethnic parties [11], and that in lab experiments subjects act
in a manner consistent with a fear of social sanctioning, “discriminat[ing] in favor of co-ethnics if
and only if they can be seen to be doing so.” [8,p. 721, emphasis in original]. To date, the link between ethnicity and network structure has not been
directly tested empirically. This paper begins to fillthis gap by pairing data on network structures withhighly disaggregated data on ethnic fractionalization in Zambia to provide one of the first systematic tests of whether ELF is correlated with network
structure, as these theories require.
By combining novel geo-referenced social network data with information on social, economic,
and political outcomes, this research will groundwell-established theories about inter-citizen dynamics in robust empirics. Moreover, this researchoffers the promise of opening new avenues of research on the effect of social network structure on
political outcomes.

tioning. Social sanctioning is the application of social pressure to induce individuals to participate inpro-social activities like political protests or community projects. Social sanctioning is core to manymodels of citizen-interactions and and is a key
strategy for overcoming the collective action problem [15]. Yet the ability of citizens to social sanction is often simply assumed [13, 3, 4, 1]. This
paper presents a novel model of social sanctioning
which expands on the work of [10], [6] and [12]to explain both the ability of individuals to sanction one another and also the potential strength ofsocial sanctioning based on a specific social network property – the degree to which people sharemutual friends. This prediction that the degree towhich individuals share mutual friends (“transitivity”) should be positively associated with measuresof political accountability and its consequences isthen tested.
The second phenomenon examined is the how
network structure impedes or facilities coordinating citizens to engage in collective action. Organizing large groups for political action requires finding
a commonly-attractive message, managing logistics, and sharing information among many people.
This is difficult in any setting, but this paper argues
that some network configurations – those in whichpeople are organized into relatively small, internally well-connected but disparate groups – may
make coordination especially challenging. This
theoretical formulation gives rise to the predictionthat measures of network fragmentation2 should be
associated with lower levels of coordinated masspolitical activity, like protests.
Finally, this paper examines how network structure affects the diffusion of information within acommunity. Numerous studies have examined theimpact of mass media on political accountability[16, 7, 18], but this study aims to fill an important
gap in our understanding of informal information
sharing, which is likely to be of particular importance in the developing country context where formal media may only be consumed by a limited subset of the population or may not a reliable sourceof objective information.3 It does so by simulat2
This is operationalized as one minus the Herfandahl index
of inductively determined modularity-optimizing community
structures.
3
Indeed, Zambia’s media has been rated as “Not Free: Not
possible to safely criticize government or government officials; government exerts indirect control over media” for mostof the last decade [19].

Poster Session 1 :: April 8

2015

References[1] George A Akerlof. A Theory of Social Custom, of Which Unemployment May be One

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Consequence. The Quarterly Journal of Eco- [13] Edward Miguel and Mary Kay Gugerty.
nomics, 94(4):749, June 1980.
Ethnic diversity, social sanctions, and public goods in Kenya.
Journal of Public[2] S. Berman. Civil society and the collapse
Economics, 89(11-12):2325–2368, Decemof the Weimar Republic. World Politics,
ber 2005.
49(03):401–429, 1997.
[14] K Munshi and M Rosenzweig. Networks,[3] T Besley and S Coate. Group lending, repayCommitment, and Competence: Caste in Inment incentives and social collateral. Journal
dian Local Politics. Unpublished manuscript,
of Development Economics, 1995.
2010.
[4] Timothy Besley, Stephen Coate, and Glenn [15] Elinor Ostrom. Governing the Commons: The
Evolution of Institutions for Collective AcLoury. The Economics of Rotating Savtion. Cambridge University Press, November
ings and Credit Associations. The American
1990.
Economic Review, 83(4):792–810, September 1993.
[16] Rohini Pande. Can Informed Voters Enforce
Better Governance? Experiments in Low[5] Kanchan Chandra. Why Ethnic Parties SucIncome Democracies. Annual Review of Ecoceed. Patronage and Ethnic Head Counts innomics, 3(1):215–237, September 2011.
India. Cambridge University Press, 2007.

Poster Session 1 :: April 8

2015[6] J.D. Fearon and D.D. Laitin. Explaining in- [17] Robert D Putnam, Robert Leonardi, and Raffaella Nanetti. Making democracy work. civic
terethnic cooperation. American Political Scitraditions
in modern Italy. Princeton Univ Pr,
ence Review, pages 715–735, 1996.
May 1994.
[7] C. Ferraz and F. Finan. Exposing Corrupt[18] Ritva Reinikka and Jakob Svensson. The
Politicians: The effects of Brazil’s publicly
power of information in public services: Evireleased audits on electoral outcomes. The
dence from education in Uganda. Journal of
Quarterly Journal of Economics, 123(2):703,
Public Economics, 95(7-8):956–966, August
2008.
2011.
[8] J. Habyarimana, M. Humphreys, Daniel N [19] Jenifer Whitten-Woodring and Douglas A
Posner, and J.M. Weinstein. Why does ethVan Belle. Historical Guide to World Menic diversity undermine public goods providia Freedom: A Country-by-Country Analysion? American Political Science Review,sis. Sage / CQ Press, 2014.
101(4):709, 2007.
[9] James Habyarimana, Macartan Humphreys,
Daniel N Posner, and Jeremy M Weinstein.
Coethnicity: Diversity and the Dilemmas ofCollective Action. Russell Sage Foundation,
August 2009.
[10] Michihiro Kandori. Social Norms and Community Enforcement. The Review of Economic Studies, 59(1):63–80, January 1992.
[11] Philip Keefer. The Ethnicity Distraction: Political Credibility, Clientelism and Partisan
Preferences in Africa. The World Bank, 2009.
[12] Jennifer M Larson. Cheating Because They
Can: Social Networks and Norm Violators.
Workshop on Conflict and, 2014.

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Discovering dependence of tweet inter-arrival times
Balázs Gerencsér∗

Christophe Cloquet∗

Vincent Blondel∗

January 12, 2015

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Poster Session 1 :: April 8

2015

Introduction

The temporal patterns of human communication behavior has been an active field of research. It has beenfound that the event inter-arrival times follow a scale-free distribution [1], therefore the process of inter-arrival
sequences is often modeled as an i.i.d. process of the appropriate scale-free distribution [2].
The goal of this work is to draw attention to the dependence appearing in this process. We claim that
we get a much better fit and consequently a more precise understanding if we allow subsequent inter-arrival
times to be dependent.
In this pilot project we use Twitter data of Belgium and propose a very simple model of dependence,
already showing a considerably better fit to the data.

2

Model description

In this section we describe the model we use for the tweet inter-arrival times Wi of a single user. First ofall, we distinguish “short” and “long” waiting times depending on whether Wi exceeds some threshold TS .
This property will be recorded by Xi which can take values S or L.
The key point of the model is the dependence structure we propose. We assume that the distributionof Wi , Xi depends on the type Xi−1 of the previous waiting time, but is conditionally independent fromanything before. Therefore the type Xi is determined using a transition probability matrix:

pS|S pS|L
P =,pL|S pL|L
where pS|L stands for the probability of observing a short waiting time after a long one. Similarly, the longwaiting times follow the scale free distribution f|S or f|L (parametrized by γS , γL ) depending on whether
Xi−1 is S or L. For short waiting times we disregard the exact value.
We get a reference model of independent inter-arrival times if we assume Xi are i.i.d. and γS = γL . Thismeans the inter-arrival times Wi follow an i.i.d. process of scale-free variables.

3

Data source

The data we use is gathered using the Twitter Streaming API which gives public access and allows to fetch
tweets as long as the amount downloaded does not exceed the 1% of the total number of tweets during theactual period.
We focused on geotagged tweets originating from Belgium, therefore the 1% limit was very far from being
reached. The tweets were gathered between December 2013 and April 2014. We also applied several filters to∗ B.

Gerencsér, C. Cloquet and V. Blondel are with ICTEAM Institute, Université Catholique de Louvain, Belgium

balazs.gerencser@uclouvain.be, c.cloquet@gmail.com and vincent.blondel@uclouvain.be Their work is supported by the DYSCO Network (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction
Poles Programme, initiated by the Belgian Federal Science Policy Office, and by the Concerted Research Action (ARC) of theFrench Community of Belgium.

1

11

remove spammers and other non-human users so that our study really focuses on human tweeting behavior.
We select users with at least 1000 tweets during this period so that it is meaningful to discuss inter-arrival
times.

4

Results

In order to justify the model we proposed, we statistically compare it with the independent model. We fix
the threshold to be TS = 60s.
We perform two tests for comparison. We analyzed which model performed the best, be it (a) for
individual users or (b) for all users.
For both tests we use the standard likelihood-ratio test. This means we perform a Maximum Likelihood
fitting of both models, we call the resulting likelihoods Lindep and Lnew . Assuming the null hypothesis thatthe independent model is correct, we have that the difference
D = 2 log

Poster Session 1 :: April 8

2015

Lnew
Lindepfollows a chi-squared distribution with degrees of freedom equal to the number of extra parameters in the
new model [3]. This is 2 for individual users and 2N for the overall comparison, where N = 1315, the numberof users. We reject the null hypothesis and claim that the new model fits better if D is unreasonably large
for the actual chi-square distribution.
For the individual users we accept the new model if D has a p-value at most 0.05. We find that out of
the 1315 users, we accept the new model for 997 of them, which is over 75%. For the overall comparison, we
get Dall ≈ 43239. Assuming the null hypothesis this should be drawn from a chi-squared distribution with
degree of freedom 2N = 2630, but this has an extremely small p-value (0 up to machine precision). In otherwords, for an overall view of inter-arrival times it is absolutely clear that the new model should be preferredto the simple, independent one.

5

Conclusions and outlook

In this work we have shown that introducing a simple dependence structure substantially improves thequality of fit to Twitter inter-arrival data.
We understand this result as a first step of better understanding this type of temporal data. The model
should be tested for other, similar scenarios, primarily on mobile phone datasets. Moreover, there are several
ways to refine the dependence structure currently proposed and these should be explored to get a better
insight on the behavior of such processes.

References[1] J. Candia, M. C. González, P. Wang, T. Schoenharl, G. Madey, and A.-L. Barabási,
Uncovering individual and collective human dynamics from mobile phone records, Journal of Physics A:
Mathematical and Theoretical, 41 (2008), p. 224015.
[2] M. Karsai, M. Kivelä, R. K. Pan, K. Kaski, J. Kertész, A.-L. Barabási, and J. Saramäki,
Small but slow world: How network topology and burstiness slow down spreading, Physical Review E, 83(2011), p. 025102.
[3] S. S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses, The
Annals of Mathematical Statistics, 9 (1938), pp. 60–62.

12
2

2015

Yingxiang Yang, Marta González
Statistical models that can characterize human mobility patterns are of importance in a broad range of
research areas from epidemiology, transportation engineering, to urban planning and mobile network
communication [1, 2, 3, 4, 5, 6, 7, 8]. Nowadays, even though detailed transportation simulation
platforms can already mimic realistic travel behaviors [9, 10, 11], simple statistical models are stillneeded not only because the detailed information required for the calibration of simulation platforms areusually not available, but also because these statistical models are amenable to mathematical analysis thatquantifies the influence of each parameter.

Poster Session 1 :: April 8

A Unified Model for Individual Spatial Temporal Mobility Patterns

Ubiquitous findings observed in data from human mobility can be expressed by five statistical
distributions on the urban population, which are repeatedly found in previous works [12, 13, 14, 15, 16,
17, 18, 19, 20, 21]. These are: the frequency of visits to each location over long term observations, thenumber of locations visited each day, the duration of the visits, the distance traveled per trip, and thetemporal span between visits to the same location. An important research question is to not only include
these patterns in a mobility model, but also explain the pervasive mechanisms that produce the observed
distributions in diverse scenarios. In this study we model these spatial and temporal human mobility
patterns and integrate them into a unified model using stay locations extracted from two million mobile
phone users’ Call Detailed Records in the Boston metro area.
Spatially, we model the heterogeneously distributed activity locations, i.e, the trip origins( ) anddestinations ( ) for the entire population, using a bivariate multiplicative cascade model. As is shown by
Fig. 1(a), we repetitively divide the entire study areainto,,… grids. Both the spatial
clustering of origins and destinations, and the degree of attraction or repulsion between them at each
granularity level are controlled by bivariate random variables. The influences ofatdifferent granularity levels combine multiplicatively. Such heterogeneously distributed activity locations,when combined with a rank based location selection model (Fig. 1(b)), could give rise to heavy-tailed trip
length distributions, which is a ubiquitous characteristic found not only in human movement, but also
various other systems.
Temporally, stay duration is modeled by a time-inhomogeneous Markovian model that includes both thecircadian pattern and burstness of travelling (Fig. 2(a, b)). The circadian pattern is captured by timedependent travel probability at each time step, while the burstness of travelling is captured by
distinguishing the steady state, which means being at home, from the active state, which means being atother places. In the proposed temporal model, both the number of daily visited location distribution andthe stay duration distribution could be analytically derived using Markov chain embedding technique.
They both compare well with the observed distributions from cell phone data, as is shown in Fig. 2(c, d).
The long term visitation pattern is incorporated by the exploration and preferential return mechanism,shown in Fig. 2(e).
At last, we show that these components could be integrated into a combined model. In the combinedmodel, each component is controlled by only a few parameters. With these easily tunable parameters, weare ready to evaluate the influence of individual movement patterns on a broad range of research areas
including epidemics spreading, wireless network routing, and transportation planning.

13

Poster Session 1 :: April 8

2015

Fig. 1 (a) The scheme of the hierarchical cascade process for generating trip origin density . Each tile isrepetitively divided into 4 smaller tiles. The location density in each tile is controlled by the cascade generator . (b)The probability to choose the rank location as the destination is ( ). The closest potential destination isrank 1. (c) The probability to travel outside tile level . The tile size of level 1 is
, level 2 is
. (d) The
trip distance distribution ( ) observed from trips generated using stay locations extracted from cell phone data,
and from the simulation of the proposed model.

Fig. 2 (a) Illustration of different choices for a non-commuting person when the person is at “home” or “other”
location. (b) The time dependent periodic transition probability ̂ ( ). (c) Number of daily visited location
distribution ( ) measured from stays extracted from cell phone data, the model’s simulation result, and themodel’s analytical result. (d) Activity duration distribution ( ) measured from stays extracted from cell phonedata and the model’s simulation result. (e) Visiting frequency ( ) to themost visited location follows( )for users visiting different number of locations in the observation period.

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2015

[1]
L. Song, D. Kotz, R. Jain, and X. He, “Evaluating next-cell predictors with extensive wi-fi
mobility data,” Mobile Computing, IEEE Transactions on, vol. 5, no. 12, pp. 1633–1649, 2006.
[2]

Y. Zheng and X. Zhou, “Computing with spatial trajectories,” 2011.

[3]
C. F. Choudhury, M. Ben-Akiva, and M. Abou-Zeid, “Dynamic latent plan models,” Journal of
Choice Modelling, vol. 3, no. 2, pp. 50–70, 2010.
[4]
M. Ben-Akiva and M. Bierlaire, “Discrete choice methods and their applications to short term
travel decisions,” in Handbook of transportation science. Springer, 1999, pp. 5–33.

Poster Session 1 :: April 8

Reference[5]
P. Waddell, “A behavioral simulation model for metropolitan policy analysis and planning:
residential location and housing market components of urbansim,” Environment and Planning B, vol. 27,
no. 2, pp. 247–264, 2000.
[6]

M. Batty, The New Science of Cities. MIT Press, 2013.

[7]
D. Brockmann, L. Hufnagel, and T. Geisel, “The scaling laws of human travel,” Nature, vol. 439,
no. 7075, pp. 462–465, 2006.
[8]
M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding individual human mobility
patterns,” Nature, vol. 453, no. 7196, pp. 779–782, 2008.
[9]
M. Balmer, “Travel demand modeling for multi-agent transport simulations: Algorithms andsystems,” Ph.D. dissertation, ETH Zurich, 2007.
[10]
K. W. Axhausen, A. Zimmermann, S. Schönfelder, G. Rindsfüser, and T. Haupt, “Observing therhythms of daily life: A six-week travel diary,” Transportation, vol. 29, no. 2, pp. 95–124, 2002.
[11]
N. Schuessler and K. W. Axhausen, “Processing raw data from global positioning systems
without additional information,” Transportation Research Record: Journal of the Transportation
Research Board, vol. 2105, no. 1, pp. 28–36, 2009.
[12]
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 Academyof Sciences, vol. 106, no. 51, pp. 21484–21489, 2009.
[13]
A. Wesolowski, N. Eagle, A. J. Tatem, D. L. Smith, A. M. Noor, R. W. Snow, and C. O. Buckee,“Quantifying the impact of human mobility on malaria,” Science, vol. 338, no. 6104, pp. 267–270, 2012.
[14]
W.-j. Hsu, T. Spyropoulos, K. Psounis, and A. Helmy, “Modeling time-variant user mobility inwireless mobile networks,” in INFOCOM 2007. 26th IEEE International Conference on Computer
Communications. IEEE. IEEE, 2007, pp. 758–766.
[15]
J. Ghosh, S. J. Philip, and C. Qiao, “Sociological orbit aware location approximation and routing(solar) in manet,” Ad Hoc Networks, vol. 5, no. 2, pp. 189–209, 2007.

15

2015

[17]
X.-P. Han, Q. Hao, B.-H. Wang, and T. Zhou, “Origin of the scaling law in human mobility:
Hierarchy of traffic systems,” Physical Review E, vol. 83, no. 3, p. 036117, 2011.
[18]
K. Lee, S. Hong, S. J. Kim, I. Rhee, and S. Chong, “Demystifying levy walk patterns in humanwalks,” CS Dept., NCSU, Raleigh, NC, USA, Tech. Rep, 2008.
[19]
R. Hidalgo and A. César, “Conditions for the emergence of scaling in the inter-event time of
uncorrelated and seasonal systems,” Physica A: Statistical Mechanics and its Applications, vol. 369, no. 2,
pp. 877–883, 2006.

Poster Session 1 :: April 8[16]
S. Petrovskii and A. Morozov, “Dispersal in a statistically structured population: fat tails
revisited,” The American Naturalist, vol. 173, no. 2, pp. 278–289, 2009.

[20]
R. D. Malmgren, D. B. Stouffer, A. E. Motter, and L. A. Amaral, “A poissonian explanation forheavy tails in e-mail communication,” Proceedings of the National Academy of Sciences, vol. 105, no. 47,
pp. 18153–18158, 2008.
[21]
A. Noulas, S. Scellato, R. Lambiotte, M. Pontil, and C. Mascolo, “A tale of many cities: universal
patterns in human urban mobility,” PloS one, vol. 7, no. 5, p. e37027, 2012.

16

Estimating the Wealth of an Individual
Based on Individual Patterns of Phone Use
Joshua E. Blumenstock
University of Washington
Seattle, WA

joshblum@uw.edu

ABSTRACT
We provide evidence that mobile phone records can be used
to predict the socioeconomic status and other welfare indicators of individual mobile phone subscribers. Combining
several terabytes of anonymized transactional mobile phonerecords with data collected through 2,200 phone-based interviews, we test the extent to which it is possible to predict
an individual’s responses to survey questions based on phonerecords alone. We observe significant correlations between
asset ownership and a rich set of measures derived from thephone data that capture phone use, social network structure,and mobility.
Simple classification methods are able to predict, withvarying degrees of accuracy, whether the respondent owns
assets such as radios and televisions, as well as fixed household characteristics such as access to plumbing and electricity. More modest results are obtained when attempting
to predict a broader set of development indicators such as
an individual’s response to the question, “Have you had to
pay unexpected medical bills in the past 12 months?” Whilethese methods offer a powerful opportunity for policymakers and researchers working in developing countries, we argue that considerable calibration and refinement is needed
before such methods can be deployed.

Keywords
Mobile phones, development, big data, call detail records,
wealth, mobility, social networks, regression

1.

INTRODUCTION AND MOTIVATION

Reliable, quantitative data is a critical input to development policy, social science research, and to the decisionmaking process of firms and organizations interested in promoting social good. However, the basic measurement of key
development outcomes – such as poverty, physical security,and happiness – is notoriously difficult in developing countries, where a lack of physical infrastructure and resources

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is often compounded by market failures and fragile institutional capacity [10].
Such problems are exacerbated in fragile and conflictaffected regions, where concerns over corruption and thephysical security of enumerators and respondents make theregular collection of representative household survey data
all but impossible. For example, Angola’s last census wasin 1970, and covered just 18 districts [11]. As a result, researchers and policymakers typically rely on data from largescale national surveys (which occur infrequently), or specialized panel survey modules (which are typically administeredto small, local populations). Neither traditional source captures fine-grained variation in development outcomes overboth space and time.
In this paper, we describe preliminary results from efforts to develop models for predicting an individual’s socioeconomic status and related development outcomes based
upon anonymous, high-frequency data passively registeredthrough use of mobile phone networks. A key innovationof this approach is our ability to link individual survey responses collected in phone interviews with incredibly rich social network and communication data obtained from mobile
phone operators. Such an approach can be used to model the
relationship between passively collected metrics of mobile
phone use and explicitly queried socioeconomic phenomena.
For instance, it will be possible to tell whether an individual’s communication history can be used to predict whether
that individual agrees with a survey-based statement suchas, “I believe the current economic situation will improve inthe coming year,” or “I feel connected to other members of
my local community.”
Here, we focus on results from the analysis of data collected in Rwanda in 2009 and 2010. This work extends a previous workshop paper that used a simple regression model
to illustrate the strong relationships between simple metricsof phone use and a composite indicator of socio-economic
status [1]. To our knowledge, no other prior work has investigated the relationship between individual communication
histories and individual development outcomes. However,a series of recent studies have shown that geographicallyaggregated communication records are strong predictors ofregional census data [5, 8, 9]. A closely related set of work
uses individual phone records to model gender and related(fixed) demographic characteristics [4, 7]. These approaches
are strongly complementary, and we expect that over the
next several years these methods will significantly advanceour ability to measure, model, understand, and improve thelives of historically marginalized populations.

Poster Session 1 :: April 8

2015

17

Sample Xit Indicator (“feature”)
Average number of outgoing phone calls per day
Number of unique contacts in social network
Number of geographic regions visited in past month
Total expenditures on mobile phone-based communication
Eigenvector centrality of respondent
Percentage of closed triangles in social network

Table 1: Sample Yit development status indicators to be modeled as a function of sample Xit features

Accuracy

RecallPrecision

F

AUC% Answered Yes

Panel A: Assets and Housing
Owns a radio
Owns a bicycle
Household has electricity
Owns a television
Has indoor plumbing
Owns a motorcycle/scooter
Owns a car/truck
Owns a refrigerator
Has landline telephone

0.976
0.676
0.819
0.855
0.887
0.899
0.945
0.954
0.992

1.000
0.552
0.533
0.497
0.250
0.011
0.213
0.180
0.125

0.976
0.678
0.761
0.738
0.842
1.000
0.867
1.000
1.000

0.988
0.609
0.627
0.594
0.386
0.022
0.342
0.305
0.222

0.899
0.722
0.828
0.814
0.843
0.772
0.849
0.878
0.562

0.973
0.456
0.285
0.214
0.142
0.102
0.068
0.055
0.009

Panel B: Social Welfare Indicators
Hospital bills in last 12 months
Very ill in last 12 months
Death in family in last 12 months
Flood or drought in last 12 months
Fired in last 12 months

0.633
0.686
0.665
0.788
0.901

0.890
0.188
0.183
0.086
0.022

0.633
0.550
0.632
0.607
1.000

0.740
0.280
0.284
0.151
0.043

0.653
0.671
0.619
0.706
0.731

0.587
0.325
0.363
0.219
0.101

Poster Session 1 :: April 8

2015
Sample Yit Indicator (“development outcome”)
Household owns a motor vehicle
Amount of land owned by individual
Recent illness or other negative economic shock
Total expenditures in last month
Value of recent agricultural harvest
Financial outlook on 7-point Likert scale

Table 2: Model performance at predicting responses from survey respondents based on call records data

2. PRELIMINARY RESULTS
In ongoing work, we are conducting additional phone surveys to collect a broader range of development outcomessuch as those listed in column 1 of Table 1. Here, we focus
on the simplified task of predicting responses to relatively
well-defined questions with concrete answers that were collected in short phone interviews with mobile phone users
in 2009 and 2010. Section 2.1 describes results from predicting asset ownership and housing characteristics; section
2.2 describes initial results at predicting more general measures of social welfare; and section 2.3 describes results frompredicting a composite index of respondent wealth.

2.1

Predicting asset ownership andhousing characteristics

In Panel A of Table 2, we present the results from the useof a logistic regression to predict binary responses to survey questions about fixed assets and housing characteristics
such as, ”Does your household own one or more radios?” or”Does your household have electricity?” We fit a version ofmodel (??) with regional fixed effects and roughly twenty
aggregated measures of phone activity such as those in column 2 of Table 1, including measures of phone use, SMS
use, geographic mobility, and social network structure. The
model is fit using 10-fold cross-validation on a sample ofroughly 900 respondents who answered all survey questions,where the binary classification threshold is determined tomaximize accuracy and the other performance metrics arereported at that threshold. Figure 1 shows the ROC curves

for three representative questions asked in the survey.
In general, this rudimentary approach to modeling therelationship between phone use and asset ownership shows
signs of modest success. For most of the outcome variables
we seek to model, we can achieve relatively high accuracy,
but these rates are only marginally higher than the naive
baseline of predicting the majority class. For instance, themodel accuracy of 85% in predicting television ownershipis only an 8 percent (6 percentage points) increase over amodel that predicts all respondents do not own televisions.

2.2 Predicting welfare indicators
Panel B of Table 2 presents similar results from our attempts to predict more subjective responses to broader development questions such as “Has your household had to pay
significant hospital bills in the past 12 months?” and “Haveyou lost your job in the last 12 months”. Here, performanceis lower than with the asset ownership questions; we findthat our models are only able to offer marginal improvements over naive baseline predictions.

2.3 Predicting composite socioeconomic status
Finally, we test the ability of this approach to predictcomposite index of socioeconomic status. To create this aggregate metric from the survey responses, which we denote
by Yc
id , we take the first principal component of the 9 asset and housing characteristics listed in Panel A of Table 2.
The first principal component of wealth explains 27.24% of
the variance of the 9 asset categories. Similar results ob-

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2015

Figure 1: ROC curve for three survey outcomestain when creating a composite based on the first principal
component of a much larger number of assets and housing
characteristcs.1
In Table 3, we present the results from fitting an ordinary least squares regression of this first principal wealthcomponent on a representative sample of mobile phone use
metrics. While the explanatory power of this regression israther limited (R2 = 0.29), there are strong relationships
between the wealth composite and several of the measuresof phone use and network structure. Note that the sign andmagnitude of each of the regression coefficients is highly dependent on the set of regressors included; because of the natural dependencies in the phone data, inclusion or exclusionof additional features substantively changes the estimatedcoefficients (though such tinkering has relatively little effecton the fit of the model).
To further illustrate the strong correlations between phone
use and wealth, we perform a second principal component
analysis on a large set of different metrics of mobile phone
activity. In this case, the first principal component of 97
metrics of phone use explains 34.63% of the variance of thefull dataset. In Figure 2, we plot for each of the survey
respondents the first principal component of wealth (y-axis)
against the first principal component derived from the phone
use data (x-axis). The strong positive relationship between
these two components is illustrated by the Nadaraya-Watson
kernel regression shown in red.

3.

DISCUSSION AND CONCLUSION

We have presented preliminary evidence that it is possible
to predict a variety of indicators of individual socioeconomic
status and welfare using mobile phone call records. If these1
In earlier work, we have taken a different approach that develops a composite index of “predicted expenditures” using
publicly available household survey (DHS) data to approximate the estimated annualized household expenditures ofsurvey respondent [1]. See [6] for a related approach to developing a composite wealth index from survey data.

Coef f icient(S.E.)
Active days
−0.04
(0.03)
Calls per day
2.49(2.28)
Outgoing calls
0.01(0.01)
Incoming calls−0.01†(0.01)
Degree0.08∗∗(0.03)
Int’l outgoing calls−0.59∗(0.26)
Int’l incoming calls−1.09(0.72)
Int’l degree
0.38∗(0.17)
Towers visited−0.03(0.21)
Avg. recharge denomination
0.01(0.03)
Daily recharge−0.27∗∗∗ (0.06)
Clustering−505.80∗∗∗ (148.09)
Betweenness
137.06∗(56.66)
N
897
R2
0.29
Results show regression of first principal component of
the wealth (Ycid ), scaled by 100 to simplify presentation.
Standard errors in parentheses. Regression includes district fixed effects but coefficients are omitted from tablefor clarity. † significant at p < .10; ∗ p < .05; ∗∗ p < .01;∗∗∗
p < .001

Poster Session 1 :: April 8

Table 3: Regression of first principal component ofassets on selected measures of phone use

results can be further calibrated and improved upon, thistechnique could provide policymakers and researchers witha novel quantitative perspective on populations for whom
good data has historically been hard to find. Compared
to traditional methods for collecting individual and household data, the use of call records represents a considerably
cheaper alternative, with dramatically higher spatial andtemporal precision. In principle, such fine-grained development indicators could be applied in a variety of settings,
from program monitoring and evaluation to social welfare
targeting and analysis.
While provocative, we do not want to overstate the accuracy of the methods tested thus far, or imply that suchtechniques will ever supplant alternative modes of data collection. The predictions presented in this paper are relatively inaccurate, and the methods, models, and data leaveconsiderable room for improvement. In ongoing work, we
are working to develop improved statistical and computational models, and are collecting a large amount of survey
data that will allow for better calibration and testing.

4.

REFERENCES[1] J. Blumenstock, Y. Shen, and N. Eagle. A method for
estimating the relationship between phone use andwealth. QualMeetsQuant Workshop at the 4thInternational IEEE/ACM Conference on Information
and Communication Technologies and Development,
2010.
[2] J. E. Blumenstock and N. Eagle. Mobile divides:
Gender, socioeconomic status, and mobile phone use
in Rwanda. 4th International IEEE/ACM Conferenceon Information and Communications Technologies and19

Poster Session 1 :: April 8

2015

Figure 2: Relationship between 1st principal components of wealth and phone use

Development, Dec. 2010.
[3] J. E. Blumenstock and N. Eagle. Divided we call:
Disparities in access and use of mobile phones in
Rwanda. Information Technology and International
Development, 8(2):1–16, 2012.
[4] J. E. Blumenstock, D. Gillick, and N. Eagle. Who’s
calling? demographics of mobile phone use in rwanda.
AAAI Symposium on Aritificial Intelligence andDevelopment, 18:116–117, 2010.
[5] N. Eagle, M. Macy, and R. Claxton. Network diversity
and economic development. Science,
328(5981):1029–1031, May 2010.
[6] D. Filmer and L. H. Pritchett. Estimating wealtheffects without expenditure data - or tears: anapplication to educational enrollments in states ofindia. Demography, 38(1):115 – 132, 2001.
[7] V. Frias-Martinez, E. Frias-Martinez, and N. Oliver. A
gender-centric analysis of calling behavior in adeveloping economy. AAAI Symposium on Aritificial
Intelligence and Development, Forthcoming, 2010.
[8] V. Frias-Martinez and J. Virseda. On the relationshipbetween socio-economic factors and cell phone usage.
In Proceedings of the Fifth International Conferenceon Information and Communication Technologies andDevelopment, ICTD ’12, pages 76–84, New York, NY,
USA, 2012. ACM.
[9] T. Gutierrez, G. Krings, and V. D. Blondel.
Evaluating socio-economic state of a country
analyzing airtime credit and mobile phone datasets.
arXiv preprint arXiv:1309.4496, 2013.
[10] M. Jerven. Poor numbers: how we are misled by
African development statistics and what to do about it.
Cornell University Press, 2013.
[11] A. J. Tatem and S. Riley. Effect of poor census dataon population maps. Science, 318(5847):43, Oct. 2007.

20

2015

Carlos Andre Reis Pinheiro1, Véronique Van Vlasselaer1, Alexandre Evsukoff2,3, Moacyr Silva2, Bart
Baesens1 and Nelson Ebecken3
1

KU Leuven, Research Center for Management Informatics,
Naamsestraat 69, Leuven, Belgium{carlos.pinheiro,bart.baeses,veronique.vanvlasselaer}@kuleuven.be2

Getúlio Vargas Foundation, School of Applied Mathematics,
Praia de Botafogo 190, Botafogo, Rio de Janeiro, Brazil
alexandre.evsukoff@fgv.br

3

Federal University of Rio de Janeiro, Department of Civil Engineering,
Centro de Tecnologia, Cidade Universitária, Rio de Janeiro, Brazil
nelson@ntt.ufrj.br

Upon an overall human mobility behavior within the cityof Rio de Janeiro, this paper describes a methodology to
predict commuting trips based on the mobile phone data.
This study is based on the mobile phone data provided by
one of the largest mobile carriers in Brazil. Mobile
phone data comprises a reasonable variety of
information about subscribers’ usage, including timeand location of call activities throughout urban areas.
This information was used to build subscribers’
trajectories, describing then the most relevantcharacteristics of commuting over time. An OriginDestination (O-D) matrix was built to support theestimation for the number of commuting trips.
Traditional approaches inherited from transportation
systems, such as gravity and radiation models –commonly employed to predict the number of commuting
trips between locations(regularly upon large geographic
scales) – are compared to statistical and data mining
techniques such as linear regression, decision tree andartificial neural network. A comparison of these models
shows that data mining models may perform slightly
better than the traditional approaches fromtransportation systems when historical information areavailable. In addition to that, data mining models may be
more stable for great variances in terms of the number ofcommuting trips between locations and upon different
geographic scales. Gravity and radiation models work
very well based on large geographic scales and they holda great advantage, they are much easier to be
implemented. On the other hand, data mining models
offer more flexibility in incorporating additional
attributes about locations – such as number of job
positions, available entertainments, schools anduniversities posts, among others –and historicalinformation about the trips over time.
Human mobility analysis reveals relevant knowledge
about displacements and overall movement behavior,
supporting disciplines such as urban and public
transportation
planning,
traffic
forecasting,communication networks optimization and insights in the
spread of diseases [1]. Human mobility studies based on
mobile phone data allow approximating human motion
pattern within particular geographic areas such as great
metropolitan areas, big cities, entire states and even

Poster Session 1 :: April 8

Predicting commuting trips based on mobile phonedata

countries, including travelling behavior and migration
trends [2].
This study was conducted by using mobile phone datafrom one of the largest telecommunications companies in
Brazil. The overall analyses were performed based on six
months of call detail records, revealing the averagebehavioral pattern of users travelling throughout the cityof Rio de Janeiro over time. In this study, we analyzed
approximately 3.1 billion mobile phone records,
comprising 2.7 million subscribers, handled by 5,300
antennas, performing 1,700 cell towers.
Mobile phone data contains transactional records about
caller and callee information, associating each call or text
message to the corresponding cell tower that is spread out
through the metropolitan areas. These cell towers process
the incoming and outgoing calls, as well as text andmultimedia messages sent and received by subscribers,
providing relevant information about their geographic
locations at particular points in time. This geographicalinformation basically consists of the latitude andlongitude, the radius covered by the cell towers andinformation about the physical addresses such as street,neighborhood, city and state. Even though such locational
information only approximates real human mobility,
recent studies [5] [19] show that by using the appropriatetechniques, mobile phone data may offer the possibility tostatistically characterize human trajectories and journeys
on an urban area scale.
The use of CDR data as a proxy for human mobility
studies must be done with care. Besides of the spatial
approximation where the position of the subscriber isassumed to be the position of the tower, subscribers do notmake a call every time they move such that the observed
trajectory if often not the real trajectory performed by theuser. Moreover, the number of call varies strongly among
the population such that the sample is highly biased
towards people of better socioeconomic levels that trend
to perform more calls. It should also be noticed that in
Brazil, particularly in the Rio de Janeiro Area, where thisstudy had been carried out, 85% of the subscribers are prepaid, such that they have very irregular calling pattern21

effectiveness.

Mobile carriers have quite often an unbalanced
distribution of prepaid and postpaid mobile phones in theircustomer database. Particularly in the Brazilian’s
marketplace, this distribution is very unbalanced, 80% forprepaid and 20% for postpaid. Due to the majority of
prepaid mobile phones in the customer database, we lackinformation about the subscribers’ home address. While
prepaid subscribers do not need to provide an address,
postpaid subscribers do, but they are able to declare any
type of address, like their workplace or their homeaddress, a different user’s address or the payer’s address.
As the information about home address – and workplace –has an indispensable value in building the completesequence of displacements in terms of human mobility
behavior, we computed in this study the presumed
domiciles and workplaces for all subscribers, irrespective
whether they are prepaid or postpaid.
Both presumed domiciles and workplaces are useful inmany human mobility analyses, and could be used toestimate the number of people present in certain urban
areas of interest. Information about domiciles andworkplaces can also be used to create an OriginDestination (OD) matrix. The OD matrix is a matrix
representation of the network connecting origin withdestination locations, where the rows represent the origins,
and the columns refer to the destinations. Each cell
specifies information about the mobility, e.g. the numberof displacements between origin and destination. Using an
OD matrix uniquely composed of presumed domiciles andworkplaces, we can apply a set of analyses to reveal
interesting patterns of human’s motion in large
metropolitan areas. For example, governmental andindustrial decisions about traffic routing and public
transportation planning may rely on analysis of homework and work-home displacements over time. Frequentconnections between two areas in the city may receive full
attention for instance. Moreover, a shift in traffic can be
anticipated by analyzing domiciles and workplaces and thepossible routes between the respective locations.
Historical analyses of the number of commuting trips
between locations over time can also allow organizations
to estimate and forecast evolving trends and eventual
issues in public transportation, traffic routing andcommunications network.

Gravity and radiation models are good approaches toestimate the number of trips between two distinct
locations. In this study, the trip is defined as the
movement between the presumed domicile and thepresumed workplace, based on a particular geographic
scale – cell towers, neighborhood and clusters of cell
towers, irrespective of the path performed.
The gravity model tracks its origin from the gravitationallaw. Two bodies are attracted to one another with a force
that is proportional to the product of their masses andinversely proportional to the square of their distance.
Gravity models are mapped to human mobility byreplacing bodies by locations and masses by importance.
Importance can be measured in terms of population, but
can also incorporate other attributes like the number ofjobs, gross domestic product, public facilities,
transportation resources, traffic routes, among others.
The gravity model usually incorporates parameters todefine constraints to paths and displacements followed bypeople, such as the cost to travel [10]. The cost to travelconsists of several attributes like the distance, theresources to cover the path, the number of people
travelling, etc. In particular, it assumes that the commuting
activity between two distinct locations is proportional tothe product of the population of these two locations andinverse proportional to the distance between these two
locations.
Individuals are attracted to other locations as a function of
the distance between two different places and the cost of
travel between them. The gravity model considers that
individuals are more attracted to close locations than tolong-distance locations. This last hypothesis is based on
the natural limited resources to travel between locations
and the higher cost involved in long distances. In
problems related to transportation systems, the distance totravel is a crucial factor in users’ decision making process
when they have to commute between locations. Trips
between two locations with the same distance may havedifferent costs, for instance based on possible routes,
traffic jam, public transportation resources etc. The
population of the locations involved in the trip is also
important to predict trips within geographic areas. Large
number of people associated to origin and destination
locations may imply more trips.
The radiation model, on the other hand, tracks its origin
based on theories about diffusion dynamics, whereparticles are emitted at a given location and have a certain
probability of being absorbed by surrounding locations.
It uses the spatial distribution for the population as input,
not needing any other additional parameters. It basicallydepends on the populations of the locations involved in thetrips and their distances [10].
As the radiation model is parameter-free, the model can bemuch easier implemented in mobility behavior analyses
and trip prediction models, especially when using mobile
phone data. Although the radiation model may not seem
sufficient to predict human mobility in low geographic
scales, this model is successfully applied in reproducing
mobile patterns at large spatial scale [16] [17] [18]. As a

According to the presumed domiciles and workplaces
identified by the aforementioned method, we are able toclearly describe where people live and work in the city of
Rio de Janeiro. Note that domiciliary information wasverified by the official city reports obtained through thegovernmental authorities, and it was therefore included in
the overall analysis of human mobility behavior. Homework and work-home routes are very important in order tounderstand possible traffic issues in great metropolitan
areas. Commuting planning is one of the biggest
challenges for big cities, both to establish and arrange
properly public transportation resources and to design
optimal traffic routes. The analysis of human mobility
behavior can definitely help this task and improve its

Poster Session 1 :: April 8

2015

depending on specific commercial offer they get.

22Considering the same geographic scales, we developed
linear regression models and applied them on our mobility
data set in order to predict the number of commuting trips
between two distinct locations. We choose to estimate two
types of linear regression models: a Quantile Regression
and a Robust Regression. We found that those two models
performed slightly better than the models inherited fromphysics – i.e. the gravity and radiation model. For
comparison purposes, the mobility’s attributes used tofeed these statistical models were the same as used in thegravity and radiation models. These attributes include thedistance between the two locations involved in the trip, thenumber of trips originated from the origin location(regardless the destination), the population of the origin
location, the population of the destination location and theradius population (the population in the circle between
origin and destination locations).
Also considering the same geographic scales, a decision
tree model is estimated in order to predict the number ofcommuting trips between locations. A decision tree is asupervised learning model for classification problems[26]. Each input variable may corresponds to a node in thetree – if it increases the classification rate. Otherwise an
input variable may be discarded. Each possible value forthe input variable corresponds to edges to split nodes. In
the case of continuous values the algorithm estimates cutoff values to properly create the edges. Each leaf node
represents a value of the target variable given the values of
the input variables, represented by the path from the rootof the tree to the leaf. Afterwards, the algorithm prunes
away some of the created paths in order to avoid
overfitting. A tree learns by splitting the input data set into
subsets by testing the attributes’ value. The model
evaluates the data on the target variable by selecting themost promising independent variable to distinguishbetween the values of the target variable. This process isrecursively repeated on each derived subset. This process
is called recursive partitioning, and it ends when thesubset at a node has all the same value of the target
variable, or when splitting no longer adds value to thepredictions.
The other data mining model that is developed to predict
the number of commuting trips between any two locationsin the city of Rio de Janeiro is an artificial neural network(ANN). This technique is inspired by the central humannervous systems. In an ANN, neurons are connected
together to form a network which simulates a biologicalneural network. This network consists of sets of adaptive
weights associated to each neuron, represented bynumerical parameters adjustable by a learning algorithm.
These neurons should be able to approximate non-linear
functions of their inputs. The weights are activated during
training and prediction phases [27].
An ANN is a model formed by the interconnection ofbasic processing units, called artificial neurons. Every

2015

node in a layer is fully connected to every node in thelayer above it. In addition to weights associated with a link
between nodes, each output node adds a constant weight.
The computation feeds forward from input nodes to output
nodes without loops. Starting with linear solution of zero
hidden units, a net can have a variable number of hidden
units, and these determine the complexity of the classifier.
This section describes the models’ performance for thetransportation systems approach, based on the gravity andthe radiation models, and the data mining approach, basedon the linear regressions, the decision tree and theartificial neural network models. The models’performance is based on the RMSE – Root Mean Square
Error – which is a good measure to describe thedifferences between observed and predicted values. Thismeasure represents the standard deviation of thedifferences between these values, called residuals (basedon the data sample used for the prediction).
The comparison of all the models deployed in this study interms of the root mean square error for the number oftrips, considering the observed and predicted trips, basedon the different geographic scales is presented in thefollowing Figure.

Poster Session 1 :: April 8

result, this type of model can reasonably forecast mobility
trends and the number of trips in great metropolitan areas,
big cities and even countries, particularly when longdistances travels are involved.

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González, M. 2013. “Unraveling daily humanmobility motifs.” Journal of The Royal SocietyInterface, vol. 10 no. 84 20130246.
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24

Real-time Event Detection Method using Location
Information from Mobile Phone Handsets
Satoshi Nishiyama, Kento Kimura, Jun Nakajima, and Shinji Kashima
Technology Strategy Department, R&D Strategy Division, KDDI, Chiyoda-ku, Tokyo, Japan{sa-nishiyama, kn-kimura, ju-nakajima, s-kashim}@kddi.com

Our contributions in this paper are new real time event
detection method using location information from mobile
phone handsets and wide-area evaluation using large
telecom data set in Japan. Our method is combination ofreal time version of LPE algorithm and the automatic
generation of training set for LPE. Evaluation result showsthat our method is better than statistical approach.
I. INTRODUCTION
Human mobility models are useful for many applications,such as city infrastructure planning, area marketing and disastermanagement. Recently, many researches on human mobility
model from location information of mobile phone users arereported [1][2]. However these researches only represent
regular human activities, i.e. commuting between home andwork place, but not for irregular activities such as attending
concerts or sport games (here we call them “events” in thispaper). If we use these models to estimate the population
distribution when a popular concert is given at a stadium, we
may underestimate it. In this paper, we propose a novel realtime event detection method using the estimated number ofmobile phone users in the target area to compensate theunderestimation of population distribution by the existing
human mobility models.
II. BACKGROUND
A. Event detection problem
We consider “events” as sports games, concerts,
conventions, conferences, festivals, troubles of public
transportations or even bad weather days, which show different
patterns of human mobility (especially positive increase cases)to the regular days. We do not consider the regularly crowded
people in the Tokyo Disney Land (TDL) as the event. If an
unusual number of people gather at TDL for the Final Count
Down on New Year’s Eve, it is an event. In the evaluation, we
selected relatively larger events (i.e. event with more than
10,000 people). But our approach is applicable to relatively
smaller events, such as surprise concerts or small troubles ofpublic transportation systems.
Problem of finding an event from the number of users in aregion is considered as a kind of outlier detection problems[3][4]. In the case where no training set is available, typicalapproaches are based on statistical model (e.g. average,
standard deviation) and various kinds of models using
proximity (e.g. distance based, density based and clustering).
However, as the statistical characteristic differs for each mesh,simple statistical model does not work well. Due to thecomputational complexity, proximity based models are notsuitable for real-time methods.
Neumann et al. proposed a proximity based method called
LPE (Localized P-value Estimator) [5] using CDRs. LPE uses
the number of CDRs for each hour of whole of day (i.e. 24 data
per day) and calculates a distance value (e.g. Euclid distance)
between the training data (days without events). Then LPE set
the distance to the k-nearest neighbors (k-NNs) of each training
day as the score of the day. A day is an event day if the scoreof the day is larger than those of α percent of the training set.
They showed that LPE has better performance to one-class
SVM using CDRs in Barcelona city for event detection.
However, methods which require training sets are not suitablefor event detection method to wider region since it is notpractical to provide training sets manually (Issue 1). Also as
LPE uses whole of the day for the feature vector, it is notsuitable for real-time detection of events (Issue 2).

