2021
DOI: 10.1016/j.scs.2021.102752
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Association of built environment attributes with the spread of COVID-19 at its initial stage in China

Abstract: Highlights Focus on the spread of COVID-19 at its initial stages across the whole China. Reveal spatial clusters and outliers of the spread of COVID-19. Estimate a mixed geographically weighted regression (GWR) model. Capture both the global and local effects of inter-/intra-city built environment. Derive important policy implications from the built environment planning.

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Cited by 78 publications
(56 citation statements)
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References 77 publications
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“…In a basic fixed parameter model with only population density, regional auto centrality index, tree canopy and nonmetropolitan county indicator as key explanatory variables, density variable and nonmetropolitan county indicator were statistically significantly and negatively correlated with COVID-19 infection rate. The negative association between (population) density and infection rate in the fixed parameter model is consistent with other studies ( Khavarian-Garmsir, Sharifi, & Moradpour, 2021 ; Li, Peng, He, Wang, & Feng, 2021 ) – while acknowledging that there could be differences in the construction of density measures in these studies. Whereas regional auto centrality index was positively correlated with infection rate.…”
supporting
confidence: 89%
“…In a basic fixed parameter model with only population density, regional auto centrality index, tree canopy and nonmetropolitan county indicator as key explanatory variables, density variable and nonmetropolitan county indicator were statistically significantly and negatively correlated with COVID-19 infection rate. The negative association between (population) density and infection rate in the fixed parameter model is consistent with other studies ( Khavarian-Garmsir, Sharifi, & Moradpour, 2021 ; Li, Peng, He, Wang, & Feng, 2021 ) – while acknowledging that there could be differences in the construction of density measures in these studies. Whereas regional auto centrality index was positively correlated with infection rate.…”
supporting
confidence: 89%
“…This result is somewhat consistent with findings from previous research [ 32 ] that suggested that dense places do not necessarily lead to more infection but more connected places, as measured by a metropolitan size and an enplanement rate, were positively associated with the infection rates. Another study conducted in China also found that connectivity, as measured by the betweenness centrality of the road networks of 255 Chinese cities, was one of the key determinants of infection rates [ 64 ].…”
Section: Discussionmentioning
confidence: 99%
“…However, it is too costly to collect sufficiently large-scale samples that can represent the whole population. A few studies have investigated the large-scale inter/intra-city contacts on public transportation through smart card data of railways 6 and flight information 7 , but such data cannot reflect human mobility in various urban facilities.…”
Section: Introductionmentioning
confidence: 99%