Disparities in pedestrian streetscape environments by income and race/ethnicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Growing evidence suggests that microscale pedestrian environment features, such as sidewalk quality, crosswalks, and neighborhood aesthetics, may affect residents' physical activity. This study examined whether disparities in microscale pedestrian features existed between neighborhoods of differing socioeconomic and racial/ethnic composition. Using the validated Microscale Audit of Pedestrian Streetscapes (MAPS), pedestrian environment features were assessed by trained observers along ¼-mile routes (N = 2117) in neighborhoods in three US metropolitan regions (San Diego, Seattle, and Baltimore) during 2009 to 2010. Neighborhoods, defined as Census block groups, were selected to maximize variability in median income and macroscale walkability factors (e.g., density). Mixed-model linear regression analyses explored main and interaction effects of income and race/ethnicity separately by region. Across all three regions, low-income neighborhoods and neighborhoods with a high proportion of racial/ethnic minorities had poorer aesthetics and social elements (e.g., graffiti, broken windows, litter) than neighborhoods with higher median income or fewer racial/ethnic minorities (p<.05). However, there were also instances where neighborhoods with higher incomes and fewer racial/ethnic minorities had worse or absent pedestrian amenities such as sidewalks, crosswalks, and intersections (p<.05). Overall, disparities in microscale pedestrian features occurred more frequently in residential as compared to mixed-use routes with one or more commercial destination. However, considerable variation existed between regions as to which microscale pedestrian features were unfavorable and whether the unfavorable features were associated with neighborhood income or racial/ethnic composition. The variation in pedestrian streetscapes across cities suggests that findings from single-city studies are not generalizable. Local streetscape audits are recommended to identify disparities and efficiently allocate pedestrian infrastructure resources to ensure access and physical activity opportunities for all residents, regardless of race, ethnicity, or income level.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it