So sánh phương pháp

	Hồi quy 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	1990s–2000s	2002
Người khởi xướng	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors	Fotheringham, Brunsdon & Charlton
Loại	Bayesian hierarchical regression	Local spatial regression
Công trình gốc	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173	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 hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Liên quan	3	5
Tóm tắt	Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates small or irregular spatial datasets via hierarchical priors.	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.