Poster Session 1 :: April 8

2015

B. CDR data used for this research
We used the CDR data from opt-in users of KDDI’s mobile
phone service in this research. The approximate number of optin users is about one percent of the population in Japan. We use
the estimated location from base station ID and the delay
information from the base station for the location of the user.
Firstly we anonymize the CDRs by hashing the user-related
information in them and generate the estimated locations of theanonymized users in chronological order. Next, we count theestimated locations of the anonymized users in every fiveminutes for 500m square meshes in Kanto region. Kanto
region is an eastern part of Japan including the Greater Tokyo
area and contains approximately one third of the total
population of Japan. The numbers of 500m meshes are 307,200.
III. REAL-TIME EVENT DETECTION METHOD
The computational cost of LPE classifier and the training
cost of it are O(n) and O(n2 ) , where n is the size of the
training set, respectively. If the size of the training set is not solarge, LPE could be used for real time event detection. Our
approach is that 1) generate the training set for LPE
automatically, and 2) use LPE with shorter time window size(e.g. 4 hours) and limited size of training set (e.g. 30 days) for
real time event detection, slide the time window along time
axis and calculate the LPE classifier by differential
computation from the previous time window, as shown in Fig.
1. To realize our approach, two issues mentioned above are
discussed as follows.

25

LPE (Original)

Proposed Method

Whole day’s data (hourly data)

Shorter window size (e.g. 4 hours)with dense data (e.g. 5 minutes)
Slide window along time axis and run repeatedly

TABLE I.

Fig. 1 Proposed method for real time event detection

Statistical Approach

A. Issue 1: Automatic generation of training set
Currently there are no general methods for classifying
regular days (days without events) from days with events forvarious places (or meshes) automatically with high accuracy
even if we use longer time window (e.g. whole day) for feature
vector. But if we allow some error to the training set, there aremany classification methods. Fortunately LPE uses k-NN
distance for the feature vector metric, LPE allows some errors
in its training set. Here we cluster the days in the training
period into 2 clusters by k-means and use the larger cluster as
the training data set, assuming that ‘event’ is not usual.
Practically we separate the training period into weekdays andweekends + holidays before constructing the training sets.

Our Proposal

B. Issue 2: Shortening window period
With shorter window period, LPE tends to generate false
reports. To improve the recall rate, we set additional thresholds,a) the Euclid distance of the target > 2 times of average of
those in training set, b) number of estimated users > 20 (whichcorresponds to minimum of 2,000 people in the mesh) and c)
number of estimated users > average number of users in the
training set for the LPE classifier.
IV. EVALUATION
We implemented the proposed method on a Hadoop clusterand simulated the real time event detection for each 500m
mesh in the Kanto region. We used the number of users in themesh as the feature, used 4 months from June 2014 to the
September of this year for generating training set and tested our
method during 2 months (July and August of 2014).
For evaluation, we selected 50 events, which are considered
initially as large (events with approximately more than 10,000
people), including sports, concerts and festivals and calculated
the F-score for the 33 meshes where the 50 events took place,compared with conventional statistical approach (using averageas the threshold parameter). Here we use the criteria of ‘true’ as
two consecutive positives from the method before 30 minutelater of the start time of an event. Table 1 shows the overall
result. Our method report 261 two consecutive positives while
we found manually 209 events from the Internet. Thus theprecision is 209/261=0.80. For the pre-selected 50 events, themethod reported 31. The recall is 31/50=0.62. The recall value
is low because 15 of the pre-selected events are smaller than
we had expected and from the number of estimated users they
are not distinguishable to the no-event days. If we remove such
events from the pre-selected set, the recall value will increase
up to 31/35=0.89 and F-score will be 0.85, respectively.
Fig. 2 shows an example of automatic generation oftraining set at the mesh with a large arena (Saitama Supera)a.

EVALUATION RESULTS
Precision

RecallF-score192/234=0.82

25/50=0.50

0.62

209/261=0.80

31/50=0.62

0.70

At the maximum of F-score (1.8 times average value as a threshold).

Weekdays

Training set

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2015

Arena) and Fig. 3 shows the detected events using the training
set. Fig. 2 shows that most of the events are clustered into nontraining sets, except a few events for weekends and holidays.
Fig.3 shows that all events reported in this mesh arecorresponded to actual events including the concert on July 5th.

Days with events
Weekends and holidays
Training set

Fig. 2 Example of generated training set (Saitama Super Arena)
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0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
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0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 88.89% 88.89% 0.00% 0.00% 0.00% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 95.89% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 95.89% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 95.95% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 95.89% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 95.89% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00% 95.95% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 95.89% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00% 97.30% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.52% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 95.89% 98.63% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00% 97.30% 94.59% 97.30% 0.00% 97.30% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 94.52% 98.63% 95.89% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 0.00%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00% 97.30% 97.30% 97.30% 0.00% 97.30% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 94.52% 98.63% 94.52% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 94.74%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 0.00% 97.30% 97.30% 97.30% 0.00% 97.30% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 94.52% 98.63% 94.52% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 94.74%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 0.00% 97.30% 97.30% 97.30% 0.00% 98.65% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 95.89% 98.63% 94.52% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 94.74%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 0.00% 98.65% 97.30% 98.65% 94.59% 98.65% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 95.89% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 94.59% 97.26% 98.63% 94.52% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 94.74%
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 0.00% 98.65% 98.65% 98.65% 97.30% 98.65% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 95.89% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 98.65% 97.26% 98.63% 94.52% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 94.74%
0.00% 0.00% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 98.65% 98.65% 98.65% 98.65% 98.65% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 93.24% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 94.52% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 98.65% 95.89% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 94.74%
0.00% 0.00% 98.63% 95.89% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 98.65% 98.65% 98.65% 98.65% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 98.65% 0.00% 94.44% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 94.52% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 98.65% 93.15% 95.89% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
0.00% 0.00% 98.63% 95.89% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 89.47% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 94.44% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 95.89% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 100.00% 91.78% 93.15% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
0.00% 0.00% 100.00% 97.26% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 95.89% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 98.63% 0.00% 0.00% 0.00% 100.00% 91.78% 93.15% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
94.74% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 95.89% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 97.26% 0.00% 0.00% 0.00% 100.00% 90.41% 91.78% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
100.00% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 95.89% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 94.52% 0.00% 0.00% 0.00% 100.00% 0.00% 91.78% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
100.00% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 95.89% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 93.15% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
100.00% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 94.44% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 95.89% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 93.15% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
94.74% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 83.33% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 95.89% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 93.15% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
94.74% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 94.52% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
94.74% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 94.52% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 94.74% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
94.74% 0.00% 100.00% 98.63% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 97.30% 0.00% 0.00% 100.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94.74% 0.00% 0.00% 0.00% 0.00% 0.00% 100.00% 100.00% 100.00%
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２０１４春
住まいの
展示即売
ミキハウス
立川新商
夢博 2014
青山学院
会
埼玉県内
展示会
おかあさ おかあさ
FTISLAND FTISLAND
２０１４春 ランド
展覧会 展覧会 展覧会 展覧会 展覧会 展覧会 展覧会 展覧会 展覧会 展覧会
ゆず ゆず
品発表会
展示会*2 展示会*2
Sexy
9:30～
大学フェア
10:00～
市町村職
10:00EXIT
エホバ集 エホバ集 エホバ集
んといっ んといっDREAMS DREAMS
Animelo Animelo Animelo
ARENA ARENA
ミキハウス 10:00～
展覧会 10:00- 10:00- 10:00- 10:00- 10:00- 10:00- 10:00- 10:00- 10:00- 10:00KOBUKU KOBUKU
17:00- 17:0013:0010:00- 10:00Zone
THE
THE
16:30
少女時代 少女時代 11:00～
16:30
員採用説
17:00
TUNES
会
会
会
超新星
しょ
しょ
COME COME
Summer Summer Summer
TOUR TOUR
ランド 16:00
10:00- 20:00 20:00 20:00 20:00 20:00 20:00 20:00 20:00 20:00 20:00
RORO
講演会 寄席
15:00
17:00 17:00
10:00ALFEE ALFEE
お盆 お盆 お盆
Taylor
17:30- 17:00- 16:00
幸福の科
明会
説明会
14:009:20- 9:20- 9:2018:3010:40- 10:40TRUE TRUE
Live
Live
Live
2014 2014
10:00～ Seiko
20:00 EXILE EXILE EXILE EXILE EXILE EXILE EXILE EXILE EXILE EXILE
17:30- 16:0013:30～ 15:00～
展示会
10:00- 10:0014:0018:00- 17:00Swift The
少女時代
学
13:00～
13:3021:00
17:00 17:00 15:55
14:10- 14:1017:00- 17:0016:00- 16:00- 16:0018:30～ 18:30～
18:00 Matsuda
19:00- 19:00- 19:00- 19:00- 19:00- 19:00- 19:00- 19:00- 19:00- 19:0016:30 17:00
10:0017:00 17:00
18:00RED Tour
16:0019:00～
18:00
18:00
17:20- 17:20Concert
17:00
18:0020:00
15:00～

MorningMidnight
90%

Event foundfrom the Internet

June 7/5 (Sat)Concert (17:30-)
Found on 18:00

AugustJuly
Detected events(One of 50 events)

Fig. 3 Events detected (Saitama Super Arena)
V. CONLUSION
In this paper, we presented a real time event detection
method from CDRs based on LPE algorithm. Throughempirical evaluation of large area in Japan with actual telecom
dataset, we showed that our method is better than theconventional approach. This research is supported by Ministryof Internal Affairs and Communications on “Research andDevelopment of Technologies for the Utilization of Real-time
Information on Geospatial Platforms”.
REFERENCES
[1]
[2]
[3]
[4]

[5]

Gautier Krings et al., “Urban gravity: a model for inter-city
telecommunication flows.” J. Stat. Mech., 2009.
Sibren Isaacman, et. al., “Human Mobility Modeling at Metropolitan
Scales,” in Proceedings of Mobisys 2012, 2012.
Varun Chandola, Arindam Banerjee and Vipin Kumar, “Anomaly
Detection : A Survey”, ACM Computing Surveys, Sept 2009.
Hans-Peter Kriegel, Peer Kroger and Arthur Zimek, “Outlier Detection
Techniques”, tutorial notes, the 2010 SIAM International Conference on
Data Mining, 2010.
Neumann, J., Zao, M., Karatzoglou, A. and Oliver, N., Event Detectionin Communication and Transportation Data, in Proceedings of IbPRIA
2013, 828-838, 2013.

26

2015

Measuring global and regional influence of cites using geolocated tweets
1

Instituto de Fı́sica Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), 07122 Palma de Mallorca, Spain
2
Aix-Marseille Université, CNRS, CPT, UMR 7332, 13288 Marseille, France
3
Université de Toulon, CNRS, CPT, UMR 7332, 83957 La Garde, France

The study of competition and interactions between
cities has a long history in fields such as Geography, Spatial Economics and Urbanism [1–3]. This research hastaken traditionally as basis information on finance exchanges, sharing of firm headquarters, number of passengers transported by air or tons of cargo dispatched fromone city to another. One can define a network relying oneach of these indicators and identify the so-called World
Cities, those with a higher level of centrality as the global
economic or logistic centers. In most of these analysis,
London and New York rank as the most central cities interms of economic influence and transport network centrality.
In this work, we take a radically different approach
to measure quantitatively the influence of a city in theworld. Nowadays, geolocalized devices generate a large
quantity of real time and geolocated data allowing us tounderstand how people move throughout time and space.
It is now possible to explore human mobility in detail using geolocalized data generated by mobile phones, credit
cards, transport cards or GPS. In this study we use a
Twitter database containing 20 millions of geo-locatedtweets worldwide recorded during a period of 1000 days
to measure and compare the influence of 58 cities. The
aim here is to search for an answer to the questions raisedby the following thought experiment: an announcementwith a particular message is displayed in the most centric
place of a city. People seeing it, whether tourist or locals,
will later travel throughout the world. Which would be
the city most efficient as source for these travels? Understating as efficient that able to spread further or more
extensively the message by personal user movements. Is
there a difference between the classification obtained withlocals or visitors?

FIG. 1: Local Twitter users mobility network between the 58
cities. Only the flows representing the top 95% of the total
flow have been plotted. The flows are drawn from the least
to the greatest.

We use the Twitter data in order to tackle these questions. First , we consider the displacements of users visiting each city. The urban areas are ranked according to
the area covered and the radius traveled by these users
as a function of time. These metrics are inspired by theframework developed for random walks and Levy flights,
which allows us to characterize the evolution of the system with well defined mathematical tools and with a clear
reference baseline in mind. The ranks change dramatically when one has into account these measures, showing
as two top rankers cities such as Rome and Hong Kongthat either are large centers of global touristic attraction
or are gates to the extensive hinterland of countries suchas China. When the users are separated by visitors andlocals, we find that the main contribution to the generalranking comes from visitors and that if only locals aretaking into account the raking is modified even though
not dramatically.

Poster Session 1 :: April 8José J. Ramasco,1 Maxime Lenormand,1 Bruno Gonçalves,2, 3 and Antònia Tugores1

For getting these previous results all the Twitter population is considered, regardless of the fact that the users
mobility takes place in a city or in rural areas. The
only condition is that the user passed at certain moment
through the city under consideration but the analysis isbased on all his/her posterior movements without discerning travels to rural and urban areas However, many
previous studies were focused mostly on cities and interchanges between them. To be able to compare, we restrict the analysis to users residing in an urban area andto their movements toward other cities. In this way, we
obtain a weighted directed network between cities, whose
links weights represent the (normalized) fluxes of users
traveling from one city to another. This network provides the basis for a more traditional centrality analysis,
in which we recover London and New York as the mostcentral cities at a global scale. Still, the network also allows us to run clustering techniques and divide the world
city network in communities or areas of influence. Thisdivision of the network with some of the most intenseconnections is shown in Figure 1. When the centrality
is studied only within each community, we obtain a regional perspective that induces a ranking of cities. The
comparison between the global and the regional ranking
provides also with important insights in the hierarchy ofworld urban areas.

27[1] Christaller, W. Die Zentralen Orte in Süddeutschland:eine Ökonomisch-Geographische Untersuchung Über die
Gesetz Massigkeit der Verbreitung und Entwicklung derSiedlungen mit Städtischen Funktionen, Fischer Verlag,
Jena (1933). (English translation: Christaller, W. &Baskin, C.W. Central places in Southern Germany, Pren-tice Hall, Englewood Cliffs, N.J. (1966))[2] Knox, P.L. & Taylor, P.J. (eds.). World cities in a worldsystem. Cambridge University Press, 1995.
[3] Batty, M. The New Science of Cities. The MIT Press(2013).

Poster Session 1 :: April 8

2015

2

28

2015

Peter Widhalm

Michael Ulm

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

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

I.

I NTRODUCTION

Over the last decade a number of methods have been
proposed in the literature to use trip data collected from
cellular networks for estimating travel demand, i.e. OriginDestination matrices, and analyzing human mobility patterns.
But how reliable are the trips extracted from cellular traces?
In previous work validation was conducted by comparing the
spatial and temporal distribution of trips to traditional mobility
surveys [1]. The authors in [2] compare different interpolation
methods for mobile phone traces and evaluate their spatial
errors by subsampling smartphone user position trajectories
with different subsampling ratios. An evaluation of trajectoryfiltering techniques to reduce spatial errors in cell phone tracks
is presented in [3]. However, there is still very little empiricalknowledge about achievable trip detection rates and the spatiotemporal precision of the extracted trip data at a disaggregatedlevel. With this study we hope to fill this gap.
We report the results of an empirical evaluation conductedwith 241 individuals who used a smartphone to record alltheir trips over a period of one week. A specialized logging
software collected their GPS traces along with the cells themobile was connected to and the points in time when thedevice communicated with the mobile network (phone usage
and Location Area Update events). In addition, we asked
the participants to annotate their tracks with trip start andend points. We generate cellular trajectories by mapping therecorded cell IDs to estimated geographic locations and subsampling the trajectory according to the empirical frequency
of mobile communications (phone usage) and logged Location
Area transitions. From the resulting cellular trajectory we
try to recover the original trips and use the annotated GPS
positions and time stamps of the recorded trips as “groundtruth” for validation. We apply a simple and frequently used
trip extraction method described, e.g., in [4], [5], [6] andalso test three extensions to the algorithm (see Sect. II). We
analyze trip detection and error rates measured as sensitivity(true positive rate) and precision (positive predictive value) of
the trip extraction method and plot it against trip length, stay
duration at the trip destination and time of day. We evaluate thetemporal errors of the extracted trips and the spatial uncertaintyof trip origins and destinations. In addition, we demonstrate
the impact of different parameter settings and evaluate how
the results are affected by the inclusion of Location Area
Update records, which are independent from phone usage andare issued whenever the mobile device enters a new Location
Area.

Poster Session 1 :: April 8

Empirical Evaluation of Disaggregated Trip Data
Collected from Cellular Networks

distance threshold d1indirection ratio ι
0.85

0.9
0.8

0.8
0.7

0.75

0.6
0.7

0.5
0.65

0.4
0

1000 2000 3000 4000 5000 6000 7000
metersstay duration threshold t

0.95

1

1.5

2

2.5

3

straight-line speed vmax
0.95

0.9

0.9

0.85

0.85
0.8

0.8

0.75

0.75
0.7

0.7

0.65

0.65
0

sensitivity
precision
f1 score

0.6

10

20

30
40
minutes

50

60

0.55
0

10

20
m/s

30

40

Fig. 1: Sensitivity analysis of the parameters used for trip
extraction.

sensitivity:
precision:F1-score:

basic
algorithm
47.0%
91.6%
62.1%

interpol.+geometry
68.7%
74.2%
71.3%

+low-passfilter
66.4%
79.7%
72.4%

without
LA-Updates
49.5%
86.5%
63.0%

TABLE I: Sensitivity, precision and F1-score of the basic trip
extraction algorithm and the proposed extensions.

II.

T RIP E XTRACTION

The basic trip extraction procedure can be summarized asfollows: consecutive cell locations where all pairwise distances
are below a threshold d are fused together and their coordinates
are averaged to compute a centroid position. The timestampsof the first and the last record are used to approximate thetime of arrival and departure. Trip origins and destinations
are distinguished from “passing-by points”, i.e. points along a
traveled route, by introducing a minimum stay duration t.
In practice there are several kinds of errors and situations
when this trip extraction approach may fail:1)

trips shorter than d are likely not to be detected,
unless spatial errors add to the actual trip length;

29

2)

3)

4)

on the other hand, if d is too small, pure signal
movement will generate fictitious trips;
if the stay duration at the trip destination is shorter
than t the trip is likely not to be detected, unlessthe stay duration is overestimated due to the spatial
uncertainty and the fusion of cell locations; on theother hand, if t is too small, many points along
a traveled route will be wrongly detected as trip
destinations (e.g. waiting time at transport hubs ordue to road congestion);
the sparse and irregular temporal sampling of cellular
trajectories introduces interpolation errors and uncertainties in the estimated arrival and departure times;
the trip detection rate therefore depends on phone
usage and actual stay duration;
Location Area (LA) Update events are triggered bymotion rather than phone usage and can therefore
improve trip detection; on the other hand theserecords can cause large interpolation errors: it can beassumed that devices issuing an LA Update event are
currently moving and that the actual trip destination
is somewhere else. As a consequence, LA Update
records can introduce large spatial and temporal errors to the extracted trip data or generate fictitious
trip destinations.

In addition to the basic trip extraction algorithm we evaluate the following extensions to reduce these kinds of errors:
1)

2)

3)

interpolating the trajectory based on space-timeprisms [7] and estimating upper and lower bounds
as well as an expected value of the stay durationin order to improve the arrival, departure and stay
duration estimates (Fig. 2a);including the trajectory’s geometry in the trip extraction procedure to better distinguish trip destinations
from “passing-by points” along the traveled route: fortriplets (A, B, C) of successive locations we define athreshold ι of the “indirection” ratio (AB +BC)/AC(Fig. 2b).
low-pass filtering based on an assumed straight-line
travel speed vmax to reduce noise and fictitious trips(Fig. 2c);

These extensions are described in detail in [8].
III.

R ESULTS

The results of the sensitivity analysis of the parameters
used in the trip extraction algorithm are shown in Figure 1. The
optimal value of distance parameter d that maximizes the F1score (i.e. the harmonic mean of sensitivity and precision) was
d = 1000m, but all settings with 500 < d < 1500m resulted
in similar scores. For large ranges of duration parametert, geometry parameter ι, and speed parameter vmax there
was almost a one-to-one trade-off between sensitivity andprecision: fixing the distance parameter to it’s optimal value
the F1-score varied only little for all parameter settings with
ι > 1.1, vmax > 8m/s, and 6 < t < 60min. The highest F1score was obtained for ι = 1.2, vmax = 18m/s and t = 24min.
Choosing parameter values maximizing the F1-score resulted
in approximately 66% sensitivity and 80% precision.

Poster Session 1 :: April 8

2015(a) space-time prisms(b) geometry(c) low-pass filter

Fig. 2: Extensions to the basic trip extraction procedure.
arrival time

departure time

stay duration

0.7

0.7

0.7

0.6

0.6

0.6

0.5

0.5

0.5

0.4

0.4

0.4

0.3

0.3

−100

0
minutes

100

0.3

−50

arrival time0
minutes

50−100

departure time
0.7

0.7

0.6

0.6

0.6

0.5

0.5

0.5

0.4

0.4

0.4

0.3

0.3
100
minutes

200

100

stay duration

0.7

0

0
minutes0.3
050
minutes

100

0

100
minutes200

Fig. 3: Errors (top row) and error magnitudes (bottom row)of arrival time, departure time and stay duration estimates
resulting from linear interpolation (red) and interpolation basedon space-time prisms (green).

The results achieved with the basic trip extraction algorithm
and it’s extensions are detailed in Table I. Combining thespace-time prism approach (extension 1) with analysis of thegeometry of the cellular trajectory (extension 2) significantly
improved the trip detection rate as compared to the basic algorithm. The reason is that linear interpolation underestimates
the stay durations at visited locations. As shown in Figure 3,
the interpolation scheme based on space-time prisms improved
the estimates of trip start times, end times, and stay durations.
However, in some cases it overestimated the stay durations, andas a consequence the number of fictitious trips was increased,
which is reflected by a lower precision percentage. Applying
a low-pass filter (extension 3) reduced the number of fictitious
trips due to noise. This improved precision by 5.5%, but on
the other hand it also reduced sensitivity by 2.3%. Excluding

30

2015
0.9

0.8

0.7

F(x)

0.6

0.5

0.4

0.3
OpenCellId Vienna
OpenCellId outside Viennaincl. LA−Upd Viennaincl. LA−Upd outside Vienna
excl. LA−Upd Vienna
excl. LA−Upd outside Vienna

0.2

0.1

0

300

600

900 1200 1500 1800 2100 2400 2700 3000 3300 3600 3900 4200 4500 4800
metersFig. 4: Spatial error.

Future work includes analysis of selection biases in cellular
data, i.e. distortions by over- or under-representation of groupsof people depending on their age, sex, income, social role orother socioeconomic factors, or biases in the detection of trips
depending on their purpose.

sensitivity vs. stay duration

sensitivity

0.8
0.75
0.7
0.65

t

0.6
0

0.5

ACKNOWLEDGMENT
1

1.5

2

2.5
3
3.5
hours
sensitivity vs. straight−line trip length

4

4.5

5

0.8
sensitivity

In summary, these results show that the evaluated extensions of the basic trip extraction algorithm improved the results
as measured by the achieved F1-score. However, there arestrong trade-offs between detection sensitivity and precision
scores, and spatio-temporal errors, which means that the optimal trip extraction algorithm and parameter settings depend
largely on the intended application and it’s requirements. As
expected, the trip detection rate is correlated to stay duration
at the trip destination and straight-line trip length. Including
Location Area Update records increases sensitivity but at thesame time reduces precision and worsens the spatio-temporalerrors. Since Location Area Update events are issued bydevices moving between Location Areas and generally do notindicate trip origins and destinations, these records requirea different interpolation approach than event records due tophone usage.

Poster Session 1 :: April 8

centroid. This could be useful to estimate spatial errors forother countries and regions without re-conducting empiricalinvestigations.

empirical CDF of positioning errors

This work was partially funded the Austrian Federal
Ministry for Transport, Innovation and Technology (BMVIT)
within the strategic program FIT-IT under grant 835946(SEMAPHORE).

0.75

R EFERENCES

0.7
d

0.65
0.6
0

0.5

1

1.5

2

2.5
3
kilometers

3.5

4

4.5

5

Fig. 5: Sensitivity vs. stay duration and straight-line trip length.

Location Area Update events from the cellular traces reduced
the trip detection rate by 16.9% but improved precision by
6.8%.
As expected, the detection rate (sensitivity) increases withthe straight-line trip length and the stay duration at the trip
destination, which is shown in Figure 5. For example, for trips
with straight-line lengths greater than 2km and stay durations
greater than 1 hour, the detection rate rose to 82%.
The spatial error distribution of the trip origins and destinations is given in Figure 4. Including Location Area Update events in the cellular traces increased the spatial error
significantly. There was also a significant difference in thepositioning errors between locations within and outside themetropolitan area of Vienna, due to different network density
and antenna ranges. The positioning errors could be roughly
approximated by analyzing the distances of GPS measurements in the OpenCellID database [9] to the estimated cell[1] S. Colak, L. P. Alexander, B. G. Alvim, S. R. Mehndiretta, and M. Gonzalez, “Analyzing cell phone location data for urban travel: Current
methods, limitations and opportunities,” in Transportation Research
Board Annual Meeting, 94th, 2015, Washington, DC, USA, 2015.
[2] S. Hoteit, S. Secci, S. Sobolevsky, C. Ratti, and G. Pujolle, “Estimating
human trajectories and hotspots through mobile phone data,” Computer
Networks, vol. 64, pp. 296–307, 2014.
[3] C. Horn, S. Klampfl, M. Cik, and T. Reiter, “Detecting outliers incell phone data: Correcting trajectories to improve traffic modeling,” inTransportation Research Board 93rd Annual Meeting, no. 14-3690, 2014.
[4] F. Calabrese, G. D. Lorenzo, L. Liu, and C. Ratti, “Estimating origindestination flows using mobile phone location data,” IEEE Pervasive
Computing, vol. 10, no. 4, pp. 36–44, 2011.
[5] H. Wang, F. Calabrese, G. Di Lorenzo, and C. Ratti, “Transportation
mode inference from anonymized and aggregated mobile phone calldetail records,” in Intelligent Transportation Systems (ITSC), 2010 13th
International IEEE Conference on. IEEE, 2010, pp. 318–323.
[6] T. Horanont, “CSIS discussion paper no. 115 - a study on urban mobility
and dynamic population estimation by using aggregate mobile phonesources,” 2012.
[7] T. Hägerstraand, “What about people in regional science?” Papers inregional science, vol. 24, no. 1, pp. 7–24, 1970.
[8] P. Widhalm, Y. Yang, M. Ulm, S. Athavale, and M. C. González,“Discovering urban activity patterns in cell phone data,” Transportation Special Issue on emerging, passively generated data for travel behavior
and policy analysis, 2015, accepted.
[9] Opencellid database. [Online]. Available: http://opencellid.org

31

“I don’t have a photograph, but you can have myfootprints.”
Chris Riederer, Sebastian Zimmeck, Coralie Phanord, Augustin Chaintreau, Steven M. BellovinComputer Science Department, Columbia University, New York, NY{mani,sebastian,augustin,smb}@cs.columbia.edu, Coralie.S.Phanord.16@dartmouth.edu
0.75
0.70

Poster Session 1 :: April 8

2015

Accuracy

0.65
0.60
0.55
0.50
0.45

Unsupervised threshold
Uninformed
Supervised threshold
Foursquare
FullGPS
10 people
10 sq.m.

Block Group
600-3000 people
0.04 sq.mi.

Tract
3k-4k people
0.14 sq.mi.

ZIP
Neighborhood
10.5k people
15k people
0.38 sq.mi.
1.54 sq.mi.
Granularity

PUMA
100k people
5.52 sq.mi.

17 Br
onx
54 20

Figure 1: Accuracy of predicting ethnicity for five algorithm
classes and various granularity ranges of location data.
42

Location data collected through GPS are routinely
available to a plethora of websites, apps, and third party
services. They are increasingly used to learn user characteristics particularly for purposes of behavioral andcontextual advertising. While the identification riskof location information has been widely reported, its
discriminative risk has received much less attention sofar. Documenting this characteristic of location datarequires an understanding of mobility at a demographic
level; it raises methodological challenges of reproducibility, extensibility, and accuracy, all of which are hard tosolve from a technical and ethical standpoint.
In our work, we fill this gap for the first time demonstrating which demographic traits can be inferred fromusers’ geographical footprints. We leverage the growing
body of public evidence in photo-sharing services. Using a corpus of geo-tagged photos from Instagram andFoursquare, we show that discriminative demographic
information can be inferred from visited locations. In
particular, we show that it is possible to predict a user’s
ethnicity with reasonable accuracy (Figure 1). Furthermore, we show that human location trends observed in
census data can be reliably reproduced from the publicly available Instagram data (Figure 2).

BoroughState
400k-2.6mil people 580k-3.8mil people
61.0 sq.mi.
72k sq.mi.

18

54
31
11 14
13 9
Hudson

25 18
48 54
13 14
11 9
New
Yor
k
Ki
ngs

17 10
64 70

Ri
ch10 20 mond
7

20 8
36 54
32 25
10 9

Demogr
aphi
cs

11 54
30
3 6

Hi
spani
c
Caucasi
an
Af
r
i
can
Amer
i
can

28 37

28

18 33
23 18
5

Queens

Asi
an

15 14
66 57

Nassau11 19
8 5

Figure 2: Top: Comparison of ethnicity according to census (leftcolumn) and Instagram location data points (right column) for
various counties in New York State. Bottom: Instagram location
data points colored by ethnicity in New York City.

32

2015

Thomas R. Kirchner1,2 Hong Gao2, Andrew Anesetti-Rothermel2, Heather Carlos3, Brian House4
1

New York University, NY, USA
The Schroeder Institute at Legacy, Washington, DC, USA
3
Norris Cotton Cancer Center, Dartmouth College, Hanover, NH, USA
4
Brown University, Providence, RI, USA
2

ABSTRACT
Health-related behaviors occur as part of a broad socio-ecologicalcontext that unfolds dynamically over time. It is important to improve
our understanding of the way health-related features within cities affectthe health of citizens traveling through their streets. In this paper we
present the development of a national density surface of convenience,
grocery and gas outlets, and explore the way real-time access to theseoutlets varies as a function of both their density and the mobility patternsof residents.

1. INTRODUCTION

final dataset included N = 269,781 retail outlets (Figure 1). To
quantify individuals’ real-time access to retail outlets, a static
bandwidth KDE approach was carried out with the spatial analyst
density toolset in ArcGIS v.10.1 software. The resulting density
surface had a fixed 5-mile bandwidth and a cell size of 250 meters.
Zonal statistics of the final density surface was calculated for zip
code tabulation areas (ZCTAs)[9] in the United States. The
average retail outlet density per ZCTA was then linked to eachreal-time mobility coordinate contributed by the participants in our
geo-location tracking sample.

Poster Session 1 :: April 8

Longitudinal Human Mobility and Real-time Access to a
National Density Surface of Retail Outlets

Figure 1. National Density Surface: US Convenience Retail

The link between cities and the health of their citizens represents a
rapidly evolving area of scientific inquiry[1]. It is increasingly
clear that there are large individual differences in mobility patterns
that drive a dynamic interaction between individuals and theirsurroundings over time[2-4]. Conceptions of the urban environmental
impact on health as static fail to account for the mobility and preferencesof citizens actively engaging with their real-time context. Evidence
from the growing literature on human mobility[2, 3] supports thenotion that mobility patterns determine environmental exposurelevels more than static factors like a person’s place of residence.
Yet methods for continuous quantification of accumulating levelsof exposure to features in the built environment are only now being
developed.
In this paper we present the development of a framework for the study ofhuman mobility and real-time access to the landscape of point-of-saleproducts available across the US, a factor with indisputable implications
for a range of health related behaviors[5-7]. This begins with a national
probability density surface representing the continuous landscape of
convenience, grocery and gas outlets in the US. We then overlay
longitudinal human mobility data collected from a sample of 550 people
who have voluntarily recorded their real-time geographic location via
their cellular phone every 10-minutes over an average of 3 months,
resulting in an average of 10,635 observations per user. Analyses
examine the extent to which the dynamic nature of participants’ mobility
patterns interacts with their real-time surroundings and thus determines
their day-to-day access to products sold in retail convenience stores.
Variations across urban areas and over time provide insight and identify
targets of intervention for both urban planners and public healthpractitioners.

2. METHOD
2.1 Retail Density Surface

2.2 Longitudinal Mobility Data
Geolocation tracking made it possible to physically link each person’s
real-time location to the probabilistically continuous landscape of
convenience stores across the US. Mobility data comes from
OpenPaths<https://openpaths.cc>, launched by the New YorkTimes Company Research and Development Lab in May of
2011[10]. OpenPaths collects GPS location information throughiOS and Android location tracking applications. These applications
utilized multiple approaches to geographic location capture, including
both direct satellite GPS coordinates (when available) and wirelessnetwork-based “assisted” geo-location estimation via trilateration among
cellular towers and wireless data access points.

A nationwide density surface of convenience and related retail
outlet locations was generated using kernel density estimation(KDE). The empirical basis for this probability density surface
was a national dataset of retail outlets, identified by North
American Industry Classification Systems (NAICS) codes[8]. The

33

2015
Figure 3. 𝒓𝒈 over 24-hr clock as function of day of week.

Poster Session 1 :: April 8

Figure 2. Human Mobility Cohort

2.4 Real-time Retail Access
The longitudinal mobility dataset contains 3,440,821 observations
collected from 859 individuals worldwide from 03/01/2012 to
12/31/2013, which was then clipped to United States using state
outline polygons published by United States Census Bureau,
yielding a US cohort of 550 individuals with 2,013,042
observations recording during the present observation period. The
total number of locations contributed by each individual ranges
from 1 to 79,602 with a mean at 10,635.39 and standard deviationof 15,189.44. The number of days falls between 1 and 670 with amean of 202.52 (SD=181.79 days).

2.2.1 States of residence
Noteworthy, 19.33% of the data (N= 389,058) falls in California,
and 14.45% (N= 290,854) in New York. All other states have
66.22% (N= 1,333,130). Given nice contrast in terms of geolocations, climate, built-in environment, etc., between New Yorkand California, the two states are entered as covariates in allmodels.

2.3 Radius of Gyration
Radius of gyration measures the distance a person travels within acertain time period[3]. It defines by the standard deviation between
locations and their center of mass:
𝑟!! =

1
𝑁

!

(𝑟! − 𝑟!"#$ )!
!!!

In this paper, hourly radius of gyration was obtained for eachindividual, resulting in 747,347 total observations. Validity of thedata is supported by observation of expected patterns of mobility,
such as that related to weekdays and weekends. Figure 3 illustrates
the daily drop in mobility across the early morning hours, followed
by a steep rise across the middle of the day, and then divergence on
Friday, Saturday and Sunday, with late Sunday revealed as thewindow of greatest mobility, as travelers who departed on either
Friday or Saturday return home.
Overall, mean radius of gyration is 1.93 kilometers per hour with a
4.41 kilometers standard deviation. Minimum and maximum are 0
and 119.51 kilometers. Within each day, 𝒓𝒈 was the lowest earlyand higher across the remainder of the day. We see a spike in 𝒓𝒈
mid-day on weekdays – and generally more variation on weekdays.

Real-time access to the retail density surface was defined as the
product of each participant’s radius of gyration within each hour
under observation and their average retail outlet density value foreach mobility coordinate recorded within the same hour.
Conceptually, this “Access” variable accumulated the number ofretail options participants had as they moved, and as expected froma count variable of this kind, the observed distribution was heavily
skewed right, and thus not a reasonable fit for the assumptions of
the general linear model (see Section 3.1). Access was therefore
stratified into deciles (plus an additional level for values of zero),effectively transforming it into a categorical variable with 11 levels
from 0 to 10 corresponding to growing access to retail products.
Non-parametric categorical data analysis methods were thenemployed as describe in Section 3.1.

3. STATISTICAL ANALYSES
3.1 Best-in-class Model Selection
Table 1. Step-down contrasts of "best-in-class" models
Model(Model'Terms)
1 (N,'W,'T,'S,'A)
2 (NTA,'WTA,'NWT,'NTS,'TSA,'NSA,'WSA)
3 (NTA,'WTA,'NWT,'NWA,'NAS,'NTS,'TSA,'WSA,'WTS,'NWS)
4 (NTSA,'NWTA)
5 (NWTA,'NWST,'NTSA,'WTSA,'NWSA)
6 (NWTSA)

Deviance
1.11E+08
258972.5
196333
188143.523238.13
K166.6045dfp
698 0.00
361 1.00
339 1.00
264 1.00
90 1.00
0 1.00G2
Δ'df χ2'(Δdf;'0.001)
110496519.6 337
135.80762639.4
22
67.4598189.475
100.425164905.4 174
135.807
23404.8
90
124.116
K
K
K

N:'NY'versus'CA
W:'weekend
T:'time'of'day
S:'season
A:'access'to'retail

Hierarchically nested model comparison techniques were used toiteratively identify the most parsimonious combination of factors
required to explain the observed data. Table 1 presents an
overview of the best-in-class model selection process that sought
to identify the most parsimonious model form, defined as theminimal set of parameters required to provide and adequate fit tothe observed data. The initial basis for comparison is Model 6,
which is the saturated model that corresponds perfectly to the raw
data, having degrees of freedom (df=0) equivalent to the total
number of cells minus all interactive combinations of the 5 factors
under study here: retail Access (the conceptual DV), State (i.e., NY
as reference), Weekend, TOD, Season. Stepping-down from thesaturated model, Model 2 in Table 1 fits the data well while only
including 342 of the total 704 cells under study. This is thus themost parsimonious model, effectively isolating an informative
pattern in the data that then becomes the basis for inference andconclusions.

34

2015
4. CONCLUSIONS
Results of this paper shed additional light on the nature of realtime retail Access, especially as it compares to the static
aggregated density of outlets alone. The contrast between New
York and California is particularly useful on this point. First of all,outlet density peaks much higher in New York than in California,
while California and its urban sprawl is characterized by more
widely distributed yet moderate to low levels of density. Second,radius of gyration is consistently higher in California than in New
York, and exhibits an entirely different cyclic pattern. This patternof results demonstrates the kind of insight that can be gained byconsidering both POIs and dynamic mobility patterns.

REFERENCES
1.
Cromley E.K., Mclafferty S.L., GIS and Public Health.
2002: The Guilford Press.

Poster Session 1 :: April 8

Figure 4. Access by Time of Day in California and New York

2.
Yuan J., Zheng Y., Xie X., Discovering regions ofdifferent functions in a city using human mobility and POIs, in Inproceedings of the ACM KDD. 2012, ACM Press: Beijing, China.
Figure 4 highlights the pattern of interaction NTA (State, Time ofDay, and Access) in Model 2, Table 1. It reveals the different
Access level across time of day between New York and California.
Elevated Access spikes in the middle of the day in New York andagain in late night, while in California, Access peaks in lateafternoon.
Figure 5. Access by Time of Day between Weekday andWeekend3.
Gonzalez M.C., Hidalgo C.A., and Barabasi A.L.,
Understanding individual human mobility patterns. Nature, 2008.
453(7196): p. 779-782.
4.
Kirchner T.R., Cantrell J., Anesetti-Rothermel A.,
Pearson J., Cha S., Kreslake J., Ganz O., Tacelosky M., Abrams
D., Vollone D., Individual mobility patterns and real-time geospatial exposure to point-of-sale tobacco marketing, in In
Proceedings of the ACM Wireless Health. 2012, ACM Press: San
Diego, CA.
5.
U.S. Department of Health and Human Services.,
Preventing tobacco use among youth and young adults: a report ofthe surgeon general. 2012, U.S. Department of Health and Human
Services, Centers for Disease Control and Prevention, National
Center for Chronic Disease Prevention and Health Promotion,
Office on Smoking and Health: Atlanta, GA.
6.
Institute of Medicine., Food marketing to children andyouth: threat or opportunity? 2006.
7.
Cairns, G., et al., Systematic reviews of the evidence on
the nature, extent and effects of food marketing to children. A
retrospective summary. Appetite, 2013. 62(0): p. 209-215.
8.
U.S. Census Bureau. Introduction to NAICS. 2014 [cited
2015 January 8th]; Available from:http://www.census.gov/eos/www/naics/.
9.
U.S. Census Bureau. ZIP Code Tabulation Areas(ZCTAs). 2014 [cited 2015 January 8]; Available from:https://www.census.gov/geo/reference/zctas.html.
10.
House B., OpenPaths: empowering personal geographic
data, in In proceeding of the ISEA. 2013: Sydney, Australia.

Figure 5 presents the different Access patterns in time of day
between weekday and weekend. On weekdays, Access spikes in
the middle of the day, probably due to high movement and highexposure to retail outlets during lunchtime. In contrast, on
weekends, Access elevates in early evening and late night, which
could probably be explained by going out and returning home forweekend activities.

	
  
35

Bandicoot: a Python toolbox to extract
behavioral indicators from mobile phone
metadata

Poster Session 1 :: April 8

2015

Yves-Alexandre de Montjoye1 , Luc Rocher, Alex ‘Sandy’ Pentland∗

Corresponding author (yvesalexandre@demontjoye.com)

Mobile phone data, also called call detail record (CDR), have been used extensively by researchers in computational social science [4]. Researchers havecompared the recent availability of large-scale behavioral data sets to the invention of the microscope [3]. Mobile phone metadata have for example beenused to detect communities inside countries [1], analyze the impact of mobility
on malaria [7], and to predict the personality [2] or gender [6] of users. Thereexist, however, a growing need for standardized and privacy-preserving methods
to analyze mobile phone metadata datasets.
Bandicoot is an open-source Python toolbox to process mobile phone metadata. Bandicoot loads individuals metadata and attribute text files for a given
user and all its contacts as well as a tower file. It then computes, on a weekly
basis, 30 behavioral indicator for the user and return their mean, median, andstandard deviation. The behavioral indicators falls into 3 categories: individuallevel (number of call, text response rate. . . ), spatial patterns (radius of gyration, entropy of places. . . ), and at the social network level (clustering coefficient,assortativity. . . ). Bandicoot then provides easy to use functions to export in a
CSV file the behavioral indicators of a set of users as well as potential missing
data.
Overall, Bandicoot provides a complete easy-to-use environment for researchers
using mobile phone metadata. It allows them to easily load their data, perform
analysis, and export their results with a few line of codes. Its standardized metrics and tested code helps researcher compare results across studies, see figure 1
for a visual overview. The toolbox is easy to extended and contains an extensive
documentation with guides and examples.
Bandicoot has already been used in combination with machine learning algorithms to label datasets and to run large-scale experiments [5].

136

Poster Session 1 :: April 8

2015

user

INTERACTION NETWORK
34 text messages / 22 calls
5 contacts

MOBILITY
14 places visited

1/1/15

1/5/15CALLS
TEXTS
light/light outgoing, dark/dark incoming

Figure 1: Visual features of a user’s records

References[1] Vincent D 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.
[2] Yves-Alexandre de Montjoye, Jordi Quoidbach, Florent Robic, andAlex Sandy Pentland. Predicting personality using novel mobile phone-based
metrics. In Social Computing, Behavioral-Cultural Modeling and Prediction,
pages 48–55. Springer, 2013.
[3] S. Higginbotham.
“For science, big data is the microscope ofthe 21st century”, 2011.
URL http://gigaom.com/2011/11/08/
for-science-big-data-is-the-microscope-of-the-21st-century/.
[4] David Lazer, Alex Sandy Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James
Fowler, Myron Gutmann, et al. Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915):721, 2009.
237

[5] Pål Sundsøy, Johannes Bjelland, Asif M Iqbal, Yves-Alexandre de Montjoye,
et al. Big data-driven marketing: How machine learning outperforms marketers’ gut-feeling. In Social Computing, Behavioral-Cultural Modeling andPrediction, pages 367–374. Springer, 2014.
[6] Pål Sundsøy, Johannes Bjelland, Asif M Iqbal, Alex Pentland, YvesAlexandre de Montjoye, et al. A cross country study of gender prediction
using mobile phone metadata. In Netsci-X. 2015.
[7] Amy Wesolowski, Nathan Eagle, Andrew J Tatem, David L Smith, Abdisalan M Noor, Robert W Snow, and Caroline O Buckee. Quantifying theimpact of human mobility on malaria. Science, 338(6104):267–270, 2012.

