geostatistics-interpolation

# Geostatistics & Interpolation Purpose: interpolation that reports what it doesn't know. The difference between a professional product and a pretty raster is the uncertainty surface and an honest cross-validation — both are non-optional here. ## Method selection | Situation | Method | |---|---| | Dense, smooth phenomenon, quick look | IDW (report power parameter; test 1-3) | | Physical phenomenon with spatial structure, need uncertainty | **Ordinary kriging** (default professional choice) | | Clear trend (elevation gradient, coastal effect) | Universal kriging or regression kriging on covariates | | Strong covariates available (DEM, land cover, distances) | Regression kriging / random-forest residual kriging | | Categorical target | Indicator kriging | | Honeycomb-free tessellation, no extrapolation wanted | Natural neighbor | IDW is a reasonable baseline but has no error model and bullseyes around extremes; say so when delivering IDW-only products. ## Exploratory phase (before any interpolation) - Map the points with values; look for duplicates at identical coordinates (average or offset them — kriging matrices go singular otherwise). - Histogram + skew: strongly skewed variables (rainfall, concentrations) usually want a log/normal-score transform; back-transform predictions properly (bias correction for lognormal kriging). - Trend check: regress value on x, y, and candidate covariates; visible trend → universal/regression kriging path. - Declustering if sampl...