Comparer des méthodes

	Régression Spatiale Bayésienne	Modèle d'erreur spatiale (SEM)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	1988
Auteur d'origine	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors	Anselin
Type	Bayesian hierarchical regression	Spatial regression (spatially autocorrelated errors)
Source fondatrice	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Alias	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Apparentées	3	5
Résumé	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.	The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares.