We propose a hybrid mixed spatial graphical model framework and novel concepts, e.g., cross-Markov Random Field (cross-MRF), to comprehensively address all feature aspects of highly multivariate high-dimensionality (HMHD) spatial data class when constructing the desired joint variance and precision matrix (where both p and n are large). Specifically, the framework accommodates any customized conditional independence (CI) among any number of p variate fields at the first stage, alleviating dynamic memory burden. Meanwhile, it facilitates parallel generation of covariance and precision matrix, with the latter's generation order scaling only linearly in p. In the second stage, we demonstrate the multivariate Hammersley-Clifford theorem from a column-wise conditional perspective and unearth the existence of cross-MRF. The link of the mixed spatial graphical framework and the cross-MRF allows for a mixed conditional approach, resulting in the sparsest possible representation of the precision matrix via accommodating the doubly CI among both p and n, with the highest possible exact-zero-value percentage. We also explore the possibility of the co-existence of geostatistical and MRF modelling approaches in one unified framework, imparting a potential solution to an open problem. The derived theories are illustrated with 1D simulation and 2D real-world spatial data.

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