  Multivariate measurements taken at irregularly sampled locations are a common
form of data, for example in geochemical analysis of soil. In practical
considerations predictions of these measurements at unobserved locations are of
great interest. For standard multivariate spatial prediction methods it is
mandatory to not only model spatial dependencies but also cross-dependencies
which makes it a demanding task. Recently, a blind source separation approach
for spatial data was suggested. When using this spatial blind source separation
method prior the actual spatial prediction, modelling of spatial
cross-dependencies is avoided, which in turn simplifies the spatial prediction
task significantly. In this paper we investigate the use of spatial blind
source separation as a pre-processing tool for spatial prediction and compare
it with predictions from Cokriging and neural networks in an extensive
simulation study as well as a geochemical dataset.
