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

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 can include an infinite number of higher-order harmonics with exponentially vanishing interaction strength, directional dependence of the bilinear interaction term between nearest neighbors, longer-distance neighbor interactions, and two types of an external bias field. Hence, in contrast with the single-parameter MPR, the GPR model features five additional parameters: the number n of higher-order terms and the decay-rate parameter α, the exchange anisotropy parameter Jnn, the further-neighbor interaction coupling Jfn, and the external field (bias) parameters K (or K′). We present numerical tests on various synthetic and real 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.