Kriging
- Estimates values at unmeasured locations using observed nearby data.
- Explicitly models spatial autocorrelation to improve prediction accuracy.
- Commonly applied in fields like agriculture and environmental science to inform decisions.
Definition
Section titled “Definition”Kriging is a spatial interpolation technique used in geostatistics to estimate the value of a variable at a given location based on its observed values at neighboring locations. It is a type of spatial prediction that accounts for spatial autocorrelation, the tendency of nearby locations to have similar values, in order to provide more accurate estimates.
Explanation
Section titled “Explanation”Kriging uses a mathematical model to capture the underlying spatial structure of the data rather than relying on simple summaries like the average or median of observed values. By incorporating spatial autocorrelation, kriging provides estimates that are intended to be more representative of the true value at an unmeasured location. This approach typically yields more accurate predictions than interpolation methods that do not account for spatial patterns.
Examples
Section titled “Examples”Agriculture
Section titled “Agriculture”By collecting data on crop yields at different locations within a field, kriging can be used to estimate the yield at unmeasured locations, taking into account the spatial patterns of yield within the field. This can be useful for determining the optimal placement of irrigation systems or fertilizers.
Environmental science
Section titled “Environmental science”By collecting data on the concentration of a contaminant at different locations within an area, kriging can be used to estimate the concentration at unmeasured locations, taking into account the spatial patterns of contamination within the area. This can be useful for identifying areas of high contamination and planning remediation efforts.
Use cases
Section titled “Use cases”- Determining the optimal placement of irrigation systems or fertilizers in agriculture.
- Identifying areas of high contamination and planning remediation efforts in environmental science.
- Applied across a variety of fields where accurate spatial prediction at unmeasured locations is needed.
Related terms
Section titled “Related terms”- Spatial interpolation
- Geostatistics
- Spatial autocorrelation
- Interpolation methods