3D Analyst Tools ArcToolbox

Geostatistical Analyst Tools

Geostatistical Analyst Tools

• With Geostatistical Analyst, you can easily create a continuous surface or map from measurements stored in a point feature layer or raster layer or by using polygon centroids. The sample points may be measurements such as elevation, depth to the water table, or levels of pollution. Geostatistical Analyst provides a comprehensive set of tools for creating surfaces that can be used to visualize, analyze, and understand spatial phenomena.
• The Geostatistical Analyst toolbox consists of groups of tools, they will be explained with an explanation of all the tools as follows:

Interpolation Toolset:

Tools for predicting 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.

Empirical Bayesian Kriging

Empirical Bayesian kriging is an interpolation method that accounts for the error in estimating the underlying semivariogram through repeated simulations.

Input features

The input point features containing the z-values to be interpolated.

Z value field

Field that holds a height or magnitude value for each point. This can be a numeric field or the Shape field if the input features contain z-values or m-values.

Output geostatistical layer (optional)

The geostatistical layer produced. This layer is required output only if no output raster is requested.

Output raster (optional)

The output raster. This raster is required output only if no output geostatistical layer is requested.

Output cell size (optional)

The cell size at which the output raster will be created.

This value can be explicitly set under Raster Analysis from the Environment Settings.

If not set, it is the shorter of the width or the height of the extent of the input point features, in the input spatial reference, divided by 250.

Data transformation type (optional)

Type of transformation to be applied to the input data.

• NONE—Do not apply any transformation. This is the default.
• EMPIRICAL—Multiplicative Skewing transformation with Empirical base function.
• LOGEMPIRICAL—Multiplicative Skewing transformation with Log Empirical base function. All data values must be positive. If this option is chosen, all predictions will be positive.

Semivariogram model type (optional)

The semivariogram model that will be used for the interpolation.

• POWER—Power semivariogram
• LINEAR—Linear semivariogram
• THIN_PLATE_SPLINE—Thin Plate Spline semivariogram
• EXPONENTIAL—Exponential semivariogram
• EXPONENTIAL_DETRENDED—Exponential semivariogram with first order trend removal
• WHITTLE—Whittle semivariogram
• WHITTLE_DETRENDED—Whittle semivariogram with first order trend removal
• K_BESSEL—K-Bessel semivariogram
• K_BESSEL_DETRENDED—K-Bessel semivariogram with first order trend removal

The available choices depend on the value of the Data transformation type parameter.

If the transformation type is set to NONE, only the first three semivariograms are available. If the type is EMPIRICAL or LOGEMPIRICAL, the last six semivariograms are available.

For more information about choosing an appropriate semivariogram for your data, see the topic What is Empirical Bayesian Kriging.

Maximum number of points in each local model (optional)

The input data will automatically be divided into groups that do not have more than this number of points.

Local model area overlap factor (optional)

A factor representing the degree of overlap between local models (also called subsets). Each input point can fall into several subsets, and the overlap factor specifies the average number of subsets that each point will fall into. A high value of the overlap factor makes the output surface smoother, but it also increases processing time. Typical values vary between 0.01 and 5.

Number of simulated semivariograms (optional)

The number of simulated semivariograms of each local model.

Search neighborhood (optional)

Defines which surrounding points will be used to control the output. Standard Circular is the default.

Standard Circular

• Maximum neighbors—The maximum number of neighbors that will be used to estimate the value at the unknown location.
• Minimum neighbors—The minimum number of neighbors that will be used to estimate the value at the unknown location.
• Sector type—The geometry of the neighborhood.
  • One sector—Single ellipse.
  • Four sectors—Ellipse divided into four sectors.
  • Four sectors shifted—Ellipse divided into four sectors and shifted 45 degrees.
  • Eight sectors—Ellipse divided into eight sectors.
• Angle—The angle of rotation for the axis (circle) or semimajor axis (ellipse) of the moving window.
• Radius—The length of the radius of the search circle.

