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Cokriging uses information on several variable types. The main variable of interest is Z_{1}, and both autocorrelation for Z_{1} and cross-correlations between Z_{1} and all other variable types are used to make better predictions. It is appealing to use information from other variables to help make predictions, but it comes at a price. Cokriging requires much more estimation, including estimating the autocorrelation for each variable as well as all cross-correlations. Theoretically, you can do no worse than kriging because if there is no cross-correlation, you can fall back on autocorrelation for Z_{1}. However, each time you estimate unknown autocorrelation parameters, you introduce more variability, so the gains in precision of the predictions may not be worth the extra effort.

Ordinary cokriging assumes the following two models:

`Z`_{1}(**s**) = µ_{1} + ε_{1}(**s**)

`Z`_{2}(**s**) = µ_{2} + ε_{2}(**s**),

where µ_{1} and µ_{2} are unknown constants.

Notice that now you have two types of random errors, ε_{1}(**s**) and ε_{2}(**s**), so there is autocorrelation for each of them and cross-correlation between them. Ordinary cokriging attempts to predict Z_{1}(**s**_{0}), just like ordinary kriging, but it uses information in the covariate Z_{2}(**s**) in an attempt to do a better job. For example, the following figure has the same data that was used for ordinary kriging, only here a second variable is added.

Notice that Z_{1} and Z_{2} both appear autocorrelated. Also notice that when Z_{1} is below its mean µ_{1}, then Z_{2} is often above its mean µ_{2}, and vice versa. Thus, Z_{1} and Z_{2} appear to have negative cross-correlation. In this example, each location **s** had both Z_{1}(**s**) and Z_{2}(**s**); however, this is not necessary, and each variable type can have its own unique set of locations. The main variable of interest is Z_{1}, and both autocorrelation and cross-correlation are used to make better predictions.

The other cokriging methods—universal, simple, indicator, probability, and disjunctive—are generalizations of the foregoing methods to the case where you have multiple datasets. For example, indicator cokriging can be implemented by using several thresholds for your data and then using the binary data on each threshold to predict the threshold of primary interest. In this way, it is similar to probability kriging but can be less sensitive to outliers and other erratic data.

Semivariograms or covariances (the mathematical forms used to express autocorrelation), transformations, trend removal, and measurement error can be used when performing ordinary, simple, or universal cokriging.