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Applying the Spatial EBLUP to Place-Based Policing. Simulation Study and Application to Confidence in Police Work
Applied Spatial Analysis and Policy ( IF 2.043 ) Pub Date : 2020-03-09 , DOI: 10.1007/s12061-020-09333-8
David Buil-Gil , Angelo Moretti , Natalie Shlomo , Juanjo Medina

There is growing need for reliable survey-based small area estimates of crime and confidence in police work to design and evaluate place-based policing strategies. Crime and confidence in policing are geographically aggregated and police resources can be targeted to areas with the most problems. High levels of spatial autocorrelation in these variables allow for using spatial random effects to improve small area estimation models and estimates’ reliability. This article introduces the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP), which borrows strength from neighboring areas, to place-based policing. It assesses the SEBLUP under different scenarios of number of areas and levels of spatial autocorrelation and provides an application to confidence in policing in London. The SEBLUP should be applied for place-based policing strategies when the variable’s spatial autocorrelation is medium/high, and the number of areas is large. Confidence in policing is higher in Central and West London and lower in Eastern neighborhoods.

中文翻译:

将空间EBLUP应用于基于位置的策略。警察工作信心仿真研究与应用

越来越需要可靠的基于调查的小范围犯罪估计和对警察工作的信心,以设计和评估基于地点的警务策略。犯罪和对治安的信心在地理上是合计的,警察资源可以用于问题最多的地区。这些变量中的高水平空间自相关性允许使用空间随机效应来改善小面积估计模型和估计的可靠性。本文介绍了空间经验最佳线性无偏预测器(SEBLUP),它借鉴了邻近地区的优势,进行了基于位置的策略。它评估了在区域数量和空间自相关级别的不同情况下的SEBLUP,并提供了对伦敦治安信心的应用。当变量的空间自相关为中/高且区域数量较大时,应将SEBLUP应用于基于位置的策略。伦敦中部和西部对警务的信心较高,而东部地区则较低。
更新日期:2020-03-09
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