Natural Bayesian Killers

In criminology, the real life CSI currently faces an interesting phase, where mathematicians are entering the cold-blooded field of forensics. Geographic profiling uses existing geocoded data to predict, solve and prevent crime. In this study, we examine probability distance strategies and a Bayesian approach used to predict the residential location of a serial offender. We compare different distance decay functions used for estimating the probability density of the residential location. Additionally, we derive a Bayesian approach for predicting the next target location of the serial offender following the formulation of O’Leary [2009]. For the measure of performance, we choose the search cost ranking. The model is tested using the leave-one-out cross-validation with a small example dataset that consists of serial murder cases. Different strategies of combining the six different methods are discussed. The tests suggest that the best predictions are achieved using the Bayesian approach with exponential decay function. However, this result is not statistically significant due to the lack of test data. Team number: #8215 Page 2 of 27