Detecting urban fire high-risk regions using colocation pattern measures.

• Investigate the colocation patterns between fire incidents and land-use facilities using two colocation measures. • Given that urban activities usually occur along roadways, this study we use the network distance in defining neighbors. • Local colocation measure can yield localized patterns, thus identifying the fire high-risk regions efficiently. • Our analysis offers valuable insights into fire risk estimation and fire service management. Understanding the spatial patterns of fire incidents and identifying high-risk locations are important for urban fire safety management. Colocation pattern analysis involving geographic information system (GIS) intends to examine the spatial proximity of distinct categories of objects. Most existing studies are conducted from a global perspective and assumes that urban events taking place occur in an isotropic Euclidean space. However, the physical environment embedded in an urban space is virtually characterized by the layout of road network, and many valid scopes of patterns are restricted to local regions. In response to the above-mentioned limitations, this study investigates the spatial correlation patterns between types of fires and their neighbouring land-use facility types based on network distance using two colocation measures: the global colocation quotient (GCLQ) and the local colocation quotient (LCLQ). The global measure helps detect the overall colocation patterns across the entire study area, whereas a LCLQ can provide more detailed site-specific patterns and can capture the variability of colocation patterns across places. For illustration purposes, we use the two methods to analyse the spatial associations between 6 types of fires and 12 types of land-use facilities in the city of Nanjing, China. Experimental results show that our analysis offers valuable insights into fire risk estimation and fire service management. [ABSTRACT FROM AUTHOR]