[
Avouac, R., B. Sakarovitch, and Z. Smoreda. 2019. “A Bayesian Approach to Improve the Estimation of Population using Mobile Phone Data.” Research Workshop on Digital Demography in the Era of Big Data, Seville, Spain, 6–7 June 2019. Available at: https://iussp.org/en/digital-demography-era-big-data (accessed March 2023).
]Search in Google Scholar
[
Batista e Silva, F., S. Freire, M. Schiavina, K. Rosina, M.A. Marìn-Herrera, L. Ziemba, M. Craglia, E. Koomen, and C. Lavalle. 2020. “Uncovering Temporal Changes in Europe’s Population Density Patterns using a Data Fusion Approach.” Nature communications 11(1): 1–11. DOI: https://doi.org/10.1038/s41467-020-18344-5.
]Search in Google Scholar
[
Beresewicz, M., R. Lehtonen, F. Reis, L. Di Consiglio, and M. Karlberg. 2018. An Overview of Methods for Treating Selectivity in Big Data Sources. Technical report. Eurostat Statistical Working Article. Available at: https://doi.org/10.2785/312232.
]Search in Google Scholar
[
Blondel, V.D, A. Decuyper, and G. Krings. 2015. “A Survey of Results on Mobile Phone Data Sets Analysis.” EPJ data science 4(1): 10. DOI: https://doi.org/10.1140/epjds/s13688-015-0046-0.
]Search in Google Scholar
[
Bonnetain, L., A. Furno, N.-E. el Faouzi, M. Fiore, R. Stanica, Z. Smoreda, and C. Ziemlicki. 2021. “TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data.” Transportation Research Part C: Emerging Technologies 130: 103257. DOI: https://doi.org/10.1016/j.trc.2021.103257.
]Search in Google Scholar
[
Bonnetain, L., A. Furno, J. Krug, and N.-E. el Faouzi. 2019. “Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environents? Data-Driven Study.” Transportation Research Record 2673(7): 74–88. DOI: https://doi.org/10.1177/0361198119847472.
]Search in Google Scholar
[
Chen, G., A. Carneiro Viana, M. Fiore, and C. Sarraute. 2019. “Complete Trajectory Reconstruction from Sparse Mobile Phone Data.” EPJ Data Science 8(1): 30. DOI: https://doi.org/10.1140/epjds/s13688-019-0206-8.
]Search in Google Scholar
[
Coudin, E., M. Poulhes, and M. Suarez Castillo. 2021. “The French Official Statistics Strategy: Combining Signaling Data from Various Mobile Network Operators for Documenting COVID-19 Crisis Effects on Population Movements and Economic Outlook.” Data & Policy 3. DOI: https://doi.org/10.1017/dap.2021.1.
]Search in Google Scholar
[
ESSnet Big Data, II. 2021. Work Package I – Mobile Networks Data Deliverables. Available at: https://ec.europa.eu/eurostat/cros/content/wpi-milestones-and-deliverables_en. (accessed March 2023).
]Search in Google Scholar
[
Fekih, M., T. Bellemans, Z. Smoreda, P. Bonnel, A. Furno, and S. Galland. 2021. “A Data-Driven Approach for Origin – Destination Matrix Construction from Cellular Network Signalling Data: a Case Study of Lyon Region (France).” Transportation 48(4): 1671–1702. DOI: https://doi.org/10.1007/s11116-020-10108-w.
]Search in Google Scholar
[
Freire, S., and C. Aubrecht. 2012. “Integrating population dynamics into mapping human exposure to seismic hazard.” Natural Hazards and Earth System Sciences 12(11): 3533–3543. DOI: https://doi.org/10.5194/nhess-12-3533-2012.
]Search in Google Scholar
[
Galiana, L., B. Sakarovitch, F. Sémécurbe, and Z. Smoreda. 2020. Residential Segregation, Daytime Segregation and Spatial Frictions: an Analysis from Mobile Phone Data, Insee Working article G2020-12. Available at: https://www.insee.fr/en/-statistiques/4925202.
]Search in Google Scholar
[
Galiana, L., M. Suarez Castillo, F. Sémécurbe, E. Coudin, and M.-P. de Bellefon. 2020b. Retour Partiel des Mouvements de Population avec le Déconfinement, Insee Analyses 54. Available at: https://www.insee.fr/fr/statistiques/4635407.
]Search in Google Scholar
[
Hoteit, S., G. Chen, A. Viana, and M. Fiore. 2016. “Filling the Gaps: On the Completion of Sparse Call Detail Records for Mobility Analysis.” In Proceedings of the eleventh ACM workshop on challenged networks: 45–50. DOI: https://doi.org/10.1145/2979683.2979685.
]Search in Google Scholar
[
Insee. 2020. Population présente sur le territoire avant et aprésledébut du confinement – Premiers résultats (communiqué de presse). Available at: https://www.insee.fr/fr/information/4477356 (accessed March 2023).
]Search in Google Scholar
[
Jahani, E., P. Sundsoy, J. Bjelland, L. Bengtsson, A. Pentland, and Y.-A. de Montjoye. 2017. “Improving Official Statistics in Emerging Markets using Machine Learning and Mobile Phone Data.” EPJ Data Science 6(1): 3. DOI: https://doi.org/10.1140/epjds/s13688-017-0099-3.
]Search in Google Scholar
[
Katsikouli, P., M. Fiore, A. Furno, and R. Stanica. 2019. “Characterizing and Removing Oscillations in Mobile Phone Location Data.” In 2019 IEEE 20th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM): 1–9. IEEE. DOI: https://doi.org/10.1109/WoWMoM.2019.8793034.
]Search in Google Scholar
[
Lamarche, P., and S. Lollivier. 2021. Fidéli, l’intégration des sources fiscales dans les données sociales Courrier des statistiques 6. Available at: https://www.insee.fr/fr/information/5398683 (accessed March 2023).
]Search in Google Scholar
[
Léetroublon, C., and C. Daniel. 2018. Le Travail en Horaires Atypiques: Quels Salariés pour quelle Organisation du Temps de Travail? Dares Analyses 2018-30. Available at: https://dares.travail-emploi.gouv.fr/publications/le-travail-en-horaires-atypiques. (accessed March 2023).
]Search in Google Scholar
[
Panczak, R., E. Charles-Edwards, and J. Corcoran. 2020. “Estimating Temporary Populations: a Systematic Review of the Empirical Literature.” Humanities and Social Sciences Communications 6(1): 1–10. DOI: https://doi.org/10.1057/s41599-020-0455-y.
]Search in Google Scholar
[
Ricciato, F., and A. 2021. “On the Estimation of Spatial Density from Mobile Network Operator Data.” IEEE Transactions on Mobile Computing, 22(6): 1. DOI: https://doi.org/10.1109/TMC.2021.3134561.
]Search in Google Scholar
[
Ricciato, F., G. Lanzieri, A. Wirthmann, and G. Seynaeve. 2020. “Towards a Methodological Framework For Estimating Present Population Density from Mobile Network Operator Data.” Pervasive and Mobile Computing 101263. DOI: https://doi.org/10.1016/j.pmcj.2020.101263.
]Search in Google Scholar
[
Ricciato, F., P. Widhalm, F. Pantisano, and M. Craglia. 2017. “Beyond the “Single-Operator, CDR-only Paradigm: An Interoperable Framework for Mobile Phone Network Data Analyses and Population Density Estimation.” Pervasive and Mobile Computing 35: 65–82. DOI: https://doi.org/10.1016/j.pmcj.2016.04.009.
]Search in Google Scholar
[
Ricroch, L., and B. Roumier. 2011. Depuis 11 Ans, Moins de T^aches Ménagères, Plus d’Internet. Insee premiére 1377. Available at: https://www.insee.fr/fr/statistiques/1281050. (accessed March 2023).
]Search in Google Scholar
[
Sakarovitch, B., M.-P. de Bellefon, P. Givord, and M. Vanhoof. 2018. “Estimating the Residential Population from Mobile Phone Data, an Initial Exploration.” Economie et Statistique 505(1): 109–132. DOI: https://doi.org/10.24187/ecostat.2018.505d.1968.
]Search in Google Scholar
[
Salat, H., Z. Smoreda, and M. Schläpfer. 2020. “A Method to Estimate Population Densities and Electricity Consumption from Mobile Phone Data in Developing Countries.” PLOS ONE 15(6): 1–11. DOI: https://doi.org/10.1371/journal.pone.0235224.
]Search in Google Scholar
[
Salgado, D., L. Sanguiao, B. Oancea, S. Barragàn, and M. Necula. 2021. “An End-to-End Statistical Process with Mobile Network Data for Official Statistics.” EPJ Data Science 10(1): 1–46. DOI: https://doi.org/10.1140/epjds/s13688-021-00275-w.
]Search in Google Scholar
[
Schiavina, M., S. Freire, K. Rosina, L. Ziemba, M. Marin Herrera, M. Craglia, C. Lavalle, T. Kemper, and F. Batista. 2020. ENACT-POP R2020A-ENACT 2011 Population Grid. European Commission, Joint Research Centre (JRC). DOI: https://doi.org/10.2905/-BE02937C-5A08-4732-A24A-03E0A48BDCDA.
]Search in Google Scholar
[
Statistics Netherlands, C.B.S. 2020. Estimating Hourly Population Flows in the Netherlands. Avalialble at: https://www.cbs.nl/en-gb/longread/diversen/2020/pilot-study-mobile-phone-meta-data-records-introduction-to-the-research-method (accessed March 2023).
]Search in Google Scholar
[
Tennekes, M., and Y. Gootzen. 2022. “Bayesian location estimation of mobile devices using a signal strength model.” Journal of Spatial Information Science 25: 29–66. DOI: https://doi.org/10.5311/JOSIS.2022.25.166.
]Search in Google Scholar
[
U.N. Global Working Group. 2019. Handbook on the Use of Mobile Phone Data for Official Statistics. Technical report. United Nations. Available at: https://unstats.un.org/bigdata/task-teams/mobile-phone/MPD%20Handbook%2020191004.pdf.
]Search in Google Scholar
[
Vanhoof, M., F. Reis, T. Ploetz, and Z. Smoreda. 2018. “Assessing the Quality of Home Detection from Mobile Phone Data for Official Statistics.” Journal of Official Statistics 34(4): 935–960. DOI: https://doi.org/10.2478/jos-2018-0046.
]Search in Google Scholar
