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Spatial Regression of Crime

Spatial regression models explain crime rates across areal units — neighborhoods, census tracts, counties — while explicitly accounting for the fact that nearby places tend to have similar crime levels. Ordinary regression assumes each unit's residual is independent, an assumption crime data routinely violate, biasing standard errors and sometimes the coefficients themselves. Spatial econometric models, formalized in Luc Anselin's 1988 framework, introduce a spatial weights matrix and add a spatial lag of the outcome or a spatially correlated error so that the dependence between neighboring areas is modeled rather than ignored.

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Spatial Regression Models for Crime Rates

Rekodi ya mbinu ya kiajenda · regression-model / criminology

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. · ISBN 9789024737352
Anselin, L., Cohen, J., Cook, D., Gorr, W., & Tita, G. (2000). Spatial analyses of crime. Criminal Justice 2000, 4, 213–262. · URL

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Used in the same domainConcentrated Disadvantage Indexmachine-suggested · Relational suggestion, not evidence.Same method familyGeographically Weighted Regressionmachine-suggested · Relational suggestion, not evidence.Used in the same domainSocial Disorganization Analysismachine-suggested · Relational suggestion, not evidence.Same method familySpatial Lag Modelmachine-suggested · Relational suggestion, not evidence.