Methoden vergleichen

	Cokriging	Inverse Distance Weighting (IDW)
Fachgebiet	Räumliche Analyse	Räumliche Analyse
Familie	Regression model	Regression model
Entstehungsjahr	1963	1968
Urheber	Georges Matheron (geostatistics); multivariate extension	Donald Shepard
Typ	Multivariate geostatistical interpolation	Deterministic spatial interpolation
Wegweisende Quelle	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.	Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.
Aliasnamen	co-kriging, multivariate kriging, ortak kriging	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon
Verwandt	3	3
Zusammenfassung	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.	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.