Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies.

The impact of urban morphology characteristics on regional thermal environments is a crucial topic in urban