Developing a research and practice tool to measure walkability: A demonstration project
Bibliographic record
Abstract
Issue addressed: Growing evidence shows that higher-density, mixed-use, pedestrian-friendly neighbourhoods encourage active transport, including transport-related walking. Despite widespread recognition of the benefits of creating more walkable neighbourhoods, there remains a gap between the rhetoric of the need for walkability and the creation of walkable neighbourhoods. Moreover, there is little objective data to benchmark the walkability of neighbourhoods within and between Australian cities in order to monitor planning and design intervention progress and to assess built environment and urban policy interventions required to achieve increased walkability. This paper describes a demonstration project that aimed to develop, trial and validate a Walkability Index Tool' that could be used by policy makers and practitioners to assess the walkability of local areas; or by researchers to access geospatial data assessing walkability. The overall aim of the project was to develop an automated geospatial tool capable of creating walkability indices for neighbourhoods at user-specified scales. Methods: The tool is based on open-source software architecture, within the Australian Urban Research Infrastructure Network (AURIN) framework, and incorporates key sub-component spatial measures of walkability (street connectivity, density and land use mix). Results: Using state-based data, we demonstrated it was possible to create an automated walkability index. However, due to the lack of availability of consistent of national data measuring land use mix, at this stage it has not been possible to create a national walkability measure. The next stage of the project is to increase useability of the tool within the AURIN portal and to explore options for alternative spatial data sources that will enable the development of a valid national walkability index. Conclusion: AURIN's open-source Walkability Index Tool is a first step in demonstrating the potential benefit of a tool that cou
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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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".