City-scale fire risk modeling based on spatial regression methods

Assessing the frequency of fires and the resulting economic losses is crucial for allocating emergency resources and firefighting personnel across different areas. In this study, spatial regression methods were employed to analyze the correlation between fire risk and various influencing factors (economic vulnerability, population vulnerability, building vulnerability, etc.), and an optimal model determination procedure was proposed and validated. The results indicate that the spatial lag model with population density, per capita income, and the degree of personnel concentration around high-risk points of interest (POIs) as indicators was the best model for predicting fire frequency. Moreover, logarithmic transformation of the indicators effectively improved the prediction accuracy in low-population density areas. An ordinary least squares model with the illiteracy rate and average surface water resources as indicators was the best model for predicting direct economic losses from fires, and the distribution of high-risk POIs can qualitatively explain the differences between the predicted results and actual data. The present work not only enriches the research on city-scale fire risk assessment but also reveals that the optimal regression model determination process can provide technical support for the application of spatial regression methods in fire risk assessment.