Applying the Spatial EBLUP to place-based policing. Simulation study and application to confidence in police workCitation formats

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@article{7ea5037b7cd049faaa2e00669e8c3446,
title = "Applying the Spatial EBLUP to place-based policing. Simulation study and application to confidence in police work",
abstract = "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{\textquoteright} 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. SEBLUP should be applied for place-based policing strategies when the variable{\textquoteright}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.",
keywords = "Spatial correlation, SAR, contiguity matrix, spatial model, police legitimacy, London",
author = "David Buil-Gil and Angelo Moretti and Natalie Shlomo and Juanjo Medina",
year = "2020",
month = mar
day = "9",
doi = "10.1007/s12061-020-09333-8",
language = "English",
journal = "Applied Spatial Analysis and Policy",
issn = "1874-4621",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - Applying the Spatial EBLUP to place-based policing. Simulation study and application to confidence in police work

AU - Buil-Gil, David

AU - Moretti, Angelo

AU - Shlomo, Natalie

AU - Medina, Juanjo

PY - 2020/3/9

Y1 - 2020/3/9

N2 - 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. 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.

AB - 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. 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.

KW - Spatial correlation

KW - SAR

KW - contiguity matrix

KW - spatial model

KW - police legitimacy

KW - London

U2 - 10.1007/s12061-020-09333-8

DO - 10.1007/s12061-020-09333-8

M3 - Article

JO - Applied Spatial Analysis and Policy

JF - Applied Spatial Analysis and Policy

SN - 1874-4621

ER -