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Home : Support : Online Help : Statistics and Data Analysis : Interpolation and Curve Fitting : Interpolation Package : Kriging Subpackage : Interpolation/Kriging

Interpolation

  

Kriging

  

an overview of Kriging objects

 

Description

Examples

Compatibility

Description

• 

Kriging is a method for unstructured spatial interpolation. It is based upon a variogram function which models the variance between two data points as a function of their distance. Typically, this is an increasing function, since points closer together are likely to have more similar values (less variance). Based on the variogram and the distances from the evaluation point to all input points, a weight for each input value is computed, and these weights are used to compute a weighted average which is the predicted value.  The variograms supported by Maple, such as the Spherical and Exponential variograms, are detailed in the SetVariogram help page.

• 

The following help pages describe the Kriging object and its methods further:

apply Kriging at a grid of values

Constructor

display the empirical variogram

fit parameters of a variogram

Interpolate at a point

Overview

Set the variogram for a Kriging object

 

Examples

withInterpolation:

Create some data that is spatially correlated:

points,dataKrigingGenerateSpatialDataSpherical1,10,1

(1)

Create a Kriging object:

kKrigingpoints,data

kKrⅈgⅈng ⅈntⅇrpolatⅈon obȷⅇct wⅈth 30 samplⅇ poⅈntsVarⅈogram: Sphⅇrⅈcal(1.25259453854485,13.6487615617233,.5525536774)

(2)

Use the Kriging object to interpolate at a given point:

k0.2,0.3

−2.75173577049668650

(3)

Compatibility

• 

The Interpolation[Kriging] command was introduced in Maple 2018.

• 

For more information on Maple 2018 changes, see Updates in Maple 2018.

See Also

Constructor

Interpolating at a point

SetVariogram