Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales
Location encoders transform geographic coordinates into high dimensional embeddings for downstream machine learning, but it is unclear how well these representations capture interpretable spatial effects. We benchmark whether GeoShapley, a game-theoretic explainer that treats all location features as a single joint player, can recover spatially varying coefficients from models built on location-encoder embeddings. Eleven encoders from the TorchSpatial framework are evaluated against a synthetic process with known coefficients, across three scales (grid, county, global), with and without raw coordinates alongside the embedding, and under untrained and contrastively trained conditions. Meas...
| Authors | Daniel Kiv, Shaowen Wang |
|---|---|
| Primary category | cs.LG |
| Published | 22 June 2026 |
| Last updated at source | 25 September 2026 |
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