Compare methods

	Spatial Regression of Crime	Geographically Weighted Regression
Field	Criminology	Spatial analysis
Family	Regression model	Regression model
Year of origin	1988	2002
Originator	Luc Anselin	Fotheringham, Brunsdon & Charlton
Type	Regression model for areal crime data with spatial dependence	Local spatial regression
Seminal source	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 9789024737352	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Aliases	Spatial Lag Model of Crime, Spatial Error Model of Crime, Geographically Weighted Regression of Crime, Spatial Econometric Crime Models	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Related	4	5
Summary	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.	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.