This page provides an overview, alphabetically by author(s), of all articles on mobile phone data useful for WP5 Mobile phone data. See here for documentation on big data in general and on the other workpackages.

• R. Ahas, A. Aasa, Y. Yuan, M. Raubal, Z. Smoreda, Y. Liu, C. Ziemlicki, M. Tiru & M. Zook (2015): Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn, International Journal of Geographical Information Science (article not freely available on internet)
• R. Ahas, A. Aasa, A. Roose, Ü. Mark & S. Silm (2007): Evaluating passive mobile positioning data for tourism surveys: An Estonian case study (article not freely available on internet)
• R. Ahas, A. Aasa, S. Silm, R. Aunap, H. Kalle & Ü. Mark (2014): Mobile Positioning in Space-Time Behaviour Studies: Social Positioning Method Experiments in Estonia
• R. Ahas, A. Aasa, S. Silm & M. Tiru (2007): Mobile Positioning Data in Tourism Studies and Monitoring: Case Study in Tartu, Estonia
• R. Ahas, S. Silm, O. Järv, E. Saluveer & M. Tiru (2010): Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones, Journal of Urban Technology, April 2010
• R. Ahas, M. Tiru, E. Saluveer & C. Demunter (2014): Mobile telephones and mobile positioning data as source for statistics: Estonian experiences
• Y. Akiyama (2013): Evaluation analysis of estimation of population distribution by DMSP/OLS satellite images using GPS log data of mobile phones (article not yet freely available on the internet, see here)
• L. Alexander, S. Jiang, M. Murga & M. C. González (2015): Origin-destination trips by purpose and time of day inferred from mobile phone data
• L. Altin, M. Tiru, E. Saluveer & A. Puura (2015): Using Passive Mobile Positioning Data in Tourism and Population Statistics, NTTS 2015 Conference abstract
• A. Arai, ZP. Fan, D. Matekenya & R. Shibasaki (2016): Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users
• F. Bahoken & A-M Olteanu (2013): Designing Origin-Destination Flow Matrices from Individual Mobile Phone Paths
• P. Bajardi, M. Delfino, A. Panisson, G. Petri & M. Tizzoni (2015): Unveiling patterns of international communities in a global city using mobile phone data
• L. Bengtsson, X. Lu, A. Thorson, R. Garfield & J. von Schreeb (2011): Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haïti
• V. D. Blondel, A. Decuyper & G. Krings (2015): A survey of results on mobile phone datasets analysis
• J. E. Blumenstock (2011): Inferring patterns of internal migration from mobile phone call records: evidence from Rwanda, Information Technology for Development, April 2012
• P. Bonnel, E. Hombourger, A-M Olteanu-Raimond & Z. Smoreda (2015): Passive mobile phone dataset to construct origin-destination matrix: potentials and limitations, 10th International Conference on Transport Survey Methods
• N. Caceres, J.P. Wideberg & F.G. Benitez (2007): Deriving origin-destination data from a mobile phone network
• F. Calabrese, M. Diao, G. Di Lorenzo, J. Ferreira, Jr., C. Ratti (2012): Understanding Individual Mobility Patterns from Urban Sensing Data: A Mobile Phone Trace Example (article not freely avalaible on internet)
• F. Calabrese, L. Ferrari & V. D. Blondel (2014): Urban Sensing Using Mobile Phone Network Data: A Survey of Research, ACM Computing Surveys, January 2014
• F. Calabrese, M. Colonna, P. Lovisolo, D. Parata & C. Ratti (2011): Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome
• F. Calabrese, G. Di Lorenzo, L. Liu & C. Ratti (2011): Estimating Origin-Destination flows using opportunistically collected mobile phone location data from one million users in Boston Metropolitan Area, IEEE Pervasive Computing, May 2011
