Visualizing the Dynamics of London's Bicycle-Hire Scheme
Bibliographic record
Abstract
Visualizing flows between origins and destinations can be straightforward when dealing with small numbers of journeys or simple geographies. Lines embedded in geographic space have commonly been used in mapping transport flows, especially when geographic patterns are important, as they are when characterizing cities or managing transportation. For larger numbers of flows, however, this approach requires careful design to avoid problems of occlusion, salience bias, and information overload. Driven by the requirements identified by users and managers of the London Bicycle Hire scheme, we present three methods of representation of bicycle-hire use and travel patterns. Flow maps with curved flow symbols are used to show overviews in flow structures. Gridded views of docking-station locations that preserve geographic relationships are used to explore docking-station status over space and time in a graphically efficient manner. Origin–Destination maps that visualize the OD matrix directly while maintaining geographic context are used to provide visual details on demand. We use these approaches to identify changes in travel behaviour over space and time, to aid station rebalancing, and to provide a framework for incorporating travel modelling and simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".