Using OpenStreetMap to inventory bicycle infrastructure: A comparison with open data from cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 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