The traditional process for selecting urban gas station sites often emphasizes economic benefits and return on investment, frequently overlooking mandatory and guiding constraints established by territorial spatial planning regulations. This neglect can compromise the effective layout and future growth of cities, potentially affecting their long-term development. To address this issue, this study develops a systematic framework for urban gas station site selection that integrates both mandatory and guiding constraints. By conducting detailed analyses of feasible construction areas and fuel demand, the framework quantifies relevant indicators and establishes a comprehensive index system for site selection. A multi-objective optimization model employing genetic algorithms was utilized to maximize fuel demand coverage, minimize inter-station redundancy, and achieve optimal site coverage. This framework was applied to the central urban area of Lishui City, China, as a case study. The site selection schemes achieved a coverage rate exceeding 90%, an inter-station redundancy rate around 30%, and a demand coverage rate surpassing 90%, optimizing the key objectives. Compared to traditional methods that often ignore territorial spatial planning constraints, this framework effectively avoids conflicts with urban planning and regulatory requirements. It enhances infrastructure coordination, supports environmental sustainability, and exhibits strong adaptability to diverse urban contexts, thus offering valuable support for practical decision-making.

Night-time light data (NTL) have been extensively utilized to map urban fringe areas, but to date, there has not been a comprehensive evaluation of the existing spatial clustering methods for delineating the urban fringe using different types of night-time light data. Therefore, we first selected three popular sources of night-time light data (i.e., NPP/VIIRS, Luojia 1-01, and NASA’s Black Marble) to identify the urban fringe. The recognition of spatial mutations across the urban–rural gradient was conducted based on changes in night light intensity using a spatial continuous wavelet transform model. Then, we employed three representative dual spatial clustering approaches (i.e., MK-Means, DBSC, and DSC) for extracting urban fringe areas using different NTL. By using dual spatial clustering, the spatial patterns of the mutation points were effectively transformed into homogeneous spatially adjacent clusters, enabling the measurement of similarity between mutation points. Taking Nanjing city, one of China’s megacities, as the study area, we found that (1) Compared with the fragmented and concentrated results obtained from the Luojia 1-01, NASA’s Black Marble and NPP/VIIRS data can effectively capture the abrupt change of urban fringes with NTL variations; (2) DSC provided a reliable approach for accurately extracting urban fringe areas using NASA’s Black Marble data.

In cellular automata (CA) modeling, spatial heterogeneity can be delineated by geographical area partitioning. The dual constrained space clustering method is a prevalent approach for providing an objective and effective representation of differences within urban regions. However, previous studies faced issues by ignoring spatial heterogeneity, which could lead to an over- or under-estimation of the simulation results. Accordingly, this study attempts to incorporate spatially heterogeneous area partitioning into vector-based cellular automata (VCA), producing more accurate and reliable simulations of urban land-use change. First, an area partition strategy with DSC algorithm was employed to generate multiple relatively homogeneous sub-regions, which can effectively capture the spatial heterogeneity in the distribution of land-use change factors. Second, UrbanVCA, a brand-new VCA-based framework, was utilized for simulating land-use changes in distinct urban partitions. Finally, the constructed partitioned VCA model was applied to simulate rapid urban development in Jiangyin city from 2012 to 2017. The results indicated that the combination of DSC clustering and UrbanVCA model could obtain satisfying results as the average FoM values for the partitions and the entire study area exceeded 0.22. Furthermore, a comparative analysis of results from traditional area-partitioned CA models revealed that the proposed area partitioning approach had the potential to yield more accurate simulation outcomes as the FoM values were higher and SHDI and LSI metrics were closer to real-world observations, indicating its good performance in simulating fragmented urban landscapes.