Poster Session 1 :: April 8

2015

3

38

Detecting Train Commuters using CDRs and GIS
information
Hiroki Ishizuka
KDDI R&D
Laboratories., Inc.

hk-ishizuka@kddilabs.jp

Nao Kobayashi
KDDI R&D
Laboratories., Inc.

no-kobayashi@kddilabs.jp

Shigeki Muramatsu
KDDI R&D
Laboratories., Inc.
mura@kddilabs.jp

Chihiro Ono
KDDI R&D
Laboratories., Inc.
ono@kddilabs.jp

ABSTRACT

When a large-scale disaster occurs in downtown Tokyo,
Japanese government has to provide a sufficient number of
temporary camp sites or shelters for stranded commuters who
cannot go back their home except that they have to keep
walking several hours. Hence, It is necessary for the government to estimate exactly a number of potential candidateof stranded commuters in daily life. As commute types, train
has been 76% of motorized transports (Train, Bus, Car) inTokyo. Therefore, we suppose train commuters to be almost
candidates of stranded commuters. For disaster measure forgovernment, we proposed detecting method of the train commuters from CDRs and GIS information of train routes. As
a result of our experiment, the average of the accuracy for
detecting a train commuter is 75%.
INTRODUCTION

There are a lot of train commuters in Downtown Tokyo, sinceTokyo has a huge train network including underground lines
has been grown compared with other city in the world. Astransport types for commute, train has actually been 76% of
motorized transports (Train, Bus, Car) in Tokyo. In otherwords, train has been a main transportation for commutes inmetropolitan Tokyo.
When a large-scale disaster occurs in a metropolitan area, agovernment has to provide sufficient number of temporary
camp sites or shelters for stranded commuters who cannot
go back their home except that they have to keep walking
for many hours. At the Great East Japan Earthquake, practically there were 3.5 million people as stranded commuters incity of Tokyo. For securing temporary evacuations for suchstranded commuters, we should estimate a number of potential candidate of stranded commuters in daily life.
In case of Tokyo, we suppose train commuters to be almost
potential candidates of stranded commuters. Therefore, we
proposed estimating method of train commuters in daily life.
To detect train commuters, our method utilizes algorithms ofsupervised machine learnings with CDRs and GIS information of train routes. And also we evaluate the accuracy ofour proposed method using CDRs data of 1,000 volunteers
during 1 month (March, 2014). As a result of our experiment, the average of the accuracy for detecting a train commuter is 75%. Figure 1 is shown an example result of our
proposed method. Black dots, Blue dots and Red dots mean
locations from CDRs which a user is on a train, all locations
from CDRs and train stations, respectively.
RELATED WORKS

Poster Session 1 :: April 8

2015

Figure 1. An Example result of our proposed method

There have been a number of conventional approaches[1-8] todetermine the transportation mode including train commute.
Hemminki et al.[3] uses only accelerometers for distinguishing different modalities while testing the system across a few
cities. They obtain impressive results around 80%. However,they needs to keep sensing accelerometer during detecting
a mode of transit without considering energy consumption.
And also the method needs to install special sensing application to a user’s smartphone.
Zhou et al.[4] utilize accelerometer, audio sensor and cell
tower sequences of a smart phone to identify whether the user
is on public transit or car, which mostly works for bus detection. The proposed method is energy efficient because theyuse cell tower sequences rather than GPS. However, a training of a cell tower sequence is required for all routes. it takes
a long time to add new routes.
Rahul et al.[5] uses several sensors on smart phone and GIS
information. Their contributions are that they reduce learning
time of sensor data using General Transit Feed Specification(GTFS) which describes schedule for public transits. An accuracy of the proposed system is around 85%. However, theymight not deploy new countries since opened GTFS are notso sufficient in the world.
Our proposed system utilizes location information from
CDRs which generate when a user just uses his phone in daily
life. Therefore, the system does not impose a user to consume
energy of his phone and install a specific app for detecting his
mode of transit.
PROPOSED SYSTEM

We show two phases of operation as the system architecturein Figure 2. Our proposed system estimates train commuters

39

2015

Figure 2. A System architecture of a proposed method

Metric

k-NN

LR

SVM

RF

Accuracy
Precision
RecallF-measure

0.734
0.734
0.733
0.733

0.721
0.721
0.731
0.726

0.742
0.735
0.747
0.741

0.751
0.751
0.751
0.751

obtain an individual permission for getting CDRs in advancefrom the applicants.

using supervised machine learnings. In the learning phase,the system gets the location data (learning data) from CDRsof some train commuters who exactly utilize a train as transport types for commute. Next, it extracts features of such dataand generates a classifier model. In the classifying phase, thesystem classifies whether a train commuter or not using thegenerated classifier model. In next section, we describe details of a method for feature extraction.

Experiment of a public interview

Feature extraction

Evaluation

Before feature extraction, as data cleaning process, we exclude location data which moving velocity is 0 km/h or over
150 km/h between previous location and it from a data set
for feature extraction. Table 1 is shown all features of our
method.
Similarity to a train line

We calculate a minimum distance di between a shape of a
train line and location data (l1 , l2 , ...li ) from CDRs. GIS information of train routes is provided from Geographical Survey Institute of Japan. A similarity to a train line is expressed
as a rate of a number of di not exceeding a threshold drail to anumber of all di . We set 100m, 300m, and 500m as threshold
drail in our system.

We held a public interview throughout the internet for gathering a type of main transport for a daily commute and forgetting an individual permission to analyze CDRs. The target of this interview is from 18 old to 60 old. The period forgetting CDRs is 1 month, March 2014. At the end of the experiment, we got results of the interview and CDRs of 1, 000
people in this experiment.
We created learning data for a machine learning from 1,000
people results of the experiment. In the results of the interview, there were 460 people who utilize trains for daily commute and 540 people who does not. We calculated featuresfrom CDRs of 1000 people in 1 month. Finally, we generated
a classifier using the results of the interview and the features.
As an assumption of this work, we do not care about subway
lines, since location from CDRs does not support a localization in underground. Therefore, above 460 people did not use
subway lines.

A moving velocity vi is expressed as a velocity between 2
locations in a sequence of time series. We adopt an average,
a median, and a maximum as value of features.

We use k-Nearest Neighbor(k-NN), Logistic regression(LR),
Support Vector Machine(SVM) and Random Forest(RF) for
classifier algorithms of this evaluation. we evaluate our classifier model using k-fold cross-validation(k = 3). Table2 isshown accuracy, precision, recall, F-measure for each algorithm. There is not much different in meaning among eachalgorithms. We seem that Random Forest is the better since
the average of each metric is 0.75.

EXPERIMENT AND EVALUATIONCONCLUSION AND FUTURE WORK

Moving velocity

We evaluate an accuracy of estimation of train commuters using a generated classifier . To collect learning data for train
commuters, we interview to applicants (commuters) to get atype of transport for a daily commute. At the same time, weTable 1. Features for Estimation of Train Commuters

Features
1. Distance between train line and location data
Threshold : drail = 100, 300, 500(m)2. Moving Velocity over vrail and Dist between train line and location
Threshold : vrail = 20, 40(km/h)
3. The Average of Moving Velocity
4. The Median of Moving Velocity
5. The Maximum of Moving Velocity
6. The Average Moving Distane in a day
7. Distance between home and work

Poster Session 1 :: April 8

Table 2. A comparison of accuracies among machine learning algorithms

In this paper, we proposed detecting method of the train commuters from CDRs and GIS information of train routes. As
contributions of this work, our system utilizes only location
information from CDRs which generate when a user just uses
his phone in daily life. Therefore, the system does not impose
a user to consume energy of his phone and install a specificapp for detecting his mode of transit. From the result of theevaluation, the estimation accuracy of mode of transportationin this work is less than other conventional works that use
sensors on mobile phones. However, our method still needs
to optimize the features from CDRs as a future work.
ACKNOWLEDGMENTS

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

40

REFERENCES

1. Stenneth L.,Wolfson O.,Yu P.S. and Xu B. Transportation
mode detection using mobile phones and GIS
information. In Proc. SIGSPATIAL International
Conference on Advances in Geographic Information
Systems, 2011.
2. Zheng Y., Chen Y., Li Q., Xie X. and Ma W.
Understanding transportation modes based on GPS data
for web applications. In Journal ACM Transactions on theWeb, vol. 4, issue 1, Jan 2010.
3. Hemminki S., Nurmi P. and Tarkoma S. Accelerometerbased transportation mode detection on smartphones. In
ACM SenSys 2013.
4. Zhou P., Zheng Y. and Li M. How long to wait?:
Predicting bus arrival time with mobile phone based
participatory sensing. In Proc. MobiSys 2012.

5. Rahul C. Shah, Chieh-yih Wan, Hong Lu, Lama
Nachman. Classifying the Mode of Transportation on
Mobile Phones using GIS Information. In Proc. The 2014
ACM International Joint Conference on Pervasive andUbiquitous Computing(UBICOMP 2014), 2014.
6. Kjaergaard M. B., Bhattacharya S., Blunck H. and Nurmi
P. Energy-efficient tracking for mobile devices. In ACM
MobiSys 2011.
7. H. Lu, W. Pan, N. D. Lane, T. Choudhury, and A. T.
Campbell. Soundsense: scalable sound sensing forpeople-centric applications on mobile phones. In
Proceedings of ACM MobiSys, pages 165178, 2009.
8. S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and
M. Srivastava. Using mobile phones to determine
transportation modes. ACM Transactions on Sensor
Networks, vol. 6(issue 2): pages 127, March 2010.

Poster Session 1 :: April 8

2015

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2015

Agent-Based Dynamic Pricing for Wireless Services
dinaelreedy@eng.cu.edu.eg,amir@alumni.caltech.edu,h_fayed@eng.cu.edu.eg
1
Department of Computer Engineering, Cairo University, Egypt
2
Department of Engineering Mathematics and Physics, Cairo University, Egypt

The tremendous surge in demand for wirelessbroadband data is testing the limits of the resources ofwireless networks. Wireless operators have to keep asatisfactory quality of service (QoS) by guaranteeing
certain levels of blocking/dropping probabilities.
However, bandwidth-hungry applications frequently
result in congestion. To cope with QoS guarantees,
operators have the option to either expand theirwireless capacity or manage the traffic using price as acontrol tool. Typically, the former is a costly approach,while the latter is the worthwhile approach. In thispaper, we present a new dynamic pricing model using
the so-called agent based approach for two purposes: topromote the agent-based approach as an effective andflexible model for pricing any type of service, and todevelop the agent-based approach for the wirelessdynamic pricing problem (with focus on data services),
as a detailed case study. Agent-based systems havebeen introduced in financial markets [1], and recently
in economics [2]. The main feature of the agent-based
approach in wireless pricing is that it treats each
subscriber separately and models how pricing willaffect his demand. Moreover, it accommodates theexistence of several rate plans from the same operator
as well as rate plans from different operators. This way
it manages to account for the heterogeneity that exists
in real situations. It will also exhibit how thisheterogeneity manifests itself globally in aggregate
behavior.
We propose here a dynamic pricing methodologythat is based on considering several candidate rateplans and optimize their parameters in order tomaximize the operator’s revenue. Each rate plan has
aspects of dynamic pricing. This means that we have avariable pricing that is based on traffic, capacity, timeof the day and other factors. The goal here is not topropose specific rate plans per se, but to present awhole framework for designing and testing rate plans.
In other words we propose a complete and large scale
simulation testbed that can be used to accurately and inflexible manner simulate the users’ actions, their
response to rate plans, and to price changes, and theeffect on QoS, and aggregate traffic.

The dynamic pricing is framed as premiums anddiscounts over the prevailing rate, in order to make it
more palatable to the operators, as it allows them torelate the proposed prices to their own set reference
price. Embedded in this proposed system is an agentbased model that simulates the behavior of the users,
including the initiation of data service usage, thedynamics of the traffic generated for each session, theeffect of pricing on the traffic generated by the user andthe long term effect of pricing in selecting the rate
plan. This simulation yields an estimate of the revenue
and the load and its output will therefore directly
influence the pricing parameters and function.
To test the proposed model, we consider a numberof rate plans as examples of static and dynamically
priced plans, and optimize their parameters in an
attempt to maximize the revenue subject to QoS
constraints. We limit our study to data services, and do
not consider voice, as data services is the mostpressing issue concerning bandwidth. These rate plans
are:Rate plan 1: Dynamic Pricing for a Fixed Internet
Usage
Assume that the mobile operator provides a
standard monthly flat rate 𝑃𝑟𝑒𝑓 for a certain bundle.
We propose an alternative adjusted dynamic rate plan
as follows. The price ranges from 𝑃𝑟𝑒𝑓 − 𝑤 to𝑃𝑟𝑒𝑓 + 𝑤 where 𝑤is a fixed predetermined value that
limits the minimum and maximum price values. So thecustomer is guaranteed not to pay more than 𝑃𝑟𝑒𝑓 + 𝑤
and will not pay less than𝑃𝑟𝑒𝑓 − 𝑤. The exact paid
price will be determined dynamically according topeak/off-peak hours and cell load (measured by boththe number of online users in the cell, and the total
bandwidth they consume).
Rate plan 2: Fixed Price for Dynamic Internet Usage
This rate plan is simply the dual of the above rate
plan. As some customers prefer to pay a fixed amountmonthly, we propose a rate plan with a fixed price𝑃𝑟𝑒𝑓 while the usage amount (in Mbytes or GBytes)
Uref ranges from 𝑈𝑟𝑒𝑓 −∆ to 𝑈𝑟𝑒𝑓 +∆ where ∆ is a
fixed predetermined value that limits the minimum andmaximum usage amount.

Poster Session 1 :: April 8

Dina Elreedy1,*, Amir Atiya1, Hatem Fayed2

42

2015

How the price varies dynamically is designed asfollows. We consider that the price should be afunction of the remaining cell capacity, the time of day
and the immediate preceding rate of usage. This will
make the price as an effective control tool for reigningin congestion and for shifting demand towards low
usage periods. We consider certain price multipliers
that vary around 1 and provide a varying
discount/premium over the reference price (which istypically taken as the prevailing rate). Each multiplieris a function (typically linear or piecewise linear) of
one of the aforementioned factors. These multipliers
will be multiplied by the reference price to obtain the
final price. They vary around the value of 1, where avalue that is lower than 1 corresponds to a discountwith respect to the reference price (for example, 0.9
means that the price is 10% lower) while a value thatis higher than 1 represents a premium over thereference
The multipliers considered are: time of the day
multiplier which has higher values for peak hours, cell
capacity multiplier which is directly proportional tocell load, number of active users in cell multiplier
which is directly proportional to the number of users
and average usage per session multiplier which
rewards a user for uniform usage over long period andpenalizes him/her for higher usage in a concentrated
period (to avoid network jamming).
An meta-heuristic optimization algorithm called
Covariance Matrix Adaption Evolution Strategy(CMA-ES) [5,6] is applied to optimize the parameters
defining the functional form of the multipliers suchthat the operator’s revenue is maximized. Any pricemodification will influence the demand via the priceelasticity. An x % change in demand is translated into
an x % change in generated traffic, which in turn will
influence the revenue. The optimization algorithm willkeep probing the space intelligently until reaching the
optimal solution. The optimization is subject to strict a
constraint on the QoS in the form of caps on theblocking probability. Thus, any solution leading to aviolation of the blocking probability will be discarded.
A training period is used to determine customers’
choices of the competing rate plans. We define autility function for the rate plan evaluation, in the formof a proposed function of the average monthly
payment, and the monthly payment variability
experienced for each customer during the training
period. A softmax function is applied to the different
rate plans’ utility to determine the probability of acustomer to select a particular rate plan. Figure 1
shows a block diagram of the proposed system.

𝐿

𝑦𝑡 =

𝜑𝑘 𝑦𝑡−𝑘 + 𝜀𝑡
𝑘=1

where 𝑦𝑡 is the forecasted demand usage, 𝐿 is numberof lags and 𝜀𝑡 is a white noise term.

2

F

Poster Session 1 :: April 8

Rate plan 3: Floating PriceIn this rate plan, the price is dynamicallydetermined according to peak/off-peak hours and thecell load. However, unlike the first rate plan there are
no minimum or maximum bounds on the paid price.
So, if a user did not use the internet, he/she will pay
nothing (and vice versa, extensive use leads to a largebill). On the other hand, the price itself is not limitless.
There are built-in pricing bounds that guarantee that it
does not diverge much from the reference price.
System Overview
We collect the data from 21 users using an Android
mobile application, that we have developed, that storesand retrieves internet data usage (3G). The averagecollected data length is 72 days per user.
In order to be able to simulate the users’ actions,
we have a first stage, where we learn the dynamics of
the users’ data services usage. We assume that thelearned dynamics of the 21 users will provide a diverse
set of usage patterns hopefully similar to what isobserved in a typical cell. There are two main aspects
about the usage that we have to learn from thegathered users’ data. For each period (for example afive minute or a ten minute period) we need to predictwhether the user is online or not (i.e. whether the user
is using some data service or not). The other aspect isto predict how much traffic is he using in the period(typically in Mbytes). To obtain these dynamics wedevelop a classifier for the former aspect, and a timeseries forecasting model for the latter aspect. Oncethese two models are estimated for each user, we cangenerate more data. Moreover, we can generate
additional hypothetical users that follow dynamics
similar to the existing users. Also, one can anticipatechanges in the dynamics in response to changes in thepricing. Limiting the use to the original 21 users’ real
data would be too rigid and therefore not flexible
enough to achieve all these goals.
We developed one classifier for each user that
estimates his/her probability of being online or in asession using Support Vector Machines (SVM)
classifier [3].We then developed one time series
forecasting model for each user that forecasts theusage amount in Megabytes (MB) for future time
periods using Autoregressive (AR) [4] model with anumber of lags determined using BayesianInformation Criterion (BIC).

43

Forecasted Demand

PriceConstraints

Cell Load

Calculate
Revenue

Optimizer

Monte Carlo
Simulation of
Calls
Forecasting
Demand Indices
Price
Elasticity
Suggested Price
Parameters
SetPrice
Parameters

Poster Session 1 :: April 8

2015[2] Tesfatsion, L., Judd, K.L. (2006), Handbook ofComputational Economics, Volume 2: AgentBased Computational Economics, Elsevier,
Amsterdam.
[3] Cortes, Corinna, and Vladimir Vapnik. "Supportvector networks." Machine learning 20.3 (1995):
273-297.
[4] Box, G., Jenkins, G. M., Reinsel, G. C. (1994).
Time Series Analysis: Forecasting and Control 3rd
ed. Prentice-Hall.
[5] Auger, A., Hansen, N. (2005) A restart CMA
evolution strategy with increasing population
size, IEEE Congress on Evolutionary Computation
2, 1769-1776.
[6] Hansen, N. (2009) Benchmarking a BI-Population
CMA-ES on the BBOB-2009 function testbed. In:
GECCO Genetic and Evolutionary Computation
Conference, 2389-2396.

Figure 1: The block diagram of the dynamic pricing
model.
Results
We applied two simulation runs for 50 generated
users (whose dynamics are extracted from the 21
available real users’ data). In the first run, only a static
plan (fixed-price but with a cap on total usage) of oneof the Egyptian operators was offered to the users.
This is considered the baseline run, over which wewould like to improve by developing new rate plans,
and tuning their parameters. In the second run, boththe static plan and our proposed dynamic plans were
offered. The simulations were done for 3-months
period, after the training period at which the user
recognizes his average payment and subsequently
makes a rate choice. (In reality the customer does notnecessarily do that, because he knows his own usage,
and can effectively predict his payment for eachpossible rate plan.) Our proposed model yields anincrease of the total revenue by about 66% comparedto that of the static plan. So the proposed plans inconjunction with existing rate plans achieved both
acceptable QoS, and improved utility to the users andhigher operator’s revenue.

References[1] LeBaron, B. (2001) Empirical regularities frominteracting long and short memory investors in an
agent based stock market, IEEE Trans.
Evol.Comput.5.5: 442–455.

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Morgan R. Frank1,∗ , Serdar Çolak1 , Jameson Toole1 , Lauren P. Alexander1 , and Marta C. González1,2
1 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA,
2 Engineering Systems Division, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

January 15, 2015∗15]. Some methods for simulating population level movement based on spatially localized information include
gravity models [16–18] and the radiation model [11, 19].
However, the trend of using increasingly available mobile
phone data to approximate population levels by reception
area is an appealing alternative because of the temporal
resolution and spatial coverage [12, 20]. We can estimate
road demand through methods, such as iterated traffic assignment, using data to approximate population level transitions between regions, such as commuting from residential to commercial areas.
High-definition traffic flow and traffic incident data
made available by TomTom [21] is a promising source of
empirical traffic information. By enhancing official government traffic reports with data from 80 million anonymous mobile phone users and 1.6 million TomTom device
users, the TomTom traffic feed provides accurate glimpsesof congestion on the road network. Note that the TomTom
feed does not allow us to track individual trajectories, but
is a suitable tool for calibrating traffic assignment methods as it allows us to verify flow distributions across theroad network.
Previous studies have revealed the non-linear behaviors inherent in traffic flow as the percolation of shockwaves in continuous models [22–24] and in agent-based
investigations [25, 26]. This non-linearity may cause difficulty in predicting travel times for individual routes.
However, empirical investigation into the deviations ofroute travel times as a function of the time of the day arelacking due to the sparsity of relevant data. This issue isin part responsible for the current interest in the traffic assignment problem.
While empirical data feeds are sufficient to validate
traffic assignment, these feeds are often too sparse or unavailable for particular U.S. cities to replace current traffic
assignment methods [27]. Our method uses census tractcommuting data from the U.S. Department of Transportation Federal Highway Administration [28] and demand
estimations through real-time mobile phone data [12, 20].
We implement an iterated traffic assignment strategy fol-

Abstract
The recent availability of high-definition
traffic flow data from sources, such as TomTom, allows for empirical investigation into
traffic prediction. Here, we examine interstatesin the Boston area to evaluate the predictability of travel times over a route throughout theday. The resulting distributions exemplify thefluctuations in travel times as a result of temporally dynamic traffic conditions. Examining
the shape of these distributions over the courseof the day shows us the difficulty of predicting travel times accurately with other means.
We compare these empirical averages to travel
times resulting from iterated traffic assignment
using mobile phone data. Our results expose
natural fluctuations in travel time statistics,
while examining the use of mobile phone data
for aggregate traffic assignment in conjunction
with traffic feeds to move beyond only solving
an optimization problem, such as iterated traffic assignment. Our results suggest that more
realistic statistical models for urban traffic networks might be generated by including empirical traffic data into the traffic assignment process; this would in turn allow for improved mobility solutions and traffic applications.

Flow dynamics on networks are a thoroughly investigatedtopic as a theoretical problem [1,2] and in several applications, such as airway transportation [3–5] and social systems [6–8]. In particular, the effects of traffic congestion
across a road network presents interesting properties withan increasing number of methods for modeling such phenomenon [9–11]. The discrepancies among these models
may be attributable to the sparsity of empirical data forcalibration [12].
A common goal for the transportation community
is to provide methods that produce efficient routes forcommuters. Currently, this task requires approximating
system-wide road conditions by assigning road demand
from aggregate commuter origin-destination data [13–Poster Session 1 :: April 8

Measuring the predictability of interstate travel times using realtime traffic monitoring data for Boston∗ corresponding author: mrfrank@mit.edu

1

45

A

B

Poster Session 1 :: April 8

2015

Figure 1: A snapshot of traffic flow on the Boston road network. (A) We plot locations of Boston roads in the OpenLR
dataset in blue. Yellow road segments indicate areas where traffic flow may be observed with the TomTom highdefinition traffic flow data. (B) Projected volume-over-capacity (voc) using iterated traffic assignment from mobile
phone data during a morning commute with purple indicating voc ≈ 2.0 and red indicating voc ≈ 5.0 .

References

lowing the methods explained in [20] using the OpenLR
road network [29]. This method allows for realistic individual routing by distributing aggregate traffic across aroad network. The output of this algorithm includes an estimation of traffic flow on each road in the road network,
which can be validated against the TomTom flow data.
Figure 1 shows example predicted areas of congestion using our iterated traffic assignment method. Furthermore,
integrating data from traffic feeds may allow researchers
to move beyond relying on only an optimization problem
for providing accurate traffic assignment.
In conclusion, we show the natural variation in route
travel times over the course of a week day using empirical TomTom data for the Boston area collected overtwo months. This result exposes the innate difficulties in
producing accurate travel time predictions. Despite this,we implement a traffic assignment strategy using mobile
phone data to successfully produce realistic travel predictions for interstates in the Boston area when compared tothe empirical travel times. This promising result highlights the predictive power of traffic assignment methods
using aggregate commuter data through mobile phones
in the absence of sparsely available traffic feeds. Furthermore, our results suggest that more realistic statisticalmodels of the urban traffic network are obtainable by integrating empirical traffic data into the traffic assignment
process. Such models could be used to inform better mobility solutions and traffic applications.

[1] Vanderbei, R. J. ”Network Flow Problems”. Linear Programming. Springer US, 2014. 199-224.
[2] Angulo, G., Ahmed, S., and Dey, S. S. ”Semicontinuous network flow problems.” Mathematical Programming 145.1-2 (2014): 565-599.
[3] Guan, X., Zhang, X., Zhu, Y., Sun, D., & Lei, J. (2014).
”An Airway Network Flow Assignment Approach Basedon an Efficient Multiobjective Optimization Framework”.
The Scientific World Journal.
[4] Cook, A., Blom, H. A., Lillo, F., Mantegna, R. N., Miccich,S., Rivas, D., ... & Zanin, M. (2014). Applying complexity
science to air traffic management. Journal of Air Transport
Management.
[5] Xu, D., Zhang, C. W., Miao, Z., & Cheung, R. K. (2014).
”A flow allocation strategy for routing over multiple flowclasses with an application to air cargo terminals”. Computers & Operations Research, 51, 1-10.
[6] Bagrow, J. P., Desu, S., Frank, M. R., Manukyan, N.,
Mitchell, L., Reagan, A., Bloedorn, E. E., Booker, L. B.,
Branting, L. K., Smith, M. J., Tivnan, B. F., Danforth, C.
M., Dodds, P. S., & Bongard, J. C. ”Shadow networks: Discovering hidden nodes with models of information flow”(2013). preprint: http://arxiv.org/abs/1312.6122[7] Coviello, L., Sohn, Y., Kramer, A. D., Marlow, C.,
Franceschetti, M., Christakis, N. A., & Fowler, J. H. (2014).
”Detecting Emotional Contagion in Massive Social Networks”. PloS one, 9(3), e90315.

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[18] Thiemann, C., Theis, F., Grady, D., Brune, R. & Brockmann, D. ”The structure of borders in a small world”. PLoS
ONE 5, e15422 (2010).

[8] Bliss, C. A., Frank, M. R., Danforth, C. M., & Dodds, P. S.
(2014). ”An evolutionary algorithm approach to link prediction in dynamic social networks”. Journal of Computational
Science.

[19] Simini, F., González, M. C., Maritan, A., & Barabási A.L. ”A unviersal model for mobility and migration patterns”(2012). Nature, 96(484) doi:10.1038/nature10856[9] Li, D., Fu, B., Wang, Y., Lu, G., Berezin, Y., Stanley, E., &Havlin, S. ”Percolation transition in dynamical traffic network with evolving critical bottlenecks” (2014), Proceedings of the National Academy of Sciences.

[20] Colak, S., Alexander, L. P., Alvim, B. G., Mehndiretta,
S. R., & González, M. C. ”Analyzing Cell Phone Location
Data for Urban Travel: Current Methods, Limitations andOpportunities”, in press, Transportation Research Records,(“Practice Ready” for Presentation in the annual TRB meeting 2014)[10] Çolak, S., Schneider, C. M., Wang, P., & González, M. C.
”On the role of spatial dynamics and topology on network
flows” (2013), New Journal of Physics, 15(11).
[11] Ren, Y., Ercsey-Ravasz, M., Wang, P., González, M. C.,& Toroczkai, Z. ”Predicting commuter flows in spatial networks using a radiation model based on temporal ranges”,
Nature Communications, in press, 2014.

[21] www.tomtom.com

Poster Session 1 :: April 82015[22] Simini, F., Maritan, A., & Néda, Z. ”Human Mobility in aContinuum Approach” (2013). PLoS ONE, 8(3)[12] Jiang, S., Via-Arias, L., Zegras, C., Ferreira, J., &González, M. (2011). ”Calling for validation: demonstrating the use of mobile phone data to validate integrated
land use transportation models”. In International Conference Virtual City and Territory (7: 2011: Lisboa)[23] Flynn, M. R., Kasimov, A. R., Nave, J.-C., Rosales, R.
R., & Selbold, B. ”Self-sustained nonlinear waves in trafficflow”. Phys. Rev. E 79, 056113. Published 26 May 2009.
[24] Aw, A., & Rascle, M. ”Resurection of the ’Second Order’ Models of Traffic Flow” (200), SIAM
Journal of Applied Mathematics, 60(3), 916-938,
doi:10.1137/S0036139997332099[13] Gupta, A., Xu, W., Perrine, K., Bell, D., & Ruiz-Juri,N. (2014, October). ”On scaling time dependent shortest
path computations for Dynamic Traffic Assignment”. In Big
Data (Big Data), 2014 IEEE International Conference on(pp. 796-801). IEEE.

[25] Gazis, D. C., Herman, R., & Rothery, R. W. ”Nonlinear
Follow-the-Leader Models of Traffic Flow” (1961). Operations Research, 9(4), 545-567, doi:10.1287/opre.9.4.545[14] Shafiei, M., Nazemi, M., & Seyedabrishami, S. (2014).
”Estimating time-dependent origin-destination demand
from traffic counts: extended gradient method”. Transportation Letters: The International Journal of Transportation
Research.

[26] Khalesian, M., & Delavar, M. R. ”A multi-agent based
traffic network micro-simulation using spatio-temporal
GIS” (2008). Intl. Archives Photogrammetry, Remote Sensing & Spatial Info. Sci, 12(7), 31-35.
[27] Yang, Y., Herrera, C., Eagle, N., & Gonzaĺez, M. C. ”Limits of Predictability in Commuting Flows in the Absenceof Data for Calibration” (2014). Nature Scientific Reports,
4(5662), do:10.1038/srep05662[15] Wismans, L., de Romph, E., Friso, K., & Zantema, K.
(2014). ”Real Time Traffic Models, Decision Support for
Traffic Management”. Procedia Environmental Sciences,22, 220-235.

[28] U.S. Department of Transportation Federal Highway Administration. CTPP 2006-2010 Census Tract
Flows.
http://www.fhwa.dot.gov/planning/census
issues/ctpp/dataproducts/2006-2010 tract flows/index.cfm.

[16] Barthélemy, M. ”Spatial networks”. Phys. Rep. 499, 1?101(2010)[17] Jung, W. S., Wang, F. & Stanley, H. E. ”Gravity model inthe Korean highway”. EPL 81, 48005 (2008)[29] http://www.openlr.org/

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Poster Session 1 :: April 8

Analysis and Modeling ofMobile Data Traffic in Mexico City
Eduardo Mucelli Rezende Oliveira,× , Aline Carneiro Viana× , K. P. Naveen× , and Carlos Sarraute?
 École Polytechnique, France × INRIA, France ? Grandata Labs, Argentina

I. I NTRODUCTION

Number of subscribers

107.5

1.0

107

The 3G cellular networks are struggling with the recent boostof mobile data consumption led by the pervasive era. The steady
growth of smartphones, the very rapid evolution of services
and their usage is accentuated in metropolitan scenarios dueto the high urbanization and concentration of mobile users. In
this context, understanding mobile data traffic demands peruser is crucial for the evaluation of data offloading solutions
designed to alleviate cellular networks [1].
The pervasive era also brought new facilities: currently smartphones provide the best means of gathering users information
about content consumption behavior on a large scale. In thiscontext, the literature is rich in work studying and modelingusers mobility, but little is publicly known about users content
consumption patterns.
Our contributions in this work are twofold: First, our analyses
provide a precise characterization of individual subscribers
traffic behavior clustered by their usage pattern, instead ofa network-wide data traffic view [2]. Second, we provide a
traffic generator that synthetically, still consistently, reproduces
real traffic demands. A synthetic traffic generator has positive
implications on network resource allocation planning andtesting, or hotspot deployment. Moreover, the synthetic trafficcarries no direct personal information from the original users,
thus greatly reducing privacy issues.

0.9

106.5
106

0.8

105.5

0.7

10

5

0.6
CDF

104.5
Number of sessions

7.5

10

0.5

107

0.4

106.5

0.3

10

6

0.2

5.5

10

0.1

105
104.5

0.0
Jul

Aug

Sep
Day

(a)

Oct

Nov

1

2

3

4

5

6

7

Number of active days(b)

Fig. 1. (a) Number of subscribers and sessions on the whole dataset. (b) CDF
of number of days in which subscribers generate traffic.

A. Traffic Dynamics
Fig. 1(a) shows the total number of subscribers and the total
number of sessions from the whole dataset. Both parameters arehighly correlated (Spearman’s correlation is 98%). On average,
the number of active subscribers is higher during the weekdays
than during the weekend (also observed in [3]).
Fig. 1(b) shows the CDF of the number of active days of thesubscribers within the week. Additionally, we observed thatmost subscribers generate traffic on few hours during the day.
Furthermore, our analysis shows that uploaded and downloaded session volumes are similar and correlated (Spearmans’scorrelation is 88%). Owing to this high correlation, in ourevaluation and traffic modeling, we take into consideration the
total volume per session.

II. DATASET A NALYSIS

Our study is performed on an anonymized dataset collected at
the core of a major 3G network of Mexico’s capital, consistingof data traffic associated with 6.8 million subscribers. The B. Temporal Dynamics
data includes information about subscribers’ sessions that took
Some hours are more active than others when it comesplace from 1st July to 31st October, 2013. The studied dataset to subscribers daily activities. Two features are important tocontains more than 1.05 billion sessions and each of them highlight: (1) there is a repetitive behavior during different
has the following fields: (1) amount of upload and download days at the same hours; (2) there are peak and non-peak hours
volumes (in kilobytes) during the session; (2) session duration in traffic demands.
in seconds; and (3) timestamp indicating when the session
Indeed, Fig. 2(a) shows the hourly dynamics of the number
starts.
of sessions during the week (for a complete evaluation of other
Due to the routinary behavior of people [1] and the large parameters, refer to [4]). We can see a clear gap on the average
scale of the dataset, it suffices to study a subset of the whole number of sessions from 4am to 8am.
dataset in order to capture the daily behavior of subscribers. We
Our analysis of all parameters shows a high similarity
have selected one week to more deeply assess the subscribers’ on number of sessions, volume of traffic, and inter-arrival
behavior, which spans from 25th August to 31st August 2013 time, when compared day-wise (even comparing weekdays
and contains information of about 2.8 million smartphone and weekends). Fig. 2(b) shows the per-parameter average
devices and activity that totalizes 104 million sessions.
Relative Standard Deviation (RSD), which considers the hourIn the following, we start our analysis by studying the wise variation from all 7 days. We have also calculated the
behavior of mobile subscribers in terms of traffic they generate maximum RSD of the parameters when compared day-wise.
and their activity on the temporal scale.
The results show that the parameters from the same hours

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MondayTuesday

WednesdayThursday

UploadDownload
●

10.0

●

3

●

1

0
FridaySaturday

Sunday

3

2

●
●

●

5.0

●
●

●

●

●
●

●

●

●
●

●

●

Volume per subscriber (KB)

2

●

106.5

2.5
0.0

Number of sessions

Inter−arrival time

10.0
7.5

●
●

●

●

●

●

106.5

●

●

●
●

●
●

●
●
●
●

●

●

●

●

●

●

106
●

105.5

5.0

●
●

●

●

●

●

●

●

●
●

●

●

●

●

●

●

●

●

●

●

●

●
●

106

●

●

HF

HO

LF

LO

●
●

●
●

●

●

●
●

●

●

●

●

●

●
●

●
●
●

0.0

0
0

4

8 12 16 20

04

8 12 16 20

04

8 12 16 20

8

10

12

14

16

Hour

(a)

18 8

10

12

14

●

●

●

HF

HO

LF

LO

105.5

●

16

18

Hour

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

2.5

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

1

Hour

(b)

(a)

Hour

(b)

Fig. 2. (a) Average number of sessions per user during the week. (b) Relative
Standard Deviation per parameter.

Fig. 3. Volume of traffic per class per hour (a) for real and (b) for synthetic
subscribers.

on different days present less variability than the parameters
within the same day on different hours.
The similarity of the temporal activity patterns among
different days of the week allows us to select one day to perform
our extensive per-hour analysis and distinguish different profilesof users.

B. Subscriber Clustering and Classification
Instead of a-priori fixing a value for the number of profiles(i.e., clusters) |P|, our goal is to obtain from the data thenumber of profiles which best represent the subscribers’ traffic
activities. For this purpose, we use the UPGMA hierarchicalclustering algorithm [6], that iteratively aggregates vertices
from the similarity graph G(S0 , E). To find the best numberof clusters, we have implemented and used 23 stopping rules(see [4] for a complete list).
Profiling occurs then in four stages: (1) building a similarity
graph with S0 subscribers, (2) hierarchically clustering it using
a similarity metric, (3) determining the best number of clusters|P|, and (4) classifying the remaining subscribers in S − S0 .
These four stages are performed in two rounds. In the firstround, the graph G(S0 , E) weighted according to the trafficϑvolume similarity wij
is used. According to stopping criteria
results, |P| = 2 weighted subgraphs G1 and G2 are created. In
the second round, G1 and G2 are weighted according to theηnumber of sessions similarity wij. Two new clusters are foundfor G1 and G2 , totalizing four subscribers profiles.

III. S UBSCRIBER PROFILING METHODOLOGYTo describe the behavior of subscribers, we group them intoa limited number of profiles, generated according to two trafficparameters: traffic demands (i.e., volume of traffic) and activity
behavior (i.e., number of sessions). We detail below our profile
definition procedure.
A. Similarity Computation

Poster Session 1 :: April 8●●

Volume per synthetic subscriber (KB)●

7.5
Relative Standard Deviation (%)

Average number of sessions per subscriber

10

107

7

Our development can hold in general for any time interval
D chosen from the week. Let S be the set of all subscribersthat generate some traffic during D, and S0 ⊆ S be a randomly
selected sample of subscribers. Our objective is to partition thesubscribers in S0 into a set of clusters P, such that subscribers C. Subscriber Profiles
belonging to the same cluster are “similar” in terms of traffic
To obtain the profiles for our dataset, we set D as 27th ofdemands. We use the Euclidean distance to measure theAugust 2013, which contains information of about 1.5 million
similarity between two subscribers [5].
smartphone devices, and randomly sampled |S0 | = 10000
Each subscriber i ∈ S can be effectively represented as a
subscribers. Our profiling methodology resulted in four profiles,
sequence of sessions generated by i. Let tik denote the time
named as follows: Light Occasional (LO), Light Frequent (LF),
instant at which his k-th session begins, and let vki be the
Heavy Occasional (HO) and Heavy Frequent (HF). Table I
volume of traffic (both upload and download) generated.
shows the characteristics of each of the profiles.
To reduce the memory and processing time required, we
divide D into time slots of length T (we use T = 1 hour).
TABLE IC HARACTERISTICS OF THE RESULTING PROFILES
Let τti denote the set of all sessions starting within time slot t.
Now, the volume of trafficPgenerated by subscriber i, in time
Light
Heavy
slot t, is given by Vti = k∈τ i vki . Similarly, we define thetVolume29 KB to 20 GB
21 GB to 625 GB
inumber of sessions Nt .
Subscribers
148924227659
Using the above expressions, the total volume and the total
Occasional
Frequent
Occasional
Frequent
numberofsessions
generatedbysubscriberiduringD
are
Sessions
1 to 278
279 to 8737
1 to 278
279 to 8737
PP
ϑi = t∈D Vti and η i = t∈D Nti . Finally, we define theSubscribers
1486496
2746
27593
66
traffic volume similarity between two subscribers i and j asϑthe difference wij= kϑi − ϑj k, and the number of sessions
In Fig. 3(a), we show the dynamics of the volume of trafficηsimilarity as wij = kη i − η j k.
per subscribers class per hour, calculated using Vti . The error
Using the subscribers in S0 as the vertices, and using either bars correspond to a 95% confidence interval. We can see thatηϑ
wi,j or wi,jas the edge weights, we obtain a complete graph our methodology well separates the profiles. For each curve in
G(S0 , E), which is given as input to our clustering algorithm. this plot, we also show the respective mean values (horizontal

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lines). We classify, for each profile of subscribers and for eachparameter, the hours above the mean as peak hours, and hours
below the mean as non-peak hours.

Number of sessions

0.8

From synthetic to originalAmong days in dataset

0.6
0.4

IV. M EASUREMENT-D RIVEN T RAFFIC M ODELINGThe goal of the traffic model is to generate synthetic subscribers, whose usage pattern is consistent with the observations
made about the real subscribers in the previous section.

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A. Fitting Empirical Distributions

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Using the original subscribers’ data, we first study for each(a)(b)
profile in peak and non-peak hour, the empirical distributionfunctions (i.e., CDF) of the traffic parameters. Detailed CDFs Fig. 4. (a) CDF of the total volume per session for real and synthetic
subscribers (b) Per-parameter Bhattacharyya distances between original andanalyses are reported in [4].
Once the CDFs are obtained, we estimate the set of distri- synthetic trace, and between original trace in D and other days from theoriginal trace.
butions that best fit them. More specifically, when considering
the volume of traffic and the inter-arrival time (consisting of distance d (pD , pD0 ) is within the 95% confidence interval ofcontinuous values), the Kolmogorov-Smirnov statistic test is the distances d (pD , pE ) for E ∈ D∗ , for all three parameters.
used. The test estimates the parameters for a set of continuous
Finally, we applied the profiling methodology on the syndistributions (namely, Log-normal, Gamma, Weibull, Logis, thetic users. Fig. 3(b) depicts the per-class behavior for theand Exponential) that best fit the corresponding empirical volume of traffic per session for the classified synthetic users.
distribution. Similarly, when considering the number of sessions This result is coherent with the behavior from the original trace(consisting of discrete values), the Chi-squared statistic test is presented in Fig. 3(a).
used to estimate the best fitting parameters for a set of discrete
V. C ONCLUSIONS AND NEXT STEPS
distributions (Negative binomial, Geometric, and Poisson). In
both cases, with the results of the fitting tests, we select theIn this paper we have first presented a characterization of a
distribution function that best fits each corresponding CDF.
4-month dataset that contains more than 1.05 billion session
connections from about 6.8 million smartphone users. Moreover,
B. Synthetic Subscriber Generation
we propose a framework that automatically classifies those users
Generating a synthetic subscriber will first require us to by their traffic demands into a limited number of profiles. Our
generate a profile type (HO, HF, LO or LF) for the subscriber. approach takes advantage of repetitive user behavior due to their
Profile types are assigned randomly, based on the distribution daily routines. Furthermore, we have calculated distributionsof profiles population observed in the real data (see Table I). that describe their traffic demands into peak and non-peak hours.
After obtaining the profile type, for a given hour t, we Finally, from these distributions we create a traffic generator
randomly sample values for each traffic parameter according to and evaluate the synthetic trace it generates. Our results show
the corresponding fitted distribution functions. That is, for each that the synthetic trace presents a consistent behavior whensubscriber i and time slot t, we sample a number of sessions compared to original dataset.
Nti , mean inter-arrival time IATti , and average session volume
As future work, we aim to investigate models to describe
Vti from the appropriate (fitted) distributions. The volume per sessions’ transfer rate and duration. Additionally, we intend tosession vki (for k ∈ τti ) and initial timestamp of each session apply and evaluate our traffic generator on different problems
are computed accordingly. By varying t over the 24 hours in such as network planning.
a day, we obtain a synthetic subscriber traffic for one day.
R EFERENCES
C. Synthetic Traffic Model Evaluation[1] E. M. R. Oliveira and A. C. Viana, “From routine to network deployment

Poster Session 1 :: April 8

Bhattacharryya Distance

0.2

for data offloading in metropolitan areas,” in Proc. of IEEE SECON, Jun.
2014.
[2] D. Naboulsi, R. Stanica, and M. Fiore, “Classifying call profiles in largescale mobile traffic datasets,” in Proc. of IEEE Infocom, Apr. 2014.
[3] U. Paul, A. Subramanian, M. Buddhikot, and S. Das, “Understanding
traffic dynamics in cellular data networks,” in Proc. of IEEE Infocom,
Apr. 2011.
[4] E. M. R. Oliveira, A. C. Viana, K. P. Naveen, and C. Sarraute,“Measurement-driven mobile data traffic modelling in a large metropolitanarea,” INRIA, Tech. Rep., 2014.
[5] M. Halkidi, Y. Batistakis, and M. Vazirgiannis, “On clustering validation
techniques,” Journal of Intelligent Information Systems, vol. 17, no. 2-3,
pp. 107–145, Dec. 2001.
[6] R. R. Sokal and C. D. Michener, “A statistical method for evaluating
systematic relationships,” University of Kansas Scientific Bulletin, vol. 28,
pp. 1409–1438, 1958.
[7] A. Bhattacharyya, “On a measure of divergence between two statistical
populations defined by their probability distributions,” Bulletin of the
Calcutta Mathematical Society, vol. 35, pp. 99–109, 1943.

