{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T17:02:37Z","timestamp":1769878957991,"version":"3.49.0"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Inria in the context of the COVID-19 mission, launched"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2022,9,30]]},"abstract":"<jats:p>\n            The idea of using mobile phone data to understand the impact of the Covid-19 pandemic and that of the sanitary constraints associated with it on human mobility imposed itself as evidence in most countries. This work uses spatiotemporal aggregated mobile phone data provided by a major French telecom operator, covering a geographical region centered on Paris for early 2020, i.e., periods before and during the first French lockdown. An essential property of this data is its fine-grained spatial resolution, which, to the best of our knowledge, is unique in the COVID-related mobility literature. Contrarily to regions or country-wide resolution, it describes population mobility flows among zones ranging from 0.025~km\n            <jats:sup>2<\/jats:sup>\n            to 5.40~km\n            <jats:sup>2<\/jats:sup>\n            , corresponding to 326 aggregated zones over the total area of 93.76~km\n            <jats:sup>2<\/jats:sup>\n            of the city of Paris. We perform a data-driven mobility investigation and modeling to quantify (in space and time) the population attendance and visiting flows in different urban areas. Second, when looking at periods both before and during the lockdown, we quantify the consequences of mobility restrictions and decisions on an urban scale. For this, per zone, we define a so-called\n            <jats:italic>signature<\/jats:italic>\n            , which captures behaviors in terms of population attendance in the corresponding geographical region (i.e., their land use) and allows us to automatically detect activity, residential, and outlier areas. We then study three different types of\n            <jats:italic>graph centrality<\/jats:italic>\n            , quantifying the importance of each zone in a time-dependent weighted graph according to the habits in the mobility of the population. Combining the three centrality measures, we compute per zone of the city, its\n            <jats:italic>impact-factor<\/jats:italic>\n            , and employ it to quantify the global importance of zones according to the population mobility. Our results firstly reveal the population\u2019s daily zone preferences in terms of attendance and mobility, with a high concentration on business and touristic zones. Second, results show that the lockdown mobility restrictions significantly reduced visitation and attendance patterns on zones, mainly in central Paris, and considerably changed the mobility habits of the population. As a side effect, most zones identified as mainly having activity-related population attendance in typical periods became residential-related zones during the lockdown, turning the entire city into a residential-like area. Shorter distance displacement restrictions imposed by the lockdown increased visitation to more \u201clocal\u201d zones, i.e., close to the population\u2019s primary residence. Decentralization was also favored by the paths preferences of the still-moving population. On the other side, \u201cjogging activities\u201d allowing people to be outside their residences impacted parks visitation, increasing their visitation during the lockdown. By combining the impact factor and the signatures of the zones, we notice that areas with a higher impact factor are more likely to maintain regular land use during the lockdown.\n          <\/jats:p>","DOI":"10.1145\/3517222","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T11:41:42Z","timestamp":1648554102000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Data-driven Mobility Analysis and Modeling: Typical and Confined Life of a Metropolitan Population"],"prefix":"10.1145","volume":"8","author":[{"given":"Haron C.","family":"Fanticelli","sequence":"first","affiliation":[{"name":"National Laboratory for Scientific Computing - LNCC, Brazil and Inria, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Solohaja","family":"Rabenjamina","sequence":"additional","affiliation":[{"name":"Univ Lyon, Inria, INSA Lyon, CITI, Villeurbanne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aline Carneiro","family":"Viana","sequence":"additional","affiliation":[{"name":"Inria, Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4479-3976","authenticated-orcid":false,"given":"Razvan","family":"Stanica","sequence":"additional","affiliation":[{"name":"Univ Lyon, INSA Lyon, Inria, CITI, Villeurbanne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucas Santos","family":"De Oliveira","sequence":"additional","affiliation":[{"name":"State University of Southwestern of Bahia - UESB, Jequi\u00e9, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artur","family":"Ziviani","sequence":"additional","affiliation":[{"name":"National Laboratory for Scientific Computing - LNCC, Petr\u00f3polis, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/990064.990073"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010226"},{"key":"e_1_3_2_4_2","unstructured":"Licia Amichi Aline Carneiro Viana Mark Crovella and Antonio A. F. Loureiro. [n.d.]. Understanding individuals\u2019 proclivity for novelty seeking. https:\/\/hal.inria.fr\/hal-02944150."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397536.3422248"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/LCN52139.2021.9524884"},{"issue":"5","key":"e_1_3_2_7_2","article-title":"Limitations of using mobile phone data to model COVID-19 transmission in the USA","volume":"21","author":"Badr Hamada S.","year":"2020","unstructured":"Hamada S. Badr and Lauren M. Gardner. 2020. Limitations of using mobile phone data to model COVID-19 transmission in the USA. The Lancet 21, 5 (Nov. 2020).","journal-title":"The Lancet"},{"key":"e_1_3_2_8_2","volume-title":"Network Science","author":"Barab\u00e1si Albert-L\u00e1szl\u00f3","year":"2016","unstructured":"Albert-L\u00e1szl\u00f3 Barab\u00e1si and M\u00e1rton P\u00f3sfai. 2016. Network Science. Cambridge University Press, Cambridge. http:\/\/barabasi.com\/networksciencebook\/."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.socnet.2007.11.001"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep19342"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature04292"},{"key":"e_1_3_2_12_2","volume-title":"ROBERT: ROBust and Privacy-PresERving Proximity Tracing","author":"Castelluccia Claude","year":"2020","unstructured":"Claude Castelluccia, Nataliia Bielova, Antoine Boutet, Mathieu Cunche, Cedric Lauradoux, Daniel Le Metayer, and Vincent Roca. 2020. ROBERT: ROBust and Privacy-PresERving Proximity Tracing. Technical Report. Inria."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2923-3"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-019-0206-8"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020579"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Mehmet \u015eim\u015fek and Henning Meyerhenke. 2020. Combined centrality measures for an improved characterization of influence spread in social networks.","DOI":"10.1093\/comnet\/cnz048"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/economies9040182"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2008.924989"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-020-01698-4"},{"key":"e_1_3_2_20_2","article-title":"A data-driven approach for origin-destination matrix construction from cellular network signalling data: A case study of Lyon region France","author":"Fekih Mariem","year":"2020","unstructured":"Mariem Fekih, Tom Bellemans, Zbigniew Smoerda, Patrick Bonnel, Angelo Furno, and Stephane Galland. 2020. A data-driven approach for origin-destination matrix construction from cellular network signalling data: A case study of Lyon region France. Transportation (2020).","journal-title":"Transportation"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2637901"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0248212"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipl.2007.07.002"},{"issue":"100167","key":"e_1_3_2_24_2","article-title":"The impact of government measures and human mobility trend on COVID-19 related deaths in the UK","volume":"6","author":"Hadjidemetriou Georgios","year":"2020","unstructured":"Georgios Hadjidemetriou, Manu Sasidharan, Georgia Kouyialis, and Ajith Parlikad. 2020. The impact of government measures and human mobility trend on COVID-19 related deaths in the UK. Transportation Research Interdisciplinary Perspectives 6, 100167 (July 2020).","journal-title":"Transportation Research Interdisciplinary Perspectives"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.3386\/w9440"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trip.2020.100288"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-00615-4_14"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505821.2505828"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3431832.3431840"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2016.05.003"},{"key":"e_1_3_2_31_2","article-title":"The impact of Covid-19 on smartphone usage","author":"Li Tong","year":"2021","unstructured":"Tong Li, Mingyang Zhang, Yong Li, Eemil Lagerspetz, Sasu Tarkoma, and Pan Hui. 2021. The impact of Covid-19 on smartphone usage. IEEE Internet of Things Journal (April 2021).","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep02923"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2016.10.016"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2016.04.005"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.53"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2015.2491361"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2014.2345255"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/sahcn.2014.6990335"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abc0764"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.6602"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235732"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1203882109"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-03095-6"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289527"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2014.6921683"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1177170"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/2000172.2000179"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3347146.3359093"},{"key":"e_1_3_2_49_2","unstructured":"James A. Throgmorton and Barbara Eckstein. 2000. Desire Lines: The Chicago Area Transportation Study and the Paradox of Self in Post-War America. http:\/\/www.nottingham.ac.uk\/3cities\/throgeck.htm."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003716"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/2346496.2346498"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/PASSAT\/SocialCom.2011.133"},{"issue":"8","key":"e_1_3_2_53_2","first-page":"1","article-title":"Improved response to disasters and outbreaks by tracking population movements with mobile phone network data","volume":"8","author":"Schreeb. L. Bengtsson, X. Lu X, A. Thorson, R. Garfield, and J. von","year":"2011","unstructured":"L. Bengtsson, X. Lu X, A. Thorson, R. Garfield, and J. von Schreeb.2011. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data. PLOS Medicine 8, 8 (2011), 1\u20139.","journal-title":"PLOS Medicine"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.3151"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1223467"},{"issue":"2","key":"e_1_3_2_56_2","article-title":"Escaping from cities during the COVID-19 crisis: Using mobile phone data to trace mobility in Finland","volume":"10","author":"Willberg Elias","year":"2021","unstructured":"Elias Willberg, Olle Jarv, Tuomas Vaisanen, and Tuuli Toivonen. 2021. Escaping from cities during the COVID-19 crisis: Using mobile phone data to trace mobility in Finland. International Journal of Geo-Information 10, 2 (Feb. 2021).","journal-title":"International Journal of Geo-Information"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1371\/currents.dis.d073fbece328e4c39087bc086d694b5c"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1093\/jtm\/taaa159"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0234522"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30165-5"}],"container-title":["ACM Transactions on Spatial Algorithms and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3517222","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3517222","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:30Z","timestamp":1750188690000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3517222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,27]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,9,30]]}},"alternative-id":["10.1145\/3517222"],"URL":"https:\/\/doi.org\/10.1145\/3517222","relation":{},"ISSN":["2374-0353","2374-0361"],"issn-type":[{"value":"2374-0353","type":"print"},{"value":"2374-0361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,27]]},"assertion":[{"value":"2021-04-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-09-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}