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Urban dynamics through the lens of human mobility

dc.contributor.authorXu Y
dc.contributor.authorOlmos L.E
dc.contributor.authorMateo D
dc.contributor.authorHernando A
dc.contributor.authorYang X
dc.contributor.authorGonzález M.C.
dc.date.accessioned2023-10-24T19:24:36Z
dc.date.available2023-10-24T19:24:36Z
dc.date.created2023
dc.identifier.issn26628457
dc.identifier.urihttp://hdl.handle.net/11407/7980
dc.description.abstractThe urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. Although it usually evolves slowly, it can change quickly during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. Here we present an approach to delineate such urban dynamics in quasi-real time through a human mobility metric, the mobility centrality index ΔKS. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. The results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, KSHBT, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure. © 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.eng
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85164345970&doi=10.1038%2fs43588-023-00484-5&partnerID=40&md5=2ae2b8db559a45430267538d0c154a2f
dc.sourceNat. Comput. Sci.
dc.sourceNature Computational Scienceeng
dc.titleUrban dynamics through the lens of human mobilityeng
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programCiencias Básicasspa
dc.type.spaArtículo
dc.identifier.doi10.1038/s43588-023-00484-5
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.affiliationXu, Y., MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China, Department of City and Regional Planning, University of California, Berkeley, CA, United States, Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
dc.affiliationOlmos, L.E., Department of City and Regional Planning, University of California, Berkeley, CA, United States, Facultad de Ciencias Básicas, Universidad de Medellín, Medellín, Colombia
dc.affiliationMateo, D., Kido Dynamics SA, Lausanne, Switzerland
dc.affiliationHernando, A., Kido Dynamics SA, Lausanne, Switzerland
dc.affiliationYang, X., MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
dc.affiliationGonzález, M.C., Department of City and Regional Planning, University of California, Berkeley, CA, United States, Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States, Department of Civil and Environmental Engineering, University of California, Berkeley, CA, United States
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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