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

TL;DR

This paper explores the effectiveness of spatial blind source separation as a preprocessing step for multivariate spatial prediction, comparing it with Cokriging and neural networks through simulations and real data analysis.

Contribution

It introduces the use of spatial blind source separation for simplifying multivariate spatial prediction and compares its performance with traditional methods.

Findings

Spatial blind source separation reduces modeling complexity.

It performs comparably or better than Cokriging and neural networks.

The method is effective in geochemical data prediction.

Abstract

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.