방법 비교

	전역 공간 오차 모형 (SEM)	지리 가중 회귀 분석 (Geographically Weighted Regression, GWR)
분야	공간분석	공간분석
계열	Regression model	Regression model
기원 연도	1988	2002
창시자	Luc Anselin	Fotheringham, Brunsdon & Charlton
유형	Spatial regression model	Local spatial regression
원전	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
별칭	SEM, spatial error model, spatial error regression, global SEM	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
관련	5	5
요약	The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations.	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.