Linguistic Cartography: Metric Spatial and Hierarchical Platial Representations in Language Models

Language models can encode where a place lies on the Earth's surface, but geographic representation is not only about the coordinate-based metric space. A city is also nested in a hierarchical platial structure: state/province, country, subregion and continent. We study how these two geographic forms coexist in the neural representations of language models. We build GPH-30K, a primary source of geographic hierarchy with 30,000 globally distributed populated places with both latitude/longitude information and a complete administrative path. This benchmark dataset supports metric spatial experiments and hierarchical platial experiments. On an open-weight Llama/Qwen model panel, coordinate probes recover latitude/longitude while hierarchy probes recover platial path from the same residual stream. Layer-wise parent-recovery maps show that the place hierarchy is not a single flat attribute: country-to-subregion relations emerge earlier, admin1-to-country relations peak late, and place-to-admin1 remains the hardest edge. Final-layer embedding geometry also varies by model family and generation: Qwen-2.5 models are consistently more spatial metric-leaning, whereas Llama and Qwen3 are closer to balanced or platial hierarchy-leaning. Finally, on a conflict diagnostic set HGCD where coordinate distance and administrative-tree distance make opposite predictions, smaller Qwen/Llama checkpoints are mostly spatial, while large Llama and Qwen-2.5 checkpoints can become mixed or platial. These results support a coexistence view of linguistic cartography: language models contain both continuous metric spatial representations and discrete hierarchical platial representations, which emerge at different depths and dominate geometry under different contexts.