An overview of the Interpolation toolset

Disponible avec une licence Geostatistical Analyst.

Tools to predict values at unmeasured locations.

Tools in the Interpolation toolset

Tool	Description
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.
Empirical Bayesian Kriging 3D	Empirical Bayesian kriging 3D is a geostatistical interpolation method that uses Empirical Bayesian Kriging to interpolate 3D point data. All points must have x-, y-, and z-coordinates and a measured value to be interpolated. The output is a 3D geostatistical layer that calculates and renders itself as a 2D transect at a given elevation. The elevation of the layer can be changed with the range slider, and the layer will update to show the interpolated predictions for the new elevation.
Global Polynomial Interpolation	Fits a smooth surface that is defined by a mathematical function (a polynomial) to the input sample points.
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.
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.