Normalizing Basis Functions: Approximate Stationary Models for Large Spatial Data

TL;DR

This paper introduces efficient algorithms for normalizing basis functions in spatial models, enabling scalable and nearly stationary analysis of large geostatistical datasets with significant computational speedups.

Contribution

The paper presents two fast algorithms for normalizing basis functions, improving the scalability and accuracy of stationary spatial models for large datasets.

Findings

Achieved significant computational speedups in spatial analysis.

Enabled fitting nearly stationary models to large, complex datasets.

Demonstrated practical effectiveness within the LatticeKrig framework.

Abstract

In geostatistics, traditional spatial models often rely on the Gaussian Process (GP) to fit stationary covariances to data. It is well known that this approach becomes computationally infeasible when dealing with large data volumes, necessitating the use of approximate methods. A powerful class of methods approximate the GP as a sum of basis functions with random coefficients. Although this technique offers computational efficiency, it does not inherently guarantee a stationary covariance. To mitigate this issue, the basis functions can be "normalized" to maintain a constant marginal variance, avoiding unwanted artifacts and edge effects. This allows for the fitting of nearly stationary models to large, potentially non-stationary datasets, providing a rigorous base to extend to more complex problems. Unfortunately, the process of normalizing these basis functions is computationally demanding. To address this, we introduce two fast and accurate algorithms to the normalization step, allowing for efficient prediction on fine grids. The practical value of these algorithms is showcased in the context of a spatial analysis on a large dataset, where significant computational speedups are achieved. While implementation and testing are done specifically within the LatticeKrig framework, these algorithms can be adapted to other basis function methods operating on regular grids.