ABSTRACT Urban vitality is crucial for measuring urban sustainable development. Despite substantial progress in unraveling the spatial distribution of urban vitality, research on its temporal forecasting is less investigated. A significant challenge in urban vitality forecasting arises from the proliferation of spatial nodes, which leads to a reduction in the distinguishability among nodes. This phenomenon reduces the ability of transformer attention to detect node‐specific differences, thereby decreasing the forecasting performance. To address this, we propose a spatial heterogeneity‐aware deep learning framework for urban vitality forecasting, namely Sphereformer. Take advantage of practice from the geographical sciences, an angular‐based attention module was proposed to deal with homogeneous noise on large‐scale spatial areas, thereby improving the differentiation representation of the model. Additionally, we use Point‐of‐Interests (POI) as a covariate to further increase differentiation, thereby better achieving spatial heterogeneity modeling. Experiments demonstrate our model outperforms state‐of‐the‐art models on a large‐scale real‐world dataset of urban vitality. This research provides a new point for integrating geographical statistics with deep learning for urban vitality forecasting.
Jiang et al. (Fri,) studied this question.