Mapping change in cycling infrastructure across Canada: What, where, and for whom?
Bibliographic record
Abstract
INTERVENTION: Governments are investing in safer cycling infrastructure to provide transportation options that support health, mobility, and environmental outcomes; these can be considered population health interventions. RESEARCH QUESTION: We aimed to measure change from 2022 to 2024 in cycling infrastructure in Canada to understand what and where changes happened, and who was impacted. METHODS: We extracted data from OpenStreetMap.org (OSM) in 2022 and 2024 and coded them according to the Canadian Bikeway Comfort and Safety (Can-BICS) classification system (high, medium, or low comfort and safety). We measured differences (2022-2024) in length and type of cycling infrastructure within census subdivisions. We related differences in cycling infrastructure metrics with population composition within dissemination areas, nationally, by city size, and for particular cities, examining specific population groups (children, older adults, recent immigrants, racialized people, and low-income populations). RESULTS: Total Can-BICS-OSM cycling infrastructure in 2024 increased to 27,098 km (15.3%) from 23,502 km in 2022. Most new infrastructure was multi-use paths (2725 km). High-comfort bike-only paths increased by nearly 50% (49 km). Nationally, access increased for recent immigrants, racialized people, and people with low incomes. Overall, areas with more children and older adults saw less increase in access to infrastructure, but within small and medium cities there were often increases. CONCLUSION: Using a national dataset, we detected an increase in cycling infrastructure in Canadian communities, with mobility and health implications for many equity-deserving population groups. The greatest increases, proportionally, in cycling infrastructure were seen in small- and medium-sized cities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".