Identification and Management of Epidemic Hazard Areas for Urban Sustainability: A Case Study of Tongzhou, China

The global epidemic is relatively stable, but urban pandemics will still exist. This study used sDNA (spatial design network analysis), spatial autocorrelation, and GWR (geographically weighted regression analysis) to identify potentially risky roads, pandemic hazard areas, and various infrastructure hazard areas in the Tongzhou District for urban sustainability. The results show that urban roads at risk during an epidemic have high proximity and aggregation effects. These roads are mainly concentrated in the core area. The hazard identification areas are focused on the urban sub-center and Yizhuang New Town. This paper derives the actual hazard areas using the POI (points of interest) data of COVID-19 (coronavirus disease 2019) and compares the results with the hazard identification areas. It is found that the hazard identification areas do not show the actual hazard area completely. In this study, GWR analyses based on gridded data of infrastructure POI proximity are used to obtain the hazard areas of various infrastructure types and develop different control ranges and methods. This provides new perspectives for identifying priority areas for epidemic prevention, control, and sustainable urban development.