Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong

Abstract

Identifying the space-time patterns of areas with a higher risk of transmission and the associated built environment and demographic characteristics during the COVID-19 pandemic is critical for developing targeted intervention measures in response to the pandemic. This study aims to identify areas with a higher risk of COVID-19 transmission in different periods in Hong Kong and analyze the associated built environment and demographic factors using data of individual confirmed cases. We detect statistically significant space-time clusters of COVID-19 at the Large Street Block Group (LSBG) level in Hong Kong between January 23 and April 14, 2020. Two types of high-risk areas are identified (residences of and places visited by confirmed cases) and two types of cases (imported and local cases) are considered. The demographic and built environment features for the identified high-risk areas are further examined. The results indicate that high transport accessibility, dense and high-rise buildings, a higher density of commercial land and higher land-use mix are associated with a higher risk for places visited by confirmed cases. More green spaces, higher median household income, lower commercial land density are linked to a higher risk for the residences of confirmed cases. The results in this study not only can inform policymakers to improve resource allocation and intervention strategies but also can provide guidance to the public to avoid conducting high-risk activities and visiting high-risk places.