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.
In the current context of aging and urbanization, the rapid increase in the prevalence of disabilities (PoDs) has become an important consideration in healthy urban planning. Previous studies have focused on the spatial prevalence of total disabilities based on large-scale survey data. However, few studies have examined different types of PoDs and the factors contributing to spatial disparities in micro-urban units at the municipal level. This study aims to fill this gap by exploring the spatial PoDs, related built environments, and socio-economic factors across Tianjin municipality in 2020. The study employed Getis-Ord GI* analysis to identify urban-rural disparities and OLS and quantile regression analyses to model the heterogeneous effects of the spatial PoDs determinants across quantiles. The results reveal that the PoDs, especially visual, hearing, and limb disabilities, in the urban centre, are significantly higher than those in rural areas, which is inconsistent with previous studies conducted in China. Urbanization rate, medical facilities, and education facilities significantly reduced total PoDs, while the elderly population, migrant population, bus route density, and road density significantly increased it. The built environments and socio-economic factors had heterogeneous impacts on different types of PoDs, which were summarized into three categories based on the dominant determinants: (1) visual and hearing disabilities were medical facility dominated; (2) intellectual and limb disabilities were urbanization, and aging dominated; and (3) mental and speech disabilities were migrant dominated. This study provides scientific advice to adapt to the expected increase in demand for disability-related medical and public health services and to expand the range of effective strategies and interventions aimed at preventing the deterioration of disability and improving disability management in the population.
The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and we finally identified six main ridership patterns. We then developed a newly built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis results revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type stations, and centrality was only associated with employment-oriented hybrid type station. The research results could inform the spatial planning and design around metro stations and the planning and design of metro systems. The built environment of pedestrian catchment areas surrounding metro stations can be enhanced through rational land use planning and the appropriate allocation of urban infrastructure and public service facilities.
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