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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 (BSS) approach for spatial data was suggested. When using this spatial BSS (SBSS) method before the actual spatial prediction, modeling of spatial cross-dependencies is avoided, which in turn simplifies the spatial prediction task significantly. In this letter, we investigate the use of SBSS as a preprocessing tool for spatial prediction and compare it with predictions from Cokriging and neural networks in an extensive simulation study as well as a geochemical data set.

On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction