Temporally Consistent Present Population from Mobile Network Signaling Data for Official Statistics

Abstract

Mobile network data records are promising for measuring temporal changes in present populations. This promise has been boosted since high-frequency passively-collected signaling data became available. Its temporal event rate is considerably higher than that of Call Detail Records – on which most of the previous literature is based. Yet, we show it remains a challenge to produce statistics consistent over time, robust to changes in the “measuring instruments” and conveying spatial uncertainty to the end user. In this article, we propose a methodology to estimate – consistently over several months – hourly population presence over France based on signaling data spatially merged with fine-grained official population counts. We draw particular attention to consistency at several spatial scales and over time and to spatial mapping reflecting spatial accuracy. We compare the results with external references and discuss the challenges which remain. We argue data fusion approaches between fine-grained official statistics data sets and mobile network data, spatially merged to preserve privacy, are promising for future methodologies.

Keywords

Big data High frequency statistics dynamic population mapping spatial accuracy

References

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).

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.

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.

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.

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.

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.

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.

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.

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).

10.

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.

11.

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.

12.

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.

13.

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.

14.

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.

15.

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).

16.

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.

17.

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.

18.

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).

19.

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).

20.

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.

21.

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.

22.

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.

23.

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.

24.

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).

25.

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.

26.

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.

27.

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.

28.

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.

29.

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).

30.

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.

31.

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.

32.

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.