Equity in Spatial Access to Bicycling Infrastructure in Mid-Sized Canadian Cities
Bibliographic record
Abstract
The impacts of active transportation planning on equity are often overlooked, potentially leading to disparities in who receives benefits of infrastructure investment. This study examined income inequalities in spatial access to bicycling infrastructure in three mid-sized Canadian cities: Victoria and Kelowna (British Columbia), and Halifax (Nova Scotia), using non-spatial and spatial methods. We compiled municipal bicycling infrastructure data and calculated access to bicycling infrastructure (m/km 2 ) for dissemination areas (DAs) within each city. We analyzed trends in access across median household income quintiles, and characterized spatial patterns using a local measure of spatial autocorrelation. DAs in Kelowna ( n = 168) had the greatest access to infrastructure (median infrastructure = 2,915 m/km 2 ), followed by Victoria ( n = 386 DAs; median = 2,157 m/km 2 ), and Halifax ( n = 312 DAs; median = 0 m/km 2 ). Lower income areas in Victoria and Kelowna had greater access to infrastructure compared with higher income areas. The majority of DAs in Halifax had no infrastructure (59%), consistent across income quintiles. Spatial pattern analysis identified clusters of low income areas with poor access in each city, which may be targets for strategic, equitable investment. Although in many cities bicycling infrastructure planning is not driven by equity considerations, there is increasing political pressure to ensure equitable access to safe bicycling. Measuring and mapping trends in access to transportation resources from an equity perspective are requisite steps in the pathway toward healthy, sustainable cities for all.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".