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A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments

2019-01-23Artigo científico Acesso aberto
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dc.contributor.authorSanta, Fernando
dc.contributor.authorHenriques, Roberto
dc.contributor.authorTorres-Sospedra, Joaquín
dc.contributor.authorPebesma, Edzer
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2019-02-05T23:43:30Z
dc.date.available2019-02-05T23:43:30Z
dc.date.issued2019-01-23
dc.descriptionSanta, F., Henriques, R., Torres-Sospedra, J., & Pebesma, E. (2019). A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments. Sustainability (Switzerland), 11(3), [595]. DOI: 10.3390/su11030595
dc.description.abstractAn in-depth descriptive approach to the dynamics of the urban population is fundamental as a first step towards promoting effective planning and designing processes in cities. Understanding the behavioral aspects of human activities can contribute to their effective management and control. We present a framework, based on statistical methods, for studying the spatio-temporal distribution of geolocated tweets as a proxy for where and when people carry out their activities. We have evaluated our proposal by analyzing the distribution of collected geolocated tweets over a two-week period in the summer of 2017 in Lisbon, London, and Manhattan. Our proposal considers a negative binomial regression analysis for the time series of counts of tweets as a first step. We further estimate a functional principal component analysis of second-order summary statistics of the hourly spatial point patterns formed by the locations of the tweets. Finally, we find groups of hours with a similar spatial arrangement of places where humans develop their activities through hierarchical clustering over the principal scores. Social media events are found to show strong temporal trends such as seasonal variation due to the hour of the day and the day of the week in addition to autoregressive schemas. We have also identified spatio-temporal patterns of clustering, i.e., groups of hours of the day that present a similar spatial distribution of human activities.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent923748
dc.identifier.doi10.3390/su11030595
dc.identifier.issn2071-1050
dc.identifier.otherPURE: 11447667
dc.identifier.otherPURE UUID: 263ecbeb-701a-4495-9da7-f0e93da3c150
dc.identifier.otherScopus: 85060500301
dc.identifier.otherWOS: 000458929500040
dc.identifier.otherORCID: /0000-0002-4862-8177/work/152174842
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85060500301&partnerID=8YFLogxK
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000458929500040
dc.identifier.urlhttps://www.scopus.com/pages/publications/85060500301
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000458929500040
dc.language.isoeng
dc.peerreviewedyes
dc.subjectFunctional principal component analysis
dc.subjectHuman activity
dc.subjectMultitype spatial point patterns
dc.subjectNegative binomial regression
dc.subjectSpatio-temporal statistics
dc.subjectGeography, Planning and Development
dc.subjectRenewable Energy, Sustainability and the Environment
dc.subjectManagement, Monitoring, Policy and Law
dc.subjectSDG 7 - Affordable and Clean Energy
dc.titleA statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environmentsen
dc.typejournal article
degois.publication.issue3
degois.publication.titleSustainability
degois.publication.volume11
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)