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Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen

Countries worldwide are reopening their businesses despite the continuing COVID-19 crisis and the emergence of new variants. In this context, knowing whether the reopening of businesses at various locations exposes higher risk to the public is essential. Whether urban density correlates with the pot...

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Autores principales: Zhou, Long, Li, Sihong, Li, Chaosu, Shen, Guoqiang, Yang, Huajie, Zhu, Pengyu, Han, Haoying, Li, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786608/
https://www.ncbi.nlm.nih.gov/pubmed/35095162
http://dx.doi.org/10.1016/j.cities.2022.103615
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author Zhou, Long
Li, Sihong
Li, Chaosu
Shen, Guoqiang
Yang, Huajie
Zhu, Pengyu
Han, Haoying
Li, Bin
author_facet Zhou, Long
Li, Sihong
Li, Chaosu
Shen, Guoqiang
Yang, Huajie
Zhu, Pengyu
Han, Haoying
Li, Bin
author_sort Zhou, Long
collection PubMed
description Countries worldwide are reopening their businesses despite the continuing COVID-19 crisis and the emergence of new variants. In this context, knowing whether the reopening of businesses at various locations exposes higher risk to the public is essential. Whether urban density correlates with the potential infection risk as concluded by previous studies of the COVID-19 pandemic remains unknown. In this study, taking the Macau Peninsula as a testbed, we first identified business locations for daily activities according to the latest point of interest (POI) data and generated the potential risk surface for COVID-19 infection. Then, using the cellular phone network and urban footprint data, we further analyzed the spatial relationship between COVID-19 potential risk and urban density of population and morphology through visual analytics. Results show that while some degree of spatial congruency exists between medium-risk peaks and urban density hotspots, apparent spatial mismatch exists for high-risk peaks, indicating that the traditional planning control based on urban density is inadequate for mitigating public health risks. POI-based spatial layout and configuration better reflecting business services and associated human activities are recommended in future planning and policy-making for more resilient cities in the post-pandemic era.
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spelling pubmed-87866082022-01-25 Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen Zhou, Long Li, Sihong Li, Chaosu Shen, Guoqiang Yang, Huajie Zhu, Pengyu Han, Haoying Li, Bin Cities Article Countries worldwide are reopening their businesses despite the continuing COVID-19 crisis and the emergence of new variants. In this context, knowing whether the reopening of businesses at various locations exposes higher risk to the public is essential. Whether urban density correlates with the potential infection risk as concluded by previous studies of the COVID-19 pandemic remains unknown. In this study, taking the Macau Peninsula as a testbed, we first identified business locations for daily activities according to the latest point of interest (POI) data and generated the potential risk surface for COVID-19 infection. Then, using the cellular phone network and urban footprint data, we further analyzed the spatial relationship between COVID-19 potential risk and urban density of population and morphology through visual analytics. Results show that while some degree of spatial congruency exists between medium-risk peaks and urban density hotspots, apparent spatial mismatch exists for high-risk peaks, indicating that the traditional planning control based on urban density is inadequate for mitigating public health risks. POI-based spatial layout and configuration better reflecting business services and associated human activities are recommended in future planning and policy-making for more resilient cities in the post-pandemic era. Elsevier Ltd. 2022-04 2022-01-25 /pmc/articles/PMC8786608/ /pubmed/35095162 http://dx.doi.org/10.1016/j.cities.2022.103615 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Zhou, Long
Li, Sihong
Li, Chaosu
Shen, Guoqiang
Yang, Huajie
Zhu, Pengyu
Han, Haoying
Li, Bin
Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen
title Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen
title_full Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen
title_fullStr Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen
title_full_unstemmed Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen
title_short Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen
title_sort spatial congruency or mismatch? analyzing the covid-19 potential infection risk and urban density as businesses reopen
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786608/
https://www.ncbi.nlm.nih.gov/pubmed/35095162
http://dx.doi.org/10.1016/j.cities.2022.103615
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