Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns
The outbreak of novel coronavirus pneumonia (COVID-19) is closely related to the intra-urban environment. It is important to understand the influence mechanism and risk characteristics of urban environment on infectious diseases from the perspective of urban environment composition. In this study, w...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2023-12-01
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Series: | Frontiers in Public Health |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1241029/full |
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author | Peng Zhou Hailu Zhang Lanjun Liu Yue Pan Yating Liu Xuanhao Sang Chaoqun Liu Zixuan Chen |
author_facet | Peng Zhou Hailu Zhang Lanjun Liu Yue Pan Yating Liu Xuanhao Sang Chaoqun Liu Zixuan Chen |
author_sort | Peng Zhou |
collection | DOAJ |
description | The outbreak of novel coronavirus pneumonia (COVID-19) is closely related to the intra-urban environment. It is important to understand the influence mechanism and risk characteristics of urban environment on infectious diseases from the perspective of urban environment composition. In this study, we used python to collect Sina Weibo help data as well as urban multivariate big data, and The random forest model was used to measure the contribution of each influential factor within to the COVID-19 outbreak. A comprehensive risk evaluation system from the perspective of urban environment was constructed, and the entropy weighting method was used to produce the weights of various types of risks, generate the specific values of the four types of risks, and obtain the four levels of comprehensive risk zones through the K-MEANS clustering of Wuhan’s central urban area for zoning planning. Based on the results, we found: ①the five most significant indicators contributing to the risk of the Wuhan COVID-19 outbreak were Road Network Density, Shopping Mall Density, Public Transport Density, Educational Facility Density, Bank Density. Floor Area Ration, Poi Functional Mix ②After streamlining five indicators such as Proportion of Aged Population, Tertiary Hospital Density, Open Space Density, Night-time Light Intensity, Number of Beds Available in Designated Hospitals, the prediction accuracy of the random forest model was the highest. ③The spatial characteristics of the four categories of new crown epidemic risk, namely transmission risk, exposure risk, susceptibility risk and Risk of Scarcity of Medical Resources, were highly differentiated, and a four-level integrated risk zone was obtained by K-MEANS clustering. Its distribution pattern was in the form of “multicenter-periphery” gradient diffusion. For the risk composition of the four-level comprehensive zones combined with the internal characteristics of the urban environment in specific zones to develop differentiated control strategies. Targeted policies were then devised for each partition, offering a practical advantage over singular COVID-19 impact factor analyses. This methodology, beneficial for future public health crises, enables the swift identification of unique risk profiles in different partitions, streamlining the formulation of precise policies. The overarching goal is to maintain regular social development, harmonizing preventive measures and economic efforts. |
first_indexed | 2024-03-08T23:56:11Z |
format | Article |
id | doaj.art-d3fb571c03c94f29ba14a77402a17a2e |
institution | Directory Open Access Journal |
issn | 2296-2565 |
language | English |
last_indexed | 2024-03-08T23:56:11Z |
publishDate | 2023-12-01 |
publisher | Frontiers Media S.A. |
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spelling | doaj.art-d3fb571c03c94f29ba14a77402a17a2e2023-12-13T05:28:19ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-12-011110.3389/fpubh.2023.12410291241029Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patternsPeng ZhouHailu ZhangLanjun LiuYue PanYating LiuXuanhao SangChaoqun LiuZixuan ChenThe outbreak of novel coronavirus pneumonia (COVID-19) is closely related to the intra-urban environment. It is important to understand the influence mechanism and risk characteristics of urban environment on infectious diseases from the perspective of urban environment composition. In this study, we used python to collect Sina Weibo help data as well as urban multivariate big data, and The random forest model was used to measure the contribution of each influential factor within to the COVID-19 outbreak. A comprehensive risk evaluation system from the perspective of urban environment was constructed, and the entropy weighting method was used to produce the weights of various types of risks, generate the specific values of the four types of risks, and obtain the four levels of comprehensive risk zones through the K-MEANS clustering of Wuhan’s central urban area for zoning planning. Based on the results, we found: ①the five most significant indicators contributing to the risk of the Wuhan COVID-19 outbreak were Road Network Density, Shopping Mall Density, Public Transport Density, Educational Facility Density, Bank Density. Floor Area Ration, Poi Functional Mix ②After streamlining five indicators such as Proportion of Aged Population, Tertiary Hospital Density, Open Space Density, Night-time Light Intensity, Number of Beds Available in Designated Hospitals, the prediction accuracy of the random forest model was the highest. ③The spatial characteristics of the four categories of new crown epidemic risk, namely transmission risk, exposure risk, susceptibility risk and Risk of Scarcity of Medical Resources, were highly differentiated, and a four-level integrated risk zone was obtained by K-MEANS clustering. Its distribution pattern was in the form of “multicenter-periphery” gradient diffusion. For the risk composition of the four-level comprehensive zones combined with the internal characteristics of the urban environment in specific zones to develop differentiated control strategies. Targeted policies were then devised for each partition, offering a practical advantage over singular COVID-19 impact factor analyses. This methodology, beneficial for future public health crises, enables the swift identification of unique risk profiles in different partitions, streamlining the formulation of precise policies. The overarching goal is to maintain regular social development, harmonizing preventive measures and economic efforts.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1241029/fullWuhan cityrandom forestspatial analysissustainable planningmachine learning |
spellingShingle | Peng Zhou Hailu Zhang Lanjun Liu Yue Pan Yating Liu Xuanhao Sang Chaoqun Liu Zixuan Chen Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns Frontiers in Public Health Wuhan city random forest spatial analysis sustainable planning machine learning |
title | Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns |
title_full | Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns |
title_fullStr | Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns |
title_full_unstemmed | Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns |
title_short | Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns |
title_sort | sustainable planning in wuhan city during covid 19 an analysis of influential factors risk profiles and clustered patterns |
topic | Wuhan city random forest spatial analysis sustainable planning machine learning |
url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1241029/full |
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