• F. Calabrese & C. Ratti (2006): Note Real Time Rome (PDF download)
• J. Candia, M. C. González, P. Wang, T. Schoenharl, G. Madey & A-L Barabási (2007): Uncovering individual and collective human dynamics from mobile phone records (article not freely avalaible on internet)
• T. Couronné, V. Kirzhner, K. Korenblat & Z. Volkovich (2013): Some features of the Users' Activities in the Mobile Telephone Network
• Y. Dan & Z. He (2010): A dynamic model for urban population density estimation using mobile phone location data (article not yet freely available on the internet, see here)
• E. De Jonge, M. Van Pelt & M. Roos (2012): Time patterns, geospatial clustering and mobility statistics based on mobile phone network data
• F. De Meersman, G. Seynaeve, M. Debusschere, P. Lusyne, P. Dewitte, Y. Baeyens, A. Wirthmann, C. Demunter, F. Reis, H.I. Reuter (2016): Assessing the Quality of Mobile Phone Data as a Source of Statistics (mirror site), Q2016 Conference paper, June 2016 (pdf download)
• Y.-A. De Montjoye, C.A. Hidalgo, M. Verleysen & V.D. Blondel (2013): Unique in the Crowd: The privacy bounds of human mobility, Sci Rep. 2013; 3: 1376. Published online 2013 Mar 25. doi: 10.1038/srep01376
• M. De Nadai, J. Staiano, R. Larcher, N. Sebe, D. Quercia & B. Lepri (2016): The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective, arXiv:1603.04012v1 [cs.CY] 13 Mar 2016 (pdf download, 1.40 MB)
• M. Debusschere, P. Lusyne, P. Dewitte, Y. Baeyens, F. De Meersman, G. Seynaeve, A. Wirthmann, C. Demunter, F. Reis, H.I. Reuter (2016a): Big data en statistiek: om het kwartier een volkstelling … (mirror site), Trefpunt Economie 2016 10 (PDF download) (in Dutch)
• M. Debusschere, P. Lusyne, P. Dewitte, Y. Baeyens, F. De Meersman, G. Seynaeve, A. Wirthmann, C. Demunter, F. Reis, H.I. Reuter (2016b): Big data et statistiques : un recensement tous les quarts d'heure… (mirror site), Carrefour de l'Economie 2016 10 (PDF download) (in French)
• M. Debusschere, F. De Meersman (2016): Statistiek en big data; een samenwerkingsmodel, STAtOR, 17/3, december 2016 (PDF download) (in Dutch)
• M. Debusschere, J. Sonck, M. Skaliotis (2016): Official statistics and mobile network operator partner up in Belgium, The OECD Statistics Newsletter, Issue No 65, November 2016
• M. Debusschere, A. Wirthmann, F. De Meersman (2017): Official statistics and mobile network operators: a business model for partnerships, NTTS Conference, 13-17 March 2017 (abstract and presentation download page)
• M. G. Demissie, G. H. Correia & C. L. Bento (2013): Exploring cellular network handover information for urban mobility analysis, Journal of Transport Geography, July 2013
• M. G. Demissie, G. H. Correia & C. L. Bento (2014): Extracting urban activities through aggregate cellphone usage, 17th meeting of the EURO Working Group on Transportation, July 2014
• M. G. Demissie, S. Phithakkitnukoon, T. Sukhvibul, F. Antunes, R. Gomes & C. Bento (2016): Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal, IEEE Transactions on Intelligent Transportation Systems, January 2016
• C. Demunter, G. Seynaeve (2017): Better quality of mobile phone data based statistics through the use of signalling information – the case of tourism statistics, NTTS Conference, 13-17 March 2017 (paper and presentation download page)
• K. Denise Bell (2011): Comparing methods for estimation of daytime population in Downtown Indianapolis, Indiana
• P. Deville, C. Linarde, S. Martine, M. Gilbert, F.R. Stevens, A.E. Gaughan, V.D. Blondela & A.J. Tatem (2014): Dynamic population mapping using mobile phone data, PNAS 2014 111 (45) 15888-15893 (full text, also downloadable as PDF)
