References

African Union. 2018. African Union Convention on Cyber Security and Personal Data Protection. https://au.int/en/treaties/african-union-convention-cyber-security-and-personal-data-protection.

Ahas, Rein, Anto Aasa, Antti Roose, Ülar Mark, and Siiri Silm. 2008. “Evaluating Passive Mobile Positioning Data for Tourism Surveys: An Estonian Case Study.” Tourism Management 29 (3): 469–86. https://doi.org/10.1016/j.tourman.2007.05.014.

Aiken, Emily, Guadalupe Bedoya, Joshua Blumenstock, and Aidan Coville. 2022. “Machine Learning and Phone Data Can Improve Targeting of Humanitarian Aid.” Nature 603: 864–70. https://doi.org/10.1038/s41586-022-04484-9.

Alexander, Lauren, Shan Jiang, Mikel Murga, and Marta C. Gonzalez. 2015. “Origin-Destination Trips by Purpose and Time of Day Inferred from Mobile Phone Data.” Transportation Research Part C: Emerging Technologies 58: 240–50. https://doi.org/10.1016/j.trc.2015.02.018.

Ascari, G., E. Cerasti, C. Faricelli, et al. 2024. “Quality Aspects Using Mobile Network Operators Data for Official Statistics.” 2nd Workshop on Methodologies for Official Statistics: Proceedings (Rome), 135–51. https://www.istat.it/wp-content/uploads/2024/11/2nd-Workshop-on-methodologies-for-official-statistics-Proceedings-1.pdf.

Barreras, Francisco, and Duncan J. Watts. 2024. “The Exciting Potential and Daunting Challenge of Using GPS Human-Mobility Data for Epidemic Modeling.” Nature Computational Science 4 (6): 398–411. https://doi.org/10.1038/s43588-024-00637-0.

Benchmark Initiative. 2021. The Locus Charter. https://ethicalgeo.org/locus-charter/.

Bengtsson, Linus, Jean Gaudart, Xin Lu, et al. 2015. “Using Mobile Phone Data to Predict the Spatial Spread of Cholera.” Scientific Reports 5: 8923. https://doi.org/10.1038/srep08923.

Blondel, Vincent D., Adeline Decuyper, and Gautier Krings. 2015. “A Survey of Results on Mobile Phone Datasets Analysis.” EPJ Data Science 4 (10). https://doi.org/10.1140/epjds/s13688-015-0046-0.

Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. 2015. “Predicting Poverty and Wealth from Mobile Phone Metadata.” Science 350 (6264): 1073–76. https://doi.org/10.1126/science.aac4420.

Cabrera, Carmen et al. 2025. Sustained Changes to Urban Mobility After COVID-19 Amplified Socio-Economic Inequalities in Latin America. https://doi.org/10.48550/arXiv.2504.15871.

Cabrera, Carmen, and Francisco Rowe. 2025. A Systematic Machine Learning Approach to Measure and Assess Biases in Mobile Phone Population Data. https://doi.org/10.48550/arXiv.2509.02603.

Caceres, N., J. P. Wideberg, and F. G. Benitez. 2007. “Deriving Origin-Destination Data from a Mobile Phone Network.” IET Intelligent Transport Systems 1 (1): 15–26. https://doi.org/10.1049/iet-its:20060020.

Calabrese, Francesco, Giusy Di Lorenzo, Liang Liu, and Carlo Ratti. 2011. “Estimating Origin-Destination Flows Using Mobile Phone Location Data.” IEEE Pervasive Computing 10 (4): 36–44. https://doi.org/10.1109/MPRV.2011.41.

Deville, Pierre, Catherine Linard, Samuel Martin, et al. 2014. “Dynamic Population Mapping Using Mobile Phone Data.” Proceedings of the National Academy of Sciences 111 (45): 15888–93. https://doi.org/10.1073/pnas.1408439111.

Eagle, Nathan, Michael Macy, and Rob Claxton. 2010. “Network Diversity and Economic Development.” Science 328 (5981): 1029–31. https://doi.org/10.1126/science.1186605.

European Statistical System. 2019. Quality Assurance Framework of the European Statistical System, Version 2.0. https://ec.europa.eu/eurostat/documents/64157/4392716/ESS-QAF-V1-2final.pdf/bbf5970c-1adf-46c8-afc3-58ce177a0646.

FlowGeek. n.d. Data Governance and Data Privacy for Mobile Phone Data. https://flowgeek.org/.

Flowminder Foundation. 2023a. Correcting Measurement Biases in the Detection of Long and Short Stay Locations in Sparse Call Detail Records. https://www.flowminder.org/resources/publications-reports/correcting-measurement-biases-in-the-detection-of-long-and-short-stay-locations-in-sparse-call-detail-records-cdrs.

