Comparer des méthodes

	Krigage global	Autocorrélation spatiale
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1960s–1993	1950
Auteur d'origine	Georges Matheron (kriging framework); global neighborhood usage formalized in applied geostatistics	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Type	Geostatistical interpolation	Spatial statistic / exploratory spatial data analysis
Source fondatrice	Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience. ISBN: 978-0471002550	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Alias	global-neighborhood kriging, full-data kriging, exhaustive kriging, non-local kriging	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Apparentées	5	5
Résumé	Global Kriging is the ordinary kriging interpolation procedure applied using all available sample points as the neighborhood — no spatial search window limits which data contribute to each prediction. It produces optimal linear unbiased predictions of an unobserved value at any target location, with associated prediction-error variances, by exploiting a fitted variogram model that encodes spatial autocorrelation across the entire dataset.	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.