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Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic

Chen, Tongxin; Zhu, Di; Cheng, Tao; Gao, Xiaowei; Chen, Huanfa

Authors

Di Zhu

Tao Cheng

Xiaowei Gao

Huanfa Chen



Abstract

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

Citation

Chen, T., Zhu, D., Cheng, T., Gao, X., & Chen, H. (2023). Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic. PLoS ONE, 18(1 January), Article e0277913. https://doi.org/10.1371/journal.pone.0277913

Journal Article Type Article
Acceptance Date Nov 5, 2022
Online Publication Date Jan 20, 2023
Publication Date Jan 20, 2023
Deposit Date Nov 6, 2024
Publicly Available Date Nov 8, 2024
Journal PLoS ONE
Print ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 18
Issue 1 January
Article Number e0277913
DOI https://doi.org/10.1371/journal.pone.0277913
Public URL https://hull-repository.worktribe.com/output/4909937

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0

Copyright Statement
Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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