Abstract:The spatial distribution of urban geographical events is largely constrained by the road network, and research on spatial clusters of fire accidents at the city level plays a crucial role in emergency rescue and urban planning. For example, by knowing where and when fire accidents usually occur, fire enforcement can conduct more efficient aid measures and planning department can work out more reasonable layout optimization of fire stations. This article proposed an integrated method by combining weighted network-constrained kernel density estimation (NKDE) and network-constrained local Moran's I (ILINCS) to detect spatial cluster pattern and identify higher-risk locations of fire accidents. The proposed NKDE-ILINCS weighted a set of crucial non-spatial attributes of point events and links, and considered the impact factors of road traffic states, intersection roads and fire severity in NKDE to reflect real urban environment. This method was tested using the fire data in 2015 in Nanjing, China. The results demonstrated that the method was appropriate to detect network-constrained fire cluster patterns and identify high-high road segments. Besides, the first 14 higher-risk road segments in Nanjing are listed. These findings of this case study enhance our knowledge to more accurately observe where fire accidents usually occur and provide a reference for fire departments to improve emergency rescue effectiveness.