• A. Dobra, N. E. Williams & N.Eagle (2015): Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data
• H. Dong, M. Wu, X. Ding, L. Chu, L. Jia, Y. Qin & X. Zhou (2015): Traffic zone division based on big data from mobile phone base stations (article not freely avalaible on internet)
• J. Doyle, P. Hung, R. Farrel & S. McLoone (2014): Population Mobility Dynamics Estimated from Mobile Telephony Data, Journal of Urban Technology, June 2014
• European Commission (2014): Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics, Eurostat, ISBN 978-92-79-39762-2, doi:2785/55051 (PDF download)
• B. Furletti, P. Cintia, C. Renso & L. Spinsanti (2013): Inferring human activities from GPS tracks (pdf download, 768 kB)
• B. Furletti, L. Gabrielli, C. Renso & S. Rinzivillo (2013): Analysis of GSM calls data for understanding user mobility behavior
• L. Gabrielli, B. Furletti, F. Giannotti, M. Nanni & S. Rinzivillo (2015): Use of mobile phone data to stimate visitors mobility flows
• L. Gabrielli, B. Furletti, R. Trasarti, F. Giannotti & D. Pedreschi (2015): City users' classification with mobile phone data (article not freely avalaible on internet)
• C. Gariazzo, A. Pelliccioni & A. Bolignano (2016): A dynamic urban air pollution population exposure assessment study using model and population density data derived by mobile phone traffic (article not yet freely available on the internet, see here)
• F. Giannotti, M. Nanni, D. Pedreschi & F. Pinelli (2007): Trajectory Pattern Mining
• M. C. González, C. A. Hidalgo & A-L Barabási (2008): Understanding individual human mobility patterns
• N. Heerschap (2014): Mobile phone data and other new sources for tourism statistics (in Dutch) Section 10.2, Statistics Netherlands book on Tourism, 158-168, The Hague, The Netherlands
• T. Holleczek, H. L. Goh, A. Spyridon, D. The Anh, Y. Jin, S. Yin, S. Low & A. Shi-Nash (2015): Traffic measurement and route recommendation system for Mass Rapid Transit (MRT)
• J. Hu, W. Cao, J. Luo & X. Yu (2009): A dynamic model for urban population density estimation using mobile phone location data(article not yet freely available on the internet, see here)
• Md. S. Iqbal (2013): Development of Origin-Destination Trip Matrices Using Mobile Phone Call Data
• Md. S. Iqbal, C. F. Choudhury, P. Wang & M. C. González (2014): Development of Origin-Destination Matrices Using Mobile Phone Call Data
• O. Järv, R. Ahas, E. Saluveer, B. Derudder & F. Witlox (2012): Mobile Phones in a Traffic Flow: A Geographical Perspective to Evening Rush Hour Traffic Analysis Using Call Detail Records
• O. Järv, K. Müürisepp, R. Ahas, B. Derudder & F. Witlox (2015): Ethnic differences in activity spaces as a characteristic of segregation: A study based on mobile phone usage in Tallinn, Estonia
• A. Kamanga, P. Moono, G. Stresman, S. Mharakurwa & C. Shiff (2009): Rural health centres, communities and malaria case detection in Zambia using mobile telephones: A means to detect potential reservoirs of infection in unstable transmission conditions, Malaria Journal, April 2010
• C. Kang, Y. Liu, X. Ma & L. Wu (2012): Towards Estimating Urban Population Distributions from Mobile Call Data (article not yet freely available on the internet, see here)
• F. H. Khan, M. E. Ali & H. Dev (2015): A Hierarchical Approach for Identifying User Activity Patterns from Mobile Phone Call Detail Records
• M-P Kwan (2016): Algorithmic Geographies: Big Data, Algorithmic Uncertainty, and the Production of Geographic Knowledge, Annals of the Association of American Geographers, March 2016
