Enhancing walkability/rollability audit tools to address qualitative measures for accessibility
Bibliographic record
Abstract
As accessibility legislation and active transportation policy become more prevalent across the country, Canadian municipalities will need a tool to evaluate walkability/rollability as a vital component of accessibility in the built environment. Existing walkability indices often overlook qualities of street infrastructure—such as curb cuts and shading—in part due to the labour- or computation-intensive data collection processes required. We piloted a method of evaluating two factors of street quality identified as important for accessibility—curb cuts and shading—as part of neighbourhood-level walkability/rollability assessment in Halifax, Canada. We rated a sample of over 2000 road segments using Google Street View (GSV). Then, we identified areas with highest need of walkable/rollable infrastructure by cross-referencing the average neighbourhood-level scores and concentration of older adults and children. Lastly, we calculated the walkability scores based on a conventional method with and without the two factors for comparison. Curb cut quality was generally low across the neighbourhoods, including some newer suburbs. Shade scores were higher in more established neighbourhoods with more mature tree canopies, as expected. Addition of the two factors had a notable effect on the scoring for several neighbourhoods, suggesting that some neighbourhoods may be lower-performing (i.e., less walkable/rollable) than the conventional scores would suggest. The rating provided a more thorough picture of neighbourhoods in need of walkable/rollable infrastructure improvement. Our methodology can be a cost- and time-effective way to collect data required to monitor the progress on accessibility in the built environment that municipalities may adopt. • Conventional walkability tools often overlook the needs of people with disabilities, older adults, and children. • GSV audits are a practical option to assess accessible pedestrian infrastructure. • The addition of qualitative data had a notable effect on conventional walkability scoring methods. • This method requires careful consideration of variable choice and geography. • GSV audits are a cost-effective data collection method that could help cities monitor accessible pedestrian infrastructure.
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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 | 0.106 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".