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Twitter mobility dynamics during the COVID-19 pandemic: A case study of London

The current COVID-19 pandemic has profoundly impacted people’s lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the lo...

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Detalles Bibliográficos
Autores principales: Zhong, Chen, Morphet, Robin, Yoshida, Mitsuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132666/
https://www.ncbi.nlm.nih.gov/pubmed/37099615
http://dx.doi.org/10.1371/journal.pone.0284902
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author Zhong, Chen
Morphet, Robin
Yoshida, Mitsuo
author_facet Zhong, Chen
Morphet, Robin
Yoshida, Mitsuo
author_sort Zhong, Chen
collection PubMed
description The current COVID-19 pandemic has profoundly impacted people’s lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people’s travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 –Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale.
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spelling pubmed-101326662023-04-27 Twitter mobility dynamics during the COVID-19 pandemic: A case study of London Zhong, Chen Morphet, Robin Yoshida, Mitsuo PLoS One Research Article The current COVID-19 pandemic has profoundly impacted people’s lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people’s travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 –Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale. Public Library of Science 2023-04-26 /pmc/articles/PMC10132666/ /pubmed/37099615 http://dx.doi.org/10.1371/journal.pone.0284902 Text en © 2023 Zhong et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhong, Chen
Morphet, Robin
Yoshida, Mitsuo
Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
title Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
title_full Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
title_fullStr Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
title_full_unstemmed Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
title_short Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
title_sort twitter mobility dynamics during the covid-19 pandemic: a case study of london
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132666/
https://www.ncbi.nlm.nih.gov/pubmed/37099615
http://dx.doi.org/10.1371/journal.pone.0284902
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