The spatiotemporal nonstationarity of shared bicycle usage, a sustainable and eco-friendly mode of transportation, is believed to be influenced by the built environment. However, the specific spatial and temporal impacts of built environment factors on shared bicycle trips are not yet fully understood. This study investigates the relationship between the built environment and shared bicycle ridership in Shenzhen, a city where the distribution of shared bicycles is relatively dense, by utilizing multisource urban big data. Key independent variables were selected based on the “5Ds” dimensions of the built environment, and the performance of two models—Geographically Weighted Regression (GWR) and Geographically and Temporally Weighted Regression (GTWR)—were compared. The analysis evaluates the impact of the built environment on the density of shared bicycle ridership, incorporating both spatial and temporal dimensions. The results of the study found that the GTWR model used in this paper can effectively explain the spatio-temporal heterogeneity of built environment-related variables on shared bicycle trips with high goodness of fit. And the regression fit coefficients of the model show that the effects of different built environment indicators on the density of shared bicycle ridership are significantly different in both time and space. Among them, road network density, catering POI density, traffic POI density and POI diversity have a facilitating effect on shared bicycle travels, particularly during peak hours on weekdays and in central urban areas. Shopping POI density shows different effects on shared bike use in different times and spaces. While the distance from the city center and the nearest distance to the bus station have a suppressive effect on shared bicycle use, they show opposite degrees of influence in the spatial distribution. The results can provide more precise guidance for future rational transportation strategies or sustainable urban planning.

Streets are an important component of urban public spaces and also a high-incidence area for urban crime. However, current research mainly involves adult crime, or fails to distinguish between adult and juvenile crime, which poses a severe challenge to the prevention of juvenile delinquency. Juveniles have lower self-control abilities and are more likely to be influenced by external environmental factors to trigger criminal behavior compared to adults. Therefore, this study uses New York’s Manhattan district as an example, based on CPTED and social disorganization theories, and utilizes street view data and deep learning techniques to extract street environment indicators. The GWR model is used to explore the influence mechanism of urban street environment on juvenile crime. The results of this study, considering spatial heterogeneity, demonstrate the impact of various physical environmental indicators of urban streets on juvenile delinquency, and reveal that some street indicators have differentiated effects on crime in different areas of the city. Overall, our research helps to uncover the relationship between juvenile delinquency and the built environment of streets in complex urban settings, providing important references for future urban street design and juvenile delinquency prevention.