방법 비교

	베이지안 다중 스케일 지리 가중 회귀(Bayesian Multiscale Geographically Weighted Regression)	지리 가중 회귀 분석 (Geographically Weighted Regression, GWR)
분야	공간분석	공간분석
계열	Regression model	Regression model
기원 연도	2017-2020	2002
창시자	Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors	Fotheringham, Brunsdon & Charlton
유형	Spatially varying coefficient regression	Local spatial regression
원전	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
별칭	Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
관련	6	5
요약	Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterior sampling, yielding full uncertainty quantification for every local coefficient surface.	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.