GeoTransformer: Enhancing Urban Forecasting with Geospatial Attention Mechanisms
<|reference_start|>GeoTransformer: Enhancing Urban Forecasting with Geospatial Attention Mechanisms: Recent advancements have focused on encoding urban spatial information into high-dimensional spaces, with notable efforts dedicated to integrating sociodemographic data and satellite imagery. These efforts have established foundational models in this field. However, the effective utilization of these spatial representations for urban forecasting applications remains under-explored. To address this gap, we introduce GeoTransformer, a novel structure that synergizes the Transformer architecture with geospatial statistics prior. GeoTransformer employs an innovative geospatial attention mechanism to incorporate extensive urban information and spatial dependencies into a unified predictive model. Specifically, we compute geospatial weighted attention scores between the target region and surrounding regions and leverage the integrated urban information for predictions. Extensive experiments on GDP and ride-share demand prediction tasks demonstrate that GeoTransformer significantly outperforms existing baseline models, showcasing its potential to enhance urban forecasting tasks.<|reference_end|>
@article{jia2024geotransformer:,
    title={GeoTransformer: Enhancing Urban Forecasting with Geospatial Attention
  Mechanisms},
    author={Yuhao Jia, Zile Wu, Shengao Yi, Yifei Sun},
    journal={arXiv preprint arXiv:2408.08852},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08852},
    primaryClass={cs.AI cs.LG}
}