方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 全局普通克里金× | 协克里金：多元地统计学插值× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1951–1963 | 1965-1978 |
| 提出者≠ | Danie G. Krige; formalized by Georges Matheron | Matheron, G.; extended by Journel & Huijbregts |
| 类型 | Geostatistical interpolation | Geostatistical interpolation |
| 开创性文献≠ | Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley. ISBN: 978-0471002550 | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 |
| 别名 | ordinary kriging, OK, global kriging, stationary ordinary kriging | cokriging, co-regionalization kriging, multivariate kriging, CK |
| 相关 | 5 | 5 |
| 摘要≠ | Global Ordinary Kriging (GOK) is the canonical geostatistical interpolation method that estimates values at unsampled locations as a weighted linear combination of nearby observations. It fits a single variogram model to the entire dataset, enforcing a global stationarity assumption, and produces optimal unbiased predictions along with quantified prediction uncertainty 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. |
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