Spatial optimisation of fire service coverage: a case study of Brisbane, Australia

Abstract

In the context of changing demand for fire services, spatial optimisation of fire coverage has attracted little scholarly attention despite its potential to improve emergency response and to inform future service planning for fire stations. Drawing on small area population forecasts, this paper extends the application of the Maximum Coverage Location Model to compute and delineate the spatial coverage of current and proposed new fire stations to align with population growth estimates for Brisbane, Australia. Our results reveal important gaps in fire cover that are likely to emerge as a result of predicted population growth, the spatial patterns of which varies across the Brisbane metropolitan area. We draw on these results to delineate a series of new potential sites for fire stations to ameliorate the reduction in spatial coverage as a consequence of predicted population growth demonstrating the utility of our analytic approach for decision‐making and operational planning in the fire services.

Measuring the spatial accessibility to fire stations using enhanced floating catchment method

“…In general, accessibility is an estimate of what many authors have called 'availability of services' (e.g., ambulance and firefighting services) within a given response time, or the probability of finding an idle vehicle within specified time limits (Larson 1974;Daskin 1983;Jarvis 1985;Hogan & ReVelle 1986;Batta & Mannur 1990;Ball & Lin 1993;Goldberg 2004;Zang et al 2013). These studies have formulated models under the banner of 'location models' of which there are arguably four broad types that have been developed and applied to emergency service contexts (Marianov & Serra 2002;Goldberg 2004;Murray 2010;Degel et al 2011;Murray 2015;KC et al 2018). The early location models such as Location Set Covering Problem (LSCP) (Toregas et al 1971) and the Maximal Covering Location Problem (MCLP) (Church & ReVelle 1974) are deterministic type of location models that aim to maximise a single coverage of a set of demand points by a fleet of vehicles.…”

“…While existing studies have shown the advantages of applying location models for fire services location planning, one area that remains subject to little research is the utility of location models to measure the spatial accessibility to fire services in relation to their coverage. Such knowledge is important in its capacity to inform future policy and planning related to fire station location, especially in regions undergoing growth in their population (Murray 2013;Jennings 2013;Murray 2015;KC et al 2018). A recent body of literature has widely acknowledged the benefits of FCA method for measuring spatial accessibility to public services such as primary health care (Luo & Wang 2003;McGrail & Humphreys 2009;Luo & Qi 2009) and transport opportunities (Langford et al 2012).…”

“…Many of these studies have employed location models to evaluate the capacity and efficiency of emergency response through linking factors such as cost, time, population/dwelling density, socioeconomic conditions and political interests (Badri et al 1998;Catay 2011;Degel et al 2011;Basar et al 2012;Pérez & Marianov 2014;Murray 2015). However, a limited number of studies have drawn on estimates of future population growth to inform location decision choices for new fire stations in areas which are likely to generate higher future demands for emergency service (Murray et al 2013;Aktas et al 2013;KC et al 2018).…”

Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)

“…Localization requirements are different depending on the scenario [ 17 ]. Travel times from fire stations are always critical, but even more so when the fire is located in the surroundings of a petrol station [ 18 ] or in high-density population areas [ 19 , 20 ].…”

Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings

“…The objective value of a 5 min response time (including 1 min for initiating action and 4 min of travel time) was stipulated in the Chinese standard of GB51080-2015 [37]. Similar response time objective values were applied in previous studies and other countries' standards [7,22,38]. In this study, the objective value of a 5 min response time was also set.…”