Comparer des méthodes

	Autocorrélation spatiale bayésienne	Autocorrélation spatiale
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1991	1950
Auteur d'origine	Besag, York & Mollie	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Type	Bayesian hierarchical spatial model	Spatial statistic / exploratory spatial data analysis
Source fondatrice	Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1–20.	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Alias	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Apparentées	6	5
Résumé	Bayesian Spatial Autocorrelation embeds spatial dependence directly into a Bayesian hierarchical model. A Conditional Autoregressive (CAR) prior encodes the expectation that neighboring areas are more similar than distant ones, and posterior inference is obtained via MCMC. This approach is especially valuable in disease mapping, ecology, and regional science, where small-area estimates need borrowing strength across neighbors.	Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations.