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dc.contributor.authorSanta, Fernando
dc.contributor.authorHenriques, Roberto
dc.contributor.authorTorres-Sospedra, Joaquín
dc.contributor.authorPebesma, Edzer
dc.date.accessioned2019-03-07T18:30:38Z
dc.date.available2019-03-07T18:30:38Z
dc.date.issued2019
dc.identifier.citationSanta, Fernando, et al. "A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments." Sustainability, 2019. vol. 11, núm. 3ca_CA
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/10234/181777
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.ca_CA
dc.format.extent29 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherMDPIca_CA
dc.relation.isPartOfSustainability, 2019. vol. 11, núm. 3ca_CA
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).ca_CA
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjecthuman activityca_CA
dc.subjectspatio-temporal statisticsca_CA
dc.subjectnegative binomial regressionca_CA
dc.subjectfunctional principal component analysisca_CA
dc.subjectmultitype spatial point patternsca_CA
dc.titleA Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environmentsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.3390/su11030595
dc.relation.projectIDThe authors gratefully acknowledge funding from the European Union through the GEO–C project (H2020-MSCA-ITN-2014, grant agreement number 642332ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.mdpi.com/2071-1050/11/3/595ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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    Articles de publicacions periòdiques escrits per professors del Departament de Llenguatges i Sistemes Informàtics

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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).