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

被引:11
作者
Xu, Yong [1 ]
Guo, Chunlan [2 ]
Yang, Jinxin [1 ]
Yuan, Zhenjie [1 ]
Ho, Hung Chak [3 ]
机构
[1] Guangzhou Univ, Sch Geog Sci & Remote Sensing, Guangzhou 510006, Peoples R China
[2] Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong 999077, Peoples R China
[3] Univ Hong Kong, Sch Clin Med, Dept Anesthesiol, Hong Kong 999077, Peoples R China
基金
中国国家自然科学基金;
关键词
urban environment; COVID-19; infection rate; population;
D O I
10.3390/ijerph20021422
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:15
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