Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns
Why this work is in the frame
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Bibliographic record
Abstract
Urban space exhibits rich and diverse organizational structures, which is difficult to characterize and interpret. Modelling urban spatial structures in the context of mobility and revealing their underlying patterns in dynamic networks are key to understanding urban spatial structures and how urban systems work. Most existing methods overlook its temporal dimension and oversimplify its spatial heterogeneity, and it is challenging to address these complex properties using one single method. Therefore, we propose a framework based on temporal networks for modeling dynamic urban mobility structures. First, we cast aggregated traffic flows into a compact and informative temporal network for structure representation. Then, we explore spatial cluster substructures and temporal evolution patterns to acquire evolution regularities. Last, the capability of the proposed framework is examined by an empirical analysis based on taxi mobility networks. The experiment results enable to quantitatively depict urban space dynamics and effectively detect spatiotemporal heterogeneity in mobility networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it