Modelling Impact of High-Rise, High-Density Built Environment on COVID-19 Risks: Empirical Results from a Case Study of Two Chinese Cities

Yong Xu (), Chunlan Guo, Jinxin Yang, Zhenjie Yuan and Hung Chak Ho
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Yong Xu: School of Geographical Science and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Chunlan Guo: Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999077, China
Jinxin Yang: School of Geographical Science and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Zhenjie Yuan: School of Geographical Science and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Hung Chak Ho: Department of Anesthesiology, School of Clinical Medicine, The University of Hong Kong, Hong Kong 999077, China

IJERPH, 2023, vol. 20, issue 2, 1-15

Abstract: Characteristics of the urban environment (e.g., building density and road network) can influence the spread and transmission of coronavirus disease 2019 (COVID-19) within cities, especially in high-density high-rise built environments. Therefore, it is necessary to identify the key attributes of high-density high-rise built environments to enhance modelling of the spread of COVID-19. To this end, case studies for testing attributes for modelling development were performed in two densely populated Chinese cities with high-rise, high-density built environments (Hong Kong and Shanghai).The investigated urban environmental features included 2D and 3D urban morphological indices (e.g., sky view factor, floor area ratio, frontal area density, height to width ratio, and building coverage ratio), socioeconomic and demographic attributes (e.g., population), and public service points-of-interest (e.g., bus stations and clinics). The modelling effects of 3D urban morphological features on the infection rate are notable in urban communities. As the spatial scale becomes larger, the modelling effect of 2D built environment factors (e.g., building coverage ratio) on the infection rate becomes more notable. The influence of several key factors (e.g., the building coverage ratio and population density) at different scales can be considered when modelling the infection risk in urban communities. The findings of this study clarify how attributes of built environments can be applied to predict the spread of infectious diseases. This knowledge can be used to develop effective planning strategies to prevent and control epidemics and ensure healthy cities.

Keywords: urban environment; COVID-19; infection rate; population (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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