The　use　of　the　current　global　and　regional　models　for　predicting　large-scale　urban　crowd　flows　often　involves　trade-offs　between　computational　efficiency　and　accuracy.　Global　models　are　computationally　efficient　but　struggle　to　fully　capture　the　spatial　heterogeneity　in　crowd　dynamics　and　often　lead　to　unsatisfactory　performance.　Region-specific　models　are　highly　accurate　in　capturing　fine-grained　spatial　heterogeneity,　but　their　computational　costs　are　high　when　applied　to　numerous　regions.　We　took　a　GeoAI　approach　to　develop　a　novel　spatiotemporal　compressed　sensing-based　prediction　framework　(STCSP)　to　address　these　challenges.　This　framework　employs　compressed　sensing　techniques　to　identify　the　shared　structures　in　crowd　flow　data.　STCSP　transforms　spatiotemporal　predictions　in　a　complex　geographical　space　into　simplified　predictions　in　an　embedding　space,　which　is　more　efficient　than　existing　models.　STCSP　combines　these　simplified　predictions,　modeling　the　spatial　heterogeneity　in　detail　to　increase　the　accuracy　of　crowd-flow　predictions.　We　evaluated　STCSP　on　a　small-scale　benchmark　dataset　and　a　large-scale　citywide　dataset　and　showed　that　STCSP　outperformed　12　baseline　models　in　accuracy　and　efficiency　in　predicting　crowd　flows.　©　2025　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.