Spatial data modeling by means of Gibbs Markov random fields based on a generalized planar rotator model

TL;DR

This paper introduces a generalized Gibbs Markov random field model for spatial data that incorporates multiple realistic features like anisotropy and higher-order interactions, improving data modeling accuracy.

Contribution

The paper presents a novel GPR model extending the MPR model with additional parameters to better capture complex spatial data features.

Findings

Enhanced modeling of spatial data features.

Numerical tests demonstrate improved prediction performance.

Analysis of parameter effects on model accuracy.

Abstract

We introduce a Gibbs Markov random field for spatial data on Cartesian grids which is based on the generalized planar rotator (GPR) model. The GPR model generalizes the recently proposed modified planar rotator (MPR) model by including in the Hamiltonian additional terms that better capture realistic features of spatial data, such as smoothness, non-Gaussianity, and geometric anisotropy. In particular, the GPR model includes up to infinite number of higher-order harmonics with exponentially vanishing interaction strength, directional dependence of the bilinear interaction term between nearest grid neighbors, longer-distance neighbor interactions, and two types of an external bias field. Hence, in contrast with the single-parameter MPR model, the GPR model features five additional parameters: the number $n$ of higher-order terms and the parameter $\alpha$ controlling their decay rate, the exchange anisotropy parameter $J^{nn}$, the further-neighbor interaction coupling $J^{fn}$, and the external field (bias) parameters $K$ (or $K'$). We present numerical tests on various synthetic data which demonstrate the effects of the respective terms on the model's prediction performance and we discuss these results in connection with the data properties.