Módszerek összehasonlítása

	Kovariancia-kriging	Földrajzilag súlyozott regresszió (GWR)	Az inverz távolság))^2 súlyozás (IDW)
Tudományterület	Térbeli elemzés	Térbeli elemzés	Térbeli elemzés
Módszercsalád	Regression model	Regression model	Regression model
Keletkezés éve	1963	2002	1968
Megalkotó	Georges Matheron (geostatistics); multivariate extension	Fotheringham, Brunsdon & Charlton	Donald Shepard
Típus	Multivariate geostatistical interpolation	Local spatial regression	Deterministic spatial interpolation
Alapmű	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	Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.
Alternatív nevek	co-kriging, multivariate kriging, ortak kriging	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon
Kapcsolódó	3	5	3
Összefoglaló	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.	Inverse distance weighting is a simple, deterministic method for estimating values at unsampled locations by taking a weighted average of nearby measured points, where closer points carry more weight. Introduced by Donald Shepard in 1968, it embodies the first law of geography — near things are more related than distant things — and is one of the most widely used interpolation methods in GIS for mapping continuous fields such as rainfall, elevation, or pollution from scattered samples.