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Table 8
Kriging interpolation: Pros and cons.
| Kriging | |
| | |
| Pros | Predictions based on a spatial statistical analysis of the data |
| Best linear unbiased estimator (BLUE) | |
| Many forms of Kriging available, applicable to various data configurations | |
| Automatically accounts for clustering and screening effects; remains efficient in conditions of sparse data | |
| Can take into account variation bias toward specific directions (anisotropy) | |
| Able to quantify interpolation errors (Kriging variance) | |
| | |
| Cons | Overall complexity |
| Requires care when modeling spatial correlation structures | |
| Assumptions of intrinsic stationarity may not be valid (drift) and be handled though an appropriate Kriging variant | |
| Most Kriging variants are exact (no smoothing) | |
| Kriging is more computationally intensive than other local methods | |
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