Порівняння методів

	Social Disorganization Analysis	Spatial Regression of Crime
Галузь	Criminology	Criminology
Родина	Process / pipeline	Regression model
Рік появи	1942	1988
Автор методу	Clifford R. Shaw & Henry D. McKay	Luc Anselin
Тип	Ecological theory and analysis of neighborhood structural sources of crime	Regression model for areal crime data with spatial dependence
Основоположне джерело	Sampson, R. J., & Groves, W. B. (1989). Community structure and crime: Testing social-disorganization theory. American Journal of Sociology, 94(4), 774–802.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 9789024737352
Інші назви	Social Disorganization Theory, Shaw and McKay Model, Neighborhood Social Disorganization Analysis, Community Structure and Crime Analysis	Spatial Lag Model of Crime, Spatial Error Model of Crime, Geographically Weighted Regression of Crime, Spatial Econometric Crime Models
Пов'язані	4	4
Підсумок	Social disorganization analysis explains why crime concentrates in some neighborhoods regardless of who lives there, tracing it to community structural conditions rather than individual pathology. Building on Shaw and McKay's classic Chicago studies, it argues that poverty, residential instability, and ethnic heterogeneity undermine a neighborhood's capacity for informal social control, which in turn raises crime and delinquency — a chain that Sampson and Groves later tested empirically with survey-based measures of community social ties.	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.