3D Analyst Tools ArcToolbox

Diffusion Interpolation with Barriers, Empirical Bayesian Kriging Tools

Diffusion Interpolation with Barriers

How to use Diffusion Interpolation with Barriers Tool in Arc Toolbox ArcMap ArcGIS??

Diffusion Interpolation with Barriers Tool

Path to access the tool

:

Diffusion Interpolation with Barriers Tool, Interpolation Toolset, Geostatistical Analyst Tools Toolbox

 

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.

Input features

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

1. 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.

2. Output geostatistical layer (optional)

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

3. Output raster (optional)

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

4. 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.

5. Input absolute barrier features (optional)

Absolute barrier features using non-Euclidean distances rather than line-of-sight distances.

6. Bandwidth (optional)

Used to specify the maximum distance at which data points are used for prediction. With increasing bandwidth, prediction bias increases and prediction variance decreases.

7. Number of iterations (optional)

The iteration count controls the accuracy of the numerical solution because the model solves the diffusion equation numerically. The larger this number, the more accurate the predictions, yet the longer the processing time. The more complex the barrier's geometry and the larger the bandwidth, the more iterations are required for accurate predictions.

8. Weight field (optional)

Used to emphasize an observation. The larger the weight, the more impact it has on the prediction. For coincident observations, assign the largest weight to the most reliable measurement.

9. Input additive barrier raster (optional)

The travel distance from one raster cell to the next based on this formula:

(average cost value in the neighboring cells) x (distance between cell centers)

10. Input cumulative barrier raster (optional)

The travel distance from one raster cell to the next based on this formula:

(difference between cost values in the neighboring cells) + (distance between cell centers)

11. Input flow barrier raster (optional)

A flow barrier is used when interpolating data with preferential direction of data variation, based on this formula:

Indicator (cost values in the to neighboring cell > cost values in the from neighboring cell) * (cost values in the to neighboring cell - cost values in the from neighboring cell) + (distance between cell centers),

where indicator(true) = 1 and indicator(false) = 0.

Empirical Bayesian Kriging

How to use Empirical Bayesian Kriging Tool in ArcToolbox ArcMap ArcGIS??

Empirical Bayesian Kriging Tool

Path to access the tool

:

Empirical Bayesian Kriging Tool, Interpolation Toolset, Geostatistical Analyst Tools Toolbox

 

Empirical Bayesian Kriging

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

1.    Input features 

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

2.    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.

3.    Output geostatistical layer (optional)

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

4.    Output raster (optional)

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

5.    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.

6.    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.

7.    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.

8.    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.

9.    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.

10. Number of simulated semivariograms (optional)

The number of simulated semivariograms of each local model.

11. 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.

oOne sector—Single ellipse.

oFour sectors—Ellipse divided into four sectors.

oFour sectors shifted—Ellipse divided into four sectors and shifted 45 degrees.

oEight 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.

12. 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.

13. Quantile value (optional)

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

14. 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.

15. Probability threshold (optional)

The probability threshold value. If left empty, the median (50th quantile) of the input data will be used.