Fixed Rank co-Kriging: a model for multivariate spatial prediction

TL;DR

This paper introduces a multivariate extension of Fixed Rank Kriging for efficient, flexible spatial prediction across multiple processes, capable of leveraging cross-process information to improve accuracy in sparse or unobserved regions.

Contribution

It develops a multiresolution coregionalization structure within FRK, enabling multivariate spatial modeling with efficient estimation and flexibility for non-stationary data.

Findings

Multivariate FRK improves predictions in sparse observation areas.

The model effectively borrows information across processes.

Application to environmental data demonstrates practical utility.

Abstract

This work develops a multivariate extension of the Fixed Rank Kriging (FRK) framework for spatial prediction in settings where multiple spatial processes may provide complementary information. The goal is to preserve the computational efficiency, the ability to operate without assuming stationarity over the domain, and the spatial support flexibility of FRK, while incorporating cross-process dependence. To this end, we employ a multiresolution coregionalization structure for the latent spatial effects, in which spatial basis functions are combined with Gaussian Markov Random Field coefficients. An estimation procedure based on the expectation-maximization algorithm is developed, designed to exploit the multiresolution latent structure. Through simulation studies, we examine when the proposed joint modeling is beneficial. We consider cases in which one process is observed more sparsely or is entirely unobserved in a subregion and find that the multivariate formulation is able to borrow information from the more densely observed process, producing coherent and accurate predictions even where direct observations are limited or absent. Finally, the model is applied to the analysis of PM10 concentrations in Northern Italy, illustrating its applicability in a real environmental context.