Methoden vergelijken

	Global Spatial Error Model	Geografisch Gewogen Regressie (GWR)
Vakgebied	Ruimtelijke analyse	Ruimtelijke analyse
Familie	Regression model	Regression model
Jaar van ontstaan	1988	2002
Grondlegger	Luc Anselin	Fotheringham, Brunsdon & Charlton
Type	Spatial regression model	Local spatial regression
Oorspronkelijke bron	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
Aliassen	SEM, spatial error model, spatial error regression, global SEM	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Verwant	5	5
Samenvatting	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.