Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Обычный кригинг× | Кокригинг: Многомерная геостатистическая интерполяция× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1963 | 1965-1978 |
| Автор метода≠ | Georges Matheron (formalising D.G. Krige's empirical work) | Matheron, G.; extended by Journel & Huijbregts |
| Тип | Geostatistical interpolation | Geostatistical interpolation |
| Основополагающий источник≠ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 |
| Другие названия | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor | cokriging, co-regionalization kriging, multivariate kriging, CK |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
|
|