Using spatial point process models, clustering and space partitioning to reconfigure fire stations layout

Abstract

Abstract Fire stations (FS) are typically non-uniformly distributed across space and their service area is defined based on administrative boundaries. Since the location of FS may considerably influence the readiness and the effectiveness of the provided services, national and regional governments need research-based information to adequately plan where to establish firefighting facilities. In this study, we propose a method to reconfigure the fire stations layout using spatial point process models, clustering and space partitioning. First, modeling fire intensity variation across space throught a point process model enables to replicate the process independently by simulation. Subsequently, for each simulation, the k-means algorithm is used to define a sitting location, minimizing the total within distance between the fire occurrences and the new position. This method allows to obtain a set of locations from which the respective distribution is infered. Assuming a bivariate normal spatial distribution, we further define confidence sitting regions. Ultimately, new FS service areas are defined by Voronoi tesselation. To exemplify the application of the method, we apply it to reconfigure the fire station layout in Aveiro, Portugal.