Using OpenStreetMap to inventory bicycle infrastructure: A comparison with open data from cities
Bibliographic record
Abstract
ABSTARCTWith rapid growth in bicycling, timely and spatially rich bicycling infrastructure data are essential for understanding determinants of ridership, equity of access, and potential for future developments. OpenStreetMap (OSM) is a collaborative global map that was built by volunteers and is promising for active transportation research. In this article, we use OSM to inventory bicycling infrastructure in six Canadian cities, compare it to municipal open data, and provide guidance for practitioners using OSM data. We conducted an evaluation of OSM and open data, overall and for four categories of bicycle infrastructure: cycle tracks; on-street bicycle lanes; paths (bicycle only or multiuse); and local street bikeways. We found that the concordance in terms of total length of OSM infrastructure to open data infrastructure very high in two of the six cities (< ±2%), and reasonably high in all cities (maximum difference ±30%). Concordance for infrastructure categories was highest for on-street bicycle lanes, which were the most common, and easily identifiable type of bicycle infrastructure in the OSM data, and lowest for cycle tracks and local street bikeways, both of which are new or relatively rare infrastructure types in some Canadian cities. In some cases, OSM was more detailed and timely than open data. A challenge in OSM is consistent tagging of bicycle infrastructure types. We encourage practitioners to consider OSM data for multicity studies, but to be mindful of potential inconsistencies in attribution and local definitions. We also recommend users of OSM to publish data queries for repeatability.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.031 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".