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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed computational preprint describing MoSS, a machine learning method that combines temporal and topological features of urban mobility to generate region embeddings. The work reports superior performance on three downstream prediction tasks in two US cities compared to stated baselines, but lacks peer review and does not address clinical, epidemiological, or health-relevant outcomes.
Preprint. Urban regions in New York City and Chicago. Intervention: MoSS (Mobility Stream-Structure Synergy) method combining Sequence view (hourly inflow/outflow) and Structure view (zigzag persistence diagrams) with synergy module. Compared with: Unspecified baselines; stated to rely on auxiliary modalities. New York City and Chicago, United States.
MoSS achieves state-of-the-art performance across three downstream tasks using mobility data alone Method outperforms baselines that rely on auxiliary modalities
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This is an unrefereed preprint presenting a machine learning method for urban region embedding; it lacks peer review and reports computational performance metrics rather than clinical or health outcomes.
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Urban region embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction. Recent methods improve representation quality by integrating mobility data with auxiliary modalities, using cross-view attention or contrastive objectives to align heterogeneous features into a unified region representation. However, leveraging the temporal dynamics of human mobility remains under-explored. Regional inflow and outflow fluctuate throughout the day, and inter-region connections emerge, persist, and dissolve over time. Moreover, prevailing fusion strategies combine views additively and miss the joint signal that emerges only when views co-occur. To address these gaps, we propose Mobility Stream-Structure Synergy (MoSS), which derives complementary views from mobility data: a Sequence view that preserves each region's hourly inflow/outflow profile, and a Structure view based on zigzag persistence diagrams that capture how regional connectivity emerges, persists, and dissolves over time. A synergy module then extracts emergent representations from the co-occurrence of these views through multi-degree interactions, explicitly capturing higher-order signal across views. Extensive experiments on New York City and Chicago show that MoSS achieves state-of-the-art performance across three downstream tasks using mobility data alone, outperforming baselines that rely on auxiliary modalities.
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