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Journal Articles Australian and New Zealand Journal of Statistics Year : 2022

Bayesian nonparametric spatial prior for traffic crash risk mapping: a case study of Victoria, Australia

Abstract

We investigate the use of Bayesian nonparametric (BNP) models coupled with Markov random fields (MRF) in a risk mappring context, to build partitions of the risk into homogeneous spatial regions. 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.
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Dates and versions

hal-03138803 , version 1 (11-02-2021)
hal-03138803 , version 2 (07-09-2022)

Identifiers

Cite

Jean-Baptiste Durand, Florence Forbes, Cong Duc Phan, Long Truong, Hien D Nguyen, et al.. Bayesian nonparametric spatial prior for traffic crash risk mapping: a case study of Victoria, Australia. Australian and New Zealand Journal of Statistics, 2022, Special Issue: Geoff McLachlan Festschrift, 64 (2), pp.171-204. ⟨10.1111/anzs.12369⟩. ⟨hal-03138803v2⟩
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