Identification of spaces with cluster infection risks in small cities in China based on spatial syntax and GIS

Abstract

Among modern cities developing in a large-scale, extensive and unbalanced manner, smaller cities are relatively lagged behind due to relatively underdeveloped infrastructure, inadequate capital and technology talents, and insufficient attention from the national government, and thus they are more vulnerable when hit by unexpected disasters. The rampant pandemic of coronavirus disease 2019 (COVID-19) has made it even clearer that small cities must be equipped with stronger abilities to timely identify and prevent potential disease outbreaks. This paper takes Zhaodong City as an example to study how to better locate spaces with cluster infection risks in small cities. It combines spatial syntax, points of interest (POI), and geographical information system (GIS), and adopts hotspot analysis, average nearest neighbour analysis, kernel density estimation and other methods, to identify and locate potentially vulnerable spaces in neighbourhoods with relatively frequent people-to-people contact and thus higher disease transmission risks. Results show that there are three point-space, four line-space, and one plane-space with high risk of outbreaks in Zhaodong City, verifying the efficacy of the identification method for small cities.