Validity of Walk Score® as a measure of neighborhood walkability in Japan
Bibliographic record
Abstract
Objective measures of environmental attributes have been used to understand how neighborhood environments relate to physical activity. However, this method relies on detailed spatial data, which are often not easily available. Walk Score® is a free, publicly available web-based tool that shows how walkable a given location is based on objectively-derived proximity to several types of local destinations and street connectivity. To date, several studies have tested the concurrent validity of Walk Score as a measure of neighborhood walkability in the USA and Canada. However, it is unknown whether Walk Score is a valid measure in other regions. The current study examined how Walk Score is correlated with objectively-derived attributes of neighborhood walkability, for residential addresses in Japan. Walk Scores were obtained for 1072 residential addresses in urban and rural areas in Japan. Five environmental attributes (residential density, intersection density, number of local destinations, sidewalk availability, and access to public transportation) were calculated using geographic information systems for each address. Pearson's correlation coefficients between Walk Score and these environmental attributes were calculated (conducted in May 2017). Significant positive correlations were observed between Walk Score and environmental attributes relevant to walking. Walk Score was most closely associated with intersection density (r = 0.82) and with the number of local destinations (r = 0.77). Walk Score appears to be a valid measure of neighborhood walkability in Japan. Walk Score will allow urban designers and public health practitioners to identify walkability of local areas without relying on detailed geographic data
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".