Comparar métodos

	Indicadores Bayesianos Locales de Asociación Espacial (Bayesian LISA)	Autocorrelación espacial
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	2000s–2010s	1950
Autor original	Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand)	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Tipo	Bayesian local spatial statistic	Spatial statistic / exploratory spatial data analysis
Fuente seminal	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Alias	Bayesian LISA, Bayesian local spatial autocorrelation, Bayesian local Moran, B-LISA	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Relacionados	6	5
Resumen	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.	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.