Kriging is a geostatistical interpolation technique widely used in GIS and spatial analysis to estimate unknown values at unsampled locations based on the spatial correlation of known data points. Rooted in the work of South African mining engineer Danie Krige, this method assumes that the distance and direction between sample points reflect an underlying spatial autocorrelation that can be quantified and modeled mathematically.

In GIS workflows, Kriging is applied through platforms such as ArcGIS, QGIS, and GRASS GIS, leveraging tools that generate semivariograms to model spatial relationships across geospatial datasets. Analysts commonly apply Kriging to environmental monitoring, soil science, hydrology, and elevation modeling, where continuous surface mapping from discrete sample points is essential.

Unlike simpler interpolation methods such as IDW or spline, Kriging provides not only predicted surface values but also statistical measures of prediction uncertainty, making it a probabilistic and transparent approach. This dual output supports more informed decision-making in resource management, land use planning, and environmental risk assessment. For GIS professionals working with sparse or irregularly distributed geospatial data, Kriging delivers statistically rigorous and spatially coherent results across complex geographic extents.