CONTACT US: Contact info
|
Citation |
Yu Y, Jiang Y, Qiu N, Guo H, Han X, Guo Y. Front. Public Health 2022; 10: e1013421. |
|
Copyright |
(Copyright © 2022, Frontiers Editorial Office) |
|
DOI |
|
PMID |
36172205 |
|
PMCID |
|
|
Abstract |
E-bike, characterized as a low-carbon and health-beneficial active travel mode, is gradually becoming popular in China. Although built environment factors are considered to be key parameters that can facilitate or hinder active transportation, such as cycling or walking, few studies have explored the impact of built environment on e-bikes. To fill this gap, this study was the first to explore the relationship between e-bike usage and built environment factors based on population level travel survey in central Jinan, China. Both macro and micro levels of built environment were measured using multi-source data. We employed ordinary least squares (OLS) and geographically weighted regression (GWR) models to explore the aggregation patterns of e-bike trips. Besides, the local Moran's I was employed to classify the aggregation patterns of e-bike trips into four types. The results from OLS model showed that eye-level greenery, building floor area, road density and public service POI were positive significantly related to e-bike trips, while open sky index and NDVI had negative association with e-bike trips. The usage of GWR model provided more subtle results, which revealed significant spatial heterogeneity on the impacts of different built environment parameters. Road density and public service POI posed positive effects on e-bike travel while NDVI and open sky index were found mainly pose negative impacts on e-bike travel. Moreover, we found similar coefficient distribution patterns of eye-level greenery, building floor area and distance to bus stop. Therefore, tailored planning interventions and policies can be developed to facilitate e-bike travel and promote individual's health level. Language: en |
|
Keywords |
Humans; China; built environment; spatial heterogeneity; *Bicycling; *Built Environment; Carbon; e-bike usage; geographical weighted regression; Jinan; LISA; multi-source data; Transportation/methods |