SOURCE-LINKED INTELLIGENCE
When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including mobility analysis, public safety forecasting, and service demand estimation. Existing multi-view methods typically improve region embeddings by strengthening interactions across views. However, such methods often overlook view-specific regional structures and may propagate correlations induced by shared latent factors, which can reduce the stability of downstream predictions. To overcome this major limitation, we pro
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T09:53:56.000Z
First collected: 2026-09-20T11:41:07.830Z. This is not the publication date.