Chapter 37: The KRIGE2D Procedure

Overview

The KRIGE2D procedure performs ordinary kriging in two dimensions. PROC KRIGE2D can handle anisotropic and nested semivariogram models. Four semivariogram models are supported: the Gaussian, exponential, spherical, and power models. A single nugget effect is also supported.

You can specify the locations of kriging estimates in a GRID statement, or they can be read from a SAS data set. The grid specification is most suitable for a regular grid; the data set specification can handle any irregular pattern of points.

Local kriging is supported through the specification of a radius around a grid point or the specification of the number of nearest neighbors to use in the kriging system. When you perform local kriging, a separate kriging system is solved at each grid point using a neighborhood of the data point established by the radius or number specification.

The KRIGE2D procedure writes the kriging estimates and associated standard errors for each grid to an output data set. When you perform local kriging, PROC KRIGE2D writes the neighborhood information for each grid point to an additional, optional data set. The KRIGE2D procedure does not produce any displayed output.

Introduction to Spatial Prediction

Spatial prediction, in general, is any prediction method that incorporates spatial dependence. A simple and popular spatial prediction method is ordinary kriging.

Ordinary kriging requires a model of the spatial continuity, or dependence. This is typically in the form of a covariance or semivariogram.

Spatial prediction, then, involves two steps. First, you model the covariance or semivariogram of the spatial process. This involves choosing both a mathematical form and the values of the associated parameters. Second, you use this dependence model in solving the kriging system at a specified set of spatial points, resulting in predicted values and associated standard errors.

The KRIGE2D procedure performs the second of these steps using ordinary kriging of two-dimensional data.