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Article

Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach

Brazil’s National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
Academic Editors: Wolfgang Kainz, Spencer Chainey, Matt Ashby, Patricio Estevez-Soto, Sophie Curtis-Ham and José Luis Hernandez
ISPRS Int. J. Geo-Inf. 2021, 10(6), 364; https://doi.org/10.3390/ijgi10060364
Received: 30 April 2021 / Revised: 20 May 2021 / Accepted: 26 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Geographic Crime Analysis)
Standardized crime rates (e.g., “homicides per 100,000 people”) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment. View Full-Text
Keywords: crime; mapping; risk; standardization; denominator dilemma; geographically weighted regression crime; mapping; risk; standardization; denominator dilemma; geographically weighted regression
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MDPI and ACS Style

Ramos, R.G. Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach. ISPRS Int. J. Geo-Inf. 2021, 10, 364. https://doi.org/10.3390/ijgi10060364

AMA Style

Ramos RG. Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach. ISPRS International Journal of Geo-Information. 2021; 10(6):364. https://doi.org/10.3390/ijgi10060364

Chicago/Turabian Style

Ramos, Rafael G. 2021. "Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach" ISPRS International Journal of Geo-Information 10, no. 6: 364. https://doi.org/10.3390/ijgi10060364

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