• A. N. Larijani, A-M Olteanu-Raimond, J. Perret, M. Brédif & C. Ziemlicki (2014): Investigating the mobile phone data to stimate the origin destination flow and analysis; case study: Paris region, 4th International Symposium of Transport Simulation-ISTS'14 (article not freely available on internet)
• M. Lenormand, M. Picornell, O. G. Cantú-Ros, A. Tugores, T. Louail, R. Herranz, M. Barthelemy, E. Frías-Martínez & J. J. Ramasco (2014): Cross-Checking Different Sources of Mobility Information
• Q. Li, B. Xu, Y. Ma & T. Chung (2016): Real-time monitoring and forecast of active population density using mobile phone data (article not yet freely available on the internet, see here)
• T. Louail, M. Lenormand, M. Picornell, O. G. Cantú, R. Herranz, E. Frías-Martínez, J. J. Ramasco & M. Barthelemy (2014): Uncovering the spatial structure of mobility networks, Nature Communications, Jan 2015
• N. Makita, M. Kimura, M. Terada, M. Kobayashi & Y. Oyabu (2013): Can mobile phone network data be used to estimate small area population? A comparison from Japan (article not yet freely available on the internet, see here)
• J. Novak & J. Temelova (2012): Everyday Life and Spatial Mobility of Young People in Prague: A Pilot Study Using Mobile Phone Location Data
• P. O'Connor, W. Höpken & U. Gretzel (2008): Information and Communication Technologies in Tourism 2008, Proceedings of the International Conference in Innsbruck, Austria, 2008
• M. Offermans & M. Tennekes (2014): Mobile Phone Metadata: A New Source for Official Statistics. Presentation at the 2014 Joint Statistical Meeting (JSM) Boston, USA
• J. R. B. Palmer, T. J. Espenshade, F. Bartumeus, C. Y. Chung, N. E. Ozgencil & K. Li (2012): New Approaches to Human Mobility: Using Mobile Phones for Demographic Research
• C. Ratti, D. Frechman, R. M. Pulselli & S. Williams (2005): Mobile Landscapes: using location data from cell phones for urban analysis, Environment and Planning B: Planning an Design, 2006
• J. Reades, F. Calabrese & C. Ratti (2007): Eigenplaces: analysing cities using the space-time structure of the mobile phone network, Environment and Planning B: Planning an Design, 2009
• F. Reis, A. Wirthmann, P. Lusyne, Y. Baeyens, F. De Meersman, M. Debusschere, H. Reuter, G. Seynaeve (2016), New opportunities for statistics on population and mobility from the use of mobile phone data, paper presented at the IAOS 2016 Conference, Abu Dhabi (UAE), Dec. 2016 (article not available online)
• F. Ricciato, P. Widhalm, M. Craglia & F. Pantisano (2015): Estimating Population Density Distribution from Network-based Mobile Phone Data, JRC Technical Report (PDF download)
• F. Ricciato, P. Widhalm, F. Pantisano & M. Craglia (2017): Beyond the "single-operator, CDR-only" paradigm: An interoperable framework for mobile phone network data analyses and population density estimation (article not yet freely available on the internet, see here)
• R. Sanya & M. Mubangizi (2016): Using mobile phone data to study dynamics of rural-urban mobility
• T. Schmid, F. Bruckschen, N. Salvati & T. Zbiranski (2016): Constructing socio-demographic indicators for National Statistical Institutes using mobile phone data: estimating literacy rates in Senegal, Freie Universität Berlin, School of Business & Economics - Discussion Paper (also: Journal of the Royal Statistical Society: Series A (Statistics in Social Sciences), 2017)
• S. Schönfelder & K.W. Axhausen (2014): Structure and innovation of human activity spaces
