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. 2021 Apr 10:764:144455.
doi: 10.1016/j.scitotenv.2020.144455. Epub 2020 Dec 16.

Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong

Coco Yin Tung Kwok  1 , Man Sing Wong  2 , Ka Long Chan  1 , Mei-Po Kwan  3 , Janet Elizabeth Nichol  4 , Chun Ho Liu  5 , Janet Yuen Ha Wong  6 , Abraham Ka Chung Wai  7 , Lawrence Wing Chi Chan  8 , Yang Xu  1 , Hon Li  1 , Jianwei Huang  9 , Zihan Kan  9
Affiliations

Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong

Coco Yin Tung Kwok et al. Sci Total Environ. .

Abstract

The World Health Organization considered the wide spread of COVID-19 over the world as a pandemic. There is still a lack of understanding of its origin, transmission, and treatment methods. Understanding the influencing factors of COVID-19 can help mitigate its spread, but little research on the spatial factors has been conducted. Therefore, this study explores the effects of urban geometry and socio-demographic factors on the COVID-19 cases in Hong Kong. For each patient, the places they visited during the incubation period before going to hospital were identified, and matched with corresponding attributes of urban geometry (i.e., building geometry, road network and greenspace) and socio-demographic factors (i.e., demographic, educational, economic, household and housing characteristics) based on the coordinates. The local cases were then compared with the imported cases using stepwise logistic regression, logistic regression with case-control of time, and least absolute shrinkage and selection operator regression to identify factors influencing local disease transmission. Results show that the building geometry, road network and certain socio-economic characteristics are significantly associated with COVID-19 cases. In addition, the results indicate that urban geometry is playing a more important role than socio-demographic characteristics in affecting COVID-19 incidence. These findings provide a useful reference to the government and the general public as to the spatial vulnerability of COVID-19 transmission and to take appropriate preventive measures in high-risk areas.

Keywords: COVID-19 pandemic; Socio-demographic characteristics; Spatial analysis; Urban geometry.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Study area and the residential spatial distribution of the confirmed cases from government dataset.
Fig. 2
Fig. 2
TPU boundaries and building data used.
Fig. 3
Fig. 3
Map of building density.
Fig. 4
Fig. 4
Map of sky view factor.
Fig. 5
Fig. 5
Examples of road network collected from “OSMnx” (a) urban area; (b) rural area.
Fig. 6
Fig. 6
NDVI map for the calculation of greenspace exposure.
Fig. 7
Fig. 7
Temporal distribution of the case class of the confirmed cases.
See this image and copyright information in PMC

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