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Forecasting Spatio-Temporal Variation in Residential Burglary with the Integrated Laplace Approximation Framework: Effects of Crime Generators, Street Networks, and Prior Crimes
Journal of Quantitative Criminology ( IF 4.330 ) Pub Date : 2020-08-13 , DOI: 10.1007/s10940-020-09469-3
Maria Mahfoud , Wim Bernasco , Sandjai Bhulai , Rob van der Mei

Objectives

We investigate the spatio-temporal variation of monthly residential burglary frequencies across neighborhoods as a function of crime generators, street network features and temporally and spatially lagged burglary frequencies. In addition, we evaluate the performance of the model as a forecasting tool.

Methods

We analyze 48 months of police-recorded residential burglaries across 20 neighborhoods in Amsterdam, the Netherlands, in combination with data on the locations of urban facilities (crime generators), frequencies of other crime types, and street network data. We apply the Integrated Laplace Approximation method, a Bayesian forecasting framework that is less computationally demanding than prior frameworks.

Results

The local number of retail stores, the number of street robberies perpetrated and the closeness of the local street network are positively related to residential burglary. Inclusion of a general spatio-temporal interaction component significantly improves forecasting performance, but inclusion of spatial proximity or temporal recency components does not.

Discussion

Our findings on crime generators and street network characteristics support evidence in the literature on environmental correlates of burglary. The significance of spatio-temporal interaction indicates that residential burglary is spatio-temporally concentrated. Our finding that recency and proximity of prior burglaries do not contribute to the performance of the forecast, probably indicates that relevant spatio-temporal interaction is limited to fine-grained spatial and temporal units of analysis, such as days and street blocks.



中文翻译:

使用综合拉普拉斯近似框架预测住宅盗窃的时空变化:犯罪生成器、街道网络和先前犯罪的影响

目标

我们调查了每个社区每月住宅入室盗窃频率的时空变化,作为犯罪生成器、街道网络特征和时间和空间上滞后的入室盗窃频率的函数。此外,我们评估模型作为预测工具的性能。

方法

我们分析了荷兰阿姆斯特丹 20 个社区中警方记录的 48 个月的住宅盗窃案,并结合了城市设施(犯罪产生者)的位置、其他犯罪类型的频率和街道网络数据。我们应用了综合拉普拉斯近似方法,这是一种贝叶斯预测框架,其计算要求低于先前框架。

结果

当地零售店数量、街头抢劫案数量以及当地街道网络的紧密程度与入室盗窃呈正相关。包含一般时空交互组件可显着提高预测性能,但包含空间邻近性或时间新近组件则不会。

讨论

我们对犯罪发生因素和街道网络特征的发现支持了有关入室盗窃环境相关性文献中的证据。时空相互作用的显着性表明住宅入室盗窃是时空集中的。我们发现先前盗窃的新近度邻近对预测的性能没有贡献,这可能表明相关的时空交互仅限于细粒度的空间和时间分析单位,例如天数和街道街区。

更新日期:2020-08-13
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