The lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cycling and running tracks in central Chengdu in December 2021 (during the COVID-19 pandemic) and develops spatial regression models for numerous 500 × 500 m grids (N = 2,788) to assess the impacts of the built environment on the cycling and running intensity indices. The findings are summarized as follows. First, land-use mix has insignificant effects on the physical activity of residents, which largely contrasts with the evidence gathered from previous studies. Second, road density, water area, green space area, number of stadiums, and number of enterprises significantly facilitate cycling and running. Third, river line length and the light index have positive associations with running but not with cycling. Fourth, housing price is positively correlated with cycling and running. Fifth, schools seem to discourage these two types of physical activities during the COVID-19 pandemic. This study provides practical implications (e.g., green space planning and public space management) for urban planners, practitioners, and policymakers.

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As a critical transportation infrastructure, high-speed rail (HSR) greatly enhances accessibility and shortens the spatial-temporal distance among cities. It is well documented that HSR significantly impacts regions and cities’ economic development and spatial structure. The proportion and frequency of business passenger trips are increasing yearly, and the demand for “station as the final destination” is becoming more and more prominent. Given the pivotal role of the design and construction of HSR station areas in achieving “station as the final destination,” the study of their development characteristics and patterns has important implications for urban planning. Previous studies have focused extensively on the macro impact of the HSR operation on regional economies, urban industries, and tourism development, whereas only a few were conducted at the station level. Furthermore, the business-commercial agglomeration effects of the HSR operation on the development and construction of station areas have neither been studied nor accurately measured. To fill this gap, we first constructed a panel data set consisting of the point of interest (POI) data, China City Statistical Yearbook data, and the HSR station operation data from 2012 to 2017. Next, we developed difference-in-differences (DID) models to decipher the impact of the HSR operation on the station-level business and commercial agglomeration. The results show that the HSR operation has increased the business-commercial agglomeration index (BCAI), the commercial agglomeration index (BAI), and the business agglomeration index (CAI) by 28.3%, 29%, and 21.3%, respectively. In other words, the HSR operation has significant business-commercial agglomeration effects in the station area, and the agglomeration effect size of business is more extensive than that of commerce. Interestingly, the BCAI grew slowly in the first 3 years after the HSR operation but started to rise much faster from the fourth year, which HSR’s catalytic effects can explain. The results also reveal that the business-commercial agglomeration effects have a clear spatial threshold as the BCAI tends to decrease from 1500 m to 3000 m away from HSR stations. The plausibility of the results has been confirmed by the parallel trend, placebo, and robustness tests.

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.

The construction of new urban areas has become one of the important ways for urban regeneration throughout the process of polycentric urban development. New urban area construction following transit-oriented development (TOD) conception advocates development around the public transportation stations. To gain more benefits around the stations, it is necessary to conduct an ex-ante evaluation of TOD projects in the new urban area. The Node-place (NP) model is a commonly used method for TOD evaluation and classification, which essentially designs an analytical framework for assessing station areas in both transport (node) and land use (place) aspects. The objective of our study is twofold. First, based on the original NP model, we propose the node-place-system support (NPS) model by introducing a novel evaluation dimension—system support—which quantitatively describes the relationship between local stations and the overall urban system. Second, taking advantage of multi-sourced data and Geographical Information System (GIS) techniques, we employ the proposed NPS model to evaluate and classify the metro stations in the Tianfu New Area of Chengdu, China. The results show that most stations present a balanced relationship between transport and land use performances. However, for a fraction of these balanced stations, we observe a mismatch between the system support and NP performances. Accordingly, we identify the system-mismatched stations and provide targeted improvement strategies for urban design.