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Leveraging a large-scale dataset of over 14 billion records, we unveil and quantify the substantial incidence of the different measures enforced in urban France to combat the COVID-19 epidemic on mobile service consumption. We present a simple but effective spatial linear model that can relate changes occurring at fine-grained spatial zoning in both global and per-service traffic to a limited set of socioeconomic indicators. Our model unveils some of the mechanisms that drove the significant evolution of mobile data traffic demands during the pandemic. It allows observing how the demand for mobile services has been affected by COVID-19 in very different ways across urban areas characterized by diverse population density, income levels and leisure area presence. It also discloses that usages of individual smartphone applications have been impacted in highly heterogeneous ways by the pandemic, even more so when considering the composite impacts of different transitions between periods characterized by diverse restrictions. Our results can aid governments in understanding how their measures were received across the space and different portions of population, and network operators to comprehend changes in usage due to extraordinary events, which can be used to optimize service provisioning.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-024-00507-9","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T09:23:47Z","timestamp":1730971427000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modeling and understanding the impact of COVID-19 containment policies on mobile service consumption in French cities"],"prefix":"10.1140","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8828-6629","authenticated-orcid":false,"given":"Andr\u00e9 Felipe","family":"Zanella","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefania","family":"Rubrichi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zbigniew","family":"Smoreda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Fiore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"issue":"12","key":"507_CR1","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1016\/S2589-7500(20)30243-0","volume":"2","author":"G Pullano","year":"2020","unstructured":"Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V (2020) Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the covid-19 epidemic in France under lockdown: a population-based study. 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Health Place 70:102580","journal-title":"Health Place"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-024-00507-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-024-00507-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-024-00507-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T09:24:02Z","timestamp":1730971442000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-024-00507-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["507"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-024-00507-9","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"6 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Our work builds on mobile network traffic generated by users of a nationwide cellular infrastructure. The traffic measurements used to derive the data set were collected by the operator for network management and research purposes, and temporarily stored within a secure platform at their own premises. The aggregation at the level of the radio access antennas was also carried out in the same platform by personnel of the network operator, in full compliance with Article 89 of the General Data Protection Regulation (GDPR) of the European Commission. The data collection and processing was approved by the Data Protection Officer (DPO) of the operator, and authorized by the French National Commission on Informatics and Liberty (CNIL), within the context of a collaborative research project.We remark that the original network measurements contained personal identifiers (<i>e.g.<\/i>, the International Mobile Subscriber Identifier, or IMSI) and sensitive data (<i>e.g.<\/i>, locations of visited antennas, or mobile services consumed) about individual users, and were deleted upon aggregation. Instead, the aggregated data consist of time series of total traffic at the antenna level with a temporal granularity of one hour, and do not contain personal identifiers or sensitive information, such as the device type, preference in terms of application consumption, or trajectories. In addition, the level of spatiotemporal aggregation ensures that no data subject can be re-identified, and that the statistics do not configure as personal data in the GDPR acceptation.The researchers involved in the work presented in this paper only had access to such aggregated and privacy-preserving statistics for the purpose of carrying out the study. Ultimately, our dataset and research do not involve risks for the mobile subscribers.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declared that there is no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"68"}}