In order to evaluate our traffic modeling, we generate asynthetic dataset and compare it with the original dataset.
Towards this goal, we generate a set R of synthetic subscribers,
with |R| = |S|, and one day of traffic, denoted as D0 .
Let pϑE denote the PDF (Probability Distribution Function)of the total volume generated per subscriber in day E. Fig. 4(a)shows the CDFs corresponding to pϑD and pϑD0 . We can observe
an almost complete overlap of the two CDFs due to a highsimilarity between the real trace and the synthetic trace.
Furthermore, to measure the similarity between datasets,
we use the Bhattacharyya distance d (see [3], [7]). Let D∗
denote the set of days contained in the dataset, excluding
the original day D. Fig. 4(b) shows the distances d (pD , pE )between D and the remaining days E ∈ D∗ , and as horizontal
line the distance d (pD , pD0 ), per parameter. We verified that the50
3Inference of Users Demographic Attributes based on
Homophily in Communication Networks
Jorge Brea

Javier Burroni

Carlos Sarraute

Grandata Labs
Buenos Aires, Argentina

Grandata Labs
Buenos Aires, Argentina

Grandata Labs
Buenos Aires, Argentina

jorge@grandata.com

javier.burroni@grandata.com

charles@grandata.com

1. INTRODUCTION
Over the past decade, mobile phones have become prevalent in all parts of the world, across all demographic backgrounds. Mobile phones are used by men and women acrossa wide age range in both developed and developing countries.
Consequently, they have become one of the most importantmechanisms for social interaction within a population, making them an increasingly important source of information tounderstand human demographics and human behaviour.
In this work we combine two sources of information: communication logs from a major mobile operator in Mexico,
and information on the demographics of a subset of theusers population. This allows us to perform an observational study of mobile phone usage, differentiated by age
groups categories [1, 2]. This study is interesting in its own
right, since it provides knowledge on the structure and demographics of the mobile phone market in Mexico.
We then tackle the problem of inferring the age group for
all users in the network. We present here an exclusively
graph based inference method relying solely on the topological structure of the mobile network, together with a topological analysis of the performance of the algorithm. The
equations for our algorithm can be described as a diffusion
process with two added properties: (i) memory of its initialstate, and (ii) the information is propagated as a probability
vector for each node attribute (instead of the value of theattribute itself). Our algorithm can successfully infer different age groups within the network population given known
values for a subset of nodes (seed nodes). Most interestingly,
we show that by carefully analysing the topological relationships between correctly predicted nodes and the seed nodes,
we can characterize particular subsets of nodes for which ourinference method has significantly higher accuracy.

2. DATA SET
The dataset used in this work consists of cell phone calls
and SMS (short message service) records (CDRs) collected
over a three month period. We aggregate this information
into an edge list (x, y, wx,y ) where wx,y is a boolean value
indicating whether users x and y have communicated at least
once within the three month period. This edge list represent
our mobile phone graph G = hN , Ei where N denotes the setof nodes (users) and E the set of communication links. Ourgraph has about 70 million nodes and 250 million edges. We
are also given the age of a subset of 500,000 nodes, which
we use as a ground truth (denoted NGT ).

3.

AGE HOMOPHILY IN THE COMMUNICATION NETWORK

Graph based methods like the one we present in this work
rely strongly on the ability of the graph topology to capture
correlations between the node attributes we are aiming topredict. A most fundamental structure is that of correlations between a node’s attribute and that of its neighbors.
Figure 1(a) shows the correlation matrix C where Ci,j is thenumber of links between users of age i and age j for thenodes in the ground truth NGT . Though we can observe
some smaller off diagonal peaks, we can see that it is moststrongly peaked along the diagonal, showing that users aremuch more likely to communicate with users of their sameage.
To account for a population density bias, we compute asurrogate correlation matrix R as the expected number ofedges between ages i and j under the null hypothesis of independence. This matrix represents a graph with the samenodes as the original, but with random edges (while maintaining the same number of edges as the original). Both C
and R are represented with a logarithmic color scale. If we
subtract the logarithm of R to the logarithm of the original matrix C, we can isolate the “social effect” (i.e. homophily) from the pure random connections, as can be seenin Fig. 1(b).

4.

Poster Session 1 :: April 8

2015

REACTION-DIFFUSION ALGORITHM

For each node x in G we define an initial state probability
vector gx,0 ∈ R4 representing the initial probability of thenodes age belonging to one of d = 4 age categories: below
24, 25 to 34, 35 to 50, and over 50 years old. More precisely,
each component of gx,0 is given by(
δi,a(x) if x ∈ NS
(gx,0 )i =(1)
1/d
if x 6∈ NS
where δi,a(x) is the Kronecker delta function, a(x) the age
category assigned to each seed node x, and NS ⊂ NGT are
the seed nodes, whose known age attribute is diffused acrossthe network. For non seed nodes, equal probabilities are
assigned to each category. These probability vectors are theset of initial conditions for the algorithm.
The evolution equations for the probability vectors g arethen as follows:Px∼y gy,t−1(2)gx,t = (1 − λ) gx,0 + λ|{y : x ∼ y}|where x ∼ y is the set of x’s neighbours and λ ∈ [0, 1] defines

51

2015
5.

RESULTSIn this section we first present the results for the predictive
power of the reaction-diffusion algorithm over the whole network G. The overall performance for the entire validation set(20% of NGT ) was 46.6%, and we note that a performancebased on random guessing without prior information would
result in an expected performance of 25%, or an expected
performance of ∼ 36% if we set all nodes age group to themost probable category (35 − 50). We now take a closer lookand see how the performance can increase or decrease as we
select particular subsets of nodes.

Topological Metrics and Performance(a) Communications matrix C.

We first look at how our algorithm performs for different
subsets of N selected according to three topological metrics:(i) number of seeds in the node’s neighborhood, (ii) distanceof node to NS and (iii) node’s degree.

Poster Session 1 :: April 8each node’s category, but constrained so that the age group
distribution for the whole network be that of the groundtruth set NGT as was described in [1, 2].

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population%

Figure 1: Age homophily plots showing (a) the communications matrix C and (b) the difference between
C and the surrogate random links matrix R.
the relative importance of each of the two terms. It is not
hard to show [1] that the above equation can be rewritten as
a discrete reaction diffusion equation on the mobile phonegraph given byaaagta − gt−1
= (1 − λ)(g0a − gt−1
) − λLgt−1(3)

where L is a normalized graph Laplacian. At each iteration,
each gx,t in (2) updates its state as a result of its initial state
and the mean field resulting from the probability vector of
its neighbors.

Preserving Age Demographics
A salient feature of our algorithm is that the demographic
information being spread is not the age group itself, but aprobability vector for each age group. In each iteration, thealgorithm does not collapse the information in each node toa preferred value; instead it allows the system to evolve asa probability state over the network, which allows us to impose further external constraints on the algorithms results.
For instance, after the last iteration, we can select the age
category of each node based on the final probability state of

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Figure 2: Performance and population as functionof SIN (seeds in neighborhood).
Figure 2 plots the performance (hits) of the algorithm as
a function of the number of seeds in the node’s neighborhood (SIN ). The algorithm performs worse for nodes withno seeds in the immediate neighborhood with hits = 41.5%,
steadily rising as the amount of seeds increase with a performance of hits = 66% for nodes with 4 seeds in theirneighbourhood. We also see that the population of nodes
decreases exponentially with the amount of seeds in theirneighbourhood.
Next we examine how the algorithm performs for nodes
in G that are at a given distance to the seed set (DTS ). In
Fig. 3 we plot the population size of nodes as a functionof their DTS. The most frequent distance to the nearest
seed is 2, and almost all nodes are at distance less than
4. This implies that after four iterations of the algorithm,
the seeds information have spread to most of the nodes in G.
This figure also shows that the performance of the algorithm
decreases as the distance of a node to NS increases.
In Fig. 4 we see that the performance of the algorithm islowest for nodes with small degree and gradually increases

52

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Figure 5: Performance as function of τ .
Figure 3: Performance and population as functionof DTS (distance to seeds set).
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population%
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degreeFigure 4: Performance and node population as function of the nodes degree.

as the degree increases reaching a plateau for nodes withd(x) > 10.

Probability Vector Information
An orthogonal approach to find an optimal subset of nodes(where our algorithm works best) is to exploit the information in the probability vector for the age group on eachnode. Namely, we examine the performance of the reactiondiffusion algorithm as we restrict our analysis to nodes whose
selected category satisfies a minimal threshold value τ in its
probability vector.
In Fig. 5 we observe a monotonic increase in the performance as the threshold is increased but, as expected there
is a monotonic decrease of the validation set. We note thatfor τ = 0.5 the performance increased to 72% with 3,492
out of the 143,240 (2.4%) of the validation nodes remaining.
The performance of the algorithm increases to ∼ 81% forτ = 0.55 but the validation set remaining sharply decreased
to only 201 nodes (0.1%).

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Poster Session 1 :: April 8

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CONCLUSIONSIn this work we have presented a novel algorithm that canharness the bare bones topology of mobile phone networks toinfer with significant accuracy the age group of the network’s
users. We show that an important reason for the success of
the algorithm is the strong age homophily among neighbours
in the network as evidenced by our observational study ofthe ground truth sample GGT .
We have shown the importance of understanding nodes
topological properties, in particular their relation to the seednodes, in order to fine grain our expectation of correctly classifying the nodes. Though we have carried out this analysis
for a specific network, we believe this approach can be usefulto study generic networks where node attribute correlations
are present.
As future work, one direction that we are investigating
is how to improve the graph based inference approach presented here by appropriately combining it with classical machine learning techniques based on node features [2]. We arealso interested in applying our methodology to predict variables related to the users’ spending behavior. In [3] the authors show correlations between social features and spending
behavior for a small population. We are currently tackling
the problem of predicting spending behavior characteristics
on the scale of millions of individuals.

7.

REFERENCES[1] J. Brea, J. Burroni, M. Minnoni, and C. Sarraute.
Harnessing mobile phone social network topology toinfer users demographic attributes. In Proceedings of
the 8th Workshop on Social Network Mining andAnalysis (SNA KDD). ACM, 2014.
[2] C. Sarraute, P. Blanc, and J. Burroni. A study of age
and gender seen through mobile phone usage patterns
in Mexico. In 2014 IEEE/ACM International
Conference on Advances in Social Networks Analysis
and Mining. ACM, 2014.
[3] V. K. Singh, L. Freeman, B. Lepri, and A. S. Pentland.
Predicting spending behavior using socio-mobile
features. In Social Computing (SocialCom), 2013
International Conference on, pages 174–179. IEEE,
2013.

53

2015

Carlos Sarraute1 , Carolina Lang1 , Nicolas B. Ponieman1 , and Sebastian Anapolsky2
1

2

Grandata Labs, Argentina
Mobility and transport specialist

I. I NTRODUCTION
Cell phone technology generates massive amounts of data.
Although this data has been gathered for billing and logging
purposes, today it has a much higher value, because its volume
makes it very useful for big data analyses. In this project, weanalyse the viability of using cell phone records to lower thecost of urban and transportation planning, in particular, to findout how people travel in a specific city (in this case, Buenos
Aires, in Argentina). We use cell phones data to estimate
the distribution of the population in the city using different
periods of time. We compare those results with traditional
methods (urban polling) using data from Buenos Aires origindestination surveys. Traditional polling methods have a muchsmaller sample, in the order of tens of thousands (or even lessfor smaller cities), to maintain reasonable costs. Furthermore,these studies are performed at most once per decade, in the bestcases, in Argentina and many other countries. Our objective
is to prove that new methods based on cell phone data arereliable, and can be used indirectly to keep a real-time track of
the flow of people among different parts of a city. We also go
further to explore new possibilities opened by these methods.

III. M ETHODOLOGY
In this section we explain the methodology we used to adapt
the Call Detail Records (CDRs) to our objective.
The first step of our method generates, for each particularuser, a Location Distribution Matrix (LDM) that shows theprobability of the user being in a commune c at a given time
t of the week. The second step defines a criteria to consider
only users whose LDMs give us enough information about
their mobility patterns. The last step scales our sample using
the population values from the census data.

Poster Session 1 :: April 8

The City Pulse of Buenos Aires

A. Generation of Location Distribution Matrices
We separated a typical week into four day groups and four
hour groups, as shown in Table I.
TABLE IDAY GROUPS AND HOUR GROUPS USED IN OUR ANALYSIS
Day groups

Hour groups

Monday to Thursday
FridaySaturday
SundayMorning
Noon
AfternoonNight

5am - 11am
11am - 3pm
3pm - 8pm
8pm - 5am (of next day)This selection is based on the fact that Monday to Thursdayaretypical working days, Fridays show different mobility
II. M OBILE DATA S OURCE
patterns (specially at night), and weekends present a completely
We applied our methodology to Buenos Aires city, the capital different pattern.
The hour group selection corresponds to an analysis realizedof Argentina, which has 2,890,151 inhabitants [1] and is thewiththe data of [2], from which we determined the peaks andmain political, financial and cultural center of the country.
valleysof mobility, for a typical working day in the city.
Buenos Aires city is formally divided in 48 neighborhoods,
LetC
be the set of communes and Ru,d,h,c the number
which are grouped for political and administrative purposes in
of
calls
made
by user u on day group d, hour group h, in
15 communes.
commune c. The proportion of calls (i.e., the cell values of
We have a dataset of geolocalized CDR (call detail records),
the LDM) that a certain user u made in commune c during afrom which we examine the mobility patterns of mobile phone
combination of day group d and hour group h isusers. The high penetration of cell phone technology in the city
Ru,d,h,callows us to estimate the mobility patterns of all the inhabitantsPu,d,h,c = P
0
from this data.
c0 ∈C Ru,d,h,c
Our dataset has about 4.95 million mobile phone users (1,000 or 0 if the denominator is zero. The matrix Pu is the Location
times the number of people in the Buenos Aires survey [2]); it Distribution Matrix of user u.
also contains more than 200 million call records generated by
B. Criteria for Filtering Users
these users during a period of five months (from November
We filter the users that don’t provide enough information on
1st, 2011 to March 30th, 2012). Each record contains thetheirlocation; more precisely we only take into account theorigin (caller), destination (callee), timestamp, duration of theusers
that have enough calls in every one of the 16 day/hour
call and antenna used to connect. In addition, we have thegroups.
That is, the user u is kept if
geolocalization of the antennas. We used that information toX
map the antennas to a certain commune, and we used the map
Ru,d,h,c0 ≥ τ[call→antenna] as dataset of geolocated calls.
0
c ∈C

54

for any combination of d and h, given a threshold τ (in ourstudy τ = 1). After filtering, we obtain a set of 73,000 users
which we denote U.

In Fig. 1, we see that the results obtained are similar (bothplots show the same growth patterns for each commune). A
more detailed analysis of the differences between the two data
sources shows that the average difference is 5%. The highest
variation appears in Commune 1 (20% in the morning hourgroup) and the second highest in Commune 6 (11% in thenoon hour group). For a more detailed analysis, we refer thereader to [4].

C. Scaling up to Census Population
First, we determine the home commune Hu for every useru ∈ U. We consider that a user is at home on weekdays, atnight:
Hu = arg max Ru,weekday,night,cB. Extension to Weekendsc∈C

Given that we have successfully validated our proxy methodology with the origin-destination survey, we can now use it toextend the analysis to other time periods. We examine here themobility during the weekends. The mobility survey [2] does
not include this information; we are thus presenting here new
results on the mobility of the citizens of Buenos Aires.
The patterns for weekends (Fig. 2) are very different:
Commune 1, the central business district of the city, is not amajor pole of attraction (as it is during weekdays), whereas
other communes (mainly Commune 14) are more attractive forcitizens on weekends. Commune 14 is well known for its bars,
restaurants and night clubs, so this pattern coincides with ourinsight on the social life in this commune.

In case of a tie, we decide randomly. We registered only 395
ties among the set of valid users U (0.56% of the cases).
With that information, we extend our predictions using thecensus data [1]. The scaling factor Fc for commune c is:
popc
Fc =
#{u ∈ U|Hu = c}where popc is the population of commune c according to
the census. The range of scaling factors goes from 17.26 incommune 2 to 93.29 in commune 8.
We now define the expected quantity of people in a communec, during a combination of day group d and hour group h,
in terms of the proportion of calls of each user in c and thescaling factor of their home commune:
X
EPd,h [c] =(Pu,d,h,c · FHu )

C1
C2

320
300
280
260
240
220
200
180
160
140
Morningu∈U

Additionally, the expected quantity of people found in communec, during a day group d and hour group h, and that live incommune c0 is given by:X
EPd,h [c][c0 ] =(Pu,d,h,c · Fc0 ) .
u∈U |Hu =c0

P

C3
C4

Noon

C5
C6

Afternoon

C7
C8

C9
C10

C11
C12

C13
C14

C15

Night

320
300
280
260
240
220
200
180
160
140
Morning

Noon

AfternoonNight

Fig. 2. Predictions for a typical Saturday (left) and Sunday (right) according
to our methodology, for all the communes in Buenos Aires. The numbers in
the legend correspond to the commune numbers and the y axis shows theestimations in thousands of people.

Note that EPd,h [c] = c0 ∈C EPd,h [c][c0 ]. Having presented
the methodology, we now describe the results obtained.
IV. R ESULTS
A. Validation Against the Survey

C. Analysis of Commune 14

We first validated the proposed methodology, by comparing
it with the most traditional method used among the urban
mobility studies in Buenos Aires: the origin-destination survey[2], [3].

We analyse in more detail Commune 14 (Palermo), which
has very particular characteristics (see Fig. 3). First of all,we remark it has a typical residential commune pattern for
weekdays (with a lower concentration of people during working
hours, and a higher concentration at night). During weekends,

C1
C2

C3
C4

C5
C6

C7
C8

600

C9
C10

C11
C12

C13
C14

C15

Morning

Noon

Afternoon

Night

500

500
400

300

300

200

200

Morning

320
310
300
290
280
270
260
250
Morning

600

400

Noon

AfternoonNight

Poster Session 1 :: April 8

2015

Fig. 1. Comparison between the ENMODO survey (left) and our analysis(right), for a typical working day, and for all the communes in Buenos Aires.
The numbers in the legend correspond to the commune numbers. The y axis
shows the estimations in thousands of people.

Mon-ThuFri

Noon

Afternoon

Sat
Sun

Night

Fig. 3. Predictions according to our method, for the different day types, forCommune 14 (Palermo). The y axis shows the estimations in thousands ofpeople.

55
2however, Commune 14 shows a special behavior due to itsrole as social and nightlife hub. During Fridays, we notice anincrease of people during the night when compared to otherweekdays, which we attribute to people going out. Saturdays
show an increase in population across all time groups, with apeak at night that is similar to the one on Friday, and Sundaynight has the same quantity of people than a regular working
day at night, probably because people will have to go to workon the following day. Moreover, we notice a similar number ofpeople on Friday night compared with Saturday morning, andon Saturday night compared with Sunday morning. This fact
may be explained considering nightlife in Buenos Aires extends
into the morning (even until 8am). All these observations arecoherent with our knowledge of the city.

locations. Commune 6, on the other hand, is one of the mostpopulated and dense communes of the city (and represents its
geographical center), but is mainly residential. The differencein the number of people and variety of provenance between acentral business district as Commune 1 and a more residential
district as Commune 6 can be seen clearly in Fig. 5. We havealso done a more complete analysis including other communes
and day and hour groups (as shown in Table I) achieving
similar results.

D. The City Pulse Matrix
The urban mobility information can be used to generate
what we call the City Pulse Matrix (CPM), a 2-dimensional
matrix such that, for any day group d and hour group h,(a)

Poster Session 1 :: April 8

2015

(b)

Fig. 5. Visualization of the number of people present on Monday to Thursday
noon period in (a) Commune 1 and (b) Commune 6 (colored in violet) thatlive in each of the other communes. The scale shows the number of people(in thousands) each color represents.

CP M [i][j] = EPd,h [i][j].
Fig. 4 shows our visualization of the matrix generated by our
predictions, on a typical weekday noon (which is the time
period that varies the most with respect to weekday nights).

V. C ONCLUSIONS AND F UTURE W ORK
We presented a methodology to estimate the flow of people
between different parts of the city using mobile phone records.
According to our validation, the method is reliable, presenting
an average difference of 5% with the origin-destination
survey [2].
We extended the analysis to weekends using the proposed
methodology, and found many interesting patterns which arecoherent with our knowledge of the city. For instance, we
showed how Commune 1, the central business district, yields
during the weekends its role as major pole of attraction to
Commune 14, which is a social and nightlife hub. We alsopresented a visualization where a business and a residential
district can be clearly differentiated. A more detailed analysisof this methodology was published in [4].
We finally introduce ideas for future work: (i) achieve afiner spatial granularity with a richer dataset; (ii) consider themetropolitan region (suburbs) of the city in the analysis, asmany people travel between the capital and its suburbs every
day; (iii) analyze the mobility of citizens during particularsituations or events (for example, an evacuation or a holiday).

Fig. 4. Visualization of the City Pulse Matrix generated with our methodology,
for a weekday (Monday to Thursday) noon, with values normalized by row.

We can see in Fig. 4 that there is a darker diagonal, meaningthat in all the communes, most of the people that spend their
weekday noon in a given commune also live there. The lightest
element in the diagonal corresponds to Commune 1 (with 24%,
followed by Commune 2 with 43%), because of the flow ofpeople from the rest of the city that work there.

R EFERENCES[1] Instituto Nacional de Estadı́stica y Censos (INDEC). Censo Nacional de
Población, Hogares y Viviendas 2010, volume 1. INDEC, October 2010.
[2] Secretarı́a de Transporte. Ministerio del Interior y Transporte. ENMODO(2009-2010). Resultados de la encuesta origen destino. Movilidad en el
area metropolitana de Buenos Aires, 2010.
[3] Sebastian Anapolsky. Los flujos de movilidad territorial: un análisis dela población y la movilidad en el área metropolitana de Buenos Aires.
Revista Digital Café de las Ciudades, 133-134, 2013.
[4] Sebastián Anapolsky, Carolina Lang, Nicolás Ponieman, and Carlos
Sarraute. Exploración y análisis de datos de telefonı́a celular para estudiar
comportamientos de movilidad en la Ciudad de Buenos Aires. In XVIII
CLATPU, Congreso Nacional de Transporte Público y Urbano, 2014.

E. Visualizing the City Pulse
Finally, Fig. 5 presents a visualization of the city pulse.
We plotted a map showing for Commune 1 and Commune 6
the number of people present there on a typical working
day (Monday to Thursday) at noon, according to their homecommunes. Commune 1 is the central business district so many
people work there during the day, coming from very diverse

56
3

2015

Barbara Furletti, Lorenzo Gabrielli, Roberto Trasarti
KDDLAB - ISTI CNR, Pisa, Italyname.surname@isti.cnr.it
Zbigniew Smoreda, Maarten Vanhoof, Cezary Ziemlicki

Sociology and Economics of Networks and Services dept., Orange Labs, Paris, France

name.surname@orange.com

Recent studies have shown the great potential of
big data such as mobile phone location data to model
human behavior. Big data allow to analyze people presence in a territory in a fast and effective
way with respect to the classical surveys (diaries
or questionnaires). One of the drawbacks of thesecollection systems is incompleteness of the users’
traces; people are localized only when they are using
their phones. In this work we define a data mining
method for identifying people presence and understanding the impact of big events in big cities. We
exploit the ability of the Sociometer for classifying
mobile phone users in mobility categories throughtheir presence profile. The experiment in cooperation with Orange Telecom has been conduced in
Paris during the event Fête de la Musique using a
privacy preserving protocol.
The objective of this study is to investigate the impact of
big events in big cities on the contemporary composition of
the population. The method foresees the application of adata mining tool, called Sociometer [5, 4] on a mobile phonedataset collected in Paris in the month in which the Fête dela Musique occurs. This event, also known as World Music Day, is an annual music festival taking place on June
21, the first day of summer in cities around the world. The
Sociometer, by analyzing aggregated presence profiles of mobile phone users, is able to classify a population in mobility
categories hereby differentiating between residents, dynamic
residents, commuters, and visitors. The presence profiles are
represented by an aggregated presence matrix on weekly basis (weekdays and weekends) and are obtained by counting
the cell phone registrations of individuals in the areas of interest. By means of a data mining strategy, the Sociometerclassifies each profile. Starting from this partition, we design
a strategy for identifying how the event impacts on the composition of the population, exploring both a temporal anda spatial dimension. The spatial dimension is characterized
by the partitioning of Paris in three areas (identified as P1,
P2, and P3 so that P2 includes P1, and P3 includes P2 Fig. 1) based on the grouping of several administrative borders. The temporal dimension is a window of one month ofmobile phone observations analyzed with weekly and daily
granularity.
Fig. 2 shows the variation of the categories population
categories over the three areas during the whole period of
observation. It is evident that the number of visitors decreases from the city center (the more touristic area) to thelarger peripheral areas. Of course, this confirms the impact

Poster Session 1 :: April 8

Detecting and understanding big events in big cities

Figure 1: Administrative partitioning of Paris area:
P1, P2, and P3.
of visitors in the city center of Paris, but it also implicates asort of interplay between the city center and it’s wider that
could be interesting to define the complex usage of the city
center by its surrounding inhabitants.

Figure 2: Variation of the composition of the population in the three Administrative areas of Paris.
To deeper investigate this result, we perform a sort ofmulti-classification analysis starting from P1 and seeing how
the classification of each category of people may change enlarging the observation area (Fig. 3). Let us consider for
example the set of visitors in P1 (clearly are 100% in P1),
this set, in P2 and P3 become progressively 70% and 55%.
This means that the 45% of them, originally classified as visitors in P1, indeed belong to a different category if we lookat a bigger area. In other words they are not “foreign” ina strict sense, in fact almost the 20% of them, are actuallyresident in P3. Moreover, Some of the visitors in P1 become
commuters in P2 and resident in P3. This may happen for
the users that work in P2, live in P3, and that visit the city
center only once in a while. This multi-classification analysis adds a new dimension to the classification allowing theanalyst to refine and extend the categories with a new class“Tourist” for users which remain visitor in all three zones,
and “Occasional visits of Resident” for users which are visitors in P1 but residents in P2 or P3.
In general, an event in the city can be detected throughthe study of the distributions of the presence of people categories, and in particular of the visitors. As shown in Fig. 4,

57

Figure 3: Multi-Classification of each category ofusers over the 3 areas.
for Paris a peak of presences is not so evident in the week ofthe event (the weekdays labeled with “25 weekdays”) when
we consider the weekly distributions. This can be justified
with the fact that the event is held for only one day, as well
as by the fact that June forms the unofficial start of the
tourist season which explains the increasing trend of presences in the whole month. The event is thus hidden by thenormal activities and daily dynamics of the city.

Figure 4: Weekly distribution of the presences inthe three Administrative areas of Paris.
For detecting big (but short in time) events in very big
cities (like Paris), we come to the conclusion that it is necessary to lower the temporal granularity (in this case fromweekends/weekday to days) when we study the presence distribution of people already classified. It is important to notice that the first step of the analysis, i.e. the classification
with the Sociometer, uses profiles aggregated on weekly basis. As shown in Fig. 5, the daily distributions on daily
basis of the presences and the calls actually highlight a peakon June 21st.

Poster Session 1 :: April 8

2015

interpretation is not surprising at all as the festival is only
one day (a Thursday even) and imbeds within a nationwide
event in which all French cities have festivities.
Due the sensitive nature of the data, we have taken into
account the privacy issues during the entire process of analysis customizing and applying the privacy risk analysis method
presented in [3] and already tested in the work presented at
CPDP in 2013 [6]. This methodology implements and satisfies the constraints issued by the European Union for dataprotection in [2] and follows the principle given in [1]. The
risk analysis follows the idea that, given a dataset and a
specific application, it is possible to define the set of attacks
w.r.t. different levels of knowledge in order to evaluate therisk of linkability and re-identification. After a risk is detected, a technique for anonymizing the data is chosen, realizing a good trade-off between privacy guarantee and qualityof service.
Conclusions
The analytical process we described shows how to use theSociometer to classify people in categories during an event,and it allows to reason about the event attractiveness. It
also points out how the concept of city may change depending on the spatial granularity. The study of an event withreference to the different categories of population instead ofan undefined group of people brings out how differently an
event impacts (attracts) people at urban level. Through theweekly analysis of the call behaviors we are able to identify
a general increasing of presences across the month, while
the event Fête de la musique emerges by computing distributions on daily basis. In the case of Paris, the fact that
it is a very important city from the touristic point of viewand that attracts many visitors especially in the period ofanalysis, contributes to hide the event behind the daily dynamics of the city. The Fête de la musique, as reported by
the domain experts, is actually a very important event that
attracts tourists and Parisian, and that involves all the city,
nevertheless, it does not affect the presences on weekly basis. With this analysis we confirm that this event has a big
effect on local residents more than external visitors. In summary, with this work we meet the following objectives: (1)Verify how our proposed data mining methodology performs
in the discovering of big events in big cities; (2) Identify thepresence of visitors during the Festival by means of the Sociometer; (3) See how the composition of the population
changes along the period of observation; (4) See how theclassification of the population changes considering different
spatial resolution of Paris.
Acknowledgments. This work has been partially funded
by EIT ICT Labs - Project City Data Fusion for Event Management (activity n. 14189).

Figure 5: Daily distribution of presences and callsin P1 in the month of June.
Computing the multi-classification during the only day ofthe event, we find that the event is mostly a big attractor
for people around Paris rather than the classical tourists
coming from outside. In fact the 41% and 58% of the visitors
in P1 does not remain visitors in P2 and P3, respectively.
This observation gives rise to the interpretation that theFête de la musique is a festival for the Parisians themselvesrather than for people coming from a long distance. Such an

References[1] E. U. for data protection. Article 6.1(b) and (c) of
directive 95/46/ec and article 4.1(b) and (c) ofregulation ec (no) 45/2001, 2001.
[2] E. U. for data protection. Opinion 05/2014 onanonymisation techniques, 2014.
[3] B. Furletti, L. Gabrielli, F. Giannotti, A. Monreale,
M. Nanni, D. Pedreschi, F. Pratesi, and S. Rinzivillo.
Assessing the privacy risk in the process of building call

58

2015

Poster Session 1 :: April 8

habit models that underlie the sociometer, 2014.
[4] B. Furletti, L. Gabrielli, C. Renso, and S. Rinzivillo.
Analysis of gsm calls data for understanding user
mobility behavior. In Proceedings of the BigData
Conference, pages 550–555. IEEE, 2013.
[5] B. Furletti, L. Gabrielli, C. Renso, and S. Rinzivillo.
Tourism fluxes observatory: deriving mobility
indicators from gsm calls habits. In In the Book of
Abstracts of NetMob., 2013.
[6] S. Mascetti, A. Monreale, A. Ricci, and A. Gerino.
Anonymity: A comparison between the legal andcomputer science perspectives. In S. Gutwirth,
R. Leenes, P. D. Hert, and Y. Poullet, editors, European
Data Protection, pages 85–115. Springer, 2013.

59

2015

Márton Hunyady

hunyadym@hunyadym.hu

Gergely Lukács

lukacs@itk.ppke.hu

András Oláholah.andras@itk.ppke.hu

Faculty of Information Technology and Bionics, Pázmány Péter Catholic University
Práter u. 50/A, 1083 Budapest, Hungary

1. INTRODUCTION
Personal mobility data is useful both for helping the journey of the data collector and, when aggregated, for improving travel infrastructure. In some application areas, suchas local or long-range public transport, taxi or car sharing,
absolute location and trajectories based on them are not sufficient; the more important question is whether a particularperson is actually in a particular vehicle.
The goal of this paper is vehicle-relative positioning withsmartphones, i.e. deciding whether a person is in a particular vehicle or just near it, possibly in another vehicle.
Simply matching the spatio-temporal trajectories is not sufficient for this, because of the inherent quality limits of thedata. Our approach combines spatio-temporal data withdata from other sensors, which serves as the basis for creating a comprehensive system.

2. RELATED WORKThe concept of trajectory flock is used to describe a set ofobjects moving close to each other for some period of time[1], though the question of a shared single vehicle is notanalysed. Activity recognition, typically using accelerometer data, [2] is used to recognize physical activities, e.g.,
walking, sitting, running. Activity recognition is also becoming part of the mobile operating systems, and special
sensors are also appearing in the devices (e.g. step detector). Vehicle and passenger accelerator data were analyzed
in [3] and [4]. Indoor positioning approaches typically use
WiFi or Bluetooth scanning or magnetometer data [5]. Similar approaches are used to detect face-to-face meetings [6].

(a) Vehicle locations for
one day

Poster Session 1 :: April 8

Vehicle-Relative Positioning System(b) User locations

Figure 1: The collected vehicle and user location data.

3.2 Trajectory matching
We matched personal trajectories to vehicle trajectories in
the following steps:
1. Efficient spatial and temporal prefiltering of distantvehicles;
2. Linear interpolation to compensate for the unsynchronized timing of vehicle and personal position data;
3. Sophisticated weighed multiple point-pair matching,providing also a mean squared error based confidence
measure. This is necessary because the matching of

3. APPROACH
Our approach is based on large-scale data collection, analysisof trajectory data and sensor data mining.

3.1

Data collection

The area of study was Budapest (2 million inhabitants). Dynamic public transport vehicle data is available in a Webapplication and was collected by our software robot. Vehicle
positions are refreshed ca. every 30 seconds. The daily number of vehicle positions is ca. 3-4 millions (Fig. 1a). At thetime of the analysis data for about one month was available.
We developed a smartphone application to assess locationand sensor data about the personal movements. Data wascollected by 15 persons, covering altogether 140 hours oftravel time, ca. 340 000 locations (ca. 1.5 seconds temporal granularity; Fig. 1b), and sensor data (accelerometer,
gravitational, magnetometer and gyroscope, appr. 200 Hz
sampling frequency).

(a) Correctness of detec- (b) Calculated confidencetion (green when correct) (green is better)
Figure 2: The results of the trajectory-based vehicle detection

60

The performance of the trajectory matching algorithm ishigh in the middle of the trajectories and low when boarding
or disembarking from a vehicle, as correctly reflected by theconfidence measure also delivered by the algorithm (Fig. 2).
Thus, additional measures are required.

3.3(a) Vertical acceleration

Sensor data based matching

To create a model for sensor based activity and vehicle type
recognition, various features of the time series data (maximum, integral, root mean square, spectrum values, all on awindow) were first calculated. Data was labelled by activity and vehicle type partly by hand, partly using trajectory
matching with past data. (Fig. 3).
Data mining algorithms were trained and tested on thisdata, where test data was selected across different vehicles
and persons. The binary prediction (vehicle/on foot) performed best with the C4.5 algorithm, achieving 97.5% accuracy. The multiclass prediction of the vehicle type (bus/tram)
had a performance of 86% using Bayesian network. The
learning algorithms selected mainly accelerometer and, toa smaller extent, magnetometer and gyroscope data as relevant. After training, prediction with these algorithms isefficient and can be run on smartphones.
The combination of trajectory matching and sensor basedmatching achieves better overall results. The uncertaintyof trajectory matching at the beginning and at the end of
the journey is compensated for by sensor data. Past data
can also be used to improve the quality of sensor data basedmatching, as it can be automatically labelled by trajectory
matching.

4.

POTENTIAL APPLICATIONS

One possible application area of vehicle-relative positioning is public transportation. Here individually customized
transportation information for users as well as improved
statistics and control information for public transportationcompanies can be obtained from the data. Beside public
transportation, dynamic car-pool organization and the detection of face-to-face meetings inside vehicles can be supported by our approach.

5.

SUMMARY AND OUTLOOKWe presented an approach for vehicle-relative positioning
based on trajectory matching and extended by sensor data
based matching. The approach was applied for a reasonableamount of data and yielded promising results.
Future work is planned on improving the algorithm, testing
its performance and streamlining data collection and processing so that the system can be scaled up for a large number users.
The research was partially financed by the Rectors’s Office ofPázmány Péter Catholic University, project number KAP1.2-14/007.

(b) Spectrum of vertical acceleration

Poster Session 1 :: April 8

2015

a single personal location to the nearest vehicle position performs poorly. First, position data has limited
accuracy. Second, in long vehicles a passenger can becloser to the tracking device of another vehicle thanthat of their own vehicle. Our approach eliminates
both problems.

(c) Magnitude of gyroscope values
Figure 3: Some of the calculated features of the sensor values. Below the graphs the colors show the actual activity:
red is walking, yellow is tram, green is cycling.

6.

REFERENCES[1] Chiara Renso, Stefano Spaccapietra, and Esteban
Zimányi. Mobility Data. Cambridge University Press,
2013.
[2] Thomas Bernecker, Franz Graf, Hans-Peter Kriegel,
Christian Moennig, Dieter Dill, and Christoph
Tuermer. Activity recognition on 3d accelerometer data(technical report). 2012.
[3] Ina Partzsch. Positioning in real-time public transportnavigation: Comparison of vehicle-based andsmartphone-generated acceleration data to determine
motion states of passengers. In Networks for Mobility
2012. 6th International Symposium. Proceedings,
page 10, Stuttgart, September 2012. Universität
Stuttgart.
[4] Samuli Hemminki, Petteri Nurmi, and Sasu Tarkoma.
Accelerometer-based transportation mode detection on
smartphones. SenSys ’13, pages 13:1–13:14, New York,NY, USA, November 2013. ACM.
[5] Jaewoo Chung, Matt Donahoe, Chris Schmandt, Ig-Jae
Kim, Pedram Razavai, and Micaela Wiseman. Indoor
location sensing using geo-magnetism. In Proceedings of
the 9th International Conference on Mobile Systems,
Applications, and Services, MobiSys ’11, pages 141–154,
New York, NY, USA, June 2011. ACM.
[6] Piotr Sapiezynski, Arkadiusz Stopczynski, and Sune
Lehmann. Detecting face-to-face meetings using
smartphone sensors. In Analysis of Mobile Phone
Datasets (NetMob), 2013 3rd International Conference
on. MIT, pages 52–53, Cambridge, MA, May 2013.

61

Change Detection in Human Mobility Patterns from
Successive OD Matrices
1,2​
1​
2
Julio C. Chaves​, Moacyr A. H. B. da Silva​, Alexandre G. Evsukoff​

1​

Getúlio Vargas Foundation, School of Applied Mathematics,
Praia de Botafogo 190, Botafogo, Rio de Janeiro, Brazil
2​

Coppe/Federal University of Rio de Janeiro,
Cidade Universitária, Rio de Janeiro, Brazil

Poster Session 1 :: April 8

2015

Origin­Destination (OD) matrices have been widely used for transportation planning andtraditionally is computed from expensive surveys, made once each several years. The
availability of mobile phone data allow to compute OD matrices easily from Call Detail
Records (CDR) data. Recent studies have shown that mobile phone data are a reliable
source of information to compute OD matrix [1,4,5] and the mobility of phone users can beextrapolated to the whole population.
This work present a study to detect changes in human mobility patterns in the Rio de Janeiro
metropolitan area. The study was carried out from a dataset collected during 2014,
comprising the activity of an average of 2,7 millions of users and 1,700 cell towers. The
dataset was provided by one mobile carrier and only outgoing calls were available. The
Voronoi regions of cells are aggregated to represent district of the Rio de Janeiro city or
suburb cities in the metropolitan area.
The dataset were cleansed before used to compute OD matrices. As only outgoing calls wereavailable, the effect of pre­paid users was expressive. In Rio de Janeiro, about 80% to 85% of
the users correspond to pre­paid contracts and those users are mostly low­income population,in contrast, post­paid users correspond to medium to high­income population.
These asymmetries were corrected from census data, in order to gather user mobility as an
approximation of the population mobility. The place of residence is estimated by the mostfrequent call location during the night and weekends (provided some constraints). The placeof residence is used to compute the user/population ratio for each neighborhood and adjust
the number of users to the number of inhabitants.
In this work, daily OD matrices are computed and only users with a recorded displacement
within a day are considered. If an user has visited more than two different places within a dayeach pair of visited locations are used to compute the OD matrix in that day. The OD matrix
computed in the number of trips is divided by the total number of trips. All the process is being
implemented in a high performance database system, such that a daily OD matrix can becomputed in seconds.
The procedure is employed to compute successive OD matrices, one for each day, during thewhole year of 2014. The OD matrix is computed with respect to the neighborhoods andcorrecting for the user/population ratio for each neighborhood. For each day ​t​, the element
OD​
(​
t
)
​
represent
themobilityfromneighborhoodi​toneighborhood
j
​
on
that
day.
As result,ij​
there is one time serie OD​
), ​
t=​
1​
,...,N for each pair of neighborhoods. The analysis of these
ij(​
​t​

62

time series will allow to identify differences of mobility in workdays and weekends as so aschanges due to seasonality or events. The change detection is computed by standard
statistical methods like CUSUM [2,3] as reference, but other methods will also be
investigated.
The year of 2014 is particularly interesting for this study as it was the year of the FIFA 2014
World Cup, which caused a great impact in the city mobility, specially in the days of thenational team other games and games in the city. Moreover, the city is under work with theintroduction of new transportation systems a weel as a complete refurbishing of the harbor
region with the dismantlement of a former important expressway. Therefore we expect toverify the impact of these changes in the mobility of the city.

Poster Session 1 :: April 8

2015[1] J. Park, D.­S. Lee, and M. C. González, “The eigenmode analysis of human motion,”
Journal of Statistical Mechanics: Theory and Experiment, vol. 2010, no. 11, p. P11021, Nov.
2010.
[2] M. Bassevile and I. Nikiforov, “Detection of abrupt changes:Theory and Application”.
Prentice Hall, 1993.
[3] OAKLAND, J. S., "Statistical Process Control Paperback ­ September 26, 2007".
Routledge, 2008. ISBN: 978­0­7506­6962­7.
[4] CANDIA, J., GONZÁLEZ, M. C., WANG, P., et al. "Uncovering individual and collective
human dynamics from mobile phone records", Journal of Physics A: Mathematical andTheoretical, v. 41, n. 22, pp. 224015, jun. 2008.
[5] SLINGSBY, A., BEECHAM, R., WOOD, J. "Visual analysis of social networks in space
and time using smartphone logs", Pervasive and Mobile Computing, v. 9, n. 6, pp. 848?864,
dez. 2013.

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2015

Inferring social status and rich club effects in enterprisecommunication networks1

Yuxiao Dong† , Jie Tang‡ , Nitesh V. Chawla†,∗ , Tiancheng Lou] , Yang Yang† , Bai Wang¶† Interdisciplinary Center for Network Science and Applications, Department ofComputer Science and Engineering, University of Notre Dame, Notre Dame, IN, U.S.A.
‡ Department of Computer Science and Technology, Tsinghua University, Beijing, China] Google Inc., Mountain View, CA, U.S.A.
¶ Department of Computer Science and Technology, Beijing University of Posts andTelecommunications, Beijing, China∗ Email: nchawla@nd.edu

Poster Session 1 :: April 8

1

Abstract
Social status, defined as the relative rank or position that an individual holds in a social hierarchy [3], is known to be among the most important motivating forces in social behaviors [5].
In this paper, we consider the notion of status from the perspective of a position or title heldby a person in an enterprise. We study whether enterprise communication logs can help reveal
how social interactions and individual status manifest themselves in social networks. To that
end, we use two enterprise datasets with three communication channels — voice call (CALL),
short message (SMS), and email (EMAIL) — to demonstrate the social-behavioral differencesamong individuals with different status. Specifically, we investigate the interplay of social status
and several well-known social theories, including structural hole, social balance, homophily andsocial clique.
Structural Hole. The principle, that individuals can benefit from serving as intermediaries
between others who are not directly connected, forms the underpinning for the theory of structural holes [1]. Our analysis clearly shows that managers are more likely (70% in CALL, 55% in
SMS, and 43% in EMAIL) to be spanned as structural holes across the three networks. In otherwords, the structural holes extracted from enterprise communication network structure reveal
the social status of staff in their company. This can be explained by the fact that managers
usually need to operate the responsibility of correspondents and organizers within the company,
especially for the experience for connecting different departments or groups to cooperate.
Link Homophily. Homophily is the tendency of individuals to associate and bond withsimilar others [4]. Lazarsfeld and Merton [4] argued that individuals with similar social status
are more likely to associate with each other, which is called status homophily. Our analysis showsthat two individuals who share more common neighbors will have a tendency to have similarsocial status in the company. Managers’ ability of creating and maintaining social connections
in enterprise networks is more prominent that subordinates’. This could have the potential tofurther promote their status in companies, which further highlights the rich club effect.
Social Balance. Triad is one of the simplest grouping of individuals that can be studied
and is mostly investigated by microsociology [3]. We find that the managers’ overall balance
ratios are larger than the subordinates’ across all the three channels. Moreover, the individuals
in organizations have the tendency to maintain balanced relationships with people of the same
status; this phenomenon coincides with the link homophily observed above. We conjecture that
1This work is accepted at PLOS ONE, 2015 and the abstract is also submitted to the International Conference
on Computational Social Science (IC 2 S 2 ).

64

2015

0.6
M
S
A

0.4
0.3
0.2

0.4

0.2
0.1

0

0

5

10
Clique size(a) CALL

15

20

M
S
A

0.5

0.3

0.1
0

0.6
M
S
A

0.5
Probability

Probability

0.5

Probability

0.6

0.4
0.3
0.2
0.1

0

5

10
Clique size(b) SMS

15

20

0

0

5

10
Clique size

1520(c) EMAIL

Figure 1. Social Clique vs. Social Status. Distribution of social clique in enterprisecommunication networks. M: Managers; S: Subordinates; A: All employees;

Poster Session 1 :: April 8

2

the relatively high status empowers the managers to connect with more people and maintainthe relationships within the enterprise, enhancing the chance to promote their status.
Social Clique. Clique is a concept in both social sciences and graph theory. In social
sciences, clique is used to describe a group of persons who interact with each other more regularly
and intensely than others in the same setting. Here we aim to examine how managers andsubordinates form cliques and to which extent they are connected. We build two sub-networks
that only contain mangers or subordinates respectively for each type of a network derived fromeach of the communication channels. Figure 1 shows the distributions of clique size, conditionedon the status of individuals (employees in the enterprise). For reference, we also plot the overallclique distribution in each full network. It is obvious that the distributions of managers andsubordinates are quite different and the maximal cliques for managers are much larger thanthese for subordinates.
The correlations between social status and several social theories provide the evidence of“rich club” [2] maintained by high-status individuals. Inspired by the observations around thesocial structure and characteristics, and their potential to infer social status in a network, we
also developed a computational model—Factor Graph Model—to predict social status using the
aforementioned characteristics as features. We demonstrate that the social status of more than
85% – 93% of individuals can be inferred from their communication interactions among theircolleagues. This prediction results further confirm our observations on communication behaviors
and social theories are general across different companies, even with different communication
channels (CALL vs. SMS vs. EMAIL).

