Urban vibrancy is described by the activities of residents and their spatio-temporal dynamics. The metro station area (MSA) is one of the densest and most populous areas of the city. Thus, creating a vibrant and diverse urban environment becomes an important goal of transit-oriented development (TOD). Existing studies indicate that the built environment decisively determines MSA-level urban vibrancy. Meanwhile, the spatio-temporal heterogeneity of such effects requires thoroughly exploration and justification. In this study, we first apply mobile signaling data to quantify and decipher the spatio-temporal distribution characteristics of the MSA-level urban vibrancy in Chengdu, China. Then, we measure the built environment of the MSA by using multi-source big data. Finally, we employ geographically and temporally weighted regression (GTWR) models to examine the spatio-temporal non-stationarity of the impact of the MSA-level built environment on urban vibrancy. The results show that: 1) The high-vibrant MSAs concentrate in the commercial center and the employment center. 2) Indicators such as residential density, overpasses, road density, road network integration index, enterprise density, and restaurant density are significantly and positively associated with urban vibrancy, while indicators such as housing price and bus stop density are negatively associated with urban vibrancy. 3) The GTWR model better fits the data than the stepwise regression model. The impact of the MSA-level built environment on urban vibrancy shows a strong non-stationarity in both spatial and temporal dimensions, which matches with the spatio-temporal dynamic patterns of the residents’ daily work, leisure, and consumption activities. The findings can provide references for planners and city managers on how to frame vibrant TOD communities.

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Transit-oriented development (TOD) has been regarded as an effective way to improve urban vibrancy and facilitate affordable, equitable, and livable communities in metro station areas (MSAs). Previous studies placed great attention on the interplay between the MSA-level built environment and overall human activities while neglecting the heterogeneity among different age groups. To address this gap, we leverage the mobile phone signaling data to quantify the spatio-temporal distribution of the MSA-level human activities among different age groups as measured by the vibrancy index (VI). Furthermore, we investigate the impact of the MSA-level built environment on the VI and its intergenerational differences by employing multiple linear regressions based on multi-sourced data. To this end, Chengdu—a TOD-thriving megacity in China—is chosen as a case study. The results indicate that: (1) Residential and bus stop density are positively associated with the VI. And the magnitudes of the correlation coefficients are similar among different age groups. (2) Distance to CBD is negatively associated with the VI of teenagers (12–18 years), middle-aged adults (40–59 years), and older adults (above 60 years) but unrelated to the VI of young adults (19–39 years). (3) Employment density is positively associated with the VI of young and middle-aged adults but insignificantly associated with the VI of teenagers and older adults. (4) The correlations between the floor area ratio and the VI are positive for all age groups. As age increases, the significance of such correlations becomes more pronounced. (5) Streetscape greenery shows a more significant positive correlation with the VI of teenagers and older adults as compared to those of young and middle-aged adults. (6) Significant negative correlations exist between housing price and the VI of different age groups. The findings can inform the development and design of vibrant TOD communities.

Developing evidence-based planning interventions for promoting physical activity (PA) is considered an effective way to address urban public health issues. However, previous studies exploring how the built environment affects PA over-relied on small-sample survey data, lacked human-centered measurements of the built environment, and overlooked spatially-varying relationships. To fill these gaps, we use cycling and running activity trajectories derived from the Strava crowdsourcing data to comprehensively measure PA in the central city area of Chengdu, China. Meanwhile, we introduce a set of human-scale, eye-level built environment factors such as green, sky, and road view indexes by extracting streetscape characteristics from the Baidu street-view map using the fully Convolutional Neural Network (CNN). Based on these data, we utilize the geographically weighted regression (GWR) model to scrutinize the spatially heterogeneous impact of the built environment on PA. The results are summarized as follows: First, model comparisons show that GWR models outperform global models in terms of the goodness-of-fit, and most built environment factors have spatially varying impacts on cycling and running activities. Second, the green view index restrains cycling activities in general. In contrast, it has a wide-ranging and positive impact on running activities while hampers them in the PA-unfriendly old town. Third, the sky view index stimulates cycling activities in most areas. However, it has a mixed influence on running activities. Fourth, the road view index widely promotes cycling and running activities but hinders them in some areas of the old town dominated by automobiles and under construction. Finally, according to these empirical findings, we propose several recommendations for PA-informed planning initiatives.