Safe Routes to Play? Pedestrian and Bicyclist Crashes Near Parks in the Los Angeles Region
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».