Salīdzināt metodes

	Kōkrigings	Ģeogrāfiski svērtā regresija (GWR)	Universālā krigēšana (krigēšana ar trendu)
Nozare	Telpiskā analīze	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1963	2002	1969
Autors	Georges Matheron (geostatistics); multivariate extension	Fotheringham, Brunsdon & Charlton	Georges Matheron
Tips	Multivariate geostatistical interpolation	Local spatial regression	Geostatistical interpolation with spatial trend
Pirmavots	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Citi nosaukumi	co-kriging, multivariate kriging, ortak kriging	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Saistītās	3	5	3
Kopsavilkums	Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation, yielding more accurate interpolations and prediction variances than kriging the primary variable alone.	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.	Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances.