Confronta i metodi

	Kriging Bayesiano (Geostatistica basata su modello)	Autocorrelazione spaziale
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Regression model	Regression model
Anno di origine	1993–1998	1950
Ideatore	Diggle, Tawn & Moyeed; Handcock & Stein	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Tipo	Bayesian spatial interpolation	Spatial statistic / exploratory spatial data analysis
Fonte seminale	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.
Alias	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Correlati	5	5
Sintesi	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.