Correlation Between Neighborhood Built Environment and Leisure Walking Time Around a Riverside Park
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate whether the distance to a riverside park and the neighborhood built environment are related to individuals' leisure walking time by examining the case of the Geumho riverside park in Daegu, South Korea. BACKGROUND: Walking, being an inexpensive means of transportation with numerous health benefits, is influenced by the conditions of neighborhood built environments. METHODS: A survey was conducted from October 12 to November 8, 2022, including 184 adults aged 18 years or older. The dependent variable was the total weekly minutes of leisure walking, and the independent variables included the neighborhood built environment measured objectively using geographic information systems as well as demographic/individual characteristics and health attitude data. Analysis of variance was conducted to determine whether leisure walking time differed depending on the distance to the riverside park, and regression analysis was conducted to examine the association between leisure walking time and the neighborhood built environment. RESULTS: Individuals living within a quarter-mile of the park walked an average of 155 min per week for leisure, which was significantly more than those living further than 1 mile (mean = 85.14 min/week). Moreover, greater access to the park, higher crosswalk density, and a lower road density were associated with more leisure walking time for residents. CONCLUSIONS: The findings of this study indicate that good access to riverside parks and pedestrian-centered neighborhood environments may be related to leisure walking among residents. These findings hold significance for urban planning and the formulation of public health policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".