Modeling SpatialTemporal Dynamics of Urban Residential Fire Risk Using a Markov Chain Technique

This article applies a Markov chain method to compute the probability of residential fire occurrence based on past fire history. Fitted with the fire incidence data gathered over a period of 10 years in Melbourne, Australia, the spatially-integrated fire risk model predicts the likely occurrence of fire incidents using space and time as key model parameters. The mapped probabilities of fire occurrence across Melbourne show a city-centric spatial pattern where inner-city areas are relatively more vulnerable to a fire than outer suburbia. Fire risk reduces in a neighborhood when there is at least one fire in the last 1 month. The results show that the time threshold of reduced fire risk after the fire occurrence is about 2 months. Fire risk increases when there is no fire in the last 1 month within the third-order neighborhood (within 5 km). A fire that occurs within this distance range, however, has no significant effect on reducing fire risk level within the neighborhood. The spatialtemporal dependencies of fire risk provide new empirical evidence useful for fire agencies to effectively plan and implement geo-targeted fire risk interventions and education programs to mitigate potential fire risk in areas where and when they are most needed