References
1. R. S. Burt. Structural Holes : The Social Structure of Competition. Cambridge, Mass.: Harvard
University Press, 1995.
2. V. Colizza, A. Flammini, M. A. Serrano, and A. Vespignani. Detecting rich-club ordering in complex
networks. Nat Phys, 2(2):110–115, Feb. 2006.
3. D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected
World. Cambridge University Press, 2010.
4. P. F. Lazarsfeld and R. K. Merton. Friendship as a social process: A substantive and methodologicalanalysis. In M. Berger, T. Abel, and C. Page, editors, Freedom and Control in Modern Society,pages 18–66. Van Nostrand, New York, 1954.
5. L. Milroy and J. Milroy. Social network and social class: Toward an integrated sociolinguistic model.
Language in Society, 21:1–26, 3 1992.

65

Home and Work Estimation from Mobile Phone Data: Improving
Accuracy and Privacy through Spatial Aggregation
Bradley Sturt1 , Jameson L. Toole1 , Serdar Çolak2 , Marta C. González2
1

2

1

Engineering Systems Division, MIT, Cambridge, MA, 02139
Department of Civil and Environmental Engineering, MIT, Cambridge, MA, 02139
bsturt@mit.edu

ABSTRACT

Analysis of mobile phone data is becoming increasingly complex. Many algorithms and applications still
use simple methods of detecting and assigning home
and work locations to users from mobile phone metadata. There are clearly errors in these simplistic approaches, but it is unclear how much they affect otherresults. There are also issues with privacy. We makeprogress on both fronts through demonstrating a new
approach for measuring home and work. Through applying our method to the Boston metropolitan area,
we show that the proposed approach can improve theaccuracy of home and work commuting flow estimations while simultaneously improving and preserving
the privacy and anonymity of users.

Figure 1: The correlation between home and work2

trips from the census (x-axis) and mobile phone data(y-axis) for the Boston metropolitan area. The Pearson Correlation Coefficient equals 0.5312

INTRODUCTION

Detecting the home and work locations of residents ofcities is vital to numerous domains, including transportation, urban planning, and sociology. The emergence of mobile phone metadata offers an opportunity to detect home and work locations of users [1].
Through leveraging techniques to find aggregate home
and work locations at the census tract-level, mobile
phone metadata can be used to dynamically estimate
population densities [2], commuting flows [3], and congestion [4].
Despite the ubiquitous use of home and work locations, the simplicity of the techniques to detect themfrom mobile phone data at census tract aggregation
have problems.
First and foremost, there are quality problems
with any Cell Detail Record (CDR) dataset, ranging from the noise inherent to the spatial component of calls, to irregularity and sparsity in CDR entries for individual users. The accurate estimation ofhome and work locations from a dataset hinges on thedataset’s quality, and in the presence of these common
problems, the accuracy will undoubtedly be degraded.

Poster Session 1 :: April 8

2015

However, the extent to which the accuracy of home
and work locations is affected is difficult to quantify,
due to the absence of a high-accuracy, ground truth
dataset. The most common candidate to compare
against is census data. At the census tract level,
the United States government publishes the Census Transportation Planning Product (CTPP), which
provides estimations of true tract-to-tract home andwork commuting flows (i.e. the number of residents
of one tract who commute to work on another). Figure 1 shows the correlation between CDRs and CTPP
at the tract level.
Due to the often low commuting flows between
tracts and the limited sample size used to conduct
the survey, there is high uncertainty with many of
the commuting flow estimations in the CTPP. Thus,
while it is tempting to interpret Figure 1 as the accuracy of the home/work estimations from a CDR, itscorrelation is of limited insight given the inaccuracies
in both datasets.
Second, there are privacy issues from finding home66

this paper, we will refer to the square cells as zones,
and we will denote any spatial area as a region.

3.1

Figure 2: The Boston metropolitan census tracts, ag-

gregated to zones of size 5.02 [km2 ]and work commuting flows at the census tract level,
stemming from the low commuting flow counts between many tracts. The privacy and anonymity ofphone users can be compromised with relatively small
data samples [5], and in the census data, many census tract pairs have commuting flows with fewer than
10 individuals. While there are certainly errors with
the CTPP, due to the prevalence of tract pairs withlow commuting flows, it is safe to assume the trend
is likely to reflect reality. Thus, calculating the home
and work locations of users has potential to infringe
upon the anonymity of users, for users that commute
between two tracts with low commuting flows.

3

METHODS

Lenormand et al.[6] analyzed the similarities and differences of the aspects of a city that were inferredusing different metadata sources (Twitter, cell phonerecords, and the census) Because the data came atdifferent spatial regions and different levels of granularity, the authors rasterized the spatial area of a city
into square-regions of 1 and 2 kilometers.
Adopting the rasterizing approach, we demonstrate that quality and anonymity of the tractlevel commuting data can be ameliorated throughspatially-aggregating the data through rasterization
at various granularities.
The intuition behind spatial aggregation is thatflows between square regions that encompass multiple census tracts will be greater and less noisy thanflows between individual census tracts. The rasterizing of the metropolitan area of Boston at a cell sizeof 5.02 [km2 ] is visualized in Figure 2. For the rest of

COMPUTING AGGREGATEDFLOWSFirst, we aggregate the tract-to-tract commutingzflows from the census data. Let Tijt and Tab
denotethe commuting flows between tracts i and j or zones
a and b, respectively. Furthermore, let Pit and Paz denote the polygons corresponding to tract i and zone a,respectively, and let |P | denote the area of a polygonP.
z
Our goal is to compute the commuting flows Tab
between each pair of zones a and b. Therefore, foreach pair of zones a and b, we compute!
X  |P t ∩ P z |  Pjt ∩ Pbz
a
i
z
Tab =
Tijt
(1)
|Pit |
Pjt

Poster Session 1 :: April 8

2015

∀i,j

|Pit ∩Paz |is the portion of tract i that overlaps|Pit |
with zone a.
Second, we aggregate the home and work locations from the CDR data. For each user, we compute
their home and work coordinates [3]. Then, for eachpair of zones, we compute the number of users in the
CDR dataset with home and work locations in the
two zones.
where

4

RESULTS

We compute the home and work commuting flows
and calculate the correlations between the census
and CDRs at various zone sizes. Figure 3 shows
the correlations between the datasets at zone sizesof 12 [km2 ],22 [km2 ],52 [km2 ], and 102 [km2 ]. The correlation between the datasets increases drastically as
the zone size increases. The correlation between thedatasets at the 12 [km2 ] zone size is 0.5041, which
is not any improvement over the tract level. At
greater zone sizes, however, the correlations improve
to 0.9360 and 0.9598 at zone sizes of 52 [km2 ] and102 [km2 ], respectively.
First, we interpret the correlation as follows: there
are underlying quality problems in both datasets, regardless of aggregation. However, if we assume thatthe errors between the datasets are uncorrelated, we
can conclude that the high correlation at larger zone
sizes implies that the computed flows are likely to
be close to reality. Furthermore, since the results ofhome and work estimations are highly correlated tothe census data, the results suggest that mobile phone67

Poster Session 1 :: April 8

2015

Figure 3: Correlations between aggregated census and mobile phone Home and Work commuting flows. The

correlation coefficients between the two datasets at grid sizes of 12 [km2 ],22 [km2 ],52 [km2 ], and 102 [km2 ] are
0.5041, 0.8459, 0.9360, and 0.9598, respectively.
data is as good as census surveys in estimating home
and work locations for residents at higher levels of
aggregation.
Second, we observe that the commuting flows between zones, compared to commuting flows between
tracts, are generally much larger. This relationshipincreases as the zone size increases. Thus, as we increase the zone size, we can also improve privacy ofusers by increasing the difficulty to deanonymize individuals who have home and work locations in tracts
with low commuting flows.
Spatial aggregation is a necessary step for researchers analyzing and integrating geographic andmobile phone data. Through our results, we havedemonstrated that our method for spatial aggregation
with zone size as a tunable parameter allows us to understand the tradeoffs between accuracy, anonymity,
and resolution. In contrast to aggregating to fixedregions like census tracts, our method enables researchers to quantitatively tune the size of those regions to best match the accuracy and privacy needsof the application.

REFERENCES

land, and Alexander Varshavsky. Identifying important places in people’s lives from cellular network data.
Pervasive Computing, pages 133–151, 2011.
[2] Pierre Deville, Catherine Linard, Samuel Martin, Marius Gilbert, Forrest R Stevens, Andrea E Gaughan,
Vincent D Blondel, and Andrew J Tatem. Dynamic
population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45):15888–15893, 2014.
[3] N Caceres, JP Wideberg, and FG Benitez. Deriving
origin destination data from a mobile phone network.
Intelligent Transport Systems, IET, 1(1):15–26, 2007.
[4] Pu Wang, Timothy Hunter, Alexandre M. Bayen,
Katja Schechtner, and Marta C. Gonzalez. Understanding road usage patterns in urban areas. Sci. Rep.,
2, 12 2012.
[5] Yves-Alexandre de Montjoye, César A Hidalgo, Michel
Verleysen, and Vincent D Blondel. Unique in thecrowd: The privacy bounds of human mobility. Scientific reports, 3, 2013.
[6] Maxime Lenormand, Miguel Picornell, Oliva G CantuRos, Antonia Tugores, Thomas Louail, Ricardo Herranz, Marc Barthelemy, Enrique Frias-Martinez, andJose J Ramasco. Cross-checking different sources ofmobility information. arXiv preprint arXiv:1404.0333,
2014.

[1] Sibren Isaacman, Richard Becker, Ramón Cáceres,
Stephen Kobourov, Margaret Martonosi, James Row-

68

2015

A. J. Morales1 , D. Pastor-Escuredo1 , Y. Torres1 , V. Frı́as-Martı́nez2 , E. Frı́as-Martı́nez3 , N. Oliver3 ,A. Rutherford4 , T. Logar4 , R. Clausen-Nielsen4 , O. De Backer4 , M. A. Luengo-Oroz4
1
Universidad Politécnica de Madrid, 2 University of Maryland
3
Telefónica Research,4 United Nations Global Pulse
Natural disasters affect hundreds of millions of people worldwide every year. Emergency response efforts depend upon the availability of timely information, such as information concerning
the movements of affected populations. The analysis of Call Detail Records (CDR) captured fromthe mobile phone infrastructure provides new possibilities to characterize human behavior during
critical events. In this study, we combine remotely sensed data and CDRs to understand how people communicated during severe floodings in the Mexican state of Tabasco in 2009. This researchdemonstrates that CDR data has the potential to provide useful information on human behavior
for improved emergency management and humanitarian response. Our results could also serve as apotential proxy indicator for flood impact and risk awareness.

The lack of timely, accurate information about movements and communications of affected populations during natural disasters can limit the effectiveness of humanitarian response. However, the growing ubiquity of mobile phones has revealed new opportunities for accessing
such information. Mobile phone data can provide valuable insights, in order to tackle issues related to economic
and humanitarian development [2], such as understanding the behavior of affected populations during a natural
disaster [1]. For example, recent research has demonstrated the potential of mobile phone data to help study
population movements after an earthquake in Haiti [3] or
to model malaria outbreaks in Kenya [4]. During these
critical events, the patterns of collective human behavior
are disrupted, as the population faces the ongoing emergency. Such effect is closely related to the emergence oflarge information cascades, since people tend to communicate with others, triggering chain reactions in the social
network [5]. The geographical distribution of the activity
can also be used to characterize the catastrophe. For instance, Twitter activity allows to locate an earthquake’s
epicenter with extraordinary accuracy by geographicallymeasuring the volume of related tweets [6].
The goal of this study is to develop and apply methods to assess the suitability of CDR data to characterize
the impact of floods on the population. Our vision is
to build CDR-based decision-support tools to help the
public sector better respond to floods and other natural disasters. We investigate the viability of using CDR
data combined with other sources of information to characterize the floods that occurred in Tabasco, Mexico in2009. In particular, we analyzed CDRs of the geographical area affected by the floods during a period of ninemonths (July 2009 to March 2010). The main technicalcontribution of this work is the development of a multimodal data integration framework that facilitates the
combination of CDR data with data from other sources,in order to characterize changes in the communication
patterns during the floods. We also contrast our resultswith external ground truth information. For a longer
description of the research presented in this short paper

Poster Session 1 :: April 8

Studying Human Behavior through the Lens of Mobile Phones during Floods

please refer to [1].
The methodological framework proposed in this study
is composed of the following steps: First, we evaluate therepresentativeness of the data by using the 2010 census[10] of Tabasco as the ground truth. For this purpose,
we compare the census data with a data-driven social
baseline that we built based on the location of the homeantenna tower (HAT) for each phone, meaning the antenna tower most used at night during the baseline (BL)period [7]. Second, we integrate additional and diverse
data sources to further understand the phenomena. We
use remote sensing through medium resolution (15 to 60
meters) ETM+ Landsat7 [8] satellite images to detectand geographically confine the submerged land. Moreover we analyze the Tropical Rainfall Measuring Mission[9] data, in order to build a temporal series of precipitations and to understand the relationship between thenatural phenomenon and mobile phone activity.
In order to detect abnormalities in the activity, we examine mobile phone activity data before, during and after the disaster. We propose the variation metric that
relies on the comparison of the number of phones placing or receiving calls per antenna x(t), against theircharacteristic variation obtained during the baseline period (BL). Mathematically, the variation metric is defined as the z-score from x(t) –i.e. the normal distribution characterizing the baseline– and defined as xnorm =(x(t)−µBL )/σBL , where the pair (µBL , σBL ) statisticallycharacterizes the activity during the baseline period. Bymeans of analyzing the normalized series, we establish
a baseline understanding of emergency behavior whichenables us to measure the rate of disaster recovery andto show how affected populations behave in response toflooding. The variations in the number of active phones
connected to each cell tower reveal abnormal activity patterns in the most affected locations during and after thefloods that could be used as signatures of the floods –both in terms of infrastructure impact assessment andpopulation information awareness.
This research demonstrates that mobile phone data has
the potential to provide timely information about human69Poster Session 1 :: April 8

2015

2

FIG. 1. Tabasco impact maps. The top panels show the absolute value of the antenna variation metric during an arbitrary
day before floods (top left) and another one during floods (top right). In these panels, each antenna is represented by a circle
with color and size proportional to the daily variation. The segmented flooded area has been colored in light blue. The insets
display the temporal series of the antenna variation metric, and the green line indicates the day of observation. In the top rightpanel, antennas near the flooding area dramatically increased their variation during the floods. In the bottom panels, we show
two visualizations of human displacement networks among cell towers. Towers are connected if a person makes two consecutive
calls. We show an average week of the network before the floods (bottom left), and an aggregated network across the floods(bottom right). The edge color means the direction of the displacement, from green to yellow. It can be noticed that during
floods (bottom right) the graph is denser, and more connections are established between the towers.

behavior for improved emergency management and humanitarian response. Insights gained from CDR analysis could also serve as a potential proxy indicator forflood impact and risk awareness. On one hand, mobile
phone data can be highly representative of the population’s behavior. A comparison between CDR data andcensus data yields a strong linear relation between official
population statistics and population estimates computedfrom CDR data. Furthermore, civil protection warnings
are not necessarily an effective way to raise awareness. In
fact, a civil protection warning was issued on the day ofhighest rainfall in Tabasco in 2009. However, big spikes
in phone activity were only observed in two cell phone
towers along the most affected road when floods alreadyshowed initial impacts, meaning that the civil protection

warning did not generate similar levels of awareness. Thisfinding reveals important behavioral insights for emergency responders on how and when affected populations
are made aware of a disaster. Finally, mobile activity
can provide signals of flooding impact. When analyzed
against the baseline activity, cell phone towers with thehighest variation metric in the number of calls made during the floods were located in the most affected locations(see top panels in Fig. 1). Note that mobility patterns
also changed significantly near the main cities and thecapital as the ground transport system and the physicalsize of the cities constrain how the people could move
during the floods (see bottom panels in Fig. 1).
In summary, aggregated and anonymized mobile phonedata can be used to assess risk awareness, understand the70effect of public communications such as disaster alerts
and measure the direct impact of floods on the population. The research findings described in this paper showthat CDR data could be a beneficial source of information for both emergency management and resilience assessment. Analyzing mobile activity during floods could
be used to potentially locate damaged areas, efficiently

assess needs and allocate resources (for example, sending
supplies to affected areas). Identifying cell phone towers
in the most affected areas of flooding might also serve
to improve and target public communications and safety
alerts, as well as help measure the effectiveness of such
early warning announcements.

[1] Pastor-Escuredo, D., Morales, A. J. et al., Flooding
through the Lens of Mobile Phone Activity. IEEE Global
Humanitarian Technology Conference, GHTC 2014.
[2] Decuyper, A. et al., Estimating Food Consumption and Poverty Indices with Mobile Phone Data,arXiv:1412.2595, (2014)[3] Bengtsson, L. et al., PLoS Med 8 (2011), no. 8, e1001083.
Improved response to disasters and outbreaks by tracking
population movements with mobile phone network data:
A post-earthquake geospatial study in haiti,[4] Wesolowski, A. et al., Noor, Robert W. Snow, and Caroline O. Buckee, Quantifying the Impact of Human Mobility on Malaria, Science 338 (2012), no. 6104, 267–270.
[5] Bagrow, J. P. et al., Collective Response of Human Popu-lations to Large-Scale Emergencies, PLOS ONE 6 (2011),
no. 3, e17680.
[6] Sakaki, T. et al. Earthquake shakes twitter users: realtime event detection by social sensors, Proceedings of the
19th international conference on World wide web (NewYork, NY, USA), WWW ’10, ACM, 2010, pp. 851–860.
[7] Becker, R. et al., Human mobility characterization from
cellular network data, Commun. ACM 56 (2013), no. 1,
74–82.
[8] http://earthexplorer.usgs.gov/[9] http:// http://trmm.gsfc.nasa.gov/[10] http://www.censo2010.org.mx/

Poster Session 1 :: April 8

2015

3

71

2015

Christopher Small1 Richard Becker2 Ramón Cáceres3 Simon Urbanek2
1 Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
2 AT&T Labs – Research, Bedminster, NJ, USA
3 Google, New York, NY, USA

We use a spatially and temporally extensive collection of voice call and SMS text message
volumes to quantify spatiotemporal communication patterns in the New York Metro area
before, during and after the Virginia earthquake and the passage of Hurricane Irene in thesame week of 2011. We compare and contrast spatial and temporal disruptions to normal
patterns of voice and text communication in response to each of these extreme events in adiverse range of environments within the New York Metro area. We have been careful topreserve privacy by using only anonymous and aggregate data. Results show bothsimilarities and differences in call and text responses to both the earthquake and thehurricane. The earthquake produces an instantaneous and pervasive response followed by
a ~90 minute temporal disruption to both call and text volume patterns. The hurricane
produces a two day, spatially varying disruption to normal call and text volume patterns.
Comparison of call and text response to these events yields less intuitive results. Both calland text volumes increase abruptly following the earthquake but call volume anomalies aremuch larger than text volume anomalies. The magnitude of the call volume anomaly
diminishes with distance from the epicenter with multiple spatial localizations of highresponse sectors in Manhattan. On the day preceding the arrival of the hurricane, coastal
evacuation zones show varying response both in location and in call versus text volume.
These spatial patterns suggest partial, but not full, compliance with evacuation orders formost low lying areas in NYC and surroundings. In most coastal areas call volumes
dropped anomalously in the afternoon before the hurricane’s arrival, but text volumes
showed a much less consistent pattern and often did not decrease in parallel with calls. In
terms of total daily volumes, most low lying coastal areas show a decrease in both calls andtexts relative to the previous week.

Poster Session 1 :: April 8

Earthquakes, Hurricanes and Mobile Communication Patterns in the New York Metro
Area: Collective Behavior during Extreme Events

Understanding dynamics of collective human behavior during extreme events has obvious
relevance to both preparedness and response. Most current knowledge of human behavior during
extreme events comes from relatively small numbers of retrospective observations – often
qualitative with unknown accuracy or degree of representation of the impacted population. In
contrast, mobile communication data can provide pervasive, quantitative observations of humancommunication patterns before, during and after extreme events [1]. In this study we use aspatially and temporally extensive collection of voice call and SMS text message volumes toquantify spatiotemporal communication patterns in the New York Metro area before, during andafter the magnitude 5.8 Virginia earthquake (2011-08-23) and the passage of Hurricane Irene(2011-08-28), both of which occurred in the same week of 2011.
Numbers of voice calls (calls) and SMS text messages (texts) were measured at 1 minuteintervals over the course of a year (2/1/2011 through 1/31/2012) at the spatial resolution of72

azimuthal sectors of almost 11,000 mobile network antennas within a 50 mile (80.5 km) radiusof Times Square in NYC. We aggregate these call and text volumes at different spatial andtemporal resolutions and use spatial correlation matrices to quantify normal spatial and temporal
patterns and their disruption before, during and after both events. We have been careful topreserve privacy throughout this work. In particular, this study uses only anonymous andaggregate data.
Spatial and temporal correlation matrices can be used to quantify both regularities anddisruptions of spatiotemporal patterns in call and text volumes. The spatial correlation matricesof daily call and text volumes clearly resolves differences between weekday and weekend spatial
patterns, as well as decorrelations associated with holidays when the normal weekly spatial
patterns are disrupted. The largest decorrelation observed in 2011 is associated with Hurricane
Irene. Spatial correlation matrices of hourly call and text volumes in the weeks before, during
and after the earthquake and hurricane show the evolution of the spatial disruptions in greater
detail (Fig. 1).

Poster Session 1 :: April 8

2015[1] Bagrow, J.P., Wang, D., & Barbási, A.-L. (2011). Collective response of human populations to largescale emergencies. PLoS ONE, 6, 1-8

73

Recent Results/Work in Progress
Application of Floating Phone Data (FPD) in Germany
January 2012 – March 2015
Dipl.-Ing. Moritz von Mörner, vonmoerner@verkehr.tu-darmstadt.deTechnische Universität Darmstadt, Germany
Department of Civil and Environmental Engineering
Chair of Transport Planning and Traffic Engineering
Objective:
Determine the state of science and technology for the use of Floating Phone Data (FPD) in
traffic and transport research. Feasibility study for the application of FPD as input value for
e.g. transport models and transport planning.

Poster Session 1 :: April 8

2015

Focus:• Long distance travel in Germany
1. rail
2. motorway• Regional public transport with busses in rural areas.
This project is conducted for : Deutsche Bahn AG
FPD provider: Motionlogic GmbH / T-Systems International GmbH
Floating Phone Data restrictions at the moment:• Raw data cannot be obtained, only aggregated and extrapolated data is available,• Aggregated data is available in 90 minutes blocks,• Max. duration for tracking: 90 minutes,• Min. five phones registered per time unit and cross section/ the same origin
destination connection are needed to obtain data,• The FPD is aggregated and extrapolated from signals of one carrier to the overall
amount of people in the area, assuming one SIM card per person.
Other Data:• Cross section counts along the A8 between Ulm (Baden-Württemberg) and Munich(Bavaria) – all cross sections that were available in the study period are included,• 9 cross sections in both directions,• Sum of all vehicles and distinction of 8 vehicle types,• Extrapolation to number of people crossing the cross section (with averageoccupancy rates),• Train occupancy for one long distance train between Munich and Ulm in passenger
counts.
Preliminary Results:
1. Stationary vehicle counts were compared to extrapolated number of people from FPD (seePicture 1).
The correlation between extrapolated data and vehicle counts is quite good, especiallyduring workdays with only a few r-values below 0.9. At two cross sections some anomalies
were detected, which might be due to interference in the measurements through other roads
close-by the surveyed sites. Converting these numbers to passenger per vehicle showed avery high fluctuation over the day, where occupancy rates were especially high during thenight. Trying to understand the fluctuation, with an average for vehicle occupancy the overall
number of people was calculated to be compared to the extrapolated data from FPD. This
comparison showed that some of the patterns did match and others did not. However, wewere not yet able to identify a clear pattern.

74

2. Passenger counts on one long distance ICE-train from Munich to Stuttgart were conducted
and compared to the corresponding extrapolated FPD counts (see Picture 2). The
comparison shows that during workdays and weekend there are different offsets. In general
extrapolated FPD counts seem to be higher than passenger counts on weekdays, whereas
the FPD counts on weekends are lower than the corresponding passenger counts. However,
due to the limited number of data samples no direct relation can be determined.
Now we’re in the midst of identifying possible flaws in the extrapolation and comparison.
Furthermore, in this project a comparison of passenger counts in busses in a rural area inGermany is planned. Passenger counts are done manually on busses and these numbers
will be compared to extrapolated occupancy from FPD.
Conclusions:

Poster Session 1 :: April 8

2015

To obtain detailed information more insight into the extrapolation process is needed as well
as more data samples to obtain viable conclusions from the data. Towards the feasibility on
using FPD as input data for e.g. transport models, regarding the mentioned restrictions, nocomment can be made at this stage of the project.

r-value ofvehicles/90 minutes andmovements/90minutes
r = 0.92

Vergleich von manuell erhobenen Fahrgastzahlen mit Messungenmittels Floating Phone Data
Picture 1: Comparison of movement counts and number of vehicles (with theoretical occupation)
ICE 1082/1132 zwischen München Pasing und Augsburg Hbf im Zeitraum von 07.07.2014 bis 20.07.2014

DB Mobility Logistics AG | Verkehrsnetzentwicklung und Verkehrsmodelle (GSV)

2

Picture 2: Comparison of long distance train passenger counts and extrapolated FPD (Source: Deutsche Bahn)

75

Evolving classification based on CDR-derived
behavior patterns
Extended abstract
Michal Mucha, Dominik Filipiak, Agata Filipowska
Department of Information Systems
Poznan University of Economics
Poznan, Poland
firstname.lastname@kie.ue.poznan.pl
The ubiquity of mobile phones, and the immense amount ofdata that is generated with their use, open up vast possibilities
for research and real life applications. Use of mobile phones by
all parts of the population enables research on both macro andmicro scale. Mobile phone datasets describe user
telecommunication activity, tagged with approximate
geographical location & time; on top of that, social relations
can be modeled with the use of graphs [1], [2]. Such a wide
spectrum of information about a great number of individuals
could prove to be a valuable resource for research in the fieldsof psychology and sociology. In fact, this potential is alreadybeing successfully explored by [3]–[7]. The authors of thispaper wish to contribute towards further efforts of this kind byproviding a useful tool.
We introduce an approach, stemming from Behavior
Informatics (BI) [8], to prepare behavior vector sequencesusing Call Detail Records (CDR). A behavior vector isprepared from each telecommunications activity represented bya CDR entry. Spatial and social information contained in CDR
datasets can be interpreted with relevant methods and used in
the composition of behavior vectors; i.e. the importance of
certain locations to individual users may be estimated [9], andthen used to label users’ actions. The resulting behavior vector
sequence may then be used for creation of econometric models,finding particularly interesting behaviors, etc. An example,
anonymized dataset of 1 month, 8000 unique locations, and3.35 million active users, served for the creation of such abehavior vector sequence. Elements of the vector, derived from
CDR, are: user ID, location category, relation strengthcategory.
Spatial description of each CDR entry is given as thetransceiver station, which was used to handle the service. Oneor more of such stations are placed together with the aim tocover a certain area [10], [11]. Thus a CDR entry means that acertain user was at a certain area. To give an example ofincorporating location into the behavior vector sequence, we
propose the use of information on identified places ofimportance to individual people, as proven to be possible by[9] and [12]. In our experimental setting, we use a simplified
approach to estimate home and workplace locations for theusers in the dataset available to us. The personal categories
assigned to locations are then used in the composition of

behavior vectors. This demonstration shows the possibility ofinclusion of research such as [13], and other kinds of methods,
into social and psychological studies based on mobile-phone
data.

Poster Session 1 :: April 8

2015

In this way results of other methods may be used tocompose the behavior vector. To illustrate how research of
relation strength estimation, such as [14]–[17], can be included
the BI framework, we propose a simplified approach towards
categorization of relations between users in the dataset
available to us. We use a simple categorization of relations into
strong and weak. Top 5 most contacted people, who alsosatisfy the arbitrary threshold of 15 connections per month, aredeemed strong relations; all others - weak. The result of thissimplified categorization is included in the behavior vectors,
and can easily be replaced by more elaborate and accurate
methods, i.e. based on community structure [18], [19].
Similarly, other methods may be used to complement or
replace the proposed modules with which the behavior vector
sequence is constructed (i.e. analysis of physical meetings[20]).
As the main contribution, we propose the use of an
Evolving Agent-based Classifier (EVABCD) (originallyproposed in [21]) with corrections and modifications that allow
temporary (instead of the default permanent) memory of user
behavior. The original version of the classifier is designed by
its authors to allow efficient processing of data streams withthe use of recursion to reduce memory and computational
requirements of the method. Moreover, the evolving nature of
the classifier means that during its functioning new classes
appear when new pattern of behavior emerge in the population,and classes representing behavior patterns that ceased to be
expressed by users are removed. These features are retained,with the additional benefit of a limited memory for past
behavior patters.
Such classification of behavior patterns is suggested byauthors to serve e.g. as a foundation for scientific comparisonof results of psychological tests (as in [4]) with mobile datasetderived behavior observations, studies of sociologicalphenomena, as well as any other scientific work that would
employ the possibility of the macroscopic scale of mobile
phone datasets.

76

In our experiments, the samples were not labeled. Different
research endeavors that are enabled with this foundation canemploy the Evolving Classifier for supervised learning –analysis of behavior prototypes together with other data.
The contribution thus consists of:•

A way to represent CDR and layers of information
derived from it in the form of behavior vector
sequences, in accordance with the Behavior
Informatics framework•

An example of a simplified method of estimating
strength of relations, as well as inclusion ofrelation strength categorization into the BI
framework•

An example of the application of personalized
location-tagging in the composition of the
behavior vector sequence•

A classifier that allows for dynamic discernmentof behavior patterns within the population, also
keeping track of the dynamics of behavior ofindividuals

Fig. 2. Distribution of the population into behavior-based classes

The classification was carried out on the modified,recursive EVABCD. In a single-machine experimental setting
we analyzed 550 thousand users who performed 8 million
actions (one day of activity for the selected population). During
the life of the classifier (designed to process data streams), we
took snapshots of its state. Overall, 267 prototypes appeared,
describing various clusters of the population. Appearance of anew prototype usually results in the removal of one or more of
the existing prototypes, when the new one describes them wellenough. Thus the number of prototypes reported in thesnapshots varied between 5 and 20, and in the final there were

Poster Session 1 :: April 8

2015

19. Fig. 1 presents the distribution of the population intoclasses, and fig. 2 contains the probability profile of the mostpopular prototype. Notably, in our experiment the classifier has
proven capable of identifying small clusters with distinct
behavior patterns, i.e. users calling only from home and only
their close contacts.

All of which may prove useful for research of sociologicaland psychological phenomena with the application of mobile
phone datasets.

Fig. 1. A profile of behavior vector occurrence frequency that is the closest representation of the profiles of 17% of the
population. WS means that the call was made from work to a strong relation contact. OS means that the call was made fromother location (other than home or work), to a strong relation contact. 2- and 3-element sequences show the frequencies ofpatterns of consecutive actions. This particular prototype represents users who call only a stable group of contacts, don’t callfrom home, and do slightly more calls from work than from other locations. Only non-zero frequencies are displayed in thisfigure.

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[1]R. Xiang, J. Neville, and M. Rogati, “Modeling RelationshipStrength in Online Social Networks,” in Proceedings of the 19thInternational Conference on World Wide Web, 2010, pp. 981–990.

[2]

M. Saravanan, G. Prasad, S. Karishma, and D. Suganthi, “Analyzing
and labeling telecom communities using structural properties,”
Social Network Analysis and Mining, vol. 1. pp. 271–286, 2011.

[3]

A. Rubio, V. Frias-Martinez, E. Frias-Martinez, and N. Oliver,“Human mobility in advanced and developing economies: A
comparative analysis,” in AAAI Spring Symposium - Technical
Report, 2010, vol. SS-10–01, pp. 79–84.

[4]

R. de Oliveira, A. Karatzoglou, P. Concejero Cerezo, A. de Vicuña,
and N. Oliver, “Towards a Psychographic User Model from Mobile
Phone Usage,” in CHI ’11 Extended Abstracts on Human Factors in
Computing Systems, 2011, pp. 2191–2196.

[5]

V. Frias-Martinez and J. Virseda, “On the relationship between
socio-economic factors and cell phone usage,” in Proceedings of theFifth International Conference on Information and Communication
Technologies and Development - ICTD ’12, 2012, p. 76.

[6]

E. Frias-Martinez, “An agent-based model of epidemic spread using
human mobility and social network information,” in … conferenceon social …, 2011, pp. 57–64.

[7]

Y. A. De Montjoye, J. Quoidbach, F. Robic, and A. Pentland,“Predicting personality using novel mobile phone-based metrics,” in
Lecture Notes in Computer Science (including subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), 2013, vol. 7812 LNCS, pp. 48–55.

[8]L. Cao, “In-depth Behavior Understanding and Use: The Behavior
Informatics Approach,” Inf. Sci., vol. 180, no. 17, pp. 3067–3085,
Sep. 2010.

[9]

[10]

S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi, J.
Rowland, and A. Varshavsky, “Identifying important places inpeople’s lives from cellular network data,” in Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6696
LNCS, pp. 133–151.
Y.-A. de Montjoye, C. a Hidalgo, M. Verleysen, and V. D. Blondel,“Unique in the Crowd: The privacy bounds of human mobility.,”
Sci. Rep., vol. 3, p. 1376, 2013.

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M. R. Vieira, V. Frías-Martínez, N. Oliver, and E. Frías-Martínez,“Characterizing dense urban areas from mobile phone-call data:
Discovery and social dynamics,” in Proceedings - SocialCom 2010:
2nd IEEE International Conference on Social Computing, PASSAT
2010: 2nd IEEE International Conference on Privacy, Security, Risk
and Trust, 2010, pp. 241–248.

[12]

Y. Qu and J. Zhang, “Trade Area Analysis Using User Generated
Mobile Location Data,” in Proceedings of the 22Nd International
Conference on World Wide Web, 2013, pp. 1053–1064.

[13]

R. Trasarti, F. Pinelli, M. Nanni, and F. Giannotti, “Mining Mobility
User Profiles for Car Pooling,” in Proceedings of the 17th ACM
SIGKDD International Conference on Knowledge Discovery andData Mining, 2011, pp. 1190–1198.

[14]

A. Filipowska, M. Mucha, B. Perkowski, E. Szczekocka, J.
Gromada, and A. Konarski, “Towards Social Telco Applications
Based on the User Behaviour and Relations Between Users,” inICIN 2015, in press, 2015.

[15]

N. Eagle, A. S. Pentland, and D. Lazer, “Inferring friendshipnetwork structure by using mobile phone data.,” Proc. Natl. Acad.
Sci. U. S. A., vol. 106, pp. 15274–15278, 2009.

[16]

J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K.
Kaski, J. Kertész, and A.-L. Barabási, “Structure and tie strengths inmobile communication networks.,” Proc. Natl. Acad. Sci. U. S. A.,
vol. 104, pp. 7332–7336, 2007.

[17]

K. Janakiraman and S. Motahari, “How are you related? predicting
the type of a social relationship using call graph data,” inProceedings - 2012 ASE/IEEE International Conference on Privacy,
Security, Risk and Trust and 2012 ASE/IEEE International
Conference on Social Computing, SocialCom/PASSAT 2012, 2012,
pp. 111–116.

[18]

V. D. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, “Fastunfolding of community hierarchies in large networks,” Networks,
pp. 1–6, 2008.

[19]

M. Newman and M. Girvan, “Finding and evaluating community
structure in networks,” Phys. Rev. E, 2004.

[20]

C. Brown, N. Lathia, C. Mascolo, A. Noulas, and V. Blondel,“Group Colocation Behavior in Technological Social Networks,”
PLoS One, vol. 9, p. e105816, 2014.

[21]

J. A. Iglesias, P. Angelov, A. Ledezma, and A. Sanchis, “Creating
Evolving User Behavior Profiles Automatically,” Knowl. Data Eng.
IEEE Trans., vol. 24, no. 5, pp. 854–867, May 2012.

Poster Session 1 :: April 8

REFERENCES

78

Sms transmission using phone users density
in big cities
Floran Berthaud1 , Yannick Léo1 , Carlos Sarraute2 , Anthony Busson1 ,and Eric Fleury1
1 Université de lyon, UMR 5668 CNRS - ENS Lyon - INRIA - UCB Lyon 1, IXXIPoster Session 1 :: April 8

2015

2 Grandata Labs, Bartolome Cruz 1818 Vicente Lopez. Buenos Aires, Argentina

This work answers the question : is it possible to transmit a sms using phones as relay in a big city such as Mexico
City? We defined a simple transport protocol to transmit sms from a source to a destination. This protocol does
not need routing, it is based on locality of sms, the density of phones in Mexico City and mobility of phone users.
We studied a mobile dataset including 8 millions users living in Mexico city. This gave use a precise estimationof the average transmission time and the global performances of our approach. After 30 minutes, half of the sms
were delivered successfully to destination.

The need of communicating in a dense city is always increasing. Every day, millions of sms are sent in a
big city like Mexico City. Phone operators have to adapt their infrastructures to provide an efficient service.
At present times, sms are not only routed with base stations. The way to communicate and exchange sms
between each other has become diversified these last years. We can now send messages with applications
like WhatsApp [1], Tango, Skype and Viber while connected to a wireless spot [4]. During rush hours,
the capacity of the operator service are almost saturated. It is becoming a great challenge to increase thecapacity of the service with the same number of relays.
In this study, we propose a new way to transmit sms and more generally data from a source to a destination. Instead of using classical routing, we use relays close to the source and phone users that are connectedto those local relays to reach the destination. A big advantage is that we do not perform a routing algorithm
as we do not need to know where the destination is. Moreover, as we only use local relays that are close to
the source, the bandwidth cost of a sms is smaller. On top of that, the density of phones and the mobility ofusers are even higher when the capacity of classic relay network is challenged during rush hours.

Figure 1: Geographical heat maps of static network parameters : average number of base station hops (left),average distance (middle) and global activity (right) around Mexico City. The Voronoi cell for a base station
represents the area in which users are connected to this base. The green colour represents low values andthe red one high values.

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We used a communication dataset [3] containing the mobile phone interactions of 8 millions of people
in Mexico City covered by 775 base stations that are part of the classic network. This anonymised dataset
contains sms and calls with some location information defined by the base station of the source and destination. Over three months, we managed to extract around 10 millions fully located sms for our study. Mostof the sms had Mexico City as source and destination.

Protocol and results
We analyzed our dataset through time and space for each base station. We showed the variability of the
global activity according to time. We noticed a constant activity for every base station, the distance between
two stations depends on the local activity. On figure 1, for each sms of the trace, we computed the distanceand the number of relay hops from the source to the destination base station. The average distance of sms
is constant whereas the number of relay hops is higher in the center where base stations are closer.
This study provides an empirical proof of the close proximity of messages. Many sms are very local witha very small number of relay hops. We applied a neighboor protocol that consists in delivering the sms to
the phone users that are attached to the same base station when the source sends the sms. Then we let theseneighboors moving with the sms. If any of the neighboors reach the destination, the sms is delivered. If
after half an hour the message has not reached the destination, then the message is dropped. In our network,
one over three messages were delivered after 10 minutes and one over two after thirty minutes. As somelocations are missing in our dataset, in reality, the results are likely to be even better.

Poster Session 1 :: April 8

Floran Berthaud , Yannick Léo , Carlos Sarraute , Anthony Busson , and Eric Fleury

Figure 2: For each base station, we performed the ratio of local sms that had less than two relay hops (left)
and the ratio of sms that had successfully reached the destination by the neighboor protocol (right).

References[1] Karen Church and Rodrigo de Oliveira. What’s up with whatsapp?: Comparing mobile instant messaging behaviors with traditional sms. In Proceedings of the 15th International Conference on Humancomputer Interaction with Mobile Devices and Services, MobileHCI ’13, pages 352–361, New York,NY, USA, 2013. ACM.
[2] Yves-Alexandre de Montjoye, César A Hidalgo, Michel Verleysen, and Vincent D Blondel. Unique inthe crowd: The privacy bounds of human mobility. Nature srep., 3, 2013.
[3] C. Sarraute, P. Blanc, and J. Burroni. A study of age and gender seen through mobile phone usage
patterns in mexico. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM
International Conference on, pages 836–843, Aug 2014.
[4] Chai K Toh. Ad hoc mobile wireless networks: protocols and systems. Pearson Education, 2001.

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Topological Properties and Temporal Dynamics ofPlace Networks in Urban Environments
Anastasios Noulas

Computer Laboratory
University of Cambridge

Renaud Lambiotte

Department of Mathematics
University of Namur

1. INTRODUCTION
Mobile user trajectories are known to exhibit structural and temporal regularities associated with the daily and weekly cycles of human activity. The spatial network formed by user movement, andits topological characteristics in particular, have been explored inrecent research including the detection of urban neighborhoods [4],place recommendation to mobile users [9], touristic route identification [7] and a broad range of applications in epidemiology [1].
However, the majority of models of human mobility focus exclusively on its spatial characteristics [2, 5, 11], and neglect both network topology and temporal dynamics. More advanced computational methods proposed recently realize some of these aspects, forinstance by incorporating information about the users’ social network [10] and their spatiotemporal dynamics [3]. The applicabilityof these approaches is limited though, as they rely on complete
knowledge of a user’s historic whereabouts and social connections
as input, which might not be readily available in most domains.
The goal of this paper is to bridge the gap between universal
mobility models and complex computational methods in mobility
modeling. As opposed to tracking the whereabouts of individual
users, our key idea is to use the aggregate trajectories of users
between real-world places to define a network of venues in thecity. Using a longitudinal dataset from the location-based service
Foursquare we empirically analyze place networks in one hundred
metropolitan areas across the globe.
Figure 1 presents a visualization of the place network shaped by
the movement of Foursquare users in New York City. One can spothubs being formed at multiple areas across the urban plane, withlocal transitions connecting them to nearby places and occasional
long jumps connecting places located further apart from each other,for example when users move between Manhattan and Brooklyn.
Exploiting a set of insights on the growth patterns, temporal dynamics and topological properties of these place networks, we thenbuild a new human mobility model that accurately predicts the future interactions between places in urban environments with mini-

Blake Shaw

Foursquare
568 Broadway, New York

Cecilia Mascolo

Computer Laboratory
University of Cambridge

Poster Session 1 :: April 8

2015

mal parameterization and computational costs. Our work is articulated into three parts:
Place network growth and temporal pattern analysis. We firstconsider the temporal properties of place networks, and focus on
their growth over time in terms of edge and node addition processes. In accordance with previous observations in online social
networks [6], we observe a densification pattern, as the number ofedges grows superlinearly to the number of nodes in the system. A
saturating effect for node growth is reached quickly nonetheless,when the large majority of Foursquare venues is added to the network. It takes almost 10 weeks for mobile users to crowdsource alarge fraction (more than 95 %) of public places in a city. Subsequently, we compare instances of place networks across consecutive time windows of observation; we find that a significant number of new links are generated over time as users form new spatial
trajectories when they navigate between places. The set of places
that generate those edges, however, remains remarkably stable overlong periods of time. These results reveal the importance of viewing connections as fleeting entities that emerge dynamically in the
network.
Topological properties of place networks. We then empiricallyanalyze the topological properties of place networks. We maketwo key observations: first, we note that place networks exhibit
the well-known characteristics of social networks such as heavily
skewed degree distributions, scale-free properties, small-world behavior and high clustering coefficients. We trace this relationshipto the inherent inter-dependence between mobility and social linkformation in geographic space [10, 3]. In contrast, we also finda striking difference compared to social networks: they show aresemblance to the web graph presenting a balanced assortative
mixing pattern with hub nodes connecting to each other but also
to low degree nodes. This non-social property arises from the different roles played by places in the network, and in particular the
existence of travel spots, such as train stations or airports, acting
as intermediate hubs between nearby places, e.g., food places, themost frequent place type in the network, typically characterized bylow degrees. These characteristics are consistent across one hundred cities.
A new gravity model for link prediction in place networks. Finally, the turnover of links in the network over time motivates thefollowing prediction task: given past observations about the connectivity of public venues in Foursquare, we would like to predictthe pairs of places that are likely to connect at a future time. Candidate prediction models need to rank highly the pairs of venues

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106

104

Los Angeles
San Francisco
x1.14

103
102
101
100
100

101

102
103
Nodes

104

105

Figure 2: Number of edges versus number of nodes in Los Angeles and San Francisco as the cities become crowdsourced by
Foursquare users.
Figure 1: A visualization of the place network for New York City
at 11pm. Each dot represents a user traveling between venues, andis color-coded by the category of the destination with blue being
nightlife and green being food. We clearly see the edges of the
network formed by people moving between places.
that are most likely to interact, a task complicated by a number ofchallenges. In particular, the highly volatile, time dependent, link
generation process and sparse data setting may hinder the use ofcomplex prediction algorithms that can be prone to overfitting. The
inherently spatial embedding of the network suggests the need formodels which integrate appropriately geographic distance as a factor. We therefore develop a generalization of gravity models [2, 5,8], popular in the mobility and transport literature, where we incorporate the temporal aspects of the system: the model combines information on venue synchronization in terms of user activity, in andout-bound movement towards places and geographic distance. In
practice, it captures the observation that nodes may act as sourcesor sinks of users in the course of time, depending on their cycleof activity. Finally, it incorporates information about the interaction of places on the network level, a valuable aspect of attractionthat has been ignored by past mobility modeling approaches. The
ranking strategy put forward by the model outperforms by at least
two points in the Area Under the Curve (AUC) score even popularsupervised learning algorithms and by a large margin the model adhering to the standard formulation of gravity in the literature (AUC
score 0.905 versus 0.811). This is achieved with minimal requirements for training and optimization, making it ideal in practical application scenarios where expensive computations can pose a tradeoff against the real time demands of many mobile applications.

