Authors: Yuhao Jia, Zile Wu, Shengao Yi, Yifei Sun
Abstract: 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.
Source: http://arxiv.org/abs/2408.08852v1
