Mobile Phone Metadata for Development

Mobile phones are now widely adopted by most of the world population. Each time a call is made (or an SMS sent), a Call Detail Record (CDR) is generated by the telecom companies for billing purpose. These metadata provide information on when, how, from where and with whom we communicate. Conceptually, they can be described as a geospatial, dynamic, weighted and directed network. Applications of CDRs for development are numerous. They have been used to model the spread of infectious diseases, study road traffic, support electrification planning strategies or map socio-economic level of population. While massive, CDRs are not statistically representative of the whole population due to several sources of bias (market, usage, spatial and temporal resolution). Furthermore, mobile phone metadata are held by telecom companies. Consequently, their access is not necessarily straightforward and can seriously hamper any operational application. Finally, a trade-off exists between privacy and utility when using sensitive data like CDRs. New initiatives such as Open Algorithm might help to deal with these fundamental questions by allowing researchers to run algorithms on the data that remain safely stored behind the firewall of the providers.

[1]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[2]  J. White,et al.  Extracting origin destination information from mobile phone data , 2002 .

[3]  Vincent D. Blondel,et al.  Evaluating socio-economic state of a country analyzing airtime credit and mobile phone datasets , 2013, ArXiv.

[4]  L. Taylor.  No place to hide? The ethics and analytics of tracking mobility using mobile phone data , 2016 .

[5]  Helmut Hlavacs,et al.  The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring , 2015, IEEE Transactions on Intelligent Transportation Systems.

[6]  A. Barabasi,et al.  Network science , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Marco Luca Sbodio,et al.  AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data , 2013, ECML/PKDD.

[8]  J. Aker,et al.  Mobile Phones and Economic Development in Africa , 2010 .

[9]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[10]  J. Blumenstock.  Calling for Better Measurement: Estimating an Individual’s Wealth and Well-Being from Mobile Phone Transaction Records , 2015 .

[11]  Saverio Niccolini,et al.  Lessons learned on the usage of call logs for security and management in IP telephony , 2010, IEEE Communications Magazine.

[12]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[13]  Zbigniew Smoreda,et al.  Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations , 2015 .

[14]  Tyler Moore,et al.  GSM Cell Site Forensics , 2006, IFIP Int. Conf. Digital Forensics.

[15]  Zbigniew Smoreda,et al.  D4D-Senegal: The Second Mobile Phone Data for Development Challenge , 2014, ArXiv.

[16]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[17]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[18]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[19]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[20]  O. Järv,et al.  Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones , 2010 .

[21]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[22]  Albert-László Barabási,et al.  Modeling bursts and heavy tails in human dynamics , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Etienne Huens,et al.  Data for Development: the D4D Challenge on Mobile Phone Data , 2012, ArXiv.

[24]  Margaret Martonosi,et al.  ON CELLULAR , 2022 .

[25]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[26]  Carlo Ratti,et al.  Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .

[27]  Joshua Blumenstock,et al.  The Economic Impacts of New Technologies in Africa , 2015 .

[28]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[29]  Vincent D. Blondel,et al.  Estimating Food Consumption and Poverty Indices with Mobile Phone Data , 2014, ArXiv.

[30]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[31]  Pierluigi Mancarella,et al.  Using Mobile Phone Data for Electricity Infrastructure Planning , 2015, ArXiv.

[32]  Víctor Soto,et al.  Prediction of socioeconomic levels using cell phone records , 2011, UMAP'11.

[33]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

[34]  Peter Widhalm,et al.  Beyond the "single-operator, CDR-only" paradigm: An interoperable framework for mobile phone network data analyses and population density estimation , 2017, Pervasive Mob. Comput..

[35]  M. Kosinski,et al.  Computer-based personality judgments are more accurate than those made by humans , 2015, Proceedings of the National Academy of Sciences.

[36]  Albert-Laszló Barabási,et al.  Bursts : the hidden patterns behind everything we do, from your e-mail to bloody crusades , 2011 .

[37]  Jari Saramäki,et al.  From seconds to months: an overview of multi-scale dynamics of mobile telephone calls , 2015, The European Physical Journal B.

[38]  Neeti Pokhriyal,et al.  Combining disparate data sources for improved poverty prediction and mapping , 2017, Proceedings of the National Academy of Sciences.

[39]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[40]  Gabriel Cadamuro,et al.  Predicting poverty and wealth from mobile phone metadata , 2015, Science.

[41]  Caroline O. Buckee,et al.  The impact of biases in mobile phone ownership on estimates of human mobility , 2013, Journal of The Royal Society Interface.

[42]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[43]  Enrique Frías-Martínez,et al.  Comparing and modelling land use organization in cities , 2015, Royal Society Open Science.

[44]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[45]  Craglia Massimo,et al.  Estimating population density distribution from network-based mobile phone data , 2015 .