Smooth Circular

• Smoothing factor—The Smooth Interpolation option creates an outer ellipse and an inner ellipse at a distance equal to the Major Semiaxis multiplied by the Smoothing factor. The points that fall outside the smallest ellipse but inside the largest ellipse are weighted using a sigmoidal function with a value between zero and one.
• Radius—The length of the radius of the search circle.

Output surface type (optional)

Surface type to store the interpolation results.

• PREDICTION—Prediction surfaces are produced from the interpolated values.
• PREDICTION_STANDARD_ERROR— Standard Error surfaces are produced from the standard errors of the interpolated values.
• PROBABILITY—Probability surface of values exceeding or not exceeding a certain threshold.
• QUANTILE—Quantile surface predicting the specified quantile of the prediction distribution.

Quantile value (optional)

The quantile value for which the output raster will be generated.

Probability threshold type (optional)

Specifies whether to calculate the probability of exceeding or not exceeding the specified threshold.

• EXCEED—Probability values exceed the threshold. This is the default.
• NOT_EXCEED—Probability values will not exceed the threshold.

Probability threshold (optional)

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.

Sampling Network Design Toolset:

Contains the tools used to help place new sampling sites. The Densify Sampling Network tool can also be used to identify sampling locations that are no longer required:

Create Spatially Balanced Points

Generates a set of sample points based on inclusion probabilities, resulting in a spatially balanced sample design. This tool is generally used for designing a monitoring network by suggesting locations to take samples, and a preference for particular locations can be defined using an inclusion probability raster.

Densify Sampling Network

Uses a predefined geostatistical kriging layer to determine where new monitoring stations should be built. It can also be used to determine which monitoring stations should be removed from an existing network.

Simulation Toolset:

The tools in this toolkit are used to perform geostatistical simulations and to aid in the analysis of results:

Extract Values To Table

Extracts cell values from a set of rasters to a table, based on a point or polygon feature class.

Gaussian Geostatistical Simulations

Performs a conditional or unconditional geostatistical simulation based on a Simple Kriging model. The simulated rasters can be considered equally probable realizations of the kriging model.

Utilities toolset:

Tools for pre- and post-processing when developing interpolation models using the Geostatistics Analyzer:

Cross Validation

Removes one data location 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.

Neighborhood Selection

Creates a layer of points based on a user-defined neighborhood.

For example, you might wish to create a selection of points in a circular neighborhood around a location defined by the Input point. The illustration above demonstrates that the output will be the 10 points, colored blue, that fall within the circle.

Semivariogram Sensitivity

This tool performs a sensitivity analysis on the predicted values and associated standard errors by changing the model's semivariogram parameters (the nugget, partial sill, and major/minor ranges) within a percentage of the original values. The tool takes a geostatistical model source in order to populate these initial values of the nugget, partial sill, and major/minor ranges. The tool's output is a table indicating which parameter values were used and what the resulting predicted and standard error values were. If there are large fluctuations in the output with small changes in the model's parameter values, then you cannot have much confidence in the output. On the other hand, if changes in the output are small, then you can be confident in the model's predictions and make decisions based on its output.

Subset Features

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.

Working With Geostatistical Layers toolset:

This toolkit contains tools used to create, modify, export, and manipulate geostatistics layer properties:

Areal Interpolation Layer To Polygons

Reaggregates the predictions of an Areal Interpolation layer to a new set of polygons.

Calculate Z-value

Uses the interpolation model in a geostatistical layer to predict a value at a single location.

Create Geostatistical Layer

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.

GA Layer To Contour

Creates a feature class of contours from a geostatistical layer. The output feature class can be either a line feature class of contour lines or a polygon feature class of filled contours.

GA Layer To Grid

Exports a Geostatistical layer to a raster.

GA Layer To Points

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.

Get Model Parameter

Gets model parameter value from an existing geostatistical model source.

Set Model Parameter

Sets parameter values in an existing geostatistical model source.

• The same topic is available in Arabic from here

Watch this video from the YouTube channel.

In the same way, as described through this site. Watch the video first, then you can search for any tool by writing its name in the search, the language of the video is Arabic, but English subtitles and any language in the world are available. Good luck and God bless you.