NDLI: Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong

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Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong

Content Provider	MDPI
Author	Huang, Jianwei Kwan, Mei-Po Kan, Zihan Kwok, Coco Yu, Xinyu
Copyright Year	2020
Description	Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics.
Starting Page	624
e-ISSN	22209964
DOI	10.3390/ijgi9110624
Journal	ISPRS International Journal of Geo-Information
Issue Number	11
Volume Number	9
Language	English
Publisher	MDPI
Publisher Date	2020-10-25
Access Restriction	Open
Subject Keyword	ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Environmental Studies Risk of Covid-19 Built Environment Global Poisson Regression Geographically Weighted Poisson Regression
Content Type	Text
Resource Type	Article

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