Compara mètodes

	Kriging Bayesià (Geoestadística Basada en Models)	Autocorrelació espacial
Camp	Anàlisi espacial	Anàlisi espacial
Família	Regression model	Regression model
Any d'origen	1993–1998	1950
Autor original	Diggle, Tawn & Moyeed; Handcock & Stein	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Tipus	Bayesian spatial interpolation	Spatial statistic / exploratory spatial data analysis
Font seminal	Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299–350.	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Àlies	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Relacionats	5	5
Resum	Bayesian Kriging embeds classical geostatistical interpolation inside a full probabilistic framework. Instead of treating variogram parameters as fixed point estimates, it places prior distributions on them and updates these priors with observed spatial data to obtain a posterior distribution. Predictions at unsampled locations are then marginalised over this uncertainty, yielding honest predictive intervals that account for both spatial dependence and parameter 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.