krigingbased
Kriging-based methods, sometimes referred to as krigingbased, are a family of geostatistical interpolation techniques that estimate the value of a spatially distributed variable at unobserved locations from observed measurements, using spatial autocorrelation described by a variogram or covariance function. The approach, named after Danie Krige and formalized by Georges Matheron, yields the best linear unbiased predictor under specified statistical assumptions and provides a kriging variance as a measure of prediction uncertainty.
Variants of kriging differ in how the mean structure is treated. Ordinary kriging assumes an unknown constant
The typical workflow involves data quality control, exploratory data analysis, variogram or covariance estimation, variogram model
Applications of kriging-based methods span mining and mineral exploration, environmental monitoring, groundwater and hydrology, agriculture, meteorology,