Skip To Content

An overview of the Interpolation toolset

Available with Geostatistical Analyst license.

Tools to predict values at unmeasured locations.


Diffusion Interpolation With Barriers

Interpolates a surface using a kernel that is based upon the heat equation and allows one to use raster and feature barriers to redefine distances between input points.

EBK Regression Prediction

EBK Regression Prediction is a geostatistical interpolation method that uses Empirical Bayesian Kriging with explanatory variable rasters that are known to affect the value of the data that you are interpolating. This approach combines kriging with regression analysis to make predictions that are more accurate than either regression or kriging can achieve on their own.

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.


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.

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 interpolate a surfaces that passes through the input points exactly.

Tools in the Interpolation toolset

Related topics