Flowminder Foundation. 2023b. Flowminder Standards in Producing Mobility and Population Estimates from Call Detail Records in Low- and Middle-Income Countries. https://www.flowminder.org/resources/publications-reports/flowminder-standards-in-producing-mobility-and-population-estimates-from-call-details-records-in-low-and-middle-income-countries.

GDPR.eu. n.d. General Data Protection Regulation Compliance Overview. https://gdpr.eu/.

Global Partnership for Sustainable Development Data, and Positium. 2025. A Roadmap to Accessing Mobile Network Data for Statistics. https://www.data4sdgs.org/roadmap-accessing-mobile-network-data-statistics.

Gonzalez, Marta C., Cesar A. Hidalgo, and Albert-Laszlo Barabasi. 2008. “Understanding Individual Human Mobility Patterns.” Nature 453 (7196): 779–82. https://doi.org/10.1038/nature06958.

GSMA. 2016. Mobile Privacy Principles. https://www.gsma.com/solutions-and-impact/connectivity-for-good/public-policy/gsma_resources/mobile-privacy-principles/.

GSMA. 2019. Big Data for Social Good: Mobile Network Operator Data Sharing. https://www.gsma.com/solutions-and-impact/connectivity-for-good/external-affairs/wp-content/uploads/2019/09/Big-Data-AI-Ethics_web.pdf.

International Telecommunication Union. n.d.a. Big Data for Measuring the Information Society. https://www.itu.int/en/ITU-D/Statistics/Documents/bigdata/ITU_SDG_case_study.pdf.

International Telecommunication Union. n.d.b. ITU Academy. https://academy.itu.int/.

Iradukunda, Rodgers, Francisco Rowe, and Elisabetta Pietrostefani. 2025. “Estimating Internal Displacement in Ukraine from High-Frequency GPS Mobile Phone Data.” Humanities and Social Sciences Communications, ahead of print. https://doi.org/10.1057/s41599-025-06137-4.

Isaacman, Sibren, Richard Becker, Ramon Caceres, et al. 2011. “Identifying Important Places in People’s Lives from Cellular Network Data.” Pervasive Computing, Lecture notes in computer science, vol. 6696: 133–51. https://doi.org/10.1007/978-3-642-21726-5_9.

Jansen, Ronald, Miglena Abels, Savvas Papadakis, Benjamin Sakarovitch, et al. 2021. “Guiding Principles to Maintain Public Trust in the Use of Mobile Operator Data for Policy Purposes.” Data & Policy 3: E24. https://doi.org/10.1017/dap.2021.21.

Li, Tong, Cordelia Bowers, Alhassan Seidu, Gloria Akoto-Bamfo, Daniel Ofori-Boateng, et al. 2021. “Analysis of Call Detail Records to Inform the COVID-19 Response in Ghana: Opportunities and Challenges.” Data & Policy 3: E11. https://doi.org/10.1017/dap.2021.5.

Louail, Thomas, Maxime Lenormand, Oliva G. Cantu Ros, et al. 2014. “From Mobile Phone Data to the Spatial Structure of Cities.” Scientific Reports 4: 5276. https://doi.org/10.1038/srep05276.

Lu, Xin, Linus Bengtsson, and Petter Holme. 2012. “Predictability of Population Displacement After the 2010 Haiti Earthquake.” Proceedings of the National Academy of Sciences 109 (29): 11576–81. https://doi.org/10.1073/pnas.1203882109.

Montjoye, Yves-Alexandre de, Sebastien Gambs, Vincent D. Blondel, et al. 2018. “On the Privacy-Conscientious Use of Mobile Phone Data.” Scientific Data 5: 180286. https://doi.org/10.1038/sdata.2018.286.

Montjoye, Yves-Alexandre de, Cesar A. Hidalgo, Michel Verleysen, and Vincent D. Blondel. 2013. “Unique in the Crowd: The Privacy Bounds of Human Mobility.” Scientific Reports 3: 1376. https://doi.org/10.1038/srep01376.

Pietrostefani, Elisabetta, Matt Mason, Rodgers Iradukunda, Hong Tran-Jones, Iryna Loktieva, and Francisco Rowe. 2025. Dynamic Estimates of Displacement in Disaster Regions: A Policy-Driven Framework Triangulating Data. https://doi.org/10.48550/arXiv.2511.01955.

Ricciato, Fabio, Giampaolo Lanzieri, Albrecht Wirthmann, and Gerdy Seynaeve. 2020. “Towards a Methodological Framework for Estimating Present Population Density from Mobile Network Operator Data.” Pervasive and Mobile Computing 68: 101263. https://doi.org/10.1016/j.pmcj.2020.101263.

Rowe, F., E. Magpantay, M. Jalagonia, et al. 2022. Methodological Guide on the Use of Mobile Phone Data: Migration Statistics. United Nations Statistics Division; UN-CEBD Task Team on Mobile Phone Data. https://unstats.un.org/wiki/spaces/MPDMS/overview.

