A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments
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Otros documentos de la autoría: Santa, Fernando; Henriques, Roberto; Torres-Sospedra, Joaquín; Pebesma, Edzer
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Título
A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban EnvironmentsFecha de publicación
2019Editor
MDPIISSN
2071-1050Cita bibliográfica
Santa, Fernando, et al. "A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments." Sustainability, 2019. vol. 11, núm. 3Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2071-1050/11/3/595Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
An 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 ... [+]
An 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. [-]
Publicado en
Sustainability, 2019. vol. 11, núm. 3Proyecto de investigación
The authors gratefully acknowledge funding from the European Union through the GEO–C project (H2020-MSCA-ITN-2014, grant agreement number 642332Derechos de acceso
info:eu-repo/semantics/openAccess
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