Bus networks are a crucial support for urban commuting. By studying the evolutionary characteristics of bus networks, we can uncover their development patterns, coverage efficiency, and changes in regional balance, providing a scientific basis for sustainable urban development and the optimization of transportation resources. This study systematically analyzes the spatiotemporal evolution characteristics of the bus network in Beijing from 2006 to 2024 using specific spatial analysis tools to analyze spatiotemporal evolution characteristics. By analyzing spatial coverage rates of transit stations using road network and administrative division data, the study reveals the convenience of bus networks in different regions. By combining the research methodology of the Sustainable Development Goals (SDGs) report, a 500-m service radius for bus stops was assessed. A complex network model was used to extract the nodes and edges of the bus network, and the betweenness centrality (BC) characteristics were analyzed. The findings indicate that Beijing’s bus network has gradually expanded from the central urban areas to peripheral regions, with notable expansion in Tongzhou and Yanqing, resulting in an improved balance in the distribution of stations and routes and the emergence of Tongzhou as a new bus hub. The diffusion characteristics of the bus network are significantly influenced by administrative boundaries and the layout of the ring roads. Bus routes and stops are highly concentrated in the central urban areas and within the Second Ring Road, while as the number of ring roads increases, various network indices gradually decrease. The distribution of bus stops shows notable clustering and an uneven directional development. Beijing’s bus stop distribution exhibits significant clustering characteristics, and the areas with a high Population Conveniently Served by Buses (PCSB) are predominantly concentrated in the central urban areas, with a large gap compared to the outer suburban districts. These conclusions expand on the exploration of isolated and static characteristics of the bus network structure, revealing the dynamic mechanisms and evolution patterns of Beijing’s bus network. They provide guidance and recommendations for improving the bus network and offer more comprehensive support for urban planning and resource allocation.

Wastewater treatment plants (WWTPs) play a vital role in controlling wastewater discharge and promoting recycling. Accurate WWTP identification and spatial analysis are crucial for environmental protection, urban planning, and sustainable development. However, the diverse shapes and scales of WWTPs and their key facilities pose challenges for traditional detection methods. This study employs a Multi-Attention Network (MANet) for WWTP extraction, integrating channel and spatial feature attention. Additionally, a Global-Local Feature Modeling Network (GLFMN) is introduced to segment key facilities, specifically sedimentation and secondary sedimentation tanks. The approach is applied to Beijing, utilizing geographic data such as WWTP locations, treatment capacities, and surrounding residential and water distributions. Results indicate that MANet achieves 80.1% accuracy with a 90.4% recall rate, while GLFMN significantly improves the extraction of key facilities compared to traditional methods. The spatial analysis reveals WWTP distribution characteristics, offering insights into treatment capacity and geographic influences. These findings contribute to emission regulation, water quality supervision, and enterprise management of WWTPs in Beijing. This research provides a valuable reference for optimizing wastewater treatment infrastructure and supports decision-making in environmental governance and sustainable urban development.

As a contemporary mode of transportation for medium-to-long distances, the widespread adoption of free-floating shared electric vehicles has the potential to reduce urban carbon emissions and alleviate traffic congestion. However, this mode of transportation also faces challenges such as irregular parking and supply-demand imbalances. This study proposes a spatial optimization method for the deployment of free-floating shared electric vehicles. The research employs data sources such as population, points of interest, and public parking lots in Shenzhen, utilizing spatial analysis methods to explore the spatial distribution relationship between public parking lots and influencing factors. By combining spatial distribution characteristics, a maximum coverage location model is established, and a deep reinforcement learning algorithm is utilized to solve the model, yielding optimized results. The efficacy of traditional algorithms is compared with that of the deep reinforcement learning algorithm, demonstrating the latters superiority in solving location models. This study presents a novel approach to optimizing the configuration of shared transportation planning in Shenzhen, offering a valuable reference for the location selection of other facilities.

Optimizing bubble tea shop locations is crucial due to the industry's rapid growth. Traditional methods often oversimplify population data and ignore overlapping service areas, leading to wasted resources or missed markets. This study proposes an optimization method calculating relative service population using Monte Carlo simulation and a genetic algorithm. By considering service area overlaps and detailed population distribution, it improves service coverage and the scientific basis of location decisions. A case study within Beijing's Fifth Ring Road demonstrates that this method effectively avoids redundant coverage and enhances the accuracy of evaluating potential service populations, offering valuable insights for practical location selection in the bubble tea industry.