Safe Routes to Play? Pedestrian and Bicyclist Crashes Near Parks in the Los Angeles Region
Bibliographic record
Abstract
Rationale: Areas near parks may present active travelers with higher risks than in other areas due to the confluence of more pedestrians and bicyclists, younger travelers, and the potential for increased numbers of motor vehicles. These risks may be amplified in low-income and minority neighborhoods due to generally higher rates of walking or lack of safety infrastructure. Objectives: We pursued three research objectives: (1) to determine if pedestrian and bicycle crashes occur at higher rates in park-adjacent neighborhoods compared to the rest of the study area; (2) to identify if demographic characteristics predict active crash risk after controlling for population and the rate of active trips; and (3) to assess if there is an amplified effect of park proximity for active crash risk in low-income and minority neighborhoods after controlling for population and the rate of active trips. Methods: With negative binomial regression modeling techniques, we used ten years of geolocated pedestrian and bicyclist crash data and a quarter mile (~400 meter) buffer around public parks to assess the risk of active travel near parks. We controlled for differential exposures to active travel risks using travel survey data. Measurements: Quarter-mile network buffers were designated around parks from the Green Visions Plan for 21st Century California in 2249 census tracts. Crashes came from the 90,846 pedestrian and bicyclist injuries and fatalities from the Statewide Integrated Traffic Reporting System, and active travel was predicted using travel data from 9135 households that participated in the Southern California Association of Governments 2001 Travel and Congestion Survey. These data were combined with demographic and income data from the U.S. Census and traffic density predictions. Results: The ratio of active crashes per 100,000 population within the quarter-mile park buffer to those outside is 1.52. The increased risk of crash for active travelers near parks remained after adjusting for varying rates of active travel in different census tracts. Minority and low-income residents of the study area are more likely to walk or bicycle than White and higher-income residents. This higher risk near parks is amplified in neighborhoods with high proportions of minority and low-income people. Higher traffic levels are highly predictive of active crashes.Conclusions: Active travelers accessing parks may lack a safe route to places for play. The socioeconomic modification of active crashes near parks found in this study is supported by existing research showing disparities in park access and higher active travel risks in low-income and minority neighborhoods.
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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 | 0.002 |
| 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) | 0.003 | 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".