4.7 Article

Do different types of urban streets lead to varying COVID-19 risk perceptions? An empirical study from a spatial heterogeneity perspective

Journal

ENVIRONMENTAL IMPACT ASSESSMENT REVIEW
Volume 104, Issue -, Pages -

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.eiar.2023.107336

Keywords

Urban street; COVID-19 risk perception; Machine learning; Spatial autocorrelation; Geographically weighted regression (GWR) model

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This study explores how different types of street environments influence people's COVID-19 risk perception. The results showed that openness was the most important influencing factor, while visual crowdedness was the least important one. High-value clusters of COVID-19 risk perception were mainly found in poor-quality commercial streets, residential streets, and leisure streets close to the plaza, while low-value aggregations were mainly found in high-quality commercial streets, residential streets, and leisure streets close to blue space. The importance of environmental factors varied greatly by location.
People's risk perception varies across different environments. However, how different environments influence people's COVID-19 risk perception (RP), especially street environments, has yet to be fully evaluated. This study explores how three different types of street environments influence people's COVID-19 RP. First, we used pedestrian-centric street-view images and machine-learning models to predict the severity of COVID-19 RP in the study area. In addition, geographically weighted regression models were constructed to assess the locally most important environmental factors and their spatial heterogeneity. The results showed that openness was the most important influencing factor on COVID-19 RP in all three types of streets, while visual crowdedness was the least important one. Moreover, the high-value clusters of COVID-19 RP mostly occurred in poor- and medium-quality commercial streets, poor-quality residential streets, and leisure streets close to the plaza. Contrastingly, the lowvalue aggregations of COVID-19 RP mainly appeared in high-quality commercial streets, medium- and highquality residential streets, and leisure streets close to blue space. We also found that the importance of environmental factors varied greatly by location. Interestingly, the RP was lower with higher visual crowdedness for all types of commercial streets. Based on this, the COVID-19 RP in different types of street environments was comprehensively evaluated so that city-relevant departments can take more effective measures to manage the public health risk based on geographical features.

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