Compară metode

	Co-kriging: Interpolare Geostatistică Multivariată	Autocorelația spațială
Domeniu	Analiză spațială	Analiză spațială
Familie	Regression model	Regression model
Anul apariției	1965-1978	1950
Autorul original	Matheron, G.; extended by Journel & Huijbregts	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Tip	Geostatistical interpolation	Spatial statistic / exploratory spatial data analysis
Sursa seminală	Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Denumiri alternative	cokriging, co-regionalization kriging, multivariate kriging, CK	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Înrudite	5	5
Rezumat	Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest.	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.