Sampling design optimization for spatial functions
Journal of the International Association for Mathematical Geology
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Abstract
A new procedure is presented for minimizing the sampling requirements necessary to estimate a mappable spatial function at a specified level of accuracy. The technique is based on universal kriging, an estimation method within the theory of regionalized variables. Neither actual implementation of the sampling nor universal kriging estimations are necessary to make an optimal design. The average standard error and maximum standard error of estimation over the sampling domain are used as global indices of sampling efficiency. The procedure optimally selects those parameters controlling the magnitude of the indices, including the density and spatial pattern of the sample elements and the number of nearest sample elements used in the estimation. As an illustration, the network of observation wells used to monitor the water table in the Equus Beds of Kansas is analyzed and an improved sampling pattern suggested. This example demonstrates the practical utility of the procedure, which can be applied equally well to other spatial sampling problems, as the procedure is not limited by the nature of the spatial function. ?? 1984 Plenum Publishing Corporation.
Suggested Citation
Olea, R., 1984, Sampling design optimization for spatial functions: Journal of the International Association for Mathematical Geology, v. 16, no. 4, p. 369-392, https://doi.org/10.1007/BF01029887.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Sampling design optimization for spatial functions |
| Series title | Journal of the International Association for Mathematical Geology |
| DOI | 10.1007/BF01029887 |
| Volume | 16 |
| Issue | 4 |
| Year Published | 1984 |
| Language | English |
| Publisher location | Kluwer Academic Publishers-Plenum Publishers |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Journal of the International Association for Mathematical Geology |
| First page | 369 |
| Last page | 392 |