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。
| 时空泛克里金× | 时空克里金× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份 | 1999 | 1999 |
| 提出者≠ | Kyriakidis & Journel (1999); foundations in Matheron's geostatistics | Cressie & Huang; Kyriakidis & Journel |
| 类型≠ | Spatiotemporal geostatistical interpolation | Geostatistical interpolation |
| 开创性文献≠ | Kyriakidis, P. C., & Journel, A. G. (1999). Geostatistical space-time models: A review. Mathematical Geology, 31(6), 651-684. DOI ↗ | Cressie, N., & Huang, H.-C. (1999). Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94(448), 1330-1340. DOI ↗ |
| 别名 | STUK, spatiotemporal universal kriging, space-time kriging with trend, universal kriging in space-time | spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time |
| 相关≠ | 5 | 4 |
| 摘要≠ | Space-Time Universal Kriging (STUK) is a geostatistical method that interpolates a continuously varying phenomenon across both space and time while explicitly modelling a deterministic trend component. It generalises Universal Kriging to the joint space-time domain, producing unbiased optimal predictions and associated uncertainty estimates at unobserved space-time locations. | Space-Time Kriging is a geostatistical interpolation method that predicts an unknown variable at any location and time by borrowing strength from nearby observations in both space and time simultaneously. It models the joint spatial-temporal covariance structure through a space-time variogram, then uses optimal linear weights to produce predictions with quantified uncertainty. |
| ScholarGate数据集 ↗ |
|
|