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Cokrigaje×Ponderación por distancia inversa (IDW)×
CampoAnálisis espacialAnálisis espacial
FamiliaRegression modelRegression model
Año de origen19631968
Autor originalGeorges Matheron (geostatistics); multivariate extensionDonald Shepard
TipoMultivariate geostatistical interpolationDeterministic spatial interpolation
Fuente seminalMatheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524. DOI ↗
Aliasco-kriging, multivariate kriging, ortak krigingIDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon
Relacionados33
ResumenCokriging 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.
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ScholarGateComparar métodos: Cokriging · Inverse Distance Weighting. Recuperado el 2026-06-19 de https://scholargate.app/es/compare