So sánh phương pháp

	Mô hình Sai số Không gian Bayes	Hồi quy Trọng số Địa lý (GWR)
Lĩnh vực	Phân tích không gian	Phân tích không gian
Họ	Regression model	Regression model
Năm ra đời	1988 (classical SEM); 2009 (Bayesian formulation)	2002
Người khởi xướng	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)	Fotheringham, Brunsdon & Charlton
Loại	Bayesian spatial regression	Local spatial regression
Công trình gốc	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Tên gọi khác	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Liên quan	6	5
Tóm tắt	The Bayesian Spatial Error Model (Bayesian SEM) estimates a regression in which spatially correlated disturbances are explicitly modelled through a spatial weights matrix, while all parameters — regression coefficients, spatial error autocorrelation, and error variance — receive full posterior distributions via Bayesian inference rather than point estimates.	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.