Concept and identification methods of fire rescue enclaves

The efficient allocation and dispatch of fire rescue resources are crucial to urban public safety. Traditional approaches assume continuous spatial distribution of fire service coverage areas and give less consideration to the impact of real-time traffic conditions on rescue route selection and response times. This study aims to introduce and define the concept of "rescue enclaves"—areas that, although not directly adjacent to fire stations, can be effectively covered by them—and proposes a method to identify and calculate these spatially discontinuous coverage areas.

This study proposed a method for identifying and calculating spatially discontinuous coverage areas by mapping points to grids. Using this method: (1) fire truck travel times were calculated using real-time traffic data, (2) geographic coordinates were converted to universal transverse Mercator (UTM) coordinates, (3) the region was divided into fine grids, (4) grid coverage status was determined, (5) transition grids were processed through neighborhood analysis, and (6) rescue enclaves were identified using a breadth-first search (BFS) algorithm. The CS-XX urban fire station in a Chinese city was selected as a case study to validate the method. In this case study, 3 818 points of interest were identified as rescue demand points across 49 evaluation periods in one day, generating 187 082 valid data samples. A target response time of 4 min was established, and an 80% reduction coefficient was applied to convert regular vehicle travel times to fire truck travel times.

The rescue enclave areas were successfully identified and calculated using the proposed method, through which the following key findings were revealed: (1) the dynamic coverage area of CS-XX was observed to vary from 1.83 to 4.57 km², with the minimum fire service coverage of 1.83 km² being recorded during the morning peak at 8:00, (2) the calculated coverage area trends were found to be consistent with the percentage of demand points accessible within 4 min, whereby the reliability of the method was validated, (3) critical rescue enclaves were identified near CS-XX, with enclave areas ranging from 0.25 to 1.12 km², accounting for 12.20%-27.53% of the total coverage area, (4) the rescue enclaves were observed to occasionally extend beyond the traditional coverage of 7.00 km² prescribed by standard area determination methods, and (5) coverage areas and rescue enclave areas were demonstrated to synchronously vary with traffic conditions, with traffic congestion leading to a significant reduction in their sizes.

The proposed conceptualization of rescue enclaves is elucidated in this study, and their substantial manifestation within fire service coverage areas is substantiated through rigorous analysis. The rescue enclaves are systematically identified and quantified via an algorithmically driven methodological framework, and it is ascertained that such enclaves may comprise up to 27.53% of the coverage area of a fire station. If rescue enclaves are integrated into fire rescue jurisdiction planning protocols, they can substantially optimize resource allocation efficacy. While real-time traffic conditions and different flow efficiencies across heterogeneous route typologies are identified as the primary determinants of enclave formation, subsequent investigations are warranted to elucidate the precise mechanistic underpinnings and contributory factors governing rescue enclave emergence as well as to establish quantitative metrics for rescue passage efficiency across diverse route configurations.