Kriging procedure call - Maple Help

Interpolation[Kriging]

 procedure_call
 use the Kriging process to predict the value at one or more points

 Calling Sequence k(coordinates) k(coordinates, outopt)

Parameters

 k - a Kriging object coordinates - sequence specifying the coordinates of the point at which the value will be predicted, or a Matrix where each row specifies the coordinates of one point outopt - (optional) keyword option of the form output=format

Description

 • A Kriging object can be used in a function call. Used in this way, the function call makes use of the data stored in the Kriging object to predict the value at the point of the given coordinates.
 • The variogram set in the Kriging object is used to carry out the prediction. This can be set by using the SetVariogram procedure.
 • By default, the predicted value is returned. This is equivalent to the behavior if output=value. If output=variance then the variance associated with the predicted value is returned, which can be used as a measure of the uncertainty in the prediction. If output=both, then the sequence val, var is returned, where val is the predicted value and var is the associated variance.
 • coordinates can be an $m$-by-$n$ dimensional matrix, where $n$ is the dimensionality of the point data stored in the Kriging object. Each of the $m$ rows is treated as the coordinates of a point where the Kriging interpolation will be performed. If output=both then an $m$-by-2 matrix will be returned, with the first column corresponding to the predicted values and the second column corresponding to the variances. Otherwise, a column vector will be returned containing either the predicted values or variances as specified.

Examples

 > $\mathrm{with}\left(\mathrm{Interpolation}\right):$

We generate some data points according to the $\mathrm{Spherical}\left(1,10,1\right)$ variogram.

 > $\mathrm{points},\mathrm{data}≔\mathrm{Kriging}:-\mathrm{GenerateSpatialData}\left(\mathrm{Spherical}\left(1,10,1\right)\right)$

The next step is to create the Kriging object.

 > $k≔\mathrm{Kriging}\left(\mathrm{points},\mathrm{data}\right)$
 ${k}{≔}\left(\begin{array}{c}{Kriging intⅇrpolation obȷⅇct with 30 samplⅇ points}\\ {Variogram: Sphⅇrical\left(1.25259453854486,13.6487615617241,.5525536774\right)}\end{array}\right)$ (1)

We can use this object as is with a variogram estimated purely from the data, but we should expect to get better results if we set the variogram that we know underlies these data.

 > $\mathrm{SetVariogram}\left(k,\mathrm{Spherical}\left(1,10,1\right)\right)$
 $\left(\begin{array}{c}{Kriging intⅇrpolation obȷⅇct with 30 samplⅇ points}\\ {Variogram: Sphⅇrical\left(1,10,1\right)}\end{array}\right)$ (2)

We can now find the interpolated value for any point, and the variance at such points, as well.

 > $k\left(0.2,0.3\right)$
 ${-2.65388575588638931}$ (3)
 > $k\left(0.2,0.3,\mathrm{output}=\mathrm{variance}\right)$
 ${2.46857252864955656}$ (4)
 > $k\left(0.2,0.3,\mathrm{output}=\mathrm{both}\right)$
 ${-2.65388575588638931}{,}{2.46857252864955656}$ (5)

Like any function of two variables, you can plot the interpolated surface with the plot3d command. One can also plot the variance.

 > $\mathrm{plot3d}\left(k\left(x,y\right),x=0..1,y=0..1\right)$
 > $\mathrm{plot3d}\left(k,0..1,0..1\right)$
 > $\mathrm{plot3d}\left(k\left(x,y,\mathrm{output}=\mathrm{variance}\right),x=0..1,y=0..1\right)$

You can even plot the interpolated surface, and use the color to indicate the variance.

 > $\mathrm{plot3d}\left(k\left(x,y\right),x=0..1,y=0..1,\mathrm{color}=\left[\frac{k\left(x,y,\mathrm{output}=\mathrm{variance}\right)}{5},0.8,0.8,\mathrm{colortype}=\mathrm{HSV}\right]\right)$

You can also approximate the area under the surface using numerical integration.

 > $\mathrm{int}\left(k,0..1,0..1,\mathrm{numeric},\mathrm{ε}=0.1,\mathrm{method}=\mathrm{_CubaCuhre}\right)$
 ${-0.915288153662591}$ (6)

Compatibility

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