• I. Semanjski & S. Gautama (2015): Smart City Mobility Application-Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data, Sensors, July 2015
• G. Seynaeve, C. Demunter, F. De Meersman, Y. Baeyens, M. Debusschere, P. Dewitte, P. Lusyne, F. Reis, H.I. Reuter, A. Wirthmann (2016), When mobile network operators and statistical offices meet - integrating mobile positioning data into the production process of tourism statistics, paper presented at 14th Global Forum on Tourism Statistics (Venice, Italy, Nov. 2016) (PDF download)
• L. Shi, G. Chi, X. Liu & Y. Liu (2015): Human mobility patterns in different communities: a mobile phone data-based social network approach
• Z. Smoreda, A-M Olteanu-Raimond & T. Couronné (2013): Spatiotemporal data from mobile phones for personal mobility assessment
• J. E. Spinney (2003): Mobile Positioning and LBS Applications, Geography, October 2003
• J. Steenbruggen, M. T. Borzacchiello, P. Nijkamp & H. Scholten (2011): Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities, GeoJournal, April 2013
• J. Steenbruggen, M. T. Borzacchiello, P. Nijkamp & H. Scholten (2014): Real-time data from mobile phone networks for urban incidence and traffic management - A review of applications and opportunities
• M. Tennekes, M. P. W. Offermans & N. Heerschap (2017): Determining an optimal time window for roaming data for tourism statistics, Paper presented at the NetMob 2017 conference, Milano, Italy.
• M. Tennekes & M. Offermans (2014): Daytime population estimations based on mobile phone metadata. Paper for the Joint Statistical Meetings, 2–7 August, Boston, MA
• M. Tiru: Overview of the Sources and Challenges of Mobile Positioning Data for Statistics
• J.L. Toole, M. Ulm, M.C. González & D. Bauer (2012): Inferring land use from mobile phone activity, arXiv:1207.1115v1 [stat.ML] 3 Jul 2012 (pdf download, 3.02 MB)
• T. Vajakas, J. Vajakas & R. Lillemets (20015): Trajectory reconstruction from mobile positioning data using cell-to-cell travel time information, International Journal of Geographical Information Science, May 2015
• M. Wang (2014): Understanding Activity Location Choice with Mobile Phone Data
• A. Wesolowski, N. Eagle, A. M. Noor, R. W. Snow &C. O. Buckee (2013): The impact of biases in mobile phone ownership on estimates of human mobility
• A. Wirthmann, F. Reis, M. Skaliotis, F. De Meersman, G. Seynaeve, M. Debusschere, H. Reuter (2016), Big Data as a Source for Official Statistics: Assessment of Using Mobile Phone Data for Population, paper presented at Data for Policy 2016 Conference, Cambridge, 15-16 Sept. 2016 (article not available on the internet)
• C. Wu, J. Thai, S. Yadlowsky, A. Pozdnoukhov & A. Bayen (2015): Cellpath: fusion of cellular and traffic sensor data for route flow estimation via convex optimization, 21st International Symposium on Transportation and Traffic Theory, 5-7 August 2015
• W. Wu, Y. Wang, J. B. Gomes, D. The Anh, S. Antonatos, M. Xue, P. Yang, G. E. Yap, X. Li, S. Krishnaswamy, J. Decraene, A. Shi-Nash (2014): Oscillation Resolution for Mobile Phone Cellular Tower Data to Enable Mobility Modelling
• Y. Xu, S. Shaw, Z. Zhao, L. Yin, Z. Fang & Q. Li (2015): Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach
• L. Yin, Q. Wang, S. Shaw, Z. Fang, J. Hu, Y. Tao & W. Wang (2015): Re-Identification Risk versus Data Utility for Aggregated Mobility Research Using Mobile Phone Location Data
• L. Yu, W. Wu, X. Li, G. Li, W. S. Ng, S-k Ng, Z. Huang, A. Arunan & H. M. Watt (2015): iVizTRANS: Interactive Visual Learning for Home and Work Place Detection from Massive Public Transportation Data (article not freely available on internet)