Rowe, Francisco. 2022. “Using Digital Footprint Data to Monitor Human Mobility and Support Rapid Humanitarian Responses.” Regional Studies, Regional Science, ahead of print. https://doi.org/10.1080/21681376.2022.2135458.

Rowe, Francisco, Alessia Calafiore, Daniel Arribas-Bel, Konstantin Samardzhiev, and Martin Fleischmann. 2023. “Urban Exodus? Understanding Human Mobility in Britain During the COVID-19 Pandemic Using Meta-Facebook Data.” Population, Space and Place, ahead of print. https://doi.org/10.1002/psp.2637.

Rowe, Francisco, and Miguel González-Leonardo. 2024. Digital Data and Population Studies. http://dx.doi.org/10.31219/osf.io/jb3e5.

Salgado, David, Luis Sanguiao, Bogdan Oancea, Sandra Barragan, and Marian Necula. 2021. “An End-to-End Statistical Process with Mobile Network Data for Official Statistics.” EPJ Data Science 10 (20). https://doi.org/10.1140/epjds/s13688-021-00275-w.

Song, Chaoming, Zehui Qu, Nicholas Blumm, and Albert-Laszlo Barabasi. 2010. “Limits of Predictability in Human Mobility.” Science 327 (5968): 1018–21. https://doi.org/10.1126/science.1177170.

Steele, Jessica E., Pal Roe Sundsoy, Carla Pezzulo, et al. 2017. “Mapping Poverty Using Mobile Phone and Satellite Data.” Journal of the Royal Society Interface 14 (127): 20160690. https://doi.org/10.1098/rsif.2016.0690.

Tizzoni, Michele, Paolo Bajardi, Adeline Decuyper, et al. 2014. “On the Use of Human Mobility Proxies for Modeling Epidemics.” PLOS Computational Biology 10 (7): e1003716. https://doi.org/10.1371/journal.pcbi.1003716.

Toole, Jameson L., Serdar Colak, Bradley Sturt, Lauren P. Alexander, Alexandre Evsukoff, and Marta C. Gonzalez. 2015. “The Path Most Traveled: Travel Demand Estimation Using Big Data Resources.” Transportation Research Part C: Emerging Technologies 58: 162–77. https://doi.org/10.1016/j.trc.2015.04.022.

UN-CEBD Task Team on Mobile Phone Data. n.d.a. Methodological Guide on the Use of Mobile Phone Data: Displacement and Disaster Statistics. United Nations Statistics Division. https://unstats.un.org/wiki/spaces/MPDDS/overview.

UN-CEBD Task Team on Mobile Phone Data. n.d.b. Methodological Guide on the Use of Mobile Phone Data: Dynamic Population Mapping. United Nations Statistics Division. https://unstats.un.org/wiki/spaces/MPDDPM/overview.

UN-CEBD Task Team on Mobile Phone Data. n.d.c. Methodological Guide on the Use of Mobile Phone Data: Measuring the Information Society. United Nations Statistics Division. https://unstats.un.org/wiki/spaces/MPDMIS/overview.

UN-CEBD Task Team on Mobile Phone Data. n.d.d. Methodological Guide on the Use of Mobile Phone Data: Tourism Statistics. United Nations Statistics Division. https://unstats.un.org/wiki/display/MPDTS.

United Nations. 2014. Fundamental Principles of Official Statistics. https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx.

United Nations Committee of Experts on Big Data and Data Science for Official Statistics. n.d. Task Team on Mobile Phone Data. https://unstats.un.org/bigdata/task-teams/mobile-phone/.

United Nations Statistics Division. 2019. Handbook on the Use of Mobile Phone Data for Official Statistics. United Nations. https://unstats.un.org/bigdata/task-teams/mobile-phone/MPD%20Handbook%2020191004.pdf.

Wesolowski, Amy, Nathan Eagle, Andrew J. Tatem, et al. 2012. “Quantifying the Impact of Human Mobility on Malaria.” Science 338 (6104): 267–70. https://doi.org/10.1126/science.1223467.

Wesolowski, Amy, Nathan Eagle, Andrew J. Tatem, et al. 2013. “The Impact of Biases in Mobile Phone Ownership on Estimates of Human Mobility.” Journal of the Royal Society Interface 10 (81): 20120986. https://doi.org/10.1098/rsif.2012.0986.

World Bank. 2021. Togo’s Novissi Platform for Social Protection Uses Machine Learning, Mobile Money, and Satellite Data to Support the Most Vulnerable. https://www.worldbank.org/en/results/2021/04/13/prioritizing-the-poorest-and-most-vulnerable-in-west-africa-togo-s-novissi-platform-for-social-protection-uses-machine-l.