2. NETWORK GROWTH AND DYNAMICS
Network densification is a fundamental phenomenon in network
dynamics and relates to the different rhythms with which nodes
and edges are added to the network. Previous work by Leskovec
et al. [6] characterizes empirically the densification process in online social and technological networks showing that the number ofedges grows superlinearly with the number of nodes in the network.
Specifically, given the number of nodes n(t) observed at a point intime t, one is interested in the number of edges e(t) and the way
this relationship forms as t grows. Formally we have:e(t) ∝ n(t)α(1)

Different values of the exponent α, imply differences in the expected number of edges over time. A graph with α = 1 maintains

Poster Session 1 :: April 8

Edges

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a stable average degree over time, whereas α > 1 corresponds to anincrease in the average degree. The findings reported in [6] suggestthat the latter is the case in many real world networks and here we
investigate whether it holds also in urban place networks. We picka random point in time t0 where we begin monitoring the evolution of a place network and then measure the number of new nodes
and edges added by users sequentially. Figure 2 shows the number of edges versus the number of nodes, in log-log scale, in thecities of Los Angeles and San Francisco, as venue information inthese cities becomes crowdsourced over time by Foursquare users.
Initially the number of links grows superlinearly with the numberof nodes. We have measured using the least squares optimization
method an exponent α = 1.14 with a standard deviation ±0.06
across a set of one hundred cities. However, at a specific city sizedependent threshold, this scaling breaks, as the number of nodes
ceases to increase whereas new links continue to appear. At thatpoint, a majority of places have been discovered by the users, andfinite-size effects induce a slowing down of new place discovery.

3.[1] P.REFERENCES
Bajardi, C. Poletto, J. Ramasco, M. Tizzoni, V. Colizza, and

A. Vespignani. Human mobility networks, travel restrictions, and theglobal spread of 2009 h1n1 pandemic. PloS ONE, 6(1):e16591, 2011.
[2] D. Brockmann, L. Hufnagel, and T. Geisel. The scaling laws ofhuman travel. Nature, 439(7075):462–465, 2006.
[3] E. Cho, S.A. Myers, and J. Leskovec. Friendship and mobility: user
movement in location-based social networks. In KDD’11.
[4] J. Cranshaw, R. Schwartz, J. Hong, and N. Sadeh. The livehoods
project: Utilizing social media to understand the dynamics of a city.
In ICWSM’12.
[5] M. González, C. Hidalgo, and A.-L. Barabási. Understanding
individual human mobility patterns. Nature, 453(7196):779–782,
2008.
[6] J. Leskovec, J. Kleinberg, and C. Faloutsos. Graphs over time:densification laws, shrinking diameters and possible explanations. In
KDD’05.
[7] C. Lucchese, R. Perego, F. Silvestri, H. Vahabi, and R. Venturini.
How random walks can help tourism. In Advances in Information
Retrieval, pages 195–206. 2012.
[8] J. H. Niedercorn and B. Bechdolt. An economic derivation of thegravity law of spatial interaction. Journal of Regional Science,9(2):273–282, 1969.
[9] A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. Mining user
mobility features for next place prediction in location-based services.
In ICDM’12.
[10] A. Sadilek, H. Kautz, and J. Bigham. Finding your friends andfollowing them to where you are. In WSDM’12.
[11] F. Simini, M. González, A. Maritan, and A.-L. Barabási. A universal
model for mobility and migration patterns. Nature,484(7392):96–100, 2012.

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The Effect of Geographical Proximity on Mobile Communication

Hyungtae Kim
Columbia University

1

Tony Jebara
Columbia University

INTRODUCTION

Average Distance vs. # of Calls

28

Geographical proximity can significantly influencecommunication patterns. At small scales, proximity
faciliates face to face interaction and relationships arenaturally formed between individuals that coincide insimilar public spaces as well as work places. A user’s
location pattern has been shown to be highly correlated with the pattern of his neighbors in a social network [4] .Call detail records (CDRs) have been used to
study a variety of user behaviors such as the statistics
of location patterns [2] and the probability of communication given a distance or proximity between pairs ofusers [3] . The correlation between user proximity andsocial connectivity have also been studied using social
networks endowed with IP-based geolocation [1]. This
article analyses a large data-set of location-augmented
call detail records (LACDRs) to more precisely characterize the influence of geographical proximity on communication frequency and the length of average communication time.

2

DATA

The data-set under consideration is a large LACDR
data-set that represents the majority of users in an
undisclosed city-scale region. The data is sampled eachtime a communication is performed by one of the users
which includes phone calls and SMS messages. Each
such event generates a record containing the eventtype, a start time, the end time as well as the GPS
location. The data set spans approximately 2.3 million devices over a period of one month and contains
approximately 300 million distinct communication andlocation events. Devices are given a unique ID which
is derived from anonymizing the phone number associated with the device. This renders the devices uniquely
identifiable (up to telephone number changes which we
assume occur with limited frequency in the data-set).
The GPS coordinates have also been transformed using a translation and a rotation such that distances
between pairs of geographical points are preserved.

Average Distance Apart (km)27

Poster Session 1 :: April 8

2015

26
25
24
23
22
21
20
19
0

20

40

60

80

100

# of Calls

Figure 1: Average Distance vs. Number of Calls. The
number of calls were grouped by buckets of 10 and the
distance was averaged for each bucket.

3

METHODOLOGYWith the LACDR data spanning n devices, a sparsen×n matrix X is constructed to represent the weighted
communication graph of the devices. Let X(i, j) be
the number of communication events between device iand j. Both SMS messages and phone calls are considered communication events. Through X, we havea surrogate measure of the degree of social interaction
between pairs of individuals.
To analyze the average calling time, a sparse n×n matrix A is estimated from data. Therein, A(i, j) is theaverage time spanned by each communication event
between device i and device j. The scalar A(i, j) is
calculated by dividing the total (undirected) communication time between i and j by the total number
communication events between device i and device j.
Fo A, only phone call events are considered since our
SMS records do not convey duration or extent of communication.
The location history of the devices consists of the
GPS coordinate along with a timestamp of when thecoordinate was recorded. For device i, let Yi ={Y1,i , Y2,i , . . . , YTi ,i } where each YT,i ∈ R3 is a threedimensional vector containing the latitude, longitude

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4

log(Distance) vs. log(# of Calls)
Fit.95 Confidence bounds

2.45

Poster Session 1 :: April 8

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70

60

50

40

30
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

log(Average Distance) (km)Figure 3: Average Call Length vs. log(Average Distance). The distances were rounded to the nearest
kilometer before the log function.

5

CONCLUSION

In this article, we present emperical evidence of two social phenomenons in the LACDR data set. Our analysis shows that individuals communicate with those in
their close proximity with greater frequency than thosefurther apart and the average communication time increases as the distance between two devices increase.

RESULTS

2.5

Average Call Length vs log(Average Distance)

90

Average Call Length (seconds)

and event of the t’th event generated by device i. Every device is considered stationary at is previously reported location until the a new location is observed.
This interpolation allows us to efficiently compute a
measure of average proximity and the time spent atthat proximity for a pair of devices. All static proximity computations were performed using the Haversine forumla between the GPS coordinates of two devices. The average distance between two devices isthen calculated as a weighted average of the Haversine distances each weighted by the amount of timespent at that distance. The average distance between
two users (or devices) i and j (or their average proximity) is stored as element D(i, j) in a sparse matrix
D. It is important to maintain a sparse representation of the n × n D matrix since n is so large. We
therefore explicitly computed the distances precisely
for pairs of users which communicated at least once
(i.e. we apply the sparsity pattern in X to the D matrix). For the non-communicating pairs, we estimate
the distances through random sampling rather than
exhaustively enumerating all possibilities.

log(D) = 2.491 + -0.0566 log(# of C)

Referenceslog(Distance) (km)

2.4

2.35

[1] M. Burke, R. Kraut, and C. Marlow. Social capital on facebook: Differentiating uses and users. In
Proceedings of the 2011 annual conference on Human factors in computing systems, page 571–580.
ACM, ACM, 2011.

2.3

2.25

2.2

2.15

2.1
0

1

2

3

4

5

6

7

log(# of Calls)

Figure 2: Relationship between Distance and Numberof Calls.
The regression analysis of C in Figure 2 shows thatthe average distance between two devices decreases as
the number of communication events increases. A linear relationship between log(distance) and log(calling
frequency) provides an especially good fit for this phenomenon.
Analysis of A in Figure 3 provides insight into another relationship. The average call time increases as
the average distance between devices increases. Thisemperical result hasn’t been previously studied to ourknowledge.

[2] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi.
Understanding individual human mobility patterns. Nature, 453(7196):779–782, June 2008.
[3] L. G. Moyano, O. R. M. Thomae, and E. FrasMartnez. Uncovering the spatio-temporal structure of social networks using cell phone records. In
J. Vreeken, C. Ling, M. J. Zaki, A. Siebes, J. X.
Yu, B. Goethals, G. I. Webb, and X. Wu, editors,
ICDM Workshops, pages 242–249. IEEE Computer
Society, 2012.
[4] D. Wang, D. Pedreschi, C. Song, F. Giannotti, and
A.-L. Barabasi. Human mobility, social ties, andlink prediction. In Proceedings of the 17th ACM
SIGKDD International Conference on Knowledge
Discovery and Data Mining, KDD ’11, pages 1100–
1108, New York, NY, USA, 2011. ACM.

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Poster Session 1 :: April 8

How mobile positioning data can contribute to urban geography: measuring ethnic
segregation in daily activity spaces in Estonia
Rein Ahas1, Siiri Silm1, Erki Saluveer1,2
1

Department of Geography, University of Tartu, Estonia, rein.ahas@ut.ee,
http://mobilitylab.ut.ee/eng/
2

Positium LBS, Estonia

The aim of this presentation is to introduce the theoretical and methodologicalaspects of using passive mobile positioning data in studying ethnic segregation. Passive
mobile positioning data is secondary data, which is recorded in various phone use logs,
for example, the Erlang data of mobile phone data (1) or Call Detail Record (CDR) data(2) is used. The presentation is based on two academic articles on the ethnic segregation
analyses in Estonia.
Ethnic segregation as spatial separation of some population groups from others is one of
the most important population processes in urban areas (3). Segregation of minorities is usuallydeemed to be negative because the isolation is associated with problems in education,
employment, poverty, safety, and health care (4). A traditional analysis of segregation on thebasis of a study of activity places (residence, place of work, leisure) may not show the complete
picture of the population processes because the activities may take place across many different
places or activities (5).
Thus, researches have increasingly highlighted the need to study segregation in the wholeextent of the places of activity of people and in the whole extent of the 24-hour activity cycle(6,7). Such studies are becoming possible and interesting in connection with taking into use ofvarious modern tracking data, which enable to observe people ubiquitously in time and space.
The use of such data is limited by many restrictions related to the right of use of the data andprivacy. There are also huge methodological and theoretical challenges. How to enrichquantitative tracking data to a level interesting to modern social sciences? How to make spacetime tracking data statistically processable? How to interpolate them spatially and temporally?
For the first case, we will be introducing the use of mobile data in studying the temporal
differences in the use of urban space.

1

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Original paper was published in Social Science Research in 2014 (8). The objective of

the CDR data over the course of three years to study temporal segregation levels in urban space
through the day, the week, and the year.
The methodological challenge of the analysis was aggregation of the mobile network
operator’s (MNO) CDR data into units of suitable temporal and spatial accuracy. It is necessary
to select units of time of the most suitable length and units of the space of the most suitablesize. This also depends on the number of respondents in the CDR database and the number ofcalls made by them.

Poster Session 1 :: April 8

this paper was to determine the temporal dimension of ethnic segregation in Tallinn. We used

As result of methodological development of data enrichment algorithms we measured
segregation for three-hour periods in city district level using traditional segregation indixes –the index of dissimilarity (ID) and the location quotient (LQ) method – and compared the resultswith residence-based indices based on 2000 census data.

Figure 1A. The index of dissimilarity during the day compared to the places of residence by
census data.

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Figure 1B. The index of dissimilarity during the week compared to the places of residence byPoster Session 1 :: April 8

census data.

Figure 1C. The index of dissimilarity during the annual cycle compared to the places ofresidence by census data.

The results indicated that the locations of people are more segregated at night, withconsiderably less segregation during the daytime (Figure 1A). The segregation is significantly
lower on workdays compared to weekends (Figure 1B). Segregation is also lower during
summer holidays compared to the winter working period (Figure 1C). The results show that
although places of residence are segregated, different ethnic groups use the city together during
the day, which increases the potential for interethnic contacts. The results demonstrate also that
temporal segregation based on mobile-phone use are considerably lower than segregation ofplaces of residence that are derived from the census.

For the second case, we will be introducing the use of mobile data in studying ethnic
variations occurring in the space usage. The original paper was published in the Annals of
Association of American Geographers in 2014 (9). The aim of the article was to find thedifferences in the leisure time space usage of ethnic groups. From the theoretical perspective,
this is an interesting challenge, because people spend the majority of their time at home and atwork and thus researches have studied these anchor points most. However, many researchers
claim that ethnic and social differences gain their true meaning outside of the routine, e.g.
outside of the daily places of activity and in the course of leisure time activities. Then people
get to choose with whom and where they go.
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From the methodological perspective, the challenge of this study is to develop an

time space usage from the CDR data. For the theoretical basis, we used the “out-of-home nonemployment activities” (10) concept of travel behaviour researchers, and developed a special
algorithm to find it from the CDR data. This is based on the anchor points model with the helpof which we can find regularly visited places, home, work and other anchor points.
Development of the anchor point model was based on a survey (11). Using the different spatial
units of GIS databases, we developed an algorithm with the help of the elimination method,which extracts the places of activity that can be defined as out-of-home non-employment

Poster Session 1 :: April 8

algorithm which would enable to distinguish home, the place of work, and the places of leisureactivity places from the CDR data. Based on the analysis of these places of activity, we
conducted a statistical analysis to find ethnic differences.

Figure 2A. Differences in districts in Tallinn visited by Estonians.

Figure 2 B. Differences in districts in Tallinn visited by the Russian-speaking minority (B).

The results show that ethnicity has a significant influence on the leisure mobility of individuals.
The biggest differences between the two population groups occur in Estonia outside the
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respondents' home city of Tallinn. The Russian minority were found to visit 45 percent fewer

districts in Tallinn and Estonia generally that are more heavily populated by the Russianspeaking population. With respect to international travel, the Russian-speaking minority visit
fewer countries and have a 3.6 times higher odds of visiting former Soviet Union countries thanEstonians. The space usage in out-of-home non-employment activities have fewer differences
between the two groups in Tallinn. Overall, our results show that ethnic differences has lesseffect on everyday space usage and a greater influence on the choices made regarding longdistance travel (Figure 2A and 2B).

Poster Session 1 :: April 8

districts in Estonia (excluding Tallinn) than Estonians. Moreover, they exhibit a preference for

Having introduced the two approaches to using mobile data in examining a “classic”
social science problem, the generalising part of this presentation will discuss the challenges
involved in using mobile data. The results showed that by using digital behavioural data, it ispossible to discover new aspects of segregation and further develop the theoretical approach tosegregation. Segregation at the place of residence can be related to inertia and the residential
property market, people may have many interethnic contacts on the daily basis. Everything also
depends on the scale of the city and the functionality of the urban space. A certain warning signof “ghettoization”, however, is the encapsulation of all of the people’s trajectories over 24 hours
in their neighbourhood or places connected to the same ethnic group. This dimension must notnecessarily be ethnic, social isolation and financial stratification have the same effect.
From the methodological perspective, we will be discussing the advantages arising fromusing the data and the limitations arising from the peculiarity of CDR data in conducting suchurban geographical researches. A reasonable amount of theoretical task setting andmethodological courage enable to develop data processing algorithms, which make the CDR
data of little promise at first sight interesting even for a more demanding researcher.

References(1) Kang, CG, Liu, Y., Ma, XJ., Wu, L., 2012. Towards Estimating Urban Population
Distributions from Mobile Call Data, Journal of Urban Technology 19(4): 3-21.
(2) Ahas, R. Aasa, A., Roose, A., Mark, Ü., Silm, S. 2008. Evaluating passive mobile
positioning data for tourism surveys: An Estonian case study. Tourism Management
29(3):469–486.

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(3) Ellis, M., Wright, R., & Parks, V. 2004. Work Together, Live Apart? Geographies ofAmerican Geographers, 94 (3), 620–637.
(4) Massey, D. S., & Denton, N. A. 1988. The dimensions of residential segregation.
Social Forces, 67, 281–315.
(5) Strömgren, M.; Tammaru, T.; Danzer, A.; van Ham, M.; Marcinczak, S.; Stjernström,
P.; Lindgren, U. (2014). Factors shaping workplace segregation between natives andimmigrants. Demography, 51(2), 645 - 671.
(6) Schnell, I., & Benjamini, Y. (2001). The sociospatial isolation of agents in everyday

Poster Session 1 :: April 8

Racial and Ethnic Segregation at Home and at Work. Annals of the Association oflife spaces as an aspect of segregation. Annals of the Association of American
Geographers, 91 (4), 622–636.
(7) Järv, O., Müürisepp, K., Ahas, R., Derudder, B., Witlox, F. 2015. Ethnic differencesin activity spaces as a characteristic of segregation: A study based on mobile phone
usage in Tallinn, Estonia Urban Studies 0042098014550459, first published on
September 22, 2014 as doi:10.1177/0042098014550459(8) Silm, S. & Ahas, R. 2014.The temporal variation of ethnic segregation in a city:
evidence from a mobile phone use dataset, Social Science Research 47: 30-43(9) Silm, S. & Ahas, R. 2014. Ethnic differences activity spaces: The study of out-ofhome non-employment activities with mobile phone data, Annals of Association ofAmerican Geographers 104(5): 542-559.
(10)
Kwan, M.-P. 1999. Gender, the home-work link, and space-time patterns of
non-employment activities. Economic Geography 75 (4): 370-94.
(11)
Ahas, R., Silm, S., Järv, O., Saluveer E., Tiru, M. 2010. Using Mobile
Positioning Data to Model Locations Meaningful to Users of Mobile Phones ,Journal of Urban Technology, 17(1): 3-27.

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Protecting the Privacy of Location Data using the
openPDS/SafeAnswers Framework
Fatima Makkia, Wassim El-Hajja, Abbas El-Hakimb, Yves-Alexandre de Montjoyeca Computer Science Dept., American University of Beirut, Lebanon, [fmm26,we07]@mail.aub.edu
b Department of Mathematics, American University of Beirut, Lebanon, aa145@aub.edu.lb
c Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA, yva@mit.edu
Abstract—Billions of applications have already been
downloaded by smartphone users. To successfully download anapplication, the application asks the user to accept a set of
permissions allowing it to access sensitive information on thephone. It is mostly unclear when such data is being accessed,
how it is being used, and for what purposes. A framework
called openPDS, was recently suggested to limit such privacy
invasion. openPDS gives the user full control over her data andonly allows access to the data/metadata via safe answers.
Although openPDS protects the user’s raw data, sensitive
personal information, such as location trace, can still beinferred by the service provider by analyzing the accumulated
answers. In this work, we focus on location privacy andappend openPDS with a module that prevents the service
provider from reconstructing the trace of users. The module
might provide a correct answer, a wrong one, or might notanswer at all. However, the module guarantees quality ofservice (QoS) by abiding with the QoS requirements
necessitated by the provider, which we define as the toleranceof the application to inaccurate answers. We tested ourapproach on 10 users whose locations traces were recorded for
10 months. The results show that no user trace was successfullyreconstructed even when high QoS levels were required.
Moreover, the adversary knowledge gained through therecurrent months was not maintained.
Keywords-component;
smartphones; mobile apps

I.

openPDS;

location

privacy;

INTRODUCTION AND BACKGROUND

Mobile phone, these little devices we carry around prettymuch 24/7, are probably the most ubiquitous behavioral andlocational sensor currently available. A recent study by
CNIL [1], the French data protection authority, showed that
22% of Android and 31% of iOS applications are getting
permission to access our location at any point in time.
While many of these applications access location data for
legitimate purposes, the proliferation of raw and large-scale
location databases is a source of concern; 62% of Americans
consider data about their exact location to be moderately or
extremely private [2], and research has shown that it is easy
to re-identify individuals in simply anonymized mobility
dataset.
Numerous solutions have consequently been developed
to protect the privacy of individuals on smartphones [3-8].
Aquifer [3] protects the user from unwanted information
disclosure that is permitted to applications by controlling thedata access after each interaction with the server provider.
This is done by designing applications that present to users aclear flow of their information and adds restrictions to data
accesses that appear to it as unlawful. Authors in [4] suggest
market-aware privacy protection framework that includes a
feedback control loop that adjusts the mobile privacy settings
based on the monetary revenue generated by advertisements.
MockDroid [5] and AppFence [6] exchange sensitive datawith fake ones, for example, by submitting fake GPS
coordinates for an application requesting them; however, it is
not location specific and works for other data as well.
TaintDroid [7] monitors the flow of privacy-sensitive dataand identifies potential misbehavior by third-party
applications. Other solutions such as the ones in [8-12]
protect the user from specific attacks and target specific data.
However, these solutions are so extreme to the extent thatthey do not permit service providers from accessing
historical data, or incorporating temporal factors [13],making their approach less practical especially for
applications that target data-science statistics and findings.
Yet, other approaches achieve privacy by protecting the user
identity within a group (mix-zone) using approaches similar
to k-anonymity [14,15] (or its variants), or by obfuscating
spatial or temporal location related data [16].
openPDS/SafeAnswers [17], on which our solution isbased, takes a different approach. In short, location
information about where a user was and is, is collected andstored under his control on his PDS. The user can then allow
applications to ask questions, in the form of code, to his
PDS. For example, an application might want to knowwhether a user is currently on AUB campus. In this case, thecode sent by the application would be run in a sandbox and a
yes/no answer will be generated depending on whether theuser is on AUB campus or not. The fundamental difference
between openPDS and other solutions is that openPDS
allows users to use and answer questions using their full
mobility trace without sharing the raw data.
Although openPDS never shares raw mobility data, a
malicious application might try to infer more information
through a specific sequence of questions-answers. For
instance, openPDS does protect the user from having theapplication provider know her exact location at a certain time
instance. In its current form, however, it does not have amodule to prevent the application provider from inferring themobility trace of the user after a certain number of questionsanswers.
We
here
proposesuchamodulefor
openPDS/SafeAnswers. The module processes location
related questions, whether a user is or is not within a given
region, by telling the truth, lying, or by not providing an

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answer. Our module prevents application providers frominferring a user’s trace while ensuring a good quality ofservice. We here define quality of service (QoS) as thepercentage of wrong/no answers the application can tolerate.
This percentage can be anywhere between 10% and 100%.
We also define privacy to be the percentage of knowledge
gained about the user’s mobility. This privacy metric will bequantified in section II.
The module works as follow: the module records
questions that have been previously asked by the applicationand creates spatiotemporal profiles of both, what theapplication knows and that of the actual user. Bothspatiotemporal profiles are created using first-order Markov
mobility chain. Using these profiles, the module can decideon providing or not a correct answer. Whether the module
provides an answer is a trade-off between the QoS and the
maximal difference between the adversary’s profile and theactual profile, which actually translates to privacy level. If
the question is answered, the spatiotemporal profile of theadversary is updated. This process is repeated for every
question. It is to be noted that the location privacy problem
we are tackling here is different than the traditional location
privacy problems mentioned before where the privacy
approach relies on achieving anonymity within a group by
using k-anonymity [14,15] or its variants, obfuscating
temporal and spatial information related to locations, tainting
private data, or protecting against specific attacks. Our
approach however is user-centric that balances between QoS
and privacy and poses no restrictions on data needed by theapplication.
We tested our proposed location privacy method on themobility traces of 10 students in the American University ofBeirut over the course of 10 months. Results show a good
privacy level with high QoS. Our module successfully
prevents an adversary from accumulating knowledge about auser’s location across time.
II.

LOCATION PRIVACY MODULE

As a first step, we create spatiotemporal profiles(mobility profiles) of the user P!"#$   and of the application
as it is learning from the recurrent questions-answers acrosstime (we call this profile P!"#$%&!%' ). Both spatiotemporalprofiles are created using first-order Markov mobility chain.
Based on the notion that users act similarly in the same time
period of the day, we integrate the time domain in themobility profile as done in [16]. The week is divided into
four time periods, three for weekdays (morning – 7 to 11 am,noon – 12 to 6 pm, and night – 7:00 pm to 6:00 am) and one
representing weekends. The spatial resolution considered in
the mobility profile is as coarse as city level; for example, we
consider the area Hamra (AUB campus included) as visited
location instead of AUB campus. It is to be noted though that
our approach allows the application to ask at anytime using
any spatial resolution. Each state s of the mobility chain isthus the spatiotemporal event of the transition, which is the
combination of the visited location with the particular time
period. An example of a state s is the probability of a user
being in Beirut (location) in the morning (time period).

Since the profile is regular and aperiodic, based on the
Ergodic theorem, the profile (the first-order Markov mobility
chain) will converge to a unique steady state distribution π.
Each entry π s   in π is the presence probability of the user instate s  i.e. each entry represents the percentage of a user
being in location x at time period t. Our objective is toanswer the adversary in such a way to keep the distance
between the stationary distribution of the adversary model
π!"#$%&!%'   and that of the real profile π!"#$ the maximum
possible, knowing that this will lead to distinct profiles andhence different traces, which is confirmed in theexperimental results.
Since the adversary gains his knowledge from thequestions he poses, the next step is to update the adversary’s
stationary distribution based on the answer provided by thelocation privacy module. Three kinds of answers can beprovided by the privacy module: true, false, and no answer.
We consider an answer to be true if it is correct, and false if
it is a noisy one. When not answering, we are sure that theattacker is learning nothing, while when submitting ananswer, the attacker is not sure if the submitted answer iscorrect or noisy. Hence, noisy answers will help in
misleading the adversary. Going forward, we propose having
three privacy zones: Favorable, Steady, and Danger. We
categorize the user to be in a Steady zone if the states in
π!"#$%&!%' are uniformly distributed with a small tolerance,
which means that the attacker cannot infer the user’s
location. If both π!"#$%&!%' and π!"#$ include a peak atlocation i in time period t, and the adversary’s question intime period t is whether the user is in location i or not, andthe user is actually in i, the zone is considered Danger,
because the adversary is getting closer to the actual mostvisited location by the user in time period t. Otherwise, thezone is considered Favorable. If the user is in a Favorable
zone f, the privacy module tends to answer truly more often
than false, for example T! =85%, F! = 10%   and N! =5%
where T! , F! , and N!    are the percentages of answering truly,
falsely, and not answering. If the user is in a Danger zone d,the privacy module tends to answer falsely more often thantruly, for example T! =45%, F! = 50%  and N! =5%. If theuser is in a Steady zone a, the privacy module tends toanswer in a way to satisfy the required QoS, for example
T! =70%, F! = 25%   and N! =5% when the QoS is 70%,
which means that the location privacy module will answer
truly 70% of the time. For the QoS to be respected, thefollowing inequality should be enforced
𝑑𝑇! + 𝑓𝑇! + 𝑎𝑇! ≥ 𝑄
where d, f, and a are the normalized number of times the user
enters into Danger, Favorable, and Steady zones
respectively, and Q is the percentage of true answers
generated by the privacy module.
Instead of fixing the percentages of answering true, false,or no answer in every zone, we resort to calculate thesepercentages based on an optimization problem where we aim
at maximizing the distance between π!"#$%&!%'   and π!"#$ , asfollows:!
!
!
!
𝐹! 𝜋!"#$ −    𝜋!"!"#$%#&     +    𝑇! 𝜋!"#$ −    𝜋!"#$%&!%'    !

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2015

!

92

where F!   and T! are the optimization variables representing
the percentages of answering falsely or truly given zone z,
and π!!"#$%&!%'   and π!!"#$%&!%'   are the stationary distributionsof the adversary knowledge after answering falsely or truly(discussed next). The optimization problem is subject to
𝑑𝑇! + 𝑓𝑇! + 𝑎𝑇! ≥ 𝑄, where Q is a given percentage (QoS
constraint).
After obtaining the optimal percentage 𝑇! and 𝐹! in every
zone z, we need to update the adversary’s knowledge basedon the answer provided by the privacy module, as follows:
𝑇! ,
𝐹! ,

𝑞 = 𝑖  𝑎𝑛𝑑  𝐴𝑛𝑠𝑤𝑒𝑟 = 𝑡𝑟𝑢𝑒𝑞 = 𝑖  𝑎𝑛𝑑  𝐴𝑛𝑠𝑤𝑒𝑟 = 𝑓𝑎𝑙𝑠𝑒

𝑇!   𝜋!"#$%&!%' 𝑖, 𝑞 ≠ 𝑖  𝑎𝑛𝑑  𝐴𝑛𝑠𝑤𝑒𝑟 = 𝑡𝑟𝑢𝑒
𝜋!"#$%& (𝑖) = 1 − 𝜋!"#$%&!%' 𝑞
𝐹!   𝜋!"#$%&!%' 𝑖, 𝑞 ≠ 𝑖  𝑎𝑛𝑑  𝐴𝑛𝑠𝑤𝑒𝑟 = 𝑓𝑎𝑙𝑠𝑒
1 − 𝜋!"#$%&!%' (𝑞)where, π!"#$%& is what the adversary is going to learn, i isthe location the adversary is learning about, and q is thelocation the adversary is asking about (hence, i and q are
indices in 𝜋).
Having determined the knowledge gained by theadversary after the submitted answer, we update theadversary profile (P!"#$%&!%' ) by updating all the possible
transitions (pt)  in P!"#$%&!%' that could have been made toreach the current period. The new adversary profile is thus
calculated as follows:!"!"#(P!"#$!"#$
pt

P!"#$%&!%' =   !"#$π!"#$%& i ∗   P!"#$%!&' (pt, i))!

!"#$where P!"#$%!&' (pt, i) is the adversary profile with only theelement at (pt, i) incremented by one transition. A newstationary distribution π!"#$%&!%' is then calculated from the
updated  P!"#$%!"#$ . The same steps are repeated every time a
new answer is given. After every answer, we quantify theprivacy level of the user. In a given time period, the user’s
top visited location is identified, meaning that the mostvisited location in a specific time period in π!"#$%&!%' is thesame as the most visited location in the same time period in
π!"#$ . We consider a user’s most likely trace to be exposed or
breached (privacy level) if the adversary was able to identify
the top visited location in each period of the available four
periods. The more locations identified, the higher the privacy
breach.
III.

!
!!! !∗!"#$%&  !"  !"#$"  !"#!  !  !"#$%&"'(  !"#$%!&!#"  !"  !  !"#$%  !"#$!

!"!#$  !"#$%&  !"  !"#$"

!"

∗   

would ask, on average, 12 questions/day. Every question
asks about a location i.e. are you in location X? X can be any
location that the user visited in the past, in addition to otherlocations that the user might visit. In our experiments we set
the number of locations to 10. Hence, the maximum numberof locations visited by our volunteers was 6. The aim of thelocation privacy module is to provide answers to the app
within the corresponding QoS limitations while achieving agood privacy level (few locations identified as mentioned in
the end of section II).
We considered two types of adversaries: Regular and
Malicious. A Malicious adversary targets in his questions the
top visited locations by the user in each time period based on
his findings. For example, if an attacker noticed that 80% of
a user’s presence in morning is in location x, the adversary
will target this location. Questions about such locations will
be increased in number and rate. Consequently, he canidentify the user trace faster. On the other hand, a Regular
adversary does not bias his questions.
We tested our privacy module using three QoS levels:
70%, 85%, and 90%. In total 6 experiments were conductedby switching between the regular and malicious adversary
and the various QoS levels.
After each month of testing, we calculate the average
number of locations identified for a user by the adversary.
Since the maximum breach is to identify the four mostvisited locations by the user, we calculate the average asfollows:

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2015

.

To know the maximum breach level for a user profile
reached by the adversary throughout the whole testing
period, we report the highest breach level reached across themonths by both malicious and regular adversary. Figure 1
shows the maximum an adversary (Malicious or Regular)
can know (highest breach level reached) vs. the 3 QoS levels
used. As shown, our privacy module achieved 60% privacy
level with 90% QoS level (40% breached). We can noticethat as the QoS increases, the privacy level decreases.
Moreover, results show that profile knowledge acquired by
the adversary in one month is not maintained in thefollowing months. This is in part due to the lying strategy
adopted by the location privacy module when the user iscategorized to be in a danger zone.

EXPERIMENTAL RESULTS

Ten student volunteers from AUB were provided with an
Android application to record their traces for a period of 10
months. 6 months were used to build their real profiles( P!"#$ ). The other 4 months were used to test theeffectiveness of the privacy module. Since there are no clear
statistics to indicate the number of questions a normal app
might ask and at what rate, we assumed that the application
Figure 1. Max privacy breach level vs. QoS level

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REFERENCES
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[3]

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[6]

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Lettre_IP_N-8-Mobilitics.pdf
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logy_strategy_consumer_products_trust_advantage_win_big_data/?chapter=2
Nadkarni, Adwait, and William Enck. "Preventing accidental data
disclosure in modern operating systems." Proceedings of the 2013
ACM SIGSAC conference on Computer & communications security.
ACM, 2013.
Leontiadis, Ilias, et al. "Don't kill my ads!: balancing privacy in an
ad-supported mobile application market." Proceedings of the Twelfth
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2012.
Beresford, Alastair R., et al. "MockDroid: trading privacy for
application functionality on smartphones." Proceedings of the 12th
Workshop on Mobile Computing Systems and Applications. ACM,
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Hornyack, Peter, et al. "These aren't the droids you're looking for:retrofitting android to protect data from imperious applications."
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[8]

Beresford A, Stajano F (2003) Location privacy in pervasive
computing. Pervasive Computing, IEEE 2: 46-55.
[9] Gedik B, Liu L (2005) Location privacy in mobile systems: A
personalized anonymization model. In: Distributed Computing
Systems, 2005. ICDCS 2005. Proceedings. 25th IEEE International
Conference on. Ieee, pp. 620-629.
[10] Zhong G, Goldberg I, Hengartner U (2007) Louis, lester and pierre:
Three protocols for location privacy. In: Proceedings of the 7thinternational conference on Privacy enhancing technologies.
Springer-Verlag, pp. 62-76.
[11] Mascetti S, Freni D, Bettini C, Wang X, Jajodia S (2011) Privacy ingeo-social networks: proximity notification with untrusted service
providers and curious buddies. The VLDB JournalThe International
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[12] Reades J (2010) Finite state machines: preserving privacy when datamining cellular phone networks. Journal of Urban Technology 17: 2940.
[13] Monreale A, Andrienko G, Andrienko N, Giannotti F, Pedreschi D, etal. (2010) Movement data anonymity through generalization.
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[14] Sweeney, Latanya. "k-anonymity: A model for protecting privacy."International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems 10.05 (2002): 557-570.
[15] Gedik, Bugra, and Ling Liu. "Location privacy in mobile systems: A
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[16] Shokri, Reza, et al. "Quantifying location privacy." Security andPrivacy (SP), 2011 IEEE Symposium on. IEEE, 2011.
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Poster Session 1 :: April 8

In conclusion, regardless of the adversary type and the
QoS level used, the adversary was only able to know
maximum 40% of a user profile on average, after 4 months.
We can deduce that the adversary cannot maintain theknowledge achieved across time, and hence the locations
that were identified were identified based on pure luck andthis will remain the case throughout the months to come.

94

Characterizing preferences and returns in humanmobility
Hugo Serrano∗1 , Fernando Buarque de Lima-Neto†2 ,Alexandre Evsukoff‡3 , and Ronaldo Menezes§1
1

BioComplex Lab, Computer Science, Florida Institute of Technology, USA

Poster Session 1 :: April 8

2015

2 Computational Intelligence Research Group, Polytechnic School of Pernambuco, Brazil
3 COPPE, Federal University of Rio de Janeiro, Brazil

In the past few years, our understanding of the fundamental laws of human mobility
has improved considerably thanks to the increasing availability of time-resolved humanmobility data, such as Call Detail Records (CDRs) [3, 7], credit card transactions [6] andlocation-based services [4, 2] data.
One fundamental characteristic of human mobility, called preferential return (PR),was proposed by Song et al. [11] and it models the strong tendency of humans to returnto previously visited locations. More precisely, it defines the probability Πi for returning
to a location i as Πi = fi , where fi is the visitation frequency of the location i. It implies
that the more visits a location receives, the more visits it is going to receive in the future,
which in different fields goes by the names of Mathew effect [8], cumulative advantage [9],or preferential attachment [1]. Although the focus of the PR mechanism (as part of the
Individual Mobility–IM model) was to reproduce some of the scaling properties of humanmobility, its general principles are grounded on plausible assumptions from the humanbehavior point of view.
However, in the long-term, the PR assumption as a property of human motion leads
to two discrepancies. First, the earlier a location is discovered, the more visits it is goingto receive. Or, in other words, the first visited location will always be the most visited
one. Second, if the cumulative advantage indeed holds true for human movements, people
would never change their favorite restaurant, change jobs or move to a different city, which
is clearly not true.
In this work, we explore the visitation return patterns under a temporal perspective.
We analyzed different ranking approaches and tested their respective correlations withthe return probabilities. Our approach is based on the premise that the longer the timesince the last visit to a location, the lower is the probability of observing a user at thislocation [11, 3], as depicted in Fig. 1A. The proposed approach overcomes the those∗hbarbosafilh2011@my.fit.edu
fbln@ecomp.poli.br‡
alexandre.evsukoff@coc.ufrj.br§
rmenzes@cs.fit.edu
†

1

95

two limitations of the PR mechanism by giving a higher importance to recently visited
locations.
Our findings are based on the analysis of 6 months of anonymized mobile phone datafrom Brazil,1 The fundamental question our approach answers is: from the visitation
frequencies point of view, what is the most likely destination of a person? One of themost visited places but whose last visit was a long ago, or a recently discovered place?
To answer these questions we compared two different visitation ranks, one based on
the visitation frequencies (kf ) and the other based on the recency (ks ), measured as afunction of the elapsed time (i.e., number of steps) since the last visit to a location. Bothranks were measured in a rolling basis from the accumulated sub-trajectories. The mostvisited location will have kf = 1 whereas the most recently visited location will have arank ks = 1. For each return, we collected both location ranks. For instance, a returnto the 10th most visited place right after visiting it is accounted as the pair (kf = 10
, ks = 1). Conversely, a return to the most visited place (e.g., home) after 10 steps isrepresented by (kf = 1, ks = 10).
When we look at the distribution of the two variables, we can see that most of the
returns are concentrated to recently and frequently visited locations (Fig. 1B). As we
can see, both rank distributions can be better approximated by similar truncated powerlaws. The lower limits of the heavy-tails ( ks min = 4 and kf min = 2) suggest that thevisitation patterns to the most visited location and the last 3 steps in human trajectories
are different and deserve further investigation.
Inspecting the returns heat map (Fig. 1C) we can see that even though the twovariables are positively correlated, for the most visited sites (first column of the heatmap) the return data points are heavily clustered around low ks values, indicating that
sub-trajectories starting and finishing at these locations are much shorter than of theother points. It is consistent with human travel patterns: on a daily basis we tend toreturn back to home [5, 10]. On the other hand, when it comes to the recently visited
locations, the data points corresponding to ks ≤ 3 (bottom-most rows in the plot) spanover a large range of kf values, supporting our hypothesis that the most recent visits
have an important role in determining our future steps. Not surprisingly, the particularrange of values of ks corresponds to recurrent visits to low-ranked locations which is the
expected outcome of long-term changes in users’ visitation patterns, such as due to arelocation or new visitation preferences.
To measure to what extent users tend to return to recently-visited places, we definedΠs (r) = p(kf = r | ks = 1) as the probability of returning to the rth most-visited locationright after visiting it (ks = 1) and Πf (r) = p(ks = r | kf = 1) as the probability of
returning to the most visited location (kf = 1) after r steps since the last visit to it.
The next step is to compare these two probabilities and how they vary with r. For
such we defined ρ(r) simply asρ(r) =p(kf = r | ks = 1)
Πs (r)=.
Πf (r)p(ks = r | kf = 1)

Poster Session 1 :: April 8

2015

(1)

A ρ(r) > 1 suggests a preference for recently visited locations. On the other hand,p(r) < 1 implies that Πf (r) > Πs (r) and hence a preference for highly visited locations.
1

Approximately 8.9 million records of 30,000 randomly sampled users from one of the largest phone
carriers in Brazil.

2

96

Poster Session 1 :: April 8

2015

Figure 1: Return characterization A. Return probabilities as a function of the elapsed
time ∆t since the last visit. Peaks are observed at 24h intervals, capturing the temporal
regularity of which humans return to previously visited locations. Also, it is possible to seethat the return probability decays very quickly as the time increases, which corroborates
with our main hypothesis. B. Return ranks (probability density function). Both
distributions can be better approximated by truncated power laws (dashed lines). The
recency-based rank has exponents αKs = 1.64 and exponential cut-off κKs = 40.94 ,whereas the frequency-based rank distribution has a better fit for αKf = 1.86 with κKf =36.88. C. Return ranks. Each point represents a return step, whereas the color encodes
the density of points. This plot represents the probability of returning to a location ofrank kf = Kf and ks = Ks . D. Recency over frequency. From the empirical data,
we can see that a recent visit to place increases significantly the probability of returning
to it. That is true especially for locations with kf ≥ 4.

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The case where ρ(r) = 1 means that both ranks have the same influence on the visitation
probabilities (null hypothesis).
From the empirical data, our analyses on the behavior of ρ(r) suggest that the preference for recently visited locations can be more than 50 times higher than the visitation
frequency which corroborates with our thesis that visitation preferences are biased infavor of the recent past trajectory. A further investigation on the ρ(r) value–including
other datasets–has shown that the range of kf < 4 in which the visitation frequency
seems to have a stronger importance than the recency, is due to the visits to the mostvisited location. When we analyze the same data after removing the most visited location(most likely the users’ homes), the influence of the recency becomes even more evident
with ρ(r) > 1 for all values of r.
In this work we explored the fundamental mechanisms of human preferences andreturns. Our results has shown that a recent visit to a location increases dramatically theprobability of returning to it in a near future, supporting the assumption of a preference
for recently visited locations, regardless their frequency-based ranks. The results can beused to explain not only long-term changes in visitation preferences but also short-termtransient trajectory changes such as temporary relocation and seasonal trends.

Poster Session 1 :: April 8

2015

References[1] A.-L. Barabási and R. Albert. Emergence of scaling in random networks. Science,
page 11, Oct. 1999. doi: 10.1126/science.286.5439.509.
[2] E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility. In Proceedings ofthe 17th ACM SIGKDD international conference on Knowledge discovery and data
mining - KDD ’11, page 1082, New York, New York, USA, 2011. ACM Press. ISBN
9781450308137. doi: 10.1145/2020408.2020579.
[3] M. C. González, C. A. Hidalgo, and A.-L. Barabási. Understanding individual humanmobility patterns. Nature, 453(7196):479–482, June 2008. ISSN 1476-4687. doi:
10.1038/nature06958.
[4] P. Grabowicz, J. Ramasco, B. Gonçalves, and V. Eguı́luz. Entangling mobility andinteractions in social media. PloS one, pages 1–16, 2014.
[5] S. Hasan, C. M. Schneider, S. V. Ukkusuri, and M. C. González. Spatiotemporal
Patterns of Urban Human Mobility. Journal of Statistical Physics, 151:304–318,
2012. ISSN 0022-4715. doi: 10.1007/s10955-012-0645-0.
[6] C. Krumme, A. Llorente, M. Cebrian, A. S. Pentland, and E. Moro. The predictability of consumer visitation patterns. Scientific reports, 3:1645, Jan. 2013.
ISSN 2045-2322. doi: 10.1038/srep01645.
[7] X. Lu, E. Wetter, N. Bharti, A. A. J. Tatem, and L. Bengtsson. Approaching thelimit of predictability in human mobility. Scientific reports, 3:2923, Jan. 2013. ISSN
2045-2322. doi: 10.1038/srep02923.

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2015

[9] D. Price. A general theory of bibliometric and other cumulative advantage processes.
Journal of the American Society for Information . . . , 1976.
[10] C. M. Schneider, V. Belik, T. Couronné, Z. Smoreda, M. C. González, andT. Couronne. Unravelling daily human mobility motifs. Journal of the Royal
Society, Interface / the Royal Society, 10:20130246, 2013. ISSN 1742-5662. doi:
10.1098/rsif.2013.0246.
[11] C. Song, T. Koren, P. Wang, and A.-l. Barabási. Modelling the scaling properties ofhuman mobility. Nature Physics, 6(10):818–823, Sept. 2010. ISSN 1745-2473. doi:
10.1038/nphys1760.

Poster Session 1 :: April 8[8] R. K. Merton. The Matthew Effect in Science. Science (New York, N.Y.), 159:
56–63, 1968. ISSN 0036-8075. doi: 10.1126/science.159.3810.56.

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2015

Utilizing Origin Destination Information obtained from Mobile Phones in
Equilibriated Path Choice1

Poster Session 1 :: April 8

Serdar Çolak1, ∗ and Marta C. González1, 2
Civil and Environmental Engineering, MIT
2
Engineering Systems Division, MIT(Dated: January 30, 2015)

Congestion is an inherent problem in networks on which agents compete for limited resources andincurred costs are a function of how many people choose to use a supply element. In the context
of mobility on road network, we can elaborate this problem as follows: Any driver with a specificdestination makes a choice about the route they will take, most often depending on their utility
which by definition tries to reflect their preferences, hence utility is inversely proportional to thetravel time of the chosen route. When a driver makes this choice, the total cost incurred to thewhole system is not only the travel time experienced by that driver, but also the marginal costthat one driver creates on all other users of that road segment. In this work, we begin by mining
billions of mobile phone call records to obtain a typical morning commute and validate our findings
against surveys. Then we make use of the existing literature to implement a framework to solve theproblem of static path selection and how it influences overall congestion. We compare our findings
with previously used, unequilibriated traffic assignment methods.
I.

