Comparar métodos

	Kriging Universal Bayesiano	Autocorrelación espacial
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	1990s–2000s	1950
Autor original	Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Tipo	Bayesian geostatistical interpolation with trend	Spatial statistic / exploratory spatial data analysis
Fuente seminal	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Alias	BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Relacionados	6	5
Resumen	Bayesian Universal Kriging (BUK) extends classical universal kriging by placing prior distributions on trend coefficients and spatial covariance parameters, then propagating full posterior uncertainty into predictions. It interpolates spatially referenced continuous data while simultaneously estimating large-scale deterministic trends driven by covariates and small-scale stochastic spatial dependence, yielding prediction intervals that honestly account for both parameter and interpolation uncertainty.	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.