Confronta i metodi

	Regressione Spaziale Bayesiana	Modello a Lag Spaziale (SAR / Autoregressivo Spaziale)
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Regression model	Regression model
Anno di origine	1990s–2000s	1988
Ideatore	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors	Anselin (textbook formalisation); LeSage & Pace
Tipo	Bayesian hierarchical regression	Spatial autoregressive regression
Fonte seminale	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	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Correlati	3	5
Sintesi	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 Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.