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Gravity-informed deep flow inference for spatial evolution modeling in panel data
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AI
DOI：10.1080/13658816.2025.2536512.png)
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Abstract
En 中文
Spatial flows between consecutive distribution snapshots describe how one configuration evolves into the next. Such panel flows are inferable, albeit challenging, from observations over space and time. Current flow generation models focus on cross-sectional scenarios and neglect the temporal nature of flows. The panel gravity model, although interpretable, relies on linear assumptions and overlooks the change of geographic contexts. Our work introduces a Deep Spatial Evolution Network (DSEN) to infer panel flows, i.e. the spatial evolution between two snapshots of spatial distributions. DSEN incorporates a cross-event context learner to encode the contextual features, and a gravity-informed spatial evolution decoder to learn latent evolutionary features. Using a device-level mobile positioning dataset in the Twin Cities Metropolitan Area, Minnesota, U.S., DSEN achieves a 14.0% correlation improvement and a 15.3% error reduction compared to baselines in inferring human flows during the 2021 Christmas holiday, while explaining evolution processes and flow directionalities via deep features. Further experiments across sampling ratios and unseen events demonstrated the robustness and generalizability of our model, respectively. Deep flow inference for panel data holds promise for advancing mobility studies at the GeoAI frontier, expanding access to flow data, and informing solutions to pressing human-environment challenges in future cities.
Keywords:
Spatial evolution
human mobility
gravity model
panel data
GeoAI
Journal
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5.1
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2.7K
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