Space-time variability in burglary risk: A Bayesian spatio-temporal modelling approach
Space-time variability in burglary risk: A Bayesian spatio-temporal modelling approach
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DOI：
10.1016/j.spasta.2014.03.006
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发表时间：
2014-08-01
影响因子：
2.3
通讯作者：
Best, N.
中科院分区：
文献类型：
--
作者：
Li, G.;Haining, R.;Best, N.
对时空犯罪数据进行建模有助于我们理解成为犯罪受害者的风险的时空特征，并对警务工作产生影响。时空相互作用在经验和理论上都深深植根于犯罪学的许多领域。在本文中，我们应用一个熟悉的贝叶斯时空模型来研究2005年至2008年英国彼得伯勒入室盗窃风险的时空变化。然而，我们对该模型进行了扩展，提出了一种新的两阶段方法，将区域划分为犯罪热点、冷点或两者都不是，并研究了每个风险类别中区域的时间动态。本文的另一个贡献是在模型中加入了协变量，以解释区域的时空分类。我们讨论了这种建模形式的优势，并确定了未来的发展方向，以分析空间和时间上的犯罪模式。文中还讨论了对犯罪研究和警务的影响。(C)2014爱思唯尔B.V.保留所有权利。
Modelling spatio-temporal offence data contributes to our understanding of the spatio-temporal characteristics of the risk of becoming a victim of crime and has implications for policing. Space-time interactions are deeply embedded both empirically and theoretically into many areas of criminology. In this paper, we apply a familiar Bayesian spatio-temporal model to explore the space-time variation in burglary risk in Peterborough, England, between 2005 and 2008. However, we extend earlier work with this model by presenting a novel two-stage method for classifying areas into crime hotspots, coldspots or neither and studying the temporal dynamics of areas within each risk category. A further contribution of this paper is the inclusion of covariates into the model in order to explain the space-time classification of areas. We discuss the advantages of, and identify future directions for, this form of modelling for analysing offence patterns in space and time. Implications for crime research and policing are also discussed. (C) 2014 Elsevier B.V. All rights reserved.