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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0284902 |
| _version_ | 1797828082586877952 |
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| author | Chen Zhong Robin Morphet Mitsuo Yoshida |
| author_facet | Chen Zhong Robin Morphet Mitsuo Yoshida |
| author_sort | Chen Zhong |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T12:58:41Z |
| format | Article |
| id | doaj.art-e352a4dc953447ca94890599ccafaedc |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-09T12:58:41Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e352a4dc953447ca94890599ccafaedc2023-05-13T05:31:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01184e028490210.1371/journal.pone.0284902Twitter mobility dynamics during the COVID-19 pandemic: A case study of London.Chen ZhongRobin MorphetMitsuo YoshidaThe 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.https://doi.org/10.1371/journal.pone.0284902 |
| spellingShingle | Chen Zhong Robin Morphet Mitsuo Yoshida Twitter mobility dynamics during the COVID-19 pandemic: A case study of London. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0284902 |
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