Mapping Bikeability: A Spatial Tool to Support Sustainable Travel
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
The built environment has been shown to influence active transportation. Although spatial data for the built environment is increasingly available, there has been little effort to use existing data and knowledge to define and map ‘bikeability’ as an approach to promoting travel by bicycle. Our goal was to build a tool to identify areas that are more conducive and less conducive to cycling. We used empirical research to develop a bikeability index and geographic information systems to map the index across the Metro Vancouver region. Results of an opinion survey, travel behaviour studies, and focus groups were used to identify the components of the index and their relative importance. Pertinent geospatial data layers were scored and combined using a flexible weighting scheme to create a composite map highlighting both high and low bikeability areas. The bikeability index was comprised of five factors shown to consistently influence cycling: Bicycle facility availability; bicycle facility quality; street connectivity; topography; and land use. For mapping purposes, we created corresponding metrics: density of bicycle facilities; separation from motor vehicle traffic; connectivity of bicycle-friendly roads (local streets, bicycle routes, and off-street paths); slope; and density of destination locations. Using empirical evidence to combine data layers for these metrics we generated a high-resolution (10 m) bikeability surface for the region, depicting bicycle-friendly areas and areas where cycling conditions need to be improved. Built environment interventions for specific locations are informed by evaluating scores for the five individual component layers. Mapping bikeability provides a powerful visual aid to identify zones where changes are needed to support sustainable travel. This evidence-based tool presents data in a user-friendly way for planners and policy makers. The overall bikeability score and its five component scores can guide local action to stimulate changes in cycling rates. It uses widely available data types, thus facilitating easy application in other cities. Furthermore, the flexible parameters and weighting scheme enable users elsewhere to tailor it to evidence about local preferences and conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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