Evaluation model for rural–urban fire service resource imbalance: A county-level perspective

Previous studies primarily focused on urban areas, neglected the imbalance of fire service resources between urban and rural regions. An imbalance coefficient model was developed in this study, utilizing real-time traffic data and Points of Interests (POI) to evaluate county-level fire service accessibility as a critical component of effective risk governance. Three variables—population density, demand points, and average travel time—are used to calculate the imbalance coefficient. A case study in Longhui County, China, validates the method. POI data identifies 19,109 fire service demand points, with one urban and 23 rural stations serving as supply points. Travel times are simulated based on real-time traffic, generating 2,293,080 valid samples from 120 scenarios. Results show accessibility within ≤4 mins, 4–14 mins, 14–24 mins, and > 24 mins accounts for 11.56 %, 46.63 %, 32.06 %, and 10.75 % of the county, respectively. The imbalance coefficient (0.5271) indicates extreme urban–rural disparity. To address urban–rural fire service imbalance and improve resilience against thermal runaway risks, strengthening rural fire infrastructure and implementing comprehensive risk governance frameworks is essential.