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- W4386574520 endingPage "e19773" @default.
- W4386574520 startingPage "e19773" @default.
- W4386574520 abstract "Since 2019, COVID-19 has triggered a renewed investigation of the urban environment and disease outbreak. While the results have been inconsistent, it has been observed that the quantity of urban green spaces (UGS) is correlated with the risk of COVID-19. However, the spatial pattern has largely been ignored, especially on the community scale. In high-density communities where it is difficult to increase UGS quantity, UGS spatial pattern could be a crucial predictive variable. Thus, this study investigated the relative contribution of quantity and spatial patterns of UGS on COVID-19 risk at the community scale using a random forest (RF) regression model based on (n = 44) communities in Wuhan. Findings suggested that 8 UGS indicators can explain 35% of the risk of COVID-19, and the four spatial pattern metrics that contributed most were core, edge, loop, and branch whereas UGS quantity contributed least. The potential mechanisms between UGS and COVID-19 are discussed, including the influence of UGS on residents' social distance and environmental factors in the community. This study offers a new perspective on optimizing UGS for public health and sustainable city design to combat pandemics and inspire future research on the specific relationship between UGS spatial patterns and pandemics and therefore help establish mechanisms of UGS and pandemics." @default.
- W4386574520 created "2023-09-10" @default.
- W4386574520 creator A5014309808 @default.
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- W4386574520 date "2023-09-01" @default.
- W4386574520 modified "2023-10-17" @default.
- W4386574520 title "Exploring the spatial pattern of community urban green space and COVID-19 risk in Wuhan based on a random forest model" @default.
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- W4386574520 doi "https://doi.org/10.1016/j.heliyon.2023.e19773" @default.
- W4386574520 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37809821" @default.
- W4386574520 hasPublicationYear "2023" @default.
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