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Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon's diversity index, number of bus stops, floor–area ratio, and distance from the city center played important roles at different scales. These non-linear results provide a more refined reference for pandemic responses at different scales from an urban planning perspective and offer useful recommendations for a sustainable COVID-19 post-pandemic response. [ABSTRACT FROM AUTHOR]

Open data is a practical source for identifying disaster risks. However, few studies have examined open data usage. This study employed CiteSpace to conduct a bibliometric analysis to determine the evolution of open data in the field of disaster risk based on the literature. The findings were as follows: first, the existing disaster-related research can be classified into four categories: introduction to risk management and its concepts, multi-hazard response, studies on the specific background or context, and analysis of the technology or methods used in disaster risk reduction. Second, the relevant literature first emerged in 1997 and has rapidly expanded in recent years. Top keywords were identified, such as "natural disaster," "risk," and "climate change." Third, the most productive country in terms of publications has been the People's Republic of China; however, the low centrality indicates a lack of international collaborations. Fourth, several bursts were found in the collected literature. The term "data analysis" appears to be one of the most pressing concerns. Finally, we identified the research frontiers. The topic "accessible global dataset" has been of primary interest to researchers recently. The results of this study can provide directional references for future research in the field. [ABSTRACT FROM AUTHOR]