Comparer des méthodes

	Indicateurs locaux bayésiens d'association spatiale (Bayesian LISA)	Autocorrélation spatiale bayésienne
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2000s–2010s	1991
Auteur d'origine	Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand)	Besag, York & Mollie
Type	Bayesian local spatial statistic	Bayesian hierarchical spatial model
Source fondatrice	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.	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.
Alias	Bayesian LISA, Bayesian local spatial autocorrelation, Bayesian local Moran, B-LISA	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA
Apparentées	6	6
Résumé	Bayesian Local Indicators of Spatial Association extend the classical LISA framework by embedding local spatial association statistics within a Bayesian hierarchical model. Rather than relying on asymptotic permutation-based significance tests, this approach places prior distributions on spatial parameters and derives posterior probabilities that a location is part of a genuine spatial cluster, accounting for uncertainty and borrowing strength across nearby units.	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.