Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Ponderación por distancia inversa (IDW)× | Cokrigaje× | |
|---|---|---|
| Campo | Análisis espacial | Análisis espacial |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1968 | 1963 |
| Autor original≠ | Donald Shepard | Georges Matheron (geostatistics); multivariate extension |
| Tipo≠ | Deterministic spatial interpolation | Multivariate geostatistical interpolation |
| Fuente seminal≠ | Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524. DOI ↗ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗ |
| Alias≠ | IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon | co-kriging, multivariate kriging, ortak kriging |
| Relacionados | 3 | 3 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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