Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Kriging Ordinar Spațio-Temporal× | Krigingul Ordinar× | |
|---|---|---|
| Domeniu | Analiză spațială | Analiză spațială |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1999 | 1963 |
| Autorul original≠ | Kyriakidis & Journel (seminal review); Cressie & Huang (covariance models) | Georges Matheron (formalising D.G. Krige's empirical work) |
| Tip | Geostatistical interpolation | Geostatistical interpolation |
| Sursa seminală≠ | Kyriakidis, P. C., & Journel, A. G. (1999). Geostatistical space-time models: a review. Mathematical Geology, 31(6), 651-684. DOI ↗ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Denumiri alternative | STOK, spatio-temporal ordinary kriging, ordinary space-time kriging, ST-OK | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Înrudite | 4 | 4 |
| Rezumat≠ | Space-Time Ordinary Kriging (STOK) is a geostatistical interpolation method that predicts a spatially and temporally varying phenomenon at unsampled space-time locations by combining the ordinary kriging assumption of an unknown, locally constant mean with a joint space-time covariance (or variogram) structure. It produces optimal, unbiased predictions along with associated estimation uncertainty. | Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point. |
| ScholarGateSet de date ↗ |
|
|