Although many geostatistical studies only study a measured attribute in relation to its spatial coordinates, this paper argues that other layers in the GIS database can be of additional use for spatial prediction purposes. They may enter the prediction equations as predictors in a regression model, or as correlated measurements. In an example we will show how this is done for predicting PCB138, a sediment pollution variable, over the North Sea floor. Issues of exploratory data analysis, required sample size, sample configuration, local versus global neighbourhoods, non-stationarity, non-linear transformations, change of support and conditional simulation will be discussed in the light of this example.
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Edzer Pebesma (2006) studied this question.
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