Bayesian non-parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Australian & New Zealand Journal of Statistics Pub Date : 2022-07-06 DOI:10.1111/anzs.12369

J.-B. Durand, F. Forbes, C.D. Phan, L. Truong, H.D. Nguyen, F. Dama

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引用次数: 1

Abstract

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.

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交通碰撞风险映射的贝叶斯非参数空间先验:以澳大利亚维多利亚州为例

我们开发了一个贝叶斯非参数(BNP)模型，结合马尔可夫随机场(mrf)进行风险映射，以推断风险的均匀空间区域。与大多数现有方法相比，所提出的方法不需要对指定数量的风险类别进行任意承诺，并自动确定其风险级别。我们考虑了相关信息计数的设置，并提出了能够处理此类数据的所谓BNP隐藏MRF (BNP- hmrf)模型。模型推理使用变分贝叶斯期望最大化算法进行，该方法在澳大利亚维多利亚州的交通事故数据上进行了说明。所得结果与交通安全文献吻合较好。更一般地说，本文提出的风险映射模型为对空间局部化计数数据进行分区提供了一种有效、方便、快速的方法。

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来源期刊

Australian & New Zealand Journal of Statistics 数学-统计学与概率论

CiteScore

1.30

自引率

9.10%

发文量

审稿时长

>12 weeks

期刊介绍： The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems.