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Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19
Urban residential districts (URDs) are a major element in the formation of cities that are essential for urban planning. Regarding the COVID-19 virus, which remains variable in aerosols for several hours, airborne transmission tends to occur in areas of poor ventilation and high occupant density. Th...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995257/ https://www.ncbi.nlm.nih.gov/pubmed/35431391 http://dx.doi.org/10.1016/j.cities.2022.103696 |
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author | Lu, Heli Guan, Ruimin Xia, Menglin Zhang, Chuanrong Miao, Changhong Ge, Yaopeng Wu, Xiaojing |
author_facet | Lu, Heli Guan, Ruimin Xia, Menglin Zhang, Chuanrong Miao, Changhong Ge, Yaopeng Wu, Xiaojing |
author_sort | Lu, Heli |
collection | PubMed |
description | Urban residential districts (URDs) are a major element in the formation of cities that are essential for urban planning. Regarding the COVID-19 virus, which remains variable in aerosols for several hours, airborne transmission tends to occur in areas of poor ventilation and high occupant density. Thus, ventilation capacity is an important factor influencing airborne transmission in URDs, which should be evaluated as part of efforts to fight COVID-19 and guide healthy city planning and implementation. Here, we develop and test systematic methods to map URDs in a typical city in northern China and quantify their ventilation capacity using very high-resolution remote sensing images. Four fundamental spatial forms of URD are identified in the research area: the point-group form, parallel form, enclosed form, and hybrid form. Our analyses indicate that the integrated ventilation capacities for well-designed URDs are nearly twice those of poorly designed URDs. Large variations in ventilation capacity are also observed within URDs, with up to 13.42 times difference between the buildings. Therefore, very high-resolution remote sensing data are fundamental for extracting building height and generating precise spatial forms, which can improve the micro-scale URD ventilation planning for the prevention of COVID-19. |
format | Online Article Text |
id | pubmed-8995257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89952572022-04-11 Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19 Lu, Heli Guan, Ruimin Xia, Menglin Zhang, Chuanrong Miao, Changhong Ge, Yaopeng Wu, Xiaojing Cities Article Urban residential districts (URDs) are a major element in the formation of cities that are essential for urban planning. Regarding the COVID-19 virus, which remains variable in aerosols for several hours, airborne transmission tends to occur in areas of poor ventilation and high occupant density. Thus, ventilation capacity is an important factor influencing airborne transmission in URDs, which should be evaluated as part of efforts to fight COVID-19 and guide healthy city planning and implementation. Here, we develop and test systematic methods to map URDs in a typical city in northern China and quantify their ventilation capacity using very high-resolution remote sensing images. Four fundamental spatial forms of URD are identified in the research area: the point-group form, parallel form, enclosed form, and hybrid form. Our analyses indicate that the integrated ventilation capacities for well-designed URDs are nearly twice those of poorly designed URDs. Large variations in ventilation capacity are also observed within URDs, with up to 13.42 times difference between the buildings. Therefore, very high-resolution remote sensing data are fundamental for extracting building height and generating precise spatial forms, which can improve the micro-scale URD ventilation planning for the prevention of COVID-19. Elsevier Ltd. 2022-07 2022-04-11 /pmc/articles/PMC8995257/ /pubmed/35431391 http://dx.doi.org/10.1016/j.cities.2022.103696 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 Lu, Heli Guan, Ruimin Xia, Menglin Zhang, Chuanrong Miao, Changhong Ge, Yaopeng Wu, Xiaojing Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19 |
title | Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19 |
title_full | Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19 |
title_fullStr | Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19 |
title_full_unstemmed | Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19 |
title_short | Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19 |
title_sort | very high-resolution remote sensing-based mapping of urban residential districts to help combat covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995257/ https://www.ncbi.nlm.nih.gov/pubmed/35431391 http://dx.doi.org/10.1016/j.cities.2022.103696 |
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