An overview of the Interpolation toolset

Tools to predict values at unmeasured locations.

Tool	Description
IDW	Uses the measured values surrounding the prediction location to predict a value for any unsampled location, based on the assumption that things that are close to one another are more alike than those that are farther apart.
Diffusion Interpolation With Barriers	Uses a kernel that is based upon the heat equation and allows one to use a combination of raster and feature datasets to act as a barrier.
Empirical_Bayesian_Kriging	Empirical Bayesian Kriging is an interpolation method that accounts for the error in estimating the underlying semivariogram through repeated simulations.
Global Polynomial Interpolation	Fits a smooth surface that is defined by a mathematical function (a polynomial) to the input sample points.
Kernel Interpolation With Barriers	A moving window predictor that uses the shortest distance between points so that points on either side of the line barriers are connected.
Local Polynomial Interpolation	Fits the specified order (zero, first, second, third, and so on) polynomial, each within specified overlapping neighborhoods, to produce an output surface.
Moving Window Kriging	Recalculates the Range, Nugget, and Partial Sill semivariogram parameters based on a smaller neighborhood, moving through all location points.
Radial Basis Functions	Uses one of five basis functions to process each measured sample value, thus creating an exact interpolation surface.

Tools in the Interpolation toolset