Analysis and Local Estimation of Spatial Data Under the Intrinsic Hypothesis of Spatial Random Fields
Public Deposited- Last Modified
- February 27, 2019
- Creator
-
Thesing, Glenn A.
- Affiliation: Gillings School of Global Public Health, Department of Environmental Sciences and Engineering
- Abstract
- The intrinsic hypothesis of random fields is applied in the analysis of spatially distributed point-value data. In particular, the intrinsic hypothesis is coupled with a kriging point estimator to study the local behavior of non-homogeneous natural processes over two-dimensional space and to produce maps of degree of spatial trend, spatial correlation features, and point estimates. An automated computer algorithm is implemented to conduct the analysis at a local scale. A rectangular grid is assigned throughout the data domain. Trend assessment, covariance modeling, and estimation are conducted at each grid node using local subsets of data. The algorithm is applied to a set of soil moisture content measurements. Sensitivity analyses are conducted and indicate estimation accuracy is dependent on the size of the local subset and the covariance model used.
- Date of publication
- July 1993
- DOI
- Resource type
- Rights statement
- In Copyright
- Advisor
- Symons, Michael J.
- Miller, Cass T.
- Christakos, George
- Degree
- Master of Science in Environmental Engineering
- Academic concentration
- Water Resources Engineering
- Degree granting institution
- University of North Carolina at Chapel Hill
- Graduation year
- 1993
- Language
- Deposit record
- c2589085-57f7-4434-bf45-a2666ba162b9
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