INTRODUCTION

In this work, our goal is to utilize an equilibriated traffic assignment procedure to analyze the effects of individualistic path choice on congestion. In addition, we aim
to build on the routing framework to further analyze urban mobility from a sociodemographic perspective to try
and answer questions that relate commuting times, pathchoices, and roads used to income, population density
and other similar qualities.
In the US the first works using mobile phone data
for transportation applications refer to traffic monitoring. Departments of Transportation in different states
carry out these studies in collaboration with private data
providers. Less is known about how the massive amountof information hidden in several months of anonymized
mobile phone bills; also known as call detailed records(CDRs), can support the models of travel behaviors.
Here we implement a method of using CDRs that canbe used to extract ODs by purpose and time of the day.
The presented results are validated against surveys andexisting ODs available from the local DOT. We show therobustness of the method comparing the results over two
types of phone data sets and in two cities.
In road networks, travel time is known to be a functionof the flow on that road segment. Though many expressions of this relationship exist, we will use the most common choice in the literature: The BPR function relating
the observed travel time te on an edge e with capacity
ke , free travel time te0 , and xe vehicles traveling on it is, β !
xe.
(1)
te (xe ) = te0 1 + αke∗ serdarc@mit.edu

where generally α = 0.15 and β = 4.
With the assumption that the observed cost for allusers is equivalent to the observed travel time, we candefine the total cost incurred by all users in a networkas,total cost = C =X

xe te (xe )

(2)

e∈E

The flow configuration obtained by trying to minimize
C to decrease the average time an average user will spend
for their trip is referred to as the socially optimal flows.
However, it is known that the actual flow configuration
is far from optimal. As drivers make selfish choices, they
push the system away from optimality. The set of flowsthat occur when every driver individually and selfishly
minimizes their own cost is referred to as the user equilibrium flows. This problem is actually a routing game,where players are the drivers and their strategy spaces areavailable paths between their origin and destination. In
this context, a Nash equilibrium refers to a set of strategy
choices of every user that results in a final outcome where
no user can unilaterally deviate from their strategy to geta better payoff, which in the context of road networks isless travel time. These principles have also been summarized under Wardrop’s principles [1] in transportation
literature.
In solving for equilibrium flows, potential functions are
made use of. By definition, a potential game is a gamethat can be represented by a potential function that when
optimized yields Nash equilibriaof the problem at hand.
0
A function φeR(xe ) such that φe (xe ) = te (xe ) can be foundxby φe (xe ) = 0 e te (x)dx. One can therefore write a potential function for the user equilibrium problem as follows:100Φ(f ) =X Z xee∈E=Xte (x)dx0

te0 xe1 + 0.03

e∈E



xe
ke

4 !

(3)

Minimization of this function results in equilibrium
flows.
Bx
1 + 100

2
0.25

A

D
x
1 + 100

2
C

FIG. 1. Example network and an analysis of its equilibriumand optimal flows. For a demand of 100 drivers going fromnode A to node D, user equilibrium would allocate the flows
between paths ABD, ACD and ABCD such that the travel
times are equal: 25 people each choose paths ABD and ACD,
and 50 people choose path ABCD; resulting in an average
travel time of 3.75 for all drivers. Optimal flows would minimize total travel time, yielding in 50 people each on paths
ABD and ACD, and zero people on path ABCD; resulting
in an average travel time of 3.5 for all drivers. The factthat edge BC remains unused captures what’s known as theBraess’ Paradox [2]; when additional capacity does not decrease social cost. The price of anarchy under these conditions
is 3.75/3.5 = 1.07.

II.

METHODOLOGY

Current methods to solve the traffic assignment problem vary in their approaches: Typical gradient descenttype algorithms, although fast to solve the problems, do
not help with the generation of the possible paths but
only generate aggregate link flows that path flows create.
On the other hand as network sizes increase, solving explicitly for paths becomes infeasible very quickly. Therefore algorithms that can efficiently compute desired equilibrium path and link flows are preferred. The literaturespans many such algorithms for this purpose, with mostbeing derivatives of three main sets of methods: Link
based, path based, origin based. Due to it’s advantages
in generating paths, fast convergence and efficiency, we
utilize an origin-based approach in this work. We will
follow Algorithm B, proposed in [3] along with modifications and improvements outlined in [4], an origin basedalgorithm that focuses on the equilibriation of a graphstructure referred to as a bush, a directed acyclic graph(DAG) emanating from every origin node. These structures are used with the assumption that in the equilibrium flows, no directed cycles should exist. In fact thecomputational efficiency of this algorithm stems from thisproperty, as DAGs can be traversed in linear time.
III.

RESULTS

We began by comparing our findings of demand fromthe mobile phone data in Fig. 2. Our findings are in good
agreement with the results obtained from traditional surveys. Comparison of the commuting trips for each origindestination pair in the morning peak shows strong correlations for both inter-town and intra-town trips, reaching ρ = 0.84 for Rio de Janeiro and ρ = 0.99 in Boston.
Fig. 2 also illustrates spatially the flow distribution of
the model and the CDR ODs for both cities by mappingcolor-coded and width adjusted lines between OD pairs
whose flow values exceed 0.10% of the total study area
trips. By visual inspection, it can be said that CDR data
manages to capture the flow distribution of that of themodel ODs, with majority of the flows concentrated towards downtown in Boston and downtown and across thebay in Rio de Janeiro.
Theoretically, for a user-equilibrium solution, the total
cost obtained by the travel time between an origin anddestination multiplied by the demand for that pair shouldbe equal to the flow on every link multiplied by the travel
time on that link; as both measure the total travel time.
To assess convergence, we use the following measure:

relative gap = 1 −

P

o,d
P

traveltimeod ∗ demandodi,j traveltimeij ∗ f lowij

Poster Session 1 :: April 8

2015

2

(4)

Fig. 3 depicts the convergence of Algorithm B compared to ITA, the incremental assignment approach, aswell as a more reasonable and accurate VoC (volume overcapacity) for Boston. These findings are preliminary,
but do have implications in further usage of user equilibrium models for assignment procedure. Consequently,
it is possible to carry out further analysis about path selection, and the effects of urban sociodemographics oncongestion.

101[1] John Glen Wardrop. Road paper. some theoretical aspectsof road traffic research. In ICE Proceedings: Engineering Divisions, volume 1, pages 325–362. Thomas Telford,
1952.
[2] Dietrich Braess, Anna Nagurney, and Tina Wakolbinger.
On a paradox of traffic planning. Transportation science,
39(4):446–450, 2005.

[3] Robert B Dial. A path-based user-equilibrium traffic assignment algorithm that obviates path storage and enumeration. Transportation Research Part B: Methodological, 40(10):917–936, 2006.
[4] Yu Marco Nie. A class of bush-based algorithms for the
traffic assignment problem. Transportation Research Part
B: Methodological, 44(1):73–89, 2010.

Poster Session 1 :: April 8

2015

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Poster Session 1 :: April 8

2015

4

FIG. 2. The flow distributions in Rio and Boston as a comparison between those obtained from the mobile phone data andthose from the survey. The correlations for the subdistrict level and the town level are depicted in the legend, for Rio andBoston, respectively.
Boston

100

0.7
0.6

10−1

0.4

P (V oC)

Relative Gap

0.5

0.3
0.2

10−3

AlgB
ITA (6 cores)

0.1
0.0

10−2

0

5

10

AlgB, µ =0.52
ITA, µ =0.77
15Time(mins)

20

25

30

10

−4

10−2

10−1

100

101

V oC

FIG. 3. The performance of algorithm B achieves higeher convergence considerable quickly, as well as returning a considerably
better Voc (volume over capacity) distribution for Boston.

103

2015

Land	
  use	
  classification	
  using	
  call	  detail	
  records	
  
1

1

1

1

1

LIRNEasia,	
  12	
  Balcombe	
  Place,	  Colombo	
  8,	
  Sri	  Lanka	
  Colombo	
  is	
  the	
  largest	
  city	  in	
  Sri	  Lanka	  by	
  population	  and	
  economic	
  activity.	
  With	  the	  post-­‐war	
  infrastructure	
  boom,	  land	  use	  patterns	
  in	
  Colombo	
  are	
  changing	
  quickly,	
  with	
  residential	
  population	
  continuously	
  decreasing	
  making	
  way	
  for	
  commercial	
  activities.	
  Having	
  an	
  up-­‐to-­‐date	
  overview	
  of	
  
land	
  use	
  characteristics	
  is	
  critical	
  in	
  for	
  urban	  planning,	
  and	  traditional	
  survey	
  and	
  census-­‐based	
  methods	
  in	
  addition	
  to	
  being	  expensive	
  cannot	  give	  high-­‐frequency	
  insights.	
  Mobile	
  network	
  big	
  data	  could	  be	
  utilized	
  as	
  a	
  low-­‐cost	
  and	
  high	  frequency	
  alternative	  for	
  understanding	
  changes	
  in	
  land	  use.	
  Our	
  method	
  potentially	
  goes	
  further	  than	
  previous	
  efforts,	
  by	
  allowing	
  for	  a	
  more	  fine-­‐grained	
  understanding	
  of	
  land	  use	  changes.	
  	
  Whenever	
  a	
  mobile	
  phone	
  is	
  used	  to	
  make	  or	  receive	  a	
  call	  or	
  SMS,	
  or	
  to	
  access	
  the	  Internet,	
  the	
  event	
  is	
  captured	
  via	
  meta-­‐data	
  stored	
  in	
  
the	
  operator’s	
  logs.	
  This	  is	
  called	
  a	
  Call	  Detail	
  Record	  (CDR).	
  The	
  CDR	
  also	  contains	
  the	  location	
  of	
  the	
  Base	  Transceiver	
  Station	
  (BTS)	
  that	
  services	
  the	
  subscriber,	
  as	
  well	  as	
  timestamp	
  for	  the	
  event.	
  Using	  these	  records	
  
it	
  is	
  possible	
  to	
  get	  a	
  time-­‐series	
  of	
  activity	
  at	
  
each	
  BTS.	
  Building	  on	
  prior	
  work	  [1,	  2,	  3,	  4],	  this	
  usage	
  pattern	
  of	
  BTS	
  activity	
  can	  be	
  leveraged	
  to	
  understand	
  the	
  characteristics	
  of	
  
the	
  land	  beneath	
  it.	  	
  For	  each	  BTS,	
  we	
  utilize	
  a	
  month	  of	
  CDR	
  data	  to	
  construct	
  a	
  time-­‐series	
  measure	
  of	
  the	
  number	  of	
  users	
  connected	
  to	
  the	
  BTS	  at	
  any	  given	
  time.	
  We	
  then	  project	
  this	  data	  into	  a	
  7-­‐day	
  week,	  excluding	
  data	  from	  public	
  holidays	  since	
  human	
  dynamics	
  change	
  dramatically	
  during	  holidays.	
  For	
  each	  BTS	  we	
  then	
  end	
  up	
  
with	
  a	
  time-­‐series	
  measure	
  for	
  each	  day	  of	
  the	  week,	
  where	
  the	
  diurnal	
  activity	
  pattern	
  for	  that	
  day	  is	
  an	
  average	
  of	
  all	  the	
  same	  weekdays	
  in	
  that	  month.	
  	
  Projecting	
  the	
  user	  data	  of	  a	
  month	  to	  a	
  week	  results	
  in	
  a	
  unique	  ‘signature’	
  of	
  mobile	
  usage	  at	
  each	  site	  as	
  seen	  in	
  Figure	
  1.	
  A	
  vector	
  of	
  
168	
  elements	
  represents	
  the	
  time-­‐series	
  signature	
  of	
  a	
  BTS.	
  This	  signature	
  provides	
  a	
  
good	
  measure	
  of	
  the	
  variation	
  in	
  people’s	
  behaviors	
  between	
  days	  and	
  even	  between	  the	
  hours	
  of	
  each	  day.	
  	
  Poster Session 1 :: April 8

Kaushalya	
  Madhawa ,	
  Sriganesh	
  Lokanathan ,	  Danaja	  Maldeniya ,	  Rohan	  Samarajiva 	
  	
  Figure	  1:	
  Hourly	
  user	  distribution	
  for	  a	
  week	  fro	  a	
  sample	  BTS	  The	
  actual	
  number	
  of	
  users	
  connected	
  to	
  a	
  
BTS	
  at	
  any	
  given	
  time	  depends	
  on	
  the	  area	  it	
  covers	  and	  the	
  place	
  it	
  is	
  located	
  at.	  To	
  remove	  these	  biases	
  we	
  normalized	
  the	
  hourly	
  user	
  distribution	
  by	
  taking	
  the	  z-­‐score.	
  The	  diurnal	  signature	
  of	
  each	  BTS	
  also	  contains	
  occasional	
  noise	
  that	  could	
  affect	
  the	
  land	  use	  classification.	
  Using	
  methods	
  articulated	
  in	  [1]	  to	
  deal	  with	  noise	  in	
  Wi-­‐Fi	
  signals,	
  we	
  remove	
  the	
  random	
  variations	  in	
  our	
  normalized	  diurnal	
  data	  by	
  decomposing	
  the	  covariance	
  matrix	  of	
  user	  time	  series	
  data	  into	  a	
  combination	
  of	
  its	
  principal	
  components.	
  	
  
Each	
  time	  series	
  vector	
  for	  a	  BTS	
  can	  be	
  represented	
  by	
  a	
  linear	
  combination	
  of	
  its	
  principal	
  eigenvectors	
  as,	  (1)	  𝑇! = 𝐶!! 𝑉! + 𝐶!! 𝑉!    + ⋯ +    𝐶!" 𝑉! 	
  	
  	
  

Here	
  𝑇! 	
  represents	
  the	
  time	  series	
  of	
  a	
  BTS	
  i,	
  
and	
  𝐶!! 	  represents	
  the	
  correlation	
  of	
  the	
  first	  eigenvector	
  𝑉! and	
  so	
  on.	  The	
  first	  eigenvector	
  accounts	  for	  the	
  general	
  diurnal	
  pattern	
  of	
  the	
  city.	
  The	
  first	
  fifteen	
  eigenvectors	
  of	
  the	  covariance	
  matrix	
  is	
  capable	  of	
  representing	
  95%	  of	
  the	
  variation	
  in	
  the	
  time	  series	
  data.	
  	
  
Then	
  the	
  time	  series	  distributions	  are	  redistributed	
  using	
  only	  the	
  first	
  fifteen	
  eigenvectors.	
  104

Land	
  use	
  classification	
  The	
  time	  series	
  of	
  each	  BTS	  is	
  then	
  assigned	
  to	
  
one	
  of	
  three	
  clusters	
  using	
  a	  k-­‐means	
  algorithm	
  considering	  each	  reconstructed	
  time	
  series	
  as	
  a	
  168-­‐dimensional	
  vector.	
  We	
  use	  the	
  signature	
  of	
  cluster	
  centroids	
  to	
  infer	  the	
  actual	
  land	  use	
  category	
  that	  each	  cluster	
  belongs	
  to.	  Whilst	
  prior	
  work	  [4]	
  had	
  identified	
  more	  than	  three	
  clusters,	
  we	
  find	  that	
  interpretation	
  of	
  the	  these	  additional	
  categories	
  is	
  problematic	
  for	
  our	
  data,	  especially	
  when	  we	
  validate	
  using	
  known	
  land	  use	
  characteristics	
  in	
  Colombo.	
  	
  Poster Session 1 :: April 8

2015
The	
  centroid	
  pattern	
  of	
  Cluster-­‐3	
  is	
  consistent	
  with	
  the	  dynamics	
  of	
  a	
  commercial	
  region,	
  since	  more	  users	
  are	
  detected	
  during	  weekdays	
  than	
  weekends	
  and	  the	
  daily	
  peak	  occurs	  during	
  the	  daytime.	
  Similarly	
  the	
  centroid	
  pattern	
  of	
  Cluster-­‐2	
  is	
  consistent	
  with	  the	
  dynamics	
  of	
  residential	
  regions,	
  with	  weekday/	
  weekend	
  variations	
  being	  minimal	
  and	
  the	
  daily	
  peak	  occurring	
  during	
  the	
  late	  evening	  each	  day.	
  Cluster-­‐2	
  is	
  classified	
  as	
  belonging	
  to	
  areas	
  of	
  mixed-­‐use	
  land	  characteristics,	
  since	
  there	  are	  no	
  discernable	
  variations	
  in	
  the	
  time	  of	
  the	
  daily	
  peaks	  or	  between	
  weekdays	
  and	
  weekends.	
  	
  Figure	  2:	
  Distribution	
  of	
  clusters	
  in	
  Colombo	
  district.	
  Cluster-­‐3	
  (Commercial)	
  areas	  are	  in	
  red,	
  Cluster-­‐2	  (residential)	
  areas	  are	  in	
  blue,	  and	
  Cluster-­‐1	
  (mixed-­‐use)	
  areas	  are	  in	
  green.	
  Areas	
  with	  blue	  dots	  signify	
  a	
  BTS	  signature	
  closer	
  to	
  Cluster-­‐2	
  than	  Cluster-­‐3.	
  Areas	
  with	  red	
  dots	  signify	
  a	
  BTS	
  signature	
  closer	
  to	
  Cluster-­‐3	
  than	  Cluster-­‐2	
  	
  	
  	
  

The	
  spatial	
  distribution	
  of	
  clusters	
  is	
  shown	  in	
  Figure	
  2.	
  	
  
Cluster-­‐1	
  also	
  exhibits	
  the	
  least	  average	
  silhouette	
  value	
  as	
  compared	
  to	
  the	
  other	
  two	  clusters.	
  We	
  then	  calculate	
  the	  distance	
  from	  each	
  point	
  in	
  Cluster-­‐1	
  to	
  the	
  centroids	
  of	
  other	
  two	
  clusters.	
  The	
  ratio	
  of	
  the	  distance	
  from	
  the	
  centroid	
  of	
  a	
  Cluster-­‐1	
  area	  to	
  the	
  centroids	
  Cluster	  2,	
  and	
  Cluster	
  3,	
  is	
  used	  to	
  understand	
  how	  the	
  mixed-­‐use	
  regions	
  compare	
  with	  other	
  two	
  categories.	
  	
  
An	
  analyses	
  of	
  these	
  ratios	
  for	
  each	  Cluster-­‐1	  area	
  (i.e.	
  mixed	
  use),	
  suggests	
  that	  mix-­‐used	
  regions	
  neighboring	
  a	
  commercial	
  area	  exhibit	
  

	
  

a	
  pattern	
  more	  similar	
  to	
  the	
  commercial	
  pattern	
  than	
  a	
  residential	
  pattern.	
  The	
  inverse	
  seems	  to	
  hold	  true	  for	
  mixed-­‐use	
  areas	  adjacent	
  to	
  mainly	
  residential	
  areas.	
  Future	  Work	  We	
  plan	  to	
  use	
  this	  technique	
  repetitively	
  
over	
  a	
  longer	
  time	  period	
  to	
  investigate	
  if	
  the	  classifications	
  of	
  the	
  silhouettes	  (blue	
  and	
  red	
  
dots	
  in	
  Figure	  2)	
  show	  changes.	
  This	  might	  reveal	  a	
  high-­‐frequency	
  measure	
  of	
  the	  changes	  in	
  land	  use	
  than	  is	
  possible	
  using	  just	  the	
  three-­‐cluster	
  classification.	
  	
  	
  

	
  

105

2015
References	
  

[2]	
  
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.	
  
[3]	
  
G.	
  Andrienko,	
  N.	
  Andrienko,	
  M.	
  Mladenov,	
  M.	
  Mock,	  and	  C.	
  Politz,	
  “Discovering	
  bits	  of	
  place	
  histories	
  from	  people’s	
  activity	
  traces,”	
  in	
  2010	
  IEEE	  Symposium	
  on	
  Visual	
  Analytics	
  Science	
  and	  Technology,	  2010,	
  pp.	
  59–66.	
  
[4]	
  
V.	
  Soto	  and	  E.	
  Frias-­‐Martinez,	
  “Robust	
  land	  use	
  characterization	
  of	
  urban	
  landscapes	
  using	
  cell	
  phone	
  data,”	
  In	
  Proceedings	
  of	
  the	
  1st	  Workshop	
  on	
  Pervasive	  Urban	  Applications	
  (Purba’11),	
  2011.	
  Poster Session 1 :: April 8

[1]	
  
F.	
  Calabrese,	
  J.	
  Reades,	  and	  C.	
  Ratti,	
  “Eigenplaces :	  Segmenting	  Space	  Through	
  Digital	  Eigenplaces :	  Segmenting	  Space	  through	
  Digital	
  Signatures,”	
  2014.	
  

	
  

106

Impact of Indoor-Outdoor Context on Crowdsourcing
based Mobile Coverage Analysis
Mahesh K. Marina* , Valentin Radu* , Konstantinos Balampekos†*

The University of Edinburgh

We consider the crowdsourcing based mobile cellular network measurement paradigm that is becoming increasingly popular. In particular, we aim to study the impact of user indoor/outdoor environment context at time of measurement. Focusing on signal strength as the measurement metric and using a real large crowdsourced measurement dataset for central London area along with estimated environment state (indoor or outdoor), we show that indoor-outdoor context has asignificant impact, suggesting that conflating indoor and outdoor measurements can lead to unreliable results. We validate
these observations using a set of diverse and controlled measurements with indoor/outdoor ground truth information.
Crowdsourcing [1–10] has recently emerged as a new approachfor mobile cellular network measurement and analysis. It exploits
smartphones (with built-in cellular network interface and location
sensing capabilities) as measurement sensors and the natural mobility of people carrying them for cost-effective, continual and finegrained spatio-temporal monitoring of mobile networks. The crowdsourcing approach has several advantages over existing approaches
like drive testing (e.g., [11]), coverage modelling (e.g., [12]) andnetwork-side passive analysis (e.g., [13]). It captures reality better than the coverage modelling approach; less expensive than thedrive testing approach; and unlike the network-based passive monitoring approach, it allows direct measurement at user side including
context. Also measurements with the crowdsourcing approach reflect user perceived mobile performance as they are obtained fromreal end-user devices. The foregoing discussion suggests that thecrowdsourcing approach will likely be an integral part of a broader
approach to meet the emerging mobile network measurement andmonitoring needs.
However the crowdsourcing approach also presents several challenges some of which have been discussed in [14]; one of the challenges mentioned concerns device and environment context and is
the focus of this paper: “.. it is not always clear where the device islocated when the test is made (so it could be indoors or outdoors,
in a bag or in the users hand) ..”. Gember et al. [5] have in fact
partially characterized the impact of some of the relevant contextual factors, especially device position (phone in hand or not), andshow that such factors have a significant impact on measurement
results. For example, moving the phone from hand to pocket cancause up to 79% difference in measured throughput.
In this paper, we complement the previous work in [5] by showing that whether a mobile user participating in a crowdsourced measurement system is indoor or outdoor at the time of measurement
matters significantly. This is an environment related aspect of user
context that has not received much attention till date. Not surprisingly, existing mobile crowdsourcing systems (e.g., [1, 2]) lack theindoor-outdoor detection capability.

†

Nokia Networks

To study the indoor-outdoor impact, we focus on signal strength(RSSI) as the measurement metric driven by at least three reasons.
Firstly, coverage or signal strength is not only an intuitive metricfor users but also the primary metric targeted by operators and regulators; this is clearly stated by the authors of [15] “Without exaggeration, we can say that coverage is the most important and thehighest-priority target that has to be achieved by cellular operators.”
and is also evident from [12, 16]. Secondly, signal strength is recently shown to correlate well with throughput and mobile device
battery energy drain [9, 17, 18]. Finally, unlike other performance
metrics like throughput that may require active measurement, signal strength can be measured passively with little or no impact ofdevice battery consumption; for this reason, it is also the mostwidely supported metric across all existing crowdsourced mobile
network measurement systems.
In particular, we highlight the downside of conflating indoor andoutdoor measurements. Such conflation of measurements from different environments and contexts is quite likely to happen in practice because it is typical to aggregate nearby measurements into
coarser geographic units such as grid squares or postcodes. It iseasy to find examples from real-world practice and research literature where aggregation of measurements over space is done. Ofcom’s mobile coverage analysis [12, 16] aggregates coverage predictions into postcodes and 200mx200m grid squares. WiScape [3]partitions the world into zones each around 0.2 sq. km. each. Online coverage checkers of web sites of almost all mobile operators
indicate coverage at the postcode level [19].
Our analysis is based on a large crowdsourced measurement dataset,
consisting of nearly 8 million measurements spanning over 3 years,
from OpenSignal [1] for central London. As the indoor-outdoor
context is not embedded in the dataset, we choose to rely on a GPS
based method used previously in [20] to infer whether a measurement was collected while indoors or outdoors.
We show that indoor and outdoor measurements have quite different signal strength characteristics (see Figure 1). As existing
crowdsourced measurement systems do not differentiate between
indoor and outdoor measurements and spatial aggregation of measurements is common, conflation of diverse measurements can thus
lead to erroneous conclusions — coverage outdoors is likely to beunderestimated, whereas indoor coverage may be overestimated.
Imprecision or uncertainty concerning location of a measurement
can compound this problem.
Figure 2 shows a sample result that highlights the risk of conflating indoor and outdoor measurements. We have also validated
these observations using a diverse set of controlled measurementswith indoor/outdoor ground truth information.

Poster Session 1 :: April 8

2015

107

Figure 2: Differences in indoor and outdoor average RSSI values for a selection of 25 cell sectors from the OpenSignal dataset.

Figure 1: CDF of indoor and outdoor RSSI values for a specific cell sector
and operator combination (OpenSignal dataset).

1.

REFERENCES[1] Open Signal Inc. http://opensignal.com/.
[2] MobiPerf. http://www.mobiperf.com/.
[3] S. Sen, J. Yoon, J. Hare, J. Ormont, and S. Banerjee. Can
they hear me now?: A Case for a Client-Assisted Approachto Monitoring Wide-Area Wireless Networks. In Proc. ACM
Internet Measurement Conference (IMC), 2011.
[4] BBC News. 3G Mobile Data Network Crowd-Sourcing
Survey. http://www.bbc.co.uk/news/business-14574816,
Aug 2011.
[5] A. Gember, A. Akella, J. Pang, A. Varshavsky, and
R. Caceres. Obtaining In-Context Measurements of Cellular
Network Performance. In Proc. ACM Internet Measurement
Conference (IMC), 2012.
[6] Ookla Speedtest Mobile Apps.
http://www.speedtest.net/mobile/.
[7] J. Sommers and P. Barford. Cell vs. WiFi: On the
Performance of Metro Area Mobile Connections. In Proc.
ACM Internet Measurement Conference (IMC), 2012.
[8] S. Sonntag, J. Manner, and L. Schulte. Netradar - Measuring
the Wireless World. In Proc. 9th International Workshop on
Wireless Network Measurements (WiNMee), 2013.
[9] A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao,and M. Welsh. Mobile Network Performance from User
Devices: A Longitudinal, Multidimensional Analysis. In
Proc. Passive and Active Measurement (PAM) Conference,

2014.
[10] Y. Xu, Z. Wang, W. K. Leong, and B. Leong. An End-to-End
Measurement Study of Modern Cellular Data Networks. In
Proc. Passive and Active Measurement (PAM) Conference,
2014.
[11] C. N. Pitas, A. D. Panagopoulos, and P. Constantinou.
Speech and Video Telephony Quality Characterization andPrediction of Live Contemporary Mobile Communication
Networks. Wireless Personal Communications,69(1):153174, Mar 2012.
[12] Ofcom. UK Mobile Services Map 2013. http://maps.ofcom.org.uk/mobile-services/.
[13] A. Gerber, J. Pang, O. Spatscheck, and S. Venkataraman.
Speed Testing without Speed Tests: Estimating Achievable
Download Speed from Passive Measurements. In Proc. ACM
Internet Measurement Conference (IMC), 2010.
[14] Ofcom. Measuring Mobile Voice and Data Quality ofExperience. http://stakeholders.ofcom.org.
uk/consultations/mobile-voice-data-experience/, Jan 2013.
[15] A. Galindo-Serrano, B. Sayrac, S. Ben Jemaa, J. Riihijarvi,
and P. Mahonen. Automated Coverage Hole Detection for
Cellular Networks Using Radio Environment Maps. In Proc.
9th International Workshop on Wireless Network
Measurements (WiNMee), 2013.
[16] Ofcom. Infrastructure Report: 2013 Update.
http://stakeholders.ofcom.org.uk/
market-data-research/other/telecoms-research/broadband-speeds/
infrastructure-report-2013/, Oct 2013.
[17] C. Cheng and P. Hsiu. Extend Your Journey: Considering
Signal Strength and Fluctuation in Location-Based
Applications. IEEE/ACM Transactions on Networking, 22,
Feb 2014.
[18] N. Ding, D. Wagner, X. Chen, A. Pathak, Y. Charlie Hu, andA. Rice. Characterizing and Modeling the Impact of WirelessSignal Strength on Smartphone Battery Drain. In Proc. ACM
SIGMETRICS, 2013.
[19] Mobile Coverage Checkers. http://consumers.
ofcom.org.uk/internet/mobile-internet/
mobile-coverage-checkers/.
[20] L. Ravindranath, C. Newport, H. Balakrishnan, and
S. Madden. Improving Wireless Network Performance Using
Sensor Hints. In Proc. USENIX NSDI, 2011.

Poster Session 1 :: April 8

2015

108

2015

Poster Session 2 :: April 9109

Anomaly detection in mobile phone data —Exploratory analysis using Self-Organizing Maps

Session 7 :: Cro

2015

Veena Mendiratta, Vijay K. Gurbani and Chitra Phadke

Abstract—Communications traffic on wireless networks generates large amounts of metadata on a continuous basis acrossthe various servers involved in the communication session. The
networks are engineered for high reliability and hence, the datafrom these networks is predominantly normal with a small
proportion being anomalous. From an operations perspective
however, it is important to detect these anomalies when they occur
to correct any vulnerabilities in the network. The objective of ourwork is anomaly detection in communication networks to improve
network performance and reliability. In this paper we explore
the use of neural network based Kohonen Self Organizing Maps
(SOM) applied to Per Call Measurement Data (PCMD) records
from a 4G network for data analysis and anomaly detection.

the Per Call Measurement Data (PCMD) of an 4G LTE system
which is a very rich source of data. To this end, in thispaper we describe work in progress which includes exploratory
data analysis; clustering and outlier detection based on neuralnetwork based Kohonen Self Organizing Map (SOM) [4].
The rest of this paper is organized as follows. Section
II gives a brief overview of the data that we used in thisanalysis. Section III presents the analysis on the data using
SOM. Related work is presented Section IV and conclusions
and future work are in Section V.
II.

I.

I NTRODUCTION

Communications networks have evolved over the years to
become large, complex and fault tolerant systems. Various
network elements in the system capture and log traffic on
a continuous basis. Given the stable nature of the networks,much of this data represents normal operations. However,
faults and errors in the network are reflected in data thatshows anomalous behavior of the network. The fault tolerant
mechanisms in the networks correct most errors and theanomalous data is typically of short duration. However, attimes, these errors may be a precursor to a larger failure in
the system. It is therefore important to detect such anomalies
in a timely fashion to detect vulnerabilities and take corrective
measures as necessary.
Anomaly detection algorithms [1], [2] can be employed
to detect anomalies in the networks. The need for detecting
anomalies in near real time necessitates the application of
anomaly detection on streaming data. But anomaly detection
for data streams remains a challenging task. Sadik et al enumerate the research issues in anomaly detection for streaming
data [3], defining streaming data as an infinite sequence ofdata points with explicit or implicit timestamps. Since communications network servers are generating data continuously,
anomaly detection on data streams would be relevant in thiscontext. Our approach is to first explore anomaly detection
algorithms with subsets of our data in batch mode and thenextend the work to streaming data. In this paper we focus ontechniques for anomaly detection when anomalies are transientand rare. The focus is on unsupervised learning techniques
since some small numbers of errors are expected in the system— considered normal — and not every error is deemed ananomaly.
The objective of our work is outlier detection in communication networks to improve network performance and reliability using performance metrics from the network, particularly

DATASET AND EXPLORATORY ANALYSISPCMD provides call measurement data on a per-procedurebasis for the important procedure interactions that a user
device (UE) has with the mobile network for voice, data
and SMS sessions in a 4G network. Examples of common
procedures triggered by the UE are: requests to register with
the network; service requests that trigger activation of radio
bearers; handovers to handle mobility and requests for releaseof a session. Each PCMD record captures key data fields
such as service type, session length, setup latency, signal
quality, data throughput, data related to handovers, the session
result code, additional levels of detail on the result code,
the sequence of intermediate procedure steps also known as
Procedure Markers (PM) required for the session, etc., thereby
providing a view into network performance. A single record istypically comprised of a large number (hundreds) of populated
fields.

Poster Session 1 :: April 8

Bell Laboratories, Alcatel-Lucent
Email: {veena.mendiratta,vijay.gurbani,chitra.phadke}@alcatel-lucent.com

For our exploratory analysis we look at the PCMD records
generated at a single server from a network that experienced
a failure. The time period of the data collection spans a few
minutes and includes: a stable period, the occurrence of the
failure event followed by a gradual recovery period, and backto a stable state. The dominant procedures (as shown in Figure1) were Procedure 6 (release), Procedure 10 (paging) andProcedure 11 (service). Due to lack of space and for illustrative
purposes we will limit our discussion in this paper to Procedure
6.
Our dataset consists of the primary and secondary fields of
PCMD records, about 250 fields in total. Guided by PCMD
domain experts, we chose the following six fields for the
analysis and modeling of anomaly detection :Duration ofProcedure; Connect Code (indicating final success/failure of
the procedure); Qualifier Primary and Qualifier Secondary,which provide further details on the resultant connect codes;
and the Sequence Index. The selection was based on theusefulness of the information in the field for determining

110Session 7 :: Cro

2015

Figure 1: Number of observations by Procedure Typethe cause of the anomalous behavior. In addition, the timingof the record as the minute relative to the start of the datacollection record was retained. This allows for differentiationof records between when the network was in a stable stateversus when the network was in a failure or recovery state.
The field Sequence Index deserves some explanation since byitself it does not occur in a PCMD record, rather it is derived
based on artifacts in the record.

Next, the SOM clustering algorithm was applied to the gridof nodes. Based on the within-cluster sum of squares (WCSS)
metric for k-means for different clustering sizes, between 8 to
12 clusters showed good results. For the case of 8 clusters,
Figure 3 shows the cluster boundaries on the grid as wellas the average value of each variable by node. The results
are interesting for anomaly detection — there is one large
cluster, as expected, and 7 small clusters of 3 nodes or 1
node each, of which 2 clusters contain no observations. This
gives 5 clusters to analyze and to study the characteristics ofthe observations to determine the causes of the anomalous
behavior. The sum of the observations in these 5 clusters
represents less than 1% of the total observations and range
between 19 and 1691 observations (412, 19, 1175, 98, 1059,
1691, 292) per cluster. However, for real-world large-scalestreaming data applications of anomaly detection it is notpractical, nor timely, to visually detect outliers. To address thisissue, future work will investigate techniques for the automatic
extraction of rules from SOMs [7].

Among other fields, each PCMD record contains up to
20 distinct Procedure Markers (PM) which are represented as
integers corresponding to the codes of intermediate events that
occur during the execution of a procedure. Based on prior field
experience with addressing failures, it is known that certain PM
sequences are likely indicators of anomalous conditions in the
network. Chandola et al. [5] indicates that often anomalies canbe found in repeating patterns (or discrete sequences) whereby
anomalies may be hidden in a pattern of sequences whose
frequency of occurrence is anomalous. Therefore, the PMs ina record were concatenated to produce a sequence. Each such
unique PM sequence was indexed and a frequency distribution
was produced for each unique PM sequence. Procedure 6 was
found to have about 100 unique sequences though only a verysmall number of sequences make up the majority of records.
III.

A NALYSIS OF RESULTS

We investigated various clustering algorithms for unsupervised learning. Given the objective of anomaly detection but
still being in the exploratory phase, we chose to work with theneural network based Kohonen Self Organizing Map (SOM)[4]. It is particularly suited to discovering input values thatare novel and for the visualization of otherwise difficult tointerpret data. A key characteristic of the SOM is its topology
preserving ability to map a multi-dimensional input into a twodimensional form.
Figure 2 shows the results of applying the SOM algorithm(using the R Kohonen package [6]) to our dataset for Procedure
6 using the 6 features described above. The figure shows the
mapping of observations to the 10x15 grid where each node in
the grid is created such that the distance to the neighbor nodeis minimized. The graphs in the figure display the heatmaps
for the 6 selected variables. Based on the characteristics of thedata, certain nodes may have no observations mapped to them— in our case about 30% of the nodes contain no observations.
Given the large number of observations and based on the data

Poster Session 1 :: April 8

distribution, we chose a grid with enough nodes (10x15) suchthat each node is distinct enough and has an optimal numberof observations in it for clustering.

Figure 3: Procedure ID 6 with 8 clustersIV.

R ELATED WORK

Anomaly detection is a mature area of research. Chandola
et al. [2] provide a comprehensive taxonomy of anomaly detection techniques across varying application domains. PCMD,
a rich data source for network state information has beenused for different objectives such as estimation of subscriber
locations [8], using that information further to improve theefficiency of paging mechanisms [9] and for load balancing[10]. Vaidyanathan [11] used PCMD to analyze the effects ofcaller traffic movement in response to emergency and nonemergency events (rugby match).
Our work, by contrast, is focused on understanding theavailability and reliability of the network by detection of
anomalies using unsupervised outlier detection learners [12]and clustering techniques.

111

Figure 2: SOM applied to Procedure 6
V.

C ONCLUSION AND FUTURE WORKIn this paper we have applied self-organizing maps for
initial exploratory analysis and as a clustering and anomaly
detection tool. One of the advantages of SOM is that it enables
one to visualize a multi-dimensional data in a two dimensional
grid form to understand the data distribution. A clusteringon the SOM nodes enables one to quickly analyze those
nodes which are minority clusters. Anomalous SOM nodes
detected via this technique can then be further investigatedto derive rules on the underlying data. The large volume ofdata generate by communications network precludes the storeand-process paradigm and streaming analytics and anomaly
detection methodologies for data streams need to be applied inpractice. We plan to also exploit the nature of the sequences of
the Procedure Markers in a PCMD record to get more details
into the interworkings in the network to perform root cause
analysis and flag anomalies. These will be the focus of future
work in this area.

[9] H. Zang and J. C. Boloy, “Mining call and mobility data to improvepaging efficiency in cellular networks,” in Proceedings of the 13th
Annual ACM International Conference on Mobile Computing and
Networking. ACM, 2007, pp. 123—134.
[10] A. Vaidyanathan, W. Wong, M. Billinghurst, and H. Sirisena, “Understanding directional load balancing using per call measurement data,”
in Performance Evaluation of Computer Telecommunication Systems,
2009. SPECTS 2009. International Symposium on, vol. 41, July 2009,
pp. 213–220.
[11] A. Vaidyanathan, “Manikarnika: Proactive crowd-sourcing for location
services,” Ph.D. dissertation, University of Canterbury, New Zealand,
2010.
[12] A. Zimek, R. J. Campello, and J. Sander, “Ensembles for unsupervised
outlier detection: Challenges and research questions,” ACM SIGKDD
Exploration Newsletter, vol. 15, no. 1, pp. 11—22, June 2013.

Poster Session 1 :: April 8

Session 7 :: Cro

2015

R EFERENCES
[1]

[2]

[3]

[4]

[5]

[6]
[7]

[8]

A. Patcha and J.-M. Park, “An overview of anomaly detection techniques: Existing solutions and latest technological trends,” Computer
Networks, vol. 51, no. 12, pp. 3448—3470, August 2007.
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A
survey,” ACM Comput. Surv., vol. 41, no. 3, pp. 15:1–15:58, Jul. 2009.
[Online]. Available: http://doi.acm.org/10.1145/1541880.1541882
S. Sadik and L. Gruenwald, “Research issues in outlier detection fordata streams,” ACM SIGKDD Exploration Newsletter, vol. 15, no. 1,
pp. 33—40, June 2013.
R. Wehrens and L. M. Buydens, “Self- and super-organizing maps in r:
The kohonen package,” Journal of Statistical Software, vol. 21, no. 5,
pp. 1—19, October 2007.
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection for
discrete sequences: A survey,” Knowledge and Data Engineering, IEEE
Transactions on, vol. 24, no. 5, pp. 823–839, May 2012.
(2014, December) R Package — Kohonen. [Online]. Available:
http://cran.r-project.org/web/packages/kohonen/kohonen.pdf
J. Malone, K. McGarry, S. Mermter, and C. Bowerman, “Data mining
using rule extraction from Kohonen self- organizing maps,” NeuralComputing and Applications, vol. 15, no. 1, pp. 9—17, August 2005.
M. J. Flanagan, L. M. Drabeck, L. A. Cohen, A. H. Diaz, andJ. Srinivasan, “Wireless Network Analysis using Per-Call Measurement
Data,” Bell Labs Technical Journal, vol. 11, no. 4, pp. 307—313, 2007.

112

Session 7 :: Cro

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Poster Session 1 :: April 8

Mobile	  Phone	  Data	  as	
  a	
  Means	
  of	
  Studying	
  Activity	
  Space	
  Segregation	
  at	
  Scale	  	  Robert	  Manduca,	
  Bradley	
  Sturt,	
  Marta	
  González	
  
	
  
Few	
  topics	
  in	
  sociology	
  are	  as	
  well	  studied	  as	
  segregation.	
  Hundreds	
  of	
  papers	
  over	  the	
  past	  several	
  decades	  have	  sought	
  to	
  determine	  both	  the	
  causes	
  and	
  effects	
  of	
  the	
  pronounced	
  segregation	
  that	  still	
  exists	
  today	
  across	  the	
  United	
  States.	
  	
  However,	
  the	
  vast	  majority	
  of	
  these	  studies	
  have	  focused	
  on	
  residential	
  segregation,	  despite	  the	
  fact	  that	  most	  of	
  the	
  impacts	
  of	
  segregation	
  are	
  felt	  through	
  the	
  environments	
  to	
  which	  people	  are	
  exposed	
  during	
  the	  day—their	
  “activity	
  spaces”	
  (Kwan,	
  2013).	
  	
  
In	
  this	
  paper,	
  we	
  utilize	
  cell	  phone	
  call	  detail	
  record	  (CDR)	
  data	  from	  metropolitan	  Boston	  to	
  examine	  segregation	
  in	
  daytime	
  activity	
  spaces.	
  We	
  articulate	
  a	
  definition	
  of	
  activity	
  space	
  that	  is	
  operable	
  on	
  CDR	
  data,	  then	
  measure	
  social	
  distances	
  between	
  census	
  tracts	
  based	  on	
  the	
  amount	
  of	
  overlap	
  between	
  the	
  activity	
  spaces	  of	
  their	
  residents.	
  We	
  find	  notable	
  clusters	
  of	
  adjacent,	
  strongly	
  connected	
  tracts,	
  particularly	
  in	
  outlying	
  parts	
  of	
  
the	
  metropolitan	
  area.	
  	
  Residential	
  segregation	
  has	
  attracted	
  enormous	
  amounts	
  of	
  scholarly	
  attention	
  over	  the	
  past	  fifty	
  years	
  as	
  
both	
  an	
  outcome	
  of	
  interest	
  and	  a	
  cause	  of	
  disparities	
  in	
  life	  outcomes	  (Bruch	
  &	
  Mare,	  2006;	
  Massey	
  &	
  Denton,	  1993;	  Sampson,	  2012;	
  Schelling,	
  1971;	
  Wilson,	  2012).	
  In	
  recent	
  years,	
  however,	
  an	
  increasing	  number	  of	
  researchers	
  in	
  sociology,	
  geography,	
  and	
  public	
  health	
  have	  noted	
  that	  many	  of	
  the	
  processes	
  thought	
  to	
  be	
  
affected	
  by	
  segregation	  occur	
  primarily	
  during	
  the	  day,	
  when	  most	  people	  are	  not	  at	
  home.	
  These	  researchers	
  have	  begun	  to	
  move	  beyond	
  simple	  residence	
  to	
  a	
  more	  complete	
  conception	
  of	
  activity	
  space	  in	
  their	
  work	  on	
  the	
  impact	  of	
  environmental	
  factors	
  on	
  social,	
  economic,	
  and	
  health	
  outcomes	
  (Crowder	
  &	
  South,	
  2011;	
  Inagami,	  Cohen,	
  &	
  Finch,	
  2007;	
  Kwan,	
  2012,	  2013;	
  Matthews,	
  2011).	
  Unfortunately,	
  thus	
  far	  there	  have	  been	  few	
  studies	
  of	
  segregation	
  in	
  activity	
  spaces	
  with	  a	
  large	
  enough	
  sample	  to	
  draw	
  conclusions	  at	
  scales	  smaller	
  than	
  the	
  overall	
  metropolitan	
  area.	
  Previous	
  studies	
  in	
  activity	
  space	
  segregation	
  have	  used	  data	  from	  travel	
  surveys	
  (Wang,	
  Li,	  &	
  Chai,	  2012;	
  Wong	
  &	
  Shaw,	
  2011),	
  cell	  phone	  GPS	  traces	  (Palmer,	  2013),	
  and	
  census	
  data	  (Ellis,	
  Wright,	
  &	
  Parks,	  2004),	
  all	  of	
  which	  are	
  limited	
  by	
  either	  a	
  sample	  of	
  not	  more	  than	  a	
  few	
  thousand	
  individuals	
  or	
  a	
  limited	
  definition	
  of	
  activity	
  space	
  centered	
  only	  on	
  home	  and	
  work.	
  	
