Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Globālā krigēšana× | Kopkrigings: Daudzdimensiju ģeostatistiskā interpolācija× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1960s–1993 | 1965-1978 |
| Autors≠ | Georges Matheron (kriging framework); global neighborhood usage formalized in applied geostatistics | Matheron, G.; extended by Journel & Huijbregts |
| Tips | Geostatistical interpolation | Geostatistical interpolation |
| Pirmavots≠ | Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience. ISBN: 978-0471002550 | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 |
| Citi nosaukumi | global-neighborhood kriging, full-data kriging, exhaustive kriging, non-local kriging | cokriging, co-regionalization kriging, multivariate kriging, CK |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Global Kriging is the ordinary kriging interpolation procedure applied using all available sample points as the neighborhood — no spatial search window limits which data contribute to each prediction. It produces optimal linear unbiased predictions of an unobserved value at any target location, with associated prediction-error variances, by exploiting a fitted variogram model that encodes spatial autocorrelation across the entire dataset. | Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest. |
| ScholarGateDatu kopa ↗ |
|
|