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We introduce a model inspired from statistical physics that is shown todisplay flexible short-range spatial correlations which are potentially usefulin geostatistical modeling. In particular, we consider a suitably modifiedplanar rotator or XY model, traditionally used for modeling continuous spinsystems in magnetism, and we demonstrate that it can capture spatialcorrelations typically present in geostatistical data. The empirical study ofthe spin configurations produced by Monte Carlo simulations at varioustemperatures and stages in the nonequilibrium regime shows that their spatialvariability can be modeled by the flexible class of Mat\'{e}rn covariancefunctions. The correlation range and the smoothness of these functions varysignificantly in the parameter space that consists of the temperature and thesimulation time. We briefly discuss the potential of the model for efficientand automatic prediction of spatial data with short-range correlations, such ascommonly encountered in geophysical and environmental applications.

Short-range correlations in modified planar rotator model