A new GeoAI framework models spatial variability as thermodynamic competition between burden (E) and capacity (S) using graph neural networks to identify regime-dependent predictor reversals and phase transitions across simulation and real datasets.
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Thermodynamic-Inspired Explainable GeoAI: Uncovering Regime-Dependent Mechanisms in Heterogeneous Spatial Systems
A new GeoAI framework models spatial variability as thermodynamic competition between burden (E) and capacity (S) using graph neural networks to identify regime-dependent predictor reversals and phase transitions across simulation and real datasets.