Comparar métodos

	Spatial Regression of Crime	Modelo de Retardo Espacial (SAR / Autoregresivo Espacial)
Campo	Criminology	Análisis espacial
Familia	Regression model	Regression model
Año de origen	1988	1988
Autor original	Luc Anselin	Anselin (textbook formalisation); LeSage & Pace
Tipo	Regression model for areal crime data with spatial dependence	Spatial autoregressive regression
Fuente seminal	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 9789024737352	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Alias	Spatial Lag Model of Crime, Spatial Error Model of Crime, Geographically Weighted Regression of Crime, Spatial Econometric Crime Models	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Relacionados	4	5
Resumen	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.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.