Contribution of streetscape audits to explanation of physical activity in four age groups based on the Microscale Audit of Pedestrian Streetscapes (MAPS)
Bibliographic record
Abstract
Ecological models of physical activity emphasize the effects of environmental influences. "Microscale" streetscape features that may affect pedestrian experience have received less research attention than macroscale walkability (e.g., residential density). The Microscale Audit of Pedestrian Streetscapes (MAPS) measures street design, transit stops, sidewalk qualities, street crossing amenities, and features impacting aesthetics. The present study examined associations of microscale attributes with multiple physical activity (PA) measures across four age groups. Areas in the San Diego, Seattle, and the Baltimore metropolitan areas, USA, were selected that varied on macro-level walkability and neighborhood income. Participants (n = 3677) represented four age groups (children, adolescents, adults, older adults). MAPS audits were conducted along a 0.25 mile route along the street network from participant residences toward the nearest non-residential destination. MAPS data were collected in 2009-2010. Subscale and overall summary scores were created. Walking/biking for transportation and leisure/neighborhood PA were measured with age-appropriate surveys. Objective PA was measured with accelerometers. Mixed linear regression analyses were adjusted for macro-level walkability. Across all age groups 51.2%, 22.1%, and 15.7% of all MAPS scores were significantly associated with walking/biking for transport, leisure/neighborhood PA, and objectively-measured PA, respectively. Supporting the ecological model principle of behavioral specificity, destinations and land use, streetscape, street segment, and intersection variables were more related to transport walking/biking, while aesthetic variables were related to leisure/neighborhood PA. The overall score was related to objective PA in children and older adults. Present findings provide strong evidence that microscale environment attributes are related to PA across the lifespan. Improving microscale features may be a feasible approach to creating activity-friendly environments.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".