# Basics of Kriging: Realistic Spatial Behavior of the Climate Variables (Especially for GATE-Geospatial 2022)

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Kriging is a geostatistical method for spatial interpolation. Kriging differs from the interpolation methods discussed so far because Kriging can assess the quality of prediction with estimated prediction errors. Originated in mining and geologic engineering in the 1950s, Kriging has since been adopted in a wide variety of disciplines.

**Kriging is a geostatistical interpolation technique** that considers both the distance and the degree of variation between known data points when estimating values in unknown areas. A kriged estimate is a weighted linear combination of the known sample values around the point to be estimated.

Kriging procedure generates an estimated surface from a scattered set of points with z-values. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology.

The predicted values are derived from the measure of relationship in samples using sophisticated weighted average technique. It uses a search radius that can be fixed or variable. The generated cell values can exceed value range of samples, and the surface does not pass through samples.

Kriging assumes that the spatial variation of an attribute as change in grade within an ore body is neither random nor deterministic. Instead, the spatial variation may consist of three components: a spatially correlated component, representing the variation of the regionalized variable; a βdriftβ or structure, representing a trend; and a random error term.