Bayesian non‐parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia. (6th July 2022) - Durand, J.‐B.; Forbes, F.; Phan, C.D.; Truong, L.; Nguyen, H.D.; Dama, F.
Summary: We develop a Bayesian non‐parametric (BNP) model coupled with Markov random fields (MRFs) for risk mapping, to infer homogeneous spatial regions in terms of risks. In contrast to most existing methods, the proposed approach does not require an arbitrary commitment to a specified number of risk classes and determines their risk levels automatically. We consider settings in which the relevant information are counts and propose a so‐called BNP hidden MRF (BNP‐HMRF) model that is able to handle such data. The model inference is carried out using a variational Bayes expectation–maximisation algorithm and the approach is illustrated on traffic crash data in the state of Victoria, Australia. The obtained results corroborate well with the traffic safety literature. More generally, the model presented here for risk mapping offers an effective, convenient and fast way to conduct partition of spatially localised count data.