Spatial forecasting of residential urban fires: A Bayesian approach

The application of GIS-based techniques to analyse incident data such as crime has received a relatively large amount of research interest, the analysis of disaggregate fire incident data, in comparison has been the focus of much less attention. This paper, for the first time applies a Bayesian methodology to generate disaggregate spatial forecasts of residential household fires across metropolitan South-East Queensland (SEQ), Australia. Expected counts of fire for a one year period are calculated for each suburbs across the SEQ region where the expected risk varies from 2 or less fires per year up to 25 fires per year. The application of the Bayesian forecast methodology has the potential to inform policy decisions both from a reactive, resource allocation perspective and a more proactive perspective, such as through spatial targeting to implement preventative measures to reduce fire risk.