  
CDR	
  data	  offers	
  the	  possibility	
  of	
  collection	
  rich	  activity	
  space	
  information	
  for	
  samples	
  of	
  hundreds	
  of	
  thousands	
  of	
  people,	
  and	  we	
  exploit	
  this	  potential	
  in	
  the	  present	
  study.	
  We	
  begin	
  by	
  extracting	
  mobility	
  patterns	
  from	
  CDRs	  in	
  metropolitan	  Boston	  over	
  two	  months	
  in	
  spring	
  2010	  in	
  the	  manner	
  described	
  by	
  Alexander	
  et	
  al.	  (2014).	
  For	
  each	  individual	
  in	
  the	
  dataset,	
  this	  method	  provides	
  an	
  estimated	
  home	
  location,	
  work	  location,	
  and	  other	  ‘stay	
  points’	
  from	  which	  they	
  made	  multiple	
  calls	
  during	  the	
  study	  period.	
  We	
  define	  an	
  individual’s	
  activity	
  space	
  from	  their	
  set	  of	
  stay	  points,	
  selecting	  those	  points	
  that	  are	
  visited	
  more	
  
than	
  a	
  threshold	
  number	
  of	
  times	  (the	
  threshold	
  is	
  not	  theoretically	
  determined,	
  and	  we	
  investigate	
  several	
  values	
  of	
  it,	
  ranging	
  from	  only	
  including	
  home	  and	
  work	  to	
  including	
  the	
  full	  set	  of	
  stay	  points).	
  These	
  activity	
  spaces	
  encompass	
  substantially	
  more	  than	
  individuals’	
  tracts	
  of	
  residence,	
  as	
  Figure	  1:	
  Daily	  movement	
  patterns	
  in	
  metropolitan	  Boston	
  indicated	
  by	
  Figure	
  1.	
  	
  
	
  	
  
113

Session 7 :: Cro

2015

Poster Session 1 :: April 8

	
  
	
  
With	
  activity	
  spaces	
  calculated	
  for	
  each	  individual,	
  we	
  examine	
  the	
  amount	  of	
  overlap	
  in	
  the	
  activity	
  spaces	
  of	
  census	
  tracts.	
  We	
  calculate	  a	
  social	
  distance	
  metric	
  for	
  census	
  tracts	
  based	  on	
  the	  percentage	
  of	
  their	
  residents’	
  activity	
  spaces	
  that	  overlap	
  with	  each	  other.	
  There	  is	
  some	  relation	
  between	
  this	
  social	
  distance	
  and	
  physical	
  distance,	
  but	  the	
  relationship	
  is	
  far	
  from	  one-­‐to-­‐one	
  (see	  Figure	  2).	
  The	
  strongest	  ties	
  are	
  found	  between	
  tracts	  in	
  outlying	
  cities	  such	  as	
  Worcester,	  Lowell,	
  and	
  Lawrence.	
  Within	
  central	
  Boston	
  the	
  ties	  are	
  weaker	
  and	
  physical	
  adjacency	  seems	
  to	
  be	
  less	  correlated	
  with	
  activity	
  space	
  overlap.	
  Analysis	
  at	
  
this	
  level	  of	
  geographical	
  detail	
  is	
  made	  feasible	  by	
  the	
  large	
  size	  of	
  the	
  CDR	  sample	  and	
  has	  not	  be	
  possible	
  in	
  previous	
  work	  on	
  activity	
  space	  segregation.	
  	
  
	
  
We	
  also	  examine	
  the	
  social	
  Figure	
  2:	
  Activity	
  space	
  overlap	
  between	
  census	
  tracts	
  (blue	
  indicates	
  higher	
  characteristics	
  of	
  connected	
  tracts.	
  levels	  of	
  overlap)	
  Tracts	
  with	  substantial	
  activity	
  space	
  overlap	
  do	
  appear	  to	
  have	  greater	
  similarity	
  in	
  social	
  characteristics,	
  although	  the	
  relationship	
  is	
  not	
  one	  to	
  one	  (see	  Figure	  3).	
  Based	  on	
  activity	
  spaces	
  we	
  calculate	
  the	
  expected	
  exposure	
  to	
  demographically	
  dissimilar	
  populations	
  for	  residents	
  of	
  each	  census	  tract.	
  	
  Further	
  work	  to	  be	
  presented	
  at	
  the	  conference	
  will	  probe	
  more	  deeply	  the	
  relationship	
  between	
  physical	
  proximity	
  and	
  activity	
  space	
  overlap,	
  investigate	
  the	
  similarity	
  of	
  overlapping	
  tracts	
  on	
  a	
  number	  of	
  demographic	
  characteristics,	
  and	
  attempt	
  to	
  locate	
  boundaries	
  in	
  the	
  Boston	  metropolitan	
  area	  that	  define	
  groups	  of	
  tracts	
  with	  substantial	
  overlap	
  from	  one	
  another.	
  	
  Each	  of	
  these	
  analyses	
  will	  be	
  novel	  to	
  the	
  activity	
  space	
  segregation	
  literature	
  because	  they	  will	  delve	  beyond	  a	
  simple	  metropolitan	
  segregation	
  Figure	
  3:	
  Tract	
  median	
  income	
  compared	
  to	
  median	
  income	
  of	
  tracts	
  with	  overlapping	
  activity	
  spaces	
  index	
  into	  the	
  specific	
  geographic	
  patterns	
  that	  create	
  daytime	
  segregation.	
  114

References	
  Session 7 :: Cro

2015

Alexander,	  L.,	
  Jiang,	
  S.,	
  Murga,	
  M.,	  &	
  González,	
  M.	
  C.	
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  Validation	
  of	
  origin-­‐destination	
  trips	
  by	
  purpose	
  and	
  time	  of	
  day	
  inferred	
  from	  mobile	
  phone	
  data.	
  Retrieved	
  from	  http://humnetlab.mit.edu/wordpress/wp-­‐
content/uploads/2010/10/TRC_BigData_612014.pdf	
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  E.	
  E.,	  &	
  Mare,	
  R.	
  D.	
  (2006).	
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  American	
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  Sociology,	  112(3),	
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  doi:10.1086/507850	
  Ellis,	
  M.,	
  Wright,	
  R.,	  &	
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  V.	
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Home	
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  American	
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  D.	
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  Social	
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Crowder,	
  K.,	  &	
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  S.	
  J.	
  (2011).	
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  temporal	
  dimensions	
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  neighborhood	
  effects	
  on	
  high	  school	
  graduation.	
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  Science	  Research,	
  40(1),	
  87–106.	
  doi:10.1016/j.ssresearch.2010.04.013	
  Massey,	
  D.	
  S.,	  &	
  Denton,	
  N.	
  A.	
  (1993).	
  American	
  apartheid:	
  Segregation	  and	  the	
  making	
  of	
  the	
  underclass.	
  Harvard	  University	
  Press.	
  Retrieved	
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  heterogeneity	
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  place:	
  studying	
  people	
  and	
  place	
  via	
  egocentric	  methods.	
  In	
  Communities,	
  Neighborhoods,	
  and	
  Health	
  (pp.	
  35–55).	
  Springer.	
  Retrieved	
  from	  http://link.springer.com.ezp-­‐prod1.hul.harvard.edu/chapter/10.1007/978-­‐1-­‐4419-­‐7482-­‐2_3	
  Palmer,	
  J.	
  R.	
  (2013).	
  Activity-­‐space	
  segregation:	
  Understanding	
  social	
  divisions	  in	
  space	
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  time.	
  Princeton	  University,	
  Princeton,	
  NJ.	  Sampson,	
  R.	
  J.	
  (2012).	
  Great	  American	
  city:	  Chicago	
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  enduring	
  neighborhood	
  effect.	
  University	
  of	
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  Press.	
  Retrieved	
  from	  http://books.google.com.ezp-­‐prod1.hul.harvard.edu/books?hl=en&lr=&id=POs5iroB7PsC&oi=fnd&pg=PR5&dq=great+american+city&ots=GjCMmtFemg&sig=i3660i6vg1HmHxIwnKI6nQZhyzw	
  Schelling,	
  T.	
  C.	
  (1971).	
  Dynamic	
  models	
  of	
  segregation†.	
  Journal	
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  Mathematical	
  Sociology,	  1(2),	
  143–186.	
  Wang,	
  D.,	
  Li,	
  F.,	  &	
  Chai,	
  Y.	
  (2012).	
  Activity	
  spaces	
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  segregation	
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  Beijing.	
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256–277.	
  Wilson,	
  W.	
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ts=gGjfzmR6h8&sig=L8_z6fxGDGB-­‐sfSOWHuXCzjnpnk	
  Wong,	  D.	
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Riyadh Alnasser∗ , Faisal Aleissa∗ , Fahad Alhasoun† , Abdullah Almaatouq† , Anas Alfaris† , Marta González†∗ King Abdulaziz City for Science and Technology KACST

Session 7 :: Cro

2015
Gender-based Characterization of Communication Behavior{ralnasser, fsaleissa}@kacst.edu.sa† Massachusetts Institute of Technology MIT{fha,amaatouq,anas,martag}@mit.edu

The rise of smart phones, apps, and mobile Internet makes thecell phone a key battleground in the fight for customer attention
by the telecom companies. Basic demographic attributes, including
gender, may be registered by the companies, where they may use thisinformation in user profiling and marketing. However, it is common
that the registered information doesn’t represent the actual users of theservice. Call Detail Records (CDRs) provide a convenient data sourcethat can be used to infer different attributes about the mobile phoneusers [1], [3]–[5], [7], [8]. Saudi Arabia is highly gender segregatedsociety. Acts of gender segregation manifest in many different forms;
for example, separate educational facilities for men and women only,
female-only shopping malls or shopping malls with access restricted
to families only. Also, many entertainment or leisure facilities arebuilt specifically for either gender. For example, attending footballmatches at various stadiums in the Kingdom is limited to men only.
II. DATA D ESCRIPTION
The dataset consists of one full month of records for an entire
country, with 3 billion mobile activities to over 10 thousands uniquecell towers, provided by a single telecom service provider.
In order to study the influence of gender in communication
behavior, we extracted a sample ground truth sample (i.e., labelsof female and male), by exploiting the gender segregation in Saudi
Arabia. Specifically, since the CDRs used in this study has no
gender information for the cell phone users, we have developed amethodology for gender identification. We were able to identify thegender of 10,000 cell phone users (5000 female students and 5000
male) students. The first step in the identification process was tolocate Point of Interests (POIs) that restrict access based on gender.
After locating these POIs, we extracted the cell towers situatedaround each POI that provide radio coverage over that area. Then,
we distinguished the people in stay versus people in move (i.e., who
is passing by and who was actually there). Finally, we identify thegender of the users based on the number of distinct gender specificlocations visited.
III. C OMMUNICATION PATTERN
In this section we attempt to understand the calling behavior of
females and males using the CDRs. We focused on examining theA. Monthly scale
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Poster Session 1 :: April 8

I. I NTRODUCTION

5,000 male and 5,000 female students activities during different daysof the week and the month. We denote the normalized activity levelsof females and males as V Female and V Male , respectively.

P(t|V)The pervasiveness of cell phones opens new horizons forunderstanding and studying human behavior. Mobile phones
are powerful sensors, and they are quickly becoming the main
source for social and behavioral data. In this paper we present
a study aimed at identifying the gender of cell phone users
based on their visited locations then investigate variations ofusage behavior given predicted gender information. The analysis
was carried out using CDRs (Call Detail Records) on 10,000
cell phone users. Our contributions can be summarized in (1)understanding the communication characteristics pertaining togender; and (2) Identifying the difference in mobility habits
between males and females. The results highlight the behavioral
differences between males and females in based on their mobile
phone usage.

Figure 1: Daily Male and Female Activities across the monthFig 1 depicts the female and male activities across the entire month,where each point represents the total number of activities in a day.
Interestingly, we observe a drop in the activities for both females
and males during weekends. Female activities during the weekend
shows a larger decrease; this might indicate that women tend to use
cell phones for professional activities or work related tasks. On theother hand, males might favor social and personal calls. We also
observe a slight increase in the mobile phone activities during the first
and the last day of the weekdays, and it appears more regularly forfemales than males. In addition, as the month progresses we observea decrease in the cell phone activities. Further investigation showsthat the last two weeks coincided with the final exams period.
B. Daily scale
In order to have a better look at the daily activity, we decompose
the daily mobile phone activities into 288 five minutes interval. This
gives us a better understanding of how the activities change within
the day. Fig 2 shows the average calling activities of females andmales in 5 minutes interval. On the left figure, we observe a peakin female activities at 6:30 am and 2:30 pm. Our hypothesis is thatsince women do not drive in Saudi Arabia, these peaks correspondto time at which they call the drivers to come and drive them to theuniversity, or in case of 2:30 pm, drive them back home. To further
investigate this, on the right figure we can see females 5-minuteinterval activities during weekends. As expected, these two peaks
disappear on weekends, thus these sharp increases in the activities
during a weekday are females calling their drivers to get them around.
The influence of the daily prayers can be observed in females andmales daily activities. Fig 2 shows significant drops occurring fourtimes across the day. Previous analysis has showed that the drops
coincide with daily prayer time [2]. An interesting observation is thatwomen tend to not pray Duhr (12pm) on time during the weekdays;
instead, we observe a slight increase in cell phone activities at thistime. An interpretation for this observation could be that women116

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Session 7 :: Cro

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(KDE) of the ROG. Although the difference in the size of the ROG between the male and female population is subtle (µ(male) = 4.056674
and µ( f emale) = 3.366997), using the two-sample KolmogorovSmirnov (KS) test, we find the difference to be statistically significant(KS = 0.15, p < 0.001) .

Female

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Figure 3: Radius of Gyration (ROG) for Males and Females
postpone this prayer until they get home. However during weekends,
women pray Duhr on time. Also, the mobile phone activities forfemales and males during weekends shows that they have similar
calling behavior. During weekends, males tend to be less active inthe first half of the day, and generate more mobile phone activities
during the second half of the day. The opposite pattern is observed
during weekdays. Females show the same pattern in the change ofcalling behavior in weekdays versus weekends. We have observedthat in fact, as it may be the cultural belief, women spend moretime on the phone than men. However, females tend to make lessnumber of phone calls in the second half of the day. We found thatfemales tend to make fewer, but longer phone calls. We calculated
the Empirical Cumulative Distribution Function (ECDF) of the calls
duration made by females and males. We find that nearly 50% of oursample of females had calls duration greater than 40,000 seconds
as opposed to their male counterparts where 50% of them had calls
duration greater than 20,000 seconds.
IV. M OBILITY D ISCREPANCY
In this section, we attempt to examine the discrepancies of mobility patterns between the female and male population. Namely,
we quantify mobility by measuring the Radius of Gyration ROG(i.e., a measure of how far from the center of mass the mass is),as implemented by the CDRs analysis package Bandicoot [3]. The
Radius of Gyration has been used previously in the literature toquantify individual trajectory tracked from CDRs, and researchers
have shown that it has a strong impact on travel distance distributions
over all users [5], [6]. Figure 3, shows the Kernel Density Estimation

In this work, we show that in some cases it is possible to infer
the gender of mobile phone users through the analysis of CDRs
coupled with POIs. The main purpose of this work is to investigate
the existence of differences in usage patterns in the CDRs given thegender of a user. This study suggests that females and males use theircell phones to communicate in a different manner and shows that
there exists a considerable variation in terms of phone usage given
the gender of a user. We acknowledge that some of the explanations
and conclusions proposed in this work might lack rigorous validations
and this is due to the nature of the CDRs where it lacks sufficientgranularity in space and time. We also lack the ground truth validationfor our proposed method of labeling. However, we believe that ouranalysis can describe well the trends and discrepancies of genderspecific communication and can be leveraged for several applications.
Future work will involve the improvement of the statistical analysis
and the investigation of additional factors that influences usage ofmobile phones such as the social structure of females and males
users. In addition, we plan to use such extracted features in training
a classifier that can predict gender using CDRs.

Poster Session 1 :: April 8

Figure 2: 5 min interval activities of males and females in 24 hours window

R EFERENCES[1] F. Alhasoun, A. Almaatouq, K. Greco, R. Campari, A. Alfaris, andC. Ratti. The city browser: Utilizing massive call data to infer city
mobility dynamics. In The 3rd International Workshop on Urban
Computing (UrbComp 2014), 2014.
[2] A. Almaatouq, F. Alhasoun, R. Campari, and A. Alfaris. The influenceof social norms on synchronous versus asynchronous communication
technologies. In Proceedings of the 1st ACM International Workshop
on Personal Data Meets Distributed Multimedia, PDM ’13, New York,NY, USA, 2013. ACM.
[3] Y.-A. de Montjoye, J. Quoidbach, F. Robic, and A. S. Pentland. Predicting
personality using novel mobile phone-based metrics. In Social Computing,
Behavioral-Cultural Modeling and Prediction. Springer, 2013.
[4] N. Eagle and A. S. Pentland. Reality mining: sensing complex social
systems. Personal Ubiquitous Comput., 10(4):255–268, Mar. 2006.
[5] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding
individual human mobility patterns. 2008.
[6] S. Hoteit, S. Secci, S. Sobolevsky, G. Pujolle, and C. Ratti. Estimating
real human trajectories through mobile phone data. In Mobile DataManagement (MDM), 2013 IEEE 14th International Conference on,volume 2, pages 148–153. IEEE, 2013.
[7] S. Jiang, J. F. Jr., and M. C. Gonzalez. Discovering urban spatial-temporal
structure from human activity patterns. pages 95–102, 2012.
[8] J. R. Kwapisz, G. M. Weiss, and S. A. Moore. 12(2).

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2015

	
  

DYNAMICS OF SOCIAL AND SPATIAL SEGREGATION

Session 7 :: Cro

Netmob	
  2015,	
  Massachusetts	
  Institute	
  of	
  Technology	  	  USING MOBILE PHONE METADATA
Johannes Bjelland, Bjørn-Atle Reme, Pål Sundsøy
Telenor Group Research, Norway{ johannes.bjelland,bjorn-atle.reme,pal-roe.sundsoy, }@telenor.com
	  Abstract
In this paper we study segregation between several ethnic groups in a European city using anonymized
mobile phone metadata collected from the major mobile phone operator. In particular we look at social
segregation (patterns of communication), spatial segregation (patterns of movement) and how these
relate to each other.
Over the last decades there has been an increased focus on the lack of social integration of immigrants
in western societies. A policy tool often considered to mitigate this problem is resettlement programs
enforcing spatial integration. This policy relies on implicit assumptions regarding the relationship andcausality between social and spatial segregation. Even though this paper is not able to answer
questions about the causal links between social and spatial segregation, we present evidence on theempirical relationship between these variables for different ethnic groups. We believe that a better
understanding of the relationship between these variables, and the mechanisms involved, is crucial todeveloping more effective integration policies.

Poster Session 1 :: April 8	  Increasing the social integration between ethnic groups is often an important objective for policy
makers. From the call data we obtain a proxy for ethnicity. This allows us to shed new light on social
integration by providing an in-depth analysis of the communication patterns of different ethnic groups
in the city. In particular we derive the extent of across-group and within-group communication for thedifferent ethnic groups in the city. Using Hofstede’s cultural dimension theory we derive a measure ofcultural distance between the different ethnicities in the sample and find how much of the social
integrations and across-group communication can be explained by this measure.
When studying the relationship between spatial and social integration we make use of a “benchmark
integration level” inspired by Blumenstock and Fratamico (2013). The benchmark is defined by what
the structure of the social network would be assuming random pairing of nodes within a geographicalarea. We then compare this benchmark with the structure of the actual social networks. Based on thisanalysis we develop the “spatial-social-integration-matrix” which provides an overall picture of theextent to which the different groups are more/less integrated than what random pairing suggests.
References
Blumenstock, JE and Fratamico L (2013). Social and Spatial Ethnic Segregation: A Framework forAnalyzing Segregation With Large-Scale Spatial Network Data, The 4th Annual ACM Symposium on
Computing for Development (DEV '13).

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2015

Behavioral Modeling for Churn Prediction:
Early Indicators and Accurate Predictors of Custom Defection and Loyalty

Session 7 :: Cro

1

Churn prediction, or the task of identifying customers
who are likely to discontinue use of a service, is an
important and lucrative concern of firms in manydifferent industries. As these firms collect an increasing
amount of large-scale, heterogeneous data on thecharacteristics and behaviors of customers, newmethods become possible for predicting churn. In thispaper, we present a unified analytic framework fordetecting the early warning signs of churn, andassigning a ”Churn Score” to each customer thatindicates the likelihood that the particular individual
will churn within a predefined amount of time. This
framework employs a brute force approach to featureengineering, then winnows the set of relevant attributes
via feature selection, before feeding the final feature-set
into a suite of supervised learning algorithms. Using
several terabytes of data from a large mobile phone
network, our method identifies several intuitive - and a
few surprising - early warning signs of churn, and ourbest model predicts whether a subscriber will churnwith 89.4% accuracy.
This paper describes a data driven quantitative andcomputational framework that can be used to find leading
indicators of churn, and identify individuals with a highlikelihood of churning. By using simple machine learningalgorithms to mine historical transaction records, thismethod discovers behavioral patterns that are empiricallycorrelated with the propensity to churn. This is in contrast
to the more traditional approach taken by many companies,
where churn strategies are determined by the intuition of
key individuals, or through direct feedback from customers.
The framework has two primary components. The first isdesigned to identify early warning signs of churn by
isolating specific and easily-measured behavioral patterns
that are highly correlated with churn. One such pattern wefind, which is indeed highly correlated with churn, is thetotal amount of observed activity of a customer. It shouldcome as no surprise that customers with low levels ofactivity are more likely to churn than customers with highlevels of activity. However, this particular metric is one
among thousands of other metrics that are correlated withchurn, and the aim of the framework is to zero in on themost predictive metrics. The method we develop is a semisupervised, brute force approach to feature engineering, inwhich our algorithm first constructs tens of thousands offeatures through combinatoric feature generation, then uses

established techniques for feature selection to prune thelong list down to the most predictive behavioral traits.
We test and calibrate this framework on a large dataset
from a mobile phone operator in South Asia. Starting witha raw dataset of several billion transactions, spanningroughly ten million prepaid mobile phone subscribers over a
period of multiple years, we extract a calibration dataset
consisting of all network-based communication for roughly
100,000 subscribers over 6 months. On this dataset, where
the natural churn rate during our evaluation period isroughly 24 percent, our method is able to predict customer
churn with just under 90 percent accuracy.
Understanding why customers terminate relationships has
been a focus of marketing research for several decades (cf.,Jain & Singh, 2002). In recent years, as data on customer
activities and characteristics becomes increasingly available
to companies, more sophisticated metrics have evolved todescribe customer behavior (cf., Gupta & Zeithaml, 2006).
Churn prediction has received recent attention from theapplied machine learning community. These approaches
have tested a battery of models including expert systems(Wei, Chiu, & others, 2002), support vector machines(Archaux, Martin, & Khenchaf, 2004), and bagging andboosting (Lemmens & Croux, 2006), to name just a few.
They further vary in terms of the approach to the data, withsome focusing on customer proﬁles and features (Qian,
Jiang, & Tsui, 2006), and others concerned primarily with
the importance of social ties and social structure (Dasgupta
et al., 2008; Bonchi, Castillo, Gionis, & Jaimes, 2011;
Karnstedt, Rowe, Chan, Alani, & Hayes, 2011). Zhang,Zhu, Xu, and Wan (2012) provide a recent overview of thediﬀerent types of subscriber attributes used to model andpredict customer churn in prior work, and Verbeke,
Dejaeger, Martens, Hur, and Baesens (2012) benchmark
several classiﬁcation techniques for prediction. Neslin,
Gupta, Kamakura, Lu, and Mason (2006) discuss theimportance of diﬀerent methods for predicting churn in thecontext of a public tournament between 33 different
competitors. In these and related studies, the behavioral
traits are pre-computed most of the times (cf., Neslin et al.
2006). By contrast, we focus on the process of generatingthese predictor variables from the raw transactional records.
Furthermore, majority of studies of churn intelecommunications focus on the post-paid network while
our method deals with the pre-paid users.

Poster Session 1 :: April 8

Muhammad Raza Khan1, Joshua Manoj1, Anikate Singh1, Joshua Blumenstock1
1
Information School, University of Washington, Seattle, WA, USA

119

Poster Session 1 :: April 8

Figure 1 Overview of Approach
Figure 1 shows the overview of our approach. We begin byrandomly selecting a subsample of roughly 100,000
subscribers from the full mobile phone subscriber base, andextract all transactions in which they are involved. For thissubset of subscribers, we then generate a large number ofaggregated metrics that describe a wide range of inferredbehavioral characteristics (Feature Engineering). With thismatrix, we then separately isolate the handful of metrics thatare most predictive of churn (Feature Selection), anddevelop a Churn Score that indicates the likelihood that asubscriber will churn (Machine Learning)
After generating an extensive list of behavioral featuresusing combinatoric approach we employ standard methodsof feature selection that determine which features are mostcorrelated with customer churn. In addition to using
statistical significance tests to evaluate the individual
predictive performance of each feature we also use a treebased method for feature selection that allows us to estimate
the conditional ability of each additional feature to improve
the overall accuracy of a joint classiﬁer (Geurts, Ernst, &Wehenkel, 2006; Hastie et al., 2009).
We quantify churn in two ways: ﬁrst, as a binary
condition that is true if the subscriber is completely inactivein the testing phase. In total, 26 % of our subscribers ﬁt thisstringent deﬁnition of churn. Second, we deﬁne a more
ﬂexible version of churn as the percentage of days on which
no activity is observed. The list of top predictive features
used in our final predictive model is shown in Table 1.
Unsurprisingly, we ﬁnd that "Percent of inactive days" – afeature indicating the fraction of days during the training
period when the subscriber had zero transactions – is highly
predictive of future churn. The next highest ranked feature
is perhaps less obvious: we ﬁnd that high variance in call
activity (measured as the maximum month-to-month change
in the ratio of incoming to outgoing calls), is strongly
predictive of churn.
While the set of features listed in Table 1 are all
unconditionally highly correlated with churn, these features
are also correlated with one another. Thus, while the"Percent of inactive days" feature may be the best single
linear discriminant between churners and non-churners, it is
not necessarily the case that the ensemble of 10 features inPanel A will be the best joint predictor of churn. Thus, inPanel B of Table 1, we provide the 10 features that are,taken together, the best joint predictors of subscriber churn.
For comparison with Panel A, we also list the R2 from theunconditional (univariate) regression for each of theSession 7 :: Cro

2015features, though it is important to note that this is theunconditional R2 and is different from the criteria used torank-order features in Panel B. Two patterns can be seen in
Table 1. First, the unconditionally predictive features (PanelA) generally reﬂect aggregate metrics of activity. However,the conditionally predictive features (Panel B) tend to be
micro-aggregates which may elude even the sharpest
marketing director.

Table 1 Top Predictors of ChurnFor evaluating predictive performance of our machinelearning models we use “Percent of inactive days” as abaseline feature, and build a linear discriminant model
based on that feature. We find that using the empiricallyfound threshold of 76% for inactive days our prediction iscorrect in 83.9% percent of cases.
Depending on the algorithm used to predict churn, we
achieve accuracy rates of roughly 88.5-89.5 percent. This
represents a modest improvement of roughly 6 percent overthe single-feature baseline, or approximately 14 percent
over the majority-class baseline. A variety of performance
characteristics of each model, as well as the linear
discriminant baseline, are given in Table 2.

Table 2 Churn Prediction Performance
While these initial empirical results are promising, we see
the primary contribution of this paper being the descriptionof a systematic framework that can be used to generateinterpretable features and predict customer outcomes.
Several of the modelling assumptions we have made, suchas the axes and dimensions used to generate features, arequite arbitrary and it is likely that more careful design ofthese behavioral metrics could yield more intuitive
predictors and more accurate predictions.

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Archaux, C., Martin, A., & Khenchaf, A. (2004). An SVM
based churn detector in prepaid mobile telephony. In
Information and communication technologies: From
theory to applications, 2004. Proceedings. 2004
international conference on (pp. 459–460). IEEE.
Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?
arnumber=13070
Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011).
Social network analysis and mining for businessapplications. ACM Transactions on Intelligent Systems
and Technology (TIST), 2(3), 22. Retrieved from http://dl.acm.org/citation.cfm?id=1961194
Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D.,
Mukherjea, S., Nanavati, A. A., & Joshi, A. (2008).
Social ties and their relevance to churn in mobile telecom
networks. In Proceedings of the 11th international
conference on extending database technology: Advancesin database technology (pp.668–677). ACM. Retrieved
from http://dl.acm.org/citation.cfm?id=1353424
Dwyer, F. R. (1997, September). Customer lifetime
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Geurts, P., Ernst, D., & Wehenkel, L. (2006, April).
Extremely randomized trees. Mach. Learn., 63(1), 3–42.
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Gupta, S., & Zeithaml, V. (2006, November). Customer
metrics and their impact on ﬁnancial performance.
Marketing Science, 25(6), 718–739. Retrieved 2014-0905, from http://pubsonline.informs.org/doi/abs/10.1287/
mksc.1060.0221
Hart, C., Heskett, J., & Sasser, W. E., Jr. (1990, July). The
proﬁtable art of service recovery. Harvard Business
Review. Retrieved from http://hbr.org/1990/07/theproﬁtable-art-of-service-recovery/ar/1
Hastie, T., Tibshirani, R., Friedman, J., Hastie, T.,Friedman, J., & Tibshirani, R. (2009). The elements ofstatistical learning (Vol. 2) (No.1). Springer. Retrieved
from http://link.springer.com/content/pdf/10.1007/978-0387-84858-7.pdf
Jain, D., & Singh, S. S. (2002, March). Customer lifetime
value research in marketing: A review and future
directions. J. Interactive Mark., 16(2), 34–46. Retrieved
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2/abstractKarnstedt, M., Rowe, M., Chan, J., Alani, H., & Hayes, C.
(2011). The eﬀect of user features on churn in social
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Lemmens, A., & Croux, C. (2006, May). Bagging andboosting classiﬁcation trees to predict churn. Journal ofMarketing Research, 43(2), 276–286. Retrieved fromhttp://journals.ama.org/doi/abs/10.1509/jmkr.43.2.276
Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C.
H. (2006). Defection detection: Measuring andunderstanding the predictive accuracy of customer churn

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the telecommunication sector: A proﬁt driven data mining
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5111002693

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REFERENCES

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Places and Mobility: the Influence of Attractions on People
Movement
Fahad Alhasoun∗ , May Alhazzani∗ , Faisal Aleissa ∗ , Riyadh Alnasser∗ , Marta González∗∗ Massachusetts Institute of Technology{fha, mayh, riyadh, aleissa, martag}@mit.edu
610

Number of Phone Calls per Cell

5

10

4

10

3

10

I. I NTRODUCTION
With the rapid adoption of pervasive technologies, a significant
portion of the worlds population utilizes mobile phones, emails andsocial media (e.g., Twitter, Facebook...etc) forming a platform forpeople to exchange information, broadcast thoughts and convey
feelings. Researchers today are using data generated from suchtechnologies to better understand human behavior at unprecedented scales. Such data driven research unveiled statistical
patterns that provide understanding of how people communicate,feel, move and so forth. Extensive research investigated theperiodicity of human mobility and showed that humans can behighly predictable [3], [5]. Such understanding of the predictability
helped significantly improve predicating social links within thesocial networks [4], [6]. Additionally, researchers were able toimprove the prediction of human mobility through social networks
features and investigated whether certain trips are motivated bysocial influences [1], [2]. In this paper, we present preliminary
insights of the relationship of human mobility to the number ofplaces around the city and the possibility of using such data to
improve mobility predictions. Exiting research have used POI data
as a proxy to the structure of cities in order to better understandhow different areas within a city attract people [7].
II. DATASET
The data set used is mobile phone billing information, also
known as Call Detail Records (CDRs), the dataset holds callinginformation including caller and receiver identifiers as well as thelocations where the call was made. First, the dataset was filteredfor users within the bounds of the city of Riyadh in Saudi Arabia.
After the initial filtration process, the data set withholds around3 million unique users and around 32 million unique social ties.
(Peeking into the structure of the social network, figure (a) shows asubset of the social network in Riyadh. In this social network, there
are around 23 thousands nodes and 93 thousands edges.) Alongwith the CDRs, we are using data pertaining to places of interests(POIs) in the city of Riyadh. We have around 12 thousands POIs
in the city of Riyadh; each POI entry contains its location and its
type (i.e. restaurant, store, bank...etc), where there are 96 types
to tag POIs.

0

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1

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2

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Number of POIs per Cell

3

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Extensive theoretical work attempts to address the predictability of human mobility in cities by means of understanding the periodicity of people movement as well as incorporating social networks features to improve the predictions.
Using mobile phone data coupled with city structure data of
the city of Riyadh in Saudi Arabia, we examine the potentials
of better predicting the mobility of users using data pertaining
to attraction areas within a city. We find that people movement
around a city is highly correlated with the distribution ofplaces around the city depending on the time of the day. We
found a correlation factor of 0.86 between the number of POIs
and the number of phone calls per location in the city. We
further explored the correlation of different POIs to different
times of the day to better understand the correlations during
regular business hours versus night times.

Figure 1: The number of phone records versus the number of POIs

III. S PATIAL D ECOMPOSITION OF POI S VERSUS P HONE ACTIVITY
In order to quantify the relationships of the number of POIs andthe level of activity across the whole day, we split the city into
squared cells of equal areas. Then, we correlate the aggregationof phone calling activity to the aggregation of number of POIs ineach cell. Figure 1 shows the plot of the number of corresponding
quantities for each cell where we have a correlation factor of 0.86
indicating very high correlation between the places people visit
and the number of POIs in a place. Spatially visualizing the heatmap of the POIs in the city of Riyadh versus the phone activity,
figure 2 shows mobile phone activity on the left versus densityof POIs on the right. The high correlation between cellular activity
and the POIs of the city of Riyadh are visually evident; we can seethat both heat maps overlap indicating similar spatial distribution.
This hints out the possibility of using such correlating quantities in
predicting human mobility around the city.
A. Time of Day Influence on Correlations
Although the spatial distribution of activity correlates highly with
the number of POIs in a given cell, the correlation coefficientshows varying values depending on the time of the day. In order togain insights, we filter phone calling activity during the day versus
the activity during the night. Table I shows correlation coefficients
between night and day activity versus the number of POIs on eachcell. The table suggests that during the day people tend to belocated around areas where the density of POIs is high having acorrelation factor is 0.825. The Table also suggests that during thenight people tend to be located in places that have POIs as well
but with a 0.487 correlation coefficient.

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POI CountDay Activity
Night Activity

POI Count
1
0.825
0.487

Day Activity
0.825
1
0.733

Night Activity
0.487
0.733
1

0.2

0.16

In order to better investigate whether certain locations are more
preferred than others, we correlate the night and day activity to
the POIs given their types. That way we can examine if certain
POIs are more preferred to be visited during the day versus thenight. Table II shows the types of POIs that correlates the mostwith phone activity during night and day. We can see that places
with shopping malls correlate the most across both day and nighttimes, but they seem more attractive during the day. We can also
see that the most attractive POIs overlap greatly.
Day Activity

Night Activity

shopping mall (0.895)
finance (0.89)
bank (0.86)
establishment (0.857)

shopping mall (0.772)
bank (0.753)
museum (0.749)
finance (0.725)

Table II: POI types correlation rank versus mobile activity

IV. F UTURE DIRECTIONS TOWARDS BETTER PREDICTING
M OBILITY
Table II shows that there isn’t much of a variation in the preference of certain types of POIs across day and night. Therefore, we
aim to investigate the preferences of POIs by spatially correlating
the POIs to the home and work locations of the users. In orderto quantify the home and work locations, we assume a number oflatent states (locations) we are going to predict. We aim to predict
two major states: home and work. We assume that the spatial
distribution of people around home and work are 2 time-invariant
Gaussian distribution [1]. The distributions are centered aroundthe home/work locations respectively. Also, we assume that user’s
phone activity times follows normal distribution. Figure 3 shows thetraces of a user modeled as a two-components Gaussian mixture
model, where each represents the underlying hidden state. The
user’s visits are classified either as home or work based on thetime of the user’s phone activity according to the temporal normal
distribution. Then, based on the predicted state, the geographic

Probability

Table I: Correlation matrix of POIs versus mobile activity

B. Most Attractive POI Types

Work
Home

0.12

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Figure 2: Mobile activity (left) and POI density (right) decomposed spatially in Riyadh where the they have a spatial correlation of 0.86

Session 7 :: Cro

2015

0.08

0.04

0
0

2

4

6

8

10

12

Time

14

16

18

20

22

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Figure 3: Gaussian mixture model for inferring home/work traces

location can be predicted from the location distribution. In addition
to investigating the correlations of home/work locations with POIs,
this prediction technique will help us identify visits that don’t belong
to one’s home or work locations. 10% to 30% of such outlier traces
can be explained by incorporating the social network information[1]. We intend to explore the possibility of explaining more about
the outlier traces through incorporating POIs. Potential research
direction includes incorporate an existing Periodicity Mobility Model(PMM) that can capture home and work for each user.
R EFERENCES[1] E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: user
movement in location-based social networks. In Proceedings of the
17th ACM SIGKDD international conference on Knowledge discovery
and data mining, pages 1082–1090. ACM, 2011.
[2] M. De Domenico, A. Lima, and M. Musolesi. Interdependence andpredictability of human mobility and social interactions. Pervasive andMobile Computing, 9(6):798–807, 2013.
[3] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding
individual human mobility patterns. Nature, 453(7196):779–782, 2008.
[4] S. Scellato, A. Noulas, and C. Mascolo. Exploiting place features inlink prediction on location-based social networks. In Proceedings of the
17th ACM SIGKDD international conference on Knowledge discovery
and data mining, pages 1046–1054. ACM, 2011.
[5] C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability
in human mobility. Science, 327(5968):1018–1021, 2010.

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Session 7 :: Cro[6] D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi.
Human mobility, social ties, and link prediction. In Proceedings of the
17th ACM SIGKDD international conference on Knowledge discovery
and data mining, pages 1100–1108. ACM, 2011.
[7] J. Yuan, Y. Zheng, and X. Xie. Discovering regions of different functions
in a city using human mobility and pois. In Proceedings of the 18th
ACM SIGKDD international conference on Knowledge discovery anddata mining, pages 186–194. ACM, 2012.

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Classification of Human Population in Hospitals

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Using Mobile Phone Data
	
   	
   	
   	
   Guanning	
  Dong	
  

	
  

	
  

	
   	
   	
   	
   Xianfeng	
  Song	
  

	
  

	
  

	
  

	
   Xinhai	  Liu	
  dongguanning@tuoming.com	
  

	
   	
   xfsong@ucas.ac.cn	
  

	
  

	
  

xinhai.liu@foxmail.com	
  

	
  

INTRODUCTION
example, the patients may crowd into the top three hospitals, increasing the burden of medical
workers, in the meantime, resulting in a waste of medical resources of other hospitals. Whenanyone is in hospital, communication with family and friends becomes an essential element. The
generated mobile phone data offer us a chance to study the behavior pattern of human populationin different level hospitals, which will help to improve the existing medical facilities and service
and adjust the quantity and capacity of hospitals according the real-time demand of patients.
Some important conclusions have been reached about human pattern based on mobile phone
data[1]. Mobile phone trajectories represent a reasonable proxy for individual mobility and canPoster Session 1 :: April 8

As the most important destinations of cities, hospitals usually have irrational arrangement. For

provide useful insights into intra-urban mobility patterns[2]. Experimental studies have exploredthe relationship between human behavior and mobile phone datasets, and identify the home-work
location[3]. In this paper, focusing on further application of the mobile phone data, we propose ananalytical process aimed at classifying human population in hospitals.
	  METHODS
We first hypothesize that human population in hospitals are mainly composed of healthcare
workers and patients. Both of them have characteristic behavior model, thus mobile phone traces.
Specifically, healthcare workers follow a relatively obvious home-hospital model. The patients,
who divide into outpatients and inpatients, have different patterns. Inpatients may also
communicate in hospital in working hours and at home in off-working hours, but the situation will
not last long. The datasets of outpatients can be generated in hospital at any time of a day. The
pattern is shown in Figure 1.

	
   	
  
(a) Inpatients

	
   	
  
(b) Outpatients	  (c) Healthcare workers

Figure 1: Spatial distribution of human population in hospitals

	
  

1	
  

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Healthcare workers

Inpatients

Outpatients

Working time

In hospital

In hospital

In hospital

Off-working time

At home

In hospital

At homeSession 7 :: Cro

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Table 1: Feature of human population in hospitals

Population

Healthcare workers

Inpatients

Outpatients

173

2126

1053

We then classify the healthcare workers and patients using the Support Vector Machines (SVM).
For modeling, three groups of training samples were used in concert with four different kernel
functions. Table 2 is the preliminary classification results of The General Hospital of People’s
Liberation Army (known as 301 hospital) on 15th December, 2014.
Moreover, we obtain the spatial distribution of healthcare workers and patients by cluster themobile phone datasets, as is presented in figure 1(301 hospital). After that, we analyze the originaldestination matrix to understand the distance human spend on the way to hospital. Finally, basedon aided-data, we research the correlation between the quantity of patients and indicator factors,

Poster Session 1 :: April 8

Table 2: Classification of human population in hospitals

such as age and gender.

RESULTSThe experimental finding importantly evaluates the potential feasibility of using the large scale ofmobile phone data to research on the healthcare allocation. The contribution is that we classified
the human in hospital into groups, including the healthcare workers, outpatient and inpatient. Thisis based on the temporal and spatial feature among them. Furthermore, the systematic study on
patients’ medical tendencies indicates that the patients of different level hospitals have certain
distinct features. Afterwards, further analysis and applications will be explored to provide
evidence and advice for healthcare facility and resource reallocation and management, aiming to
improve the service level and efficiency of healthcare system in Beijing and other main cities ofChina.
	  REFERENCES
1.

Hoteit S, Secci S, Sobolevsky S, Ratti C, Pujolle G. Estimating human trajectories andhotspots through mobile phone data. Computer Networks. 2014; 64(0): 296-307.

2.

Calabrese F, Diao M, Di Lorenzo G, Ferreira Jr J, Ratti C. Understanding individual mobility
patterns from urban sensing data: A mobile phone trace example. Transportation ResearchPart C: Emerging Technologies. 2013; 26(0): 301-13.

3.

Csáji BC, Browet A, Traag VA, Delvenne J-C, Huens E, Van Dooren P, et al. Exploring themobility of mobile phone users. Physica A: Statistical Mechanics and its Applications. 2013;
392(6): 1459-73.

	
  

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Campaign Optimization through Mobility Network Analysis

Session 7 :: Cro

2015

Yaniv Altshuler1 , Erez Shmueli2 , Guy Zyskind3 , Oder Lederman3 , Nuria Oliver4 , Sandy Pentland3
1
Athena Wisdom, Israel
Department of Industrial Engineering, Tel-Aviv University, Israel
3
MIT Media Lab, USA
4
Telefónica Research, Spain

In a world of limited resources, behavior change campaigns (e.g.
marketing, service provision, political or homeland security) canrely on creativity and attractiveness up to a certain point. The
success of a campaign can generally be defined as the product ofreach (portion of the population exposed to the campaign messages) and value of a single interaction (the capacity of a message
to induce a certain behavior in an exposed audience) [1]. Hence,
campaign managers typically distribute their budget between content enhancement (to increase the value a single interaction) andwide reach. Yet, to date it seems that the optim trade-off between
these two factors is found as a result of “intuition” rather than basedon well established analysis.
In this work, we propose a novel mathematical method that,given the characteristics of the target audience and its ability tobe persuaded, generates an optimized campaign strategy in termsof: (a) the quantity of interacting units, also referred to as insertions and (b) the monetary allocation to each unit. The model takes
into account the population’s mobility in an urban environment as
it can be inferred from real data received from a large mobile phone
carrier. Even though different populations located in different environments would be tailored with different campaign strategies, theoptimality of each strategy would be maintained.
A major contribution in our optimization model is the use of network analysis methods to approximate the reach of a campaign.
More specifically, given the network of mobility between the different geographic locations, and a subset of locations, we use theGroup Betweenness Centrality (GBC) [2] – a network measure that
calculates the percentage of shortest paths among all pairs of network nodes that pass through a pre-defined sub-set of the network’s
nodes – to approximate the reach of this subset of locations. We
then demonstrate that this function can be approximated using asmooth and easily analyzed Gompertz function. This tackles themain limitation of works on campaign optimization hitherto – efficiently estimating the campaign reach as a function of the numberof units and their locations.
We have validated our results by using two comprehensive realworld geo-spatial datasets. The first dataset included a large number of mobile phone records, from which we have produced a mobility pattern model which was in turn analyzed in order to derive
an optimized campaign for the region in question. The second
dataset comprised of a large number of taxi rides in the city of New
York. While analyzing these dataset we have first shown a way toanalytically calculate the exact optimal cost for units in a certain
campaign, of generic nature. We then demonstrated how the optimal number of such units can be produced, that would guarantee amaximal utilization of a campaign’s budget.
Finally, we have discussed several campaign scenarios, involving various utilization schemes, demonstrating the usability of thetechniques presented in this chapter for real world use.

Figure 1: GBC Deployment fort the two mobility datasets: (a)CDR (left) and (b) taxi rides (right). The appropriate Gompertz fitof the curves is also included.

Poster Session 1 :: April 8

2

Figure 2: The optimal number of units, as a function of the ratio
between the cost of a unit and the cost of the best unit available in
the two mobility datasets. For example, in the CDR dataset (left),when a single unit costs 1% of the maximal campaign impact, theoptimal number of campaign units would be 25, whereas if cheaper
units are used (such as units that cost merely 21 % of the maximal
campaign impact) the optimal number of units would be 28. Similarly, in the taxi rides dataset (right), when a single unit costs 1%of the maximal campaign impact, the optimal number of campaign
units would be 350, whereas if cheaper units are used (such as units
that cost merely 12 % of the maximal campaign impact) the optimal
number of units would be 380.

1.

REFERENCES[1] Peter J Danaher and Roland T Rust. Determining the optimal level ofmedia spending. Journal of Advertising Research, 34(1):28–34, 1994.
[2] M. G. Everett and S. P. Borgatti. The centrality of groups and classes.
Mathematical Sociology, 23(3):181–201, 1999.

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