Archetypes of Wildfire Arsonists: An Approach by Using Bayesian Networks

Wildfires are a phenomenon of great importance because of their environmental and economic consequences, as well as the human losses they cause. The rate of resolution of arson-caused wildfires is extremely low when compared to other criminal activities. This fact highlights the importance of developing methodologies to assist investigators in the criminal profiling. For that we propose the use of Bayesian networks (BNs), which are a methodology belonging to the field of machine learning. BNs are probabilistic models that have only recently been applied to criminal profiling.We learn a BN model from real data of solved arson-caused wildfires in Spain, and after validation we use it to construct archetypes of the forest fires/arsonists with the aim of better understanding of this phenomenon and help in the task of identification of the culprits. We characterize five different archetypes around author motivation from a quantitative and objective point of view, which are in correspondence with the modes of operation in criminal activities of Shye