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Sustainability 2019, 11(3), 595; https://doi.org/10.3390/su11030595

A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments

1
Nova Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal
2
Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón, Spain
3
Institute for Geoinformatics, University of Münster, 48149 Münster, Germany
*
Author to whom correspondence should be addressed.
Received: 14 December 2018 / Revised: 13 January 2019 / Accepted: 20 January 2019 / Published: 23 January 2019
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Abstract

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. View Full-Text
Keywords: human activity; spatio-temporal statistics; negative binomial regression; functional principal component analysis; multitype spatial point patterns human activity; spatio-temporal statistics; negative binomial regression; functional principal component analysis; multitype spatial point patterns
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Santa, F.; Henriques, R.; Torres-Sospedra, J.; Pebesma, E. A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments. Sustainability 2019, 11, 595.

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