Park Features, Neighborhood Environment, and Time Factors Affect Park Visitor Volume: A Meta-Analysis
Bibliographic record
Abstract
Urban parks are essential for sustainable urban planning, but their usage patterns remain complex. This meta-analysis of 30 studies identifies factors influencing park visitor volume, focusing on park attributes, neighborhood environments, and temporal aspects. Random-effect models reveal positive associations with park size, diverse facilities, organized activities, trails, maintenance, and quality. Neighborhood population density and points of interest also increase visitation, while socio-economically disadvantaged areas see reduced use. Temporal factors, such as time of day and season, significantly shape patterns. However, features like water, greenness, crime safety, and transit accessibility show mixed or insignificant effects. Regional differences highlight stronger impacts of population density and transit accessibility in the U.S. compared to Asian studies. These findings provide actionable insights for urban planners and landscape architects to design parks that cater to diverse needs, boost visitation, and maximize their community benefits.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.036 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".