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GeoTransformer: Enhancing Urban Forecasting with Dependency Retrieval and Geospatial Attention

Recent advances in urban forecasting have leveraged high-dimensional spatial data through two primary approaches: graph-based methods that rely on predefined spatial structures and region-based methods that use satellite imagery for local features. Although these methods have laid an important foundation, they struggle to integrate holistic urban information and dynamically model spatial dependencies. To address this gap, we propose GeoTransformer, a framework combining high-dimensional regional embeddings with dynamic spatial modeling. GeoTransformer features two innovations: (1) a dependency retrieval module identifying spatial dependencies to select relevant regions, and (2) a geospatial attention mechanism leveraging global urban information. These components unify structural and global urban information for better predictions. Extensive experiments on GDP and ride-share demand forecasting show that GeoTransformer outperforms baselines, highlighting its effectiveness in advancing urban forecasting tasks.

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