The precise recognition of urban fringes is vital to monitor urban sprawl and map urban management planning. The spatial clustering method is a prevalent way to identify urban fringes due to its objectivity and convenience. However, previous studies had problems with ignoring spatial heterogeneity, which could overestimate or underestimate the recognition results. Nighttime light can reflect the transitional urban–rural regions’ regional spatial characteristics and can be used to identify urban fringes. Accordingly, a new model has been established for urban fringe identification by combining spatial continuous wavelet transform (SCWT) and dual spatial clustering. Then, Nanjing City, China, as a case study, is employed to validate the model through the NPP/VIIRS nighttime light data. The identification of mutated points across the urban–rural gradient is conducted by utilizing the SCWT. By using dual spatial clustering in the urban fringe identification, it transmits the mutation points’ spatial patterns to the homogeneous spatially neighboring clusters effectively, which measures the similarity between mutation points regarding spatial and attribute domains. A comparison of the identified results by various spatial clustering approaches revealed that our method could be more suitable for the impacts of mutation points’ local spatial patterns on different density values over the whole density surface, thus leading to more accurate spatial boundaries featured by differentiating actual differences of mutation points between adjacent clusters.

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.

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.