The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data
dc.contributor.author | Romanillos Arroyo, Gustavo | |
dc.contributor.author | García Palomares, Juan Carlos | |
dc.contributor.author | Moya Gómez, Borja | |
dc.contributor.author | Gutiérrez Puebla, Javier | |
dc.contributor.author | Torres, Javier | |
dc.contributor.author | López, Mario | |
dc.contributor.author | Cantú-Ros, Oliva G. | |
dc.contributor.author | Herranz, Ricardo | |
dc.date.accessioned | 2023-06-16T14:18:24Z | |
dc.date.available | 2023-06-16T14:18:24Z | |
dc.date.issued | 2021-07-28 | |
dc.description | CRUE-CSIC (Acuerdos Transformativos 2021) | |
dc.description.abstract | Due to the rapid expansion of the COVID-19 pandemic, many countries ordained lockdowns, establishing different restrictions on people’s mobility. Exploring to what extent these measures have been effective is critical in order to better respond to similar future scenarios. This article uses anonymous mobile phone data to study the impact of the Spanish lockdown on the daily dynamics of the Madrid metropolitan area (Spain). The analysis has been carried out for a reference week prior to the lockdown and during several weeks of the lockdown in which different restrictions were in place. During these weeks, population distribution is compared during the day and at night and presence profiles are obtained throughout the day for each type of land use. In addition, a spatial multiple regression analysis is carried out to determine the impact of the different land uses on the local population. The results in the reference week, pre-COVID-19, show how the population in activity areas increases in each time slot on a specific day and how in residential areas it decreases. However, during the lockdown, activity areas cease to attract population during the day and the residential areas therefore no longer show a decrease. Only basic essential commercial activities, or others that require the presence of workers (industrial or logistics) maintain some activity during lockdown. | |
dc.description.department | Depto. de Geografía | |
dc.description.faculty | Fac. de Geografía e Historia | |
dc.description.refereed | TRUE | |
dc.description.sponsorship | Ministerio de Ciencia e Innovación (MICINN)/FEDER | |
dc.description.sponsorship | Comunidad de Madrid | |
dc.description.sponsorship | Ministerio de Ciencia e Innovación (MICINN) | |
dc.description.status | pub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/69367 | |
dc.identifier.doi | 10.1016/j.apgeog.2021.102524 | |
dc.identifier.issn | 0143-6228 | |
dc.identifier.officialurl | https://doi.org/10.1016/j.apgeog.2021.102524 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/4608 | |
dc.journal.title | Applied Geography | |
dc.language.iso | eng | |
dc.page.initial | 102524 | |
dc.publisher | Elsevier | |
dc.relation.projectID | DynMobility (RTI2018-098402-B-I00) | |
dc.relation.projectID | INNJOBMAD-CM (H2019/HUM-5761) | |
dc.relation.projectID | Juan de la Cierva–2018 (FJC2018-036613-I) | |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | |
dc.rights.accessRights | open access | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/es/ | |
dc.subject.keyword | COVID-19 | |
dc.subject.keyword | Lockdown | |
dc.subject.keyword | Spatio-temporal demographics | |
dc.subject.keyword | Mobile phone data | |
dc.subject.keyword | Land use | |
dc.subject.keyword | Urban geography | |
dc.subject.keyword | Madrid (Spain) | |
dc.subject.ucm | Demografía (Geografía) | |
dc.subject.ucm | Geografía humana | |
dc.subject.unesco | 52 Demografía | |
dc.subject.unesco | 5403 Geografía Humana | |
dc.title | The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data | |
dc.type | journal article | |
dc.volume.number | 134 | |
dspace.entity.type | Publication | |
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relation.isAuthorOfPublication | b25b5524-305e-4aa0-a30e-5b15a398806c | |
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relation.isAuthorOfPublication | 0b4c21b2-a5c3-4dfb-8088-c1ba13c4dd17 | |
relation.isAuthorOfPublication.latestForDiscovery | 7af12201-dd9c-4deb-8f3e-16bacc43e6dd |
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