Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Обычный пространственно-временной кригинг× | Обычный кригинг× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1999 | 1963 |
| Автор метода≠ | Kyriakidis & Journel (seminal review); Cressie & Huang (covariance models) | Georges Matheron (formalising D.G. Krige's empirical work) |
| Тип | Geostatistical interpolation | Geostatistical interpolation |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | STOK, spatio-temporal ordinary kriging, ordinary space-time kriging, ST-OK | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Связанные | 4 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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