Basic methods include inverse distance weighting: this attenuates the variable with decreasing proximity from the observed location.

Shepard implemented not just basic inverse distance weighting, but also he allowed barriers (permeable and absolute) to interpolation.

Application areas of kernel methods are diverse and include geostatistics, kriging, inverse distance weighting, 3D reconstruction, bioinformatics, chemoinformatics, information extraction and handwriting recognition.

Inverse Distance Weighting (IDW) is a type of deterministic method for multivariate interpolation with a known scattered set of points.

A number of simpler interpolation methods/algorithms, such as inverse distance weighting, bilinear interpolation and nearest-neighbor interpolation, were already well known before geostatistics.

A popular and simple technique called inverse distance weighting (IDW) varies the influence of surrounding points based on the inverse of the distance between the control point and the interpolated point.

Digital elevation models, triangulated irregular networks, edge-finding algorithms, Thiessen polygons, Fourier analysis, (weighted) moving averages, inverse distance weighting, kriging, spline, and trend surface analysis are all mathematical methods to produce interpolative data.