Green space in urban areas is one of the most critical infrastructures for the well-being of residents, and its spatial accessibility configuration is a key indicator of urban planning layout and ecological civilization construction. Using the Gaussian two-step floating catchment area (G2SFCA) method, K-means cluster analysis, and Kriging spatial interpolation, this study examines the spatial characteristics of green space accessibility in the Hongkou District, focusing on the relationship between “green space-community” supply. The findings indicate the following: (1) green space accessibility in Hongkou District decreases as the level of green spaces decreases. Higher levels of accessibility are associated with smaller variations in spatial distribution. (2) The green space accessibility in Hongkou District is affected by the surrounding large green space. Compared to other areas, the community green spaces near Lu Xun Park and Peace Park have higher accessibility. (3) The green space accessibility in Hongkou District is impacted by the mismatch between population density and green spaces. An overlay analysis of accessibility and population density reveals that high accessibility areas have average or low-average population density. Based on the results of the green space accessibility analysis, recommendations are proposed to optimize the green space layout in Hongkou District from the perspective of spatial justice. These suggestions are aimed at promoting the equalization of green space services in Hongkou District, improving the overall level of urban green space services, with a view to improving the quality of urban living environment and creating a green and livable urban area.

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities.

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing's six central districts. Subsequent to this, a comprehensive analysis of the network's structural attributes was carried out. A walktrap method rooted in modularity analysis was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies.