Effect of neighbourhood motorization rates on walking levels
Bibliographic record
Abstract
BACKGROUND: Motorized traffic may discourage people walking. This study analyses the influence of motorization on pedestrian mobility in the neighbourhoods of a European city, controlling for environmental, sociodemographic, mobility and road safety characteristics of the neighbourhood in which each trip was made. METHODS: Cross-sectional ecological study using the 38 neighbourhoods of Barcelona as the unit of analysis. Mobility information was obtained from the 2006 Daily Mobility Survey. Walking rates were calculated for 32.343 men and women who made walking trips, per 1000 men and women who made trips in the 38 neighbourhoods. Data were aggregated to calculate the total number of motorized trips made in each neighbourhood. β coefficients and their confidence intervals were calculated using Poisson regression, in order to study the relationship between walking and motorization, in the different tertiles of motorization and adjusting for contextual factors and their corresponding interactions with motorization. RESULTS: Levels of motorization in the neighbourhood negatively influence walking, even when environmental variables of the neighbourhood are considered. In men we observe a gradient whereby walking rates fall as motorization rises (β = -0.248; P < 0.001 and β = -0.363; P < 0.001 in the second and third quartiles of motorization, respectively). In the case of women we find that only high levels of motorization have a negative influence on the rates of women who walk. (β = -0.256; P < 0.001). CONCLUSION: Motorized traffic discourages people walking. Motorization is a modifiable environment-related factor which should be taken into account when designing policies to promote safe active travel.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".