“…Zhou found that there were scaled differences in the built environment’s influences on the elderly’s health, depending on their duration in different activity sites, the purpose of their trips, and the rate of green space in the community [ 30 ]. Cheng used a geographically weighted regression model to explore the relationship between the built environment and the daily walking time of the elderly at a local scale and found spatial heterogeneity in built environment effects [ 31 , 32 ]. Therefore, the determination of the built environment scale or geographical context is a crucial task in studies exploring the relationship between the built environment and daily activity behavior [ 33 , 34 ], and multilevel models offer the possibility of addressing the spatial heterogeneity of the built environment and have been validated in related studies [ 3 , 35 , 36 ].…”
Built environment characteristics such as walkability, land use diversity, infrastructure accessibility and attractiveness may support or hinder the elderly’s leisure activities, which in turn affects their health. Promoting the elderly’s leisure activities through the creation of a positive built environment is of great relevance to healthy aging. In the context of the continuous increasing of aging in China, promoting leisure activities for the elderly through improving the built environment has become an essential issue in urban geography and urban planning. Based on the questionnaire survey data of the elderly in Hefei City, a multilevel ordered probit regression model was used to investigate the mechanism of the multi-scale built environment on leisure activities of the elderly. The results show that: (1) more than 60% of the elderly can carry out leisure activities more than seven times a week, more than 50% of the elderly have a duration of fewer than 30 min for each leisure activity, and there are significant spatial differences in the frequency and duration of their trips at multiple scales in city, community and residential district. (2) Residential quality and community-level land use mixture, the density of leisure facilities, proximity to high-level urban roads, community security, living in the old city, and individual characteristic variables such as age, education, and satisfaction with neighborhood interaction positively contribute to the leisure activities of the elderly. In contrast, the community activity participation and the location close to expressways and railway lines have a significantly negative impact on the leisure activities of the elderly. (3) The mechanism of interactions between multi-scale built environments on the leisure activities of the elderly is mainly summarized as the transmission effect and substitution effect. The transmission effect shows that the differences in the community-level built environment are primarily caused by the differences in the city-level built environment. In contrast, the substitution effect shows that the multi-scale built environment such as residential districts, communities, and cities jointly affect the leisure activities of the elderly. Based on the mechanism of the built environment at different scales, this study can provide theoretical references and planning implications to improve the built environment through planning means such as enhancing the walkability of streets, the accessibility of facilities and the scale of greenery in order to promote active leisure activities and improve the health of the elderly.
“…Zhou found that there were scaled differences in the built environment’s influences on the elderly’s health, depending on their duration in different activity sites, the purpose of their trips, and the rate of green space in the community [ 30 ]. Cheng used a geographically weighted regression model to explore the relationship between the built environment and the daily walking time of the elderly at a local scale and found spatial heterogeneity in built environment effects [ 31 , 32 ]. Therefore, the determination of the built environment scale or geographical context is a crucial task in studies exploring the relationship between the built environment and daily activity behavior [ 33 , 34 ], and multilevel models offer the possibility of addressing the spatial heterogeneity of the built environment and have been validated in related studies [ 3 , 35 , 36 ].…”
Built environment characteristics such as walkability, land use diversity, infrastructure accessibility and attractiveness may support or hinder the elderly’s leisure activities, which in turn affects their health. Promoting the elderly’s leisure activities through the creation of a positive built environment is of great relevance to healthy aging. In the context of the continuous increasing of aging in China, promoting leisure activities for the elderly through improving the built environment has become an essential issue in urban geography and urban planning. Based on the questionnaire survey data of the elderly in Hefei City, a multilevel ordered probit regression model was used to investigate the mechanism of the multi-scale built environment on leisure activities of the elderly. The results show that: (1) more than 60% of the elderly can carry out leisure activities more than seven times a week, more than 50% of the elderly have a duration of fewer than 30 min for each leisure activity, and there are significant spatial differences in the frequency and duration of their trips at multiple scales in city, community and residential district. (2) Residential quality and community-level land use mixture, the density of leisure facilities, proximity to high-level urban roads, community security, living in the old city, and individual characteristic variables such as age, education, and satisfaction with neighborhood interaction positively contribute to the leisure activities of the elderly. In contrast, the community activity participation and the location close to expressways and railway lines have a significantly negative impact on the leisure activities of the elderly. (3) The mechanism of interactions between multi-scale built environments on the leisure activities of the elderly is mainly summarized as the transmission effect and substitution effect. The transmission effect shows that the differences in the community-level built environment are primarily caused by the differences in the city-level built environment. In contrast, the substitution effect shows that the multi-scale built environment such as residential districts, communities, and cities jointly affect the leisure activities of the elderly. Based on the mechanism of the built environment at different scales, this study can provide theoretical references and planning implications to improve the built environment through planning means such as enhancing the walkability of streets, the accessibility of facilities and the scale of greenery in order to promote active leisure activities and improve the health of the elderly.
“…To potentially play as a solution for first and last mile connectivity with the existing public transportation network, the catchment area of metro stations near bike-sharing stations needs to be extended ( Shaheen & Chan, 2016 ). This suggests that micro mobility acts as an alternative urban mobility solution and is used as a means to solve first and last mile accessibility and connectivity issues ( Alcorn & Jiao, 2019 ; Ma et al, 2018 ; Nikitas et al, 2016 ; Zhao et al, 2019 ; Wu et al, 2021 ; Bieliński & Ważna, 2020 ). Fig.…”
“…Thus, some scholars applied the geographically weighted regression (GWR) model to analysis the local effect of built environment on bike-sharing usage. For example, Wu et al [17] demonstrated that the goodness of fit in the GWR model is better than that of the OLS model by applying a OLS model and a GWR model to examine the global and local influences of the built environment on bike usage. Li et al [35] analyzed how the built environment and social-demographic characteristics influence bike-sharing utilization with the OLS model and the GWR model, and found that the shared bikes mainly serve a certain area instead of the whole city.…”
Section: Literature Reviewmentioning
confidence: 99%
“…However, the spatially varying effects of built environment factors are neglected by the studies using the OLS model. Some authors have proposed a geographically weighted regression model to capture the spatial relationship between built environment factors and bike-sharing usage [16][17][18]. Compared with the OLS model, the geographically weighted regression (GWR) model can illustrate the spatial varieties of built environment's impact on dockless bike-sharing.…”
Dockless bike-sharing systems have become one of the important transport methods for urban residents as they can effectively expand the metro’s service area. We applied the ordinary least square (OLS) model, the geographically weighted regression (GWR) model and the multiscale geographically weighted regression (MGWR) model to capture the spatial relationship between the urban built environment and the usage of bike-sharing connected to the metro. A case study in Beijing, China, was conducted. The empirical result demonstrates that the MGWR model can explain the varieties of spatial relationship more precisely than the OLS model and the GWR model. The result also shows that, among the proposed built environment factors, the integrated usage of bike-sharing and metro is mainly affected by the distance to central business district (CBD), the Hotels-Residences points of interest (POI) density, and the road density. It is noteworthy that the effect of population density on dockless bike-sharing usage is only significant at weekends. In addition, the effects of the built environment variables on dockless bike-sharing usage also vary across space. A common feature is that most of the built environment factors have a more obvious impact on the metro-oriented dockless bike-sharing usage in the eastern part of the study area. This finding can provide support for governments and urban planners to efficiently develop a bike-sharing-friendly built environment that promotes the integration of bike-sharing and metro.
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