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

Detalhes bibliográficos
Autor(a) principal: Santa, Fernando
Data de Publicação: 2019
Outros Autores: Henriques, Roberto, Torres-Sospedra, Joaquín, Pebesma, Edzer
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.3390/su11030595
Resumo: Santa, 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
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spelling A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environmentsFunctional principal component analysisHuman activityMultitype spatial point patternsNegative binomial regressionSpatio-temporal statisticsGeography, Planning and DevelopmentRenewable Energy, Sustainability and the EnvironmentManagement, Monitoring, Policy and LawSDG 7 - Affordable and Clean EnergySanta, 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/su11030595An 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNSanta, FernandoHenriques, RobertoTorres-Sospedra, JoaquínPebesma, Edzer2019-02-05T23:43:30Z2019-01-232019-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/su11030595eng2071-1050PURE: 11447667http://www.scopus.com/inward/record.url?scp=85060500301&partnerID=8YFLogxKhttps://doi.org/10.3390/su11030595info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:28:40Zoai:run.unl.pt:10362/59718Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:26.148629Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
title A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
spellingShingle A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
Santa, Fernando
Functional principal component analysis
Human activity
Multitype spatial point patterns
Negative binomial regression
Spatio-temporal statistics
Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Management, Monitoring, Policy and Law
SDG 7 - Affordable and Clean Energy
title_short A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
title_full A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
title_fullStr A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
title_full_unstemmed A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
title_sort A statistical approach for studying the spatio-temporal distribution of geolocated tweets in urban environments
author Santa, Fernando
author_facet Santa, Fernando
Henriques, Roberto
Torres-Sospedra, Joaquín
Pebesma, Edzer
author_role author
author2 Henriques, Roberto
Torres-Sospedra, Joaquín
Pebesma, Edzer
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Santa, Fernando
Henriques, Roberto
Torres-Sospedra, Joaquín
Pebesma, Edzer
dc.subject.por.fl_str_mv Functional principal component analysis
Human activity
Multitype spatial point patterns
Negative binomial regression
Spatio-temporal statistics
Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Management, Monitoring, Policy and Law
SDG 7 - Affordable and Clean Energy
topic Functional principal component analysis
Human activity
Multitype spatial point patterns
Negative binomial regression
Spatio-temporal statistics
Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Management, Monitoring, Policy and Law
SDG 7 - Affordable and Clean Energy
description Santa, 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
publishDate 2019
dc.date.none.fl_str_mv 2019-02-05T23:43:30Z
2019-01-23
2019-01-23T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.3390/su11030595
url https://doi.org/10.3390/su11030595
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2071-1050
PURE: 11447667
http://www.scopus.com/inward/record.url?scp=85060500301&partnerID=8YFLogxK
https://doi.org/10.3390/su11030595
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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