| | Class | Description |
|---|
 | CreateSpatiallyBalancedPoints |
Generates a set of sample points based on inclusion probabilities resulting in a spatially balanced, i.e. maximized, and thus more efficient sample design. |
| CrossValidation | Removes one data location and then predicts the associated data using the data at the rest of the locations. The primary use for this tool is to compare the predicted value to the observed value in order to obtain useful information about some of your model parameters. |
| DensifySamplingNetwork | Uses inter alia, the Standard Error of Prediction surface on a predefined geostatistical kriging layer to determine where new locations are required or which can be removed. |
| DiffusionInterpolationWithBarriers | 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. |
| ExtractValuesToTable | Extracts cell values from a set of rasters to a table, based on a point or polygon feature class. |
| GACalculateZValue | Uses the interpolation model in a geostatistical layer to predict a value at a single location. |
| GACreateGeostatisticalLayer | Creates a new geostatistical layer. An existing geostatistical layer or geostatistical model is required to populate the initial values for the new layer. The input to this tool can be created using the Geostatistical Wizard. |
| GAGetModelParameter | Gets model parameter value from an existing geostatistical model source. |
| GALayerToContour | Creates a feature class of coutours from a geostatiscal analysis layer. The output feature class can be either a line feature class of contour lines or a polygon feature class of filled contours. |
| GALayerToGrid | Exports a Geostatistical layer to a raster. |
| GALayerToPoints | Exports a geostatistical layer to points. The tool can also be used to predict values at unmeasured locations or to validate predictions made at measured locations. |
| GAMovingWindowKriging | Recalculates the Range, Nugget, and Partial Sill semivariogram parameters based on a smaller neighborhood, moving through all location points. |
| GANeighborhoodSelection | Creates a layer of points based on a user-defined neighborhood. |
| GASemivariogramSensitivity | Performs a sensitivity analysis with varying Nugget, Partial Sill, and Range values. |
| GASetModelParameter | Sets parameter value(s) in an existing geostatistical model source. |
| GaussianGeostatisticalSimulations | Performs a conditional or unconditional geostatistical simulation based on a Simple Kriging model. |
| GlobalPolynomialInterpolation | 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. |
| KernelInterpolationWithBarriers | A moving window predictor that uses the shortest distance between points so that points on either side of the line barriers are connected. |
| LocalPolynomialInterpolation | Fits the specified order (zero, first, second, third, and so on) polynomial, each within specified overlapping neighborhoods, to produce an output surface. |
| RadialBasisFunctions | Uses one of five basis functions to process each measured sample value, thus creating an exact interpolation surface. |
| SubsetFeatures | Divides the original dataset into two parts: one part to be used to model the spatial structure and produce a surface, the other to be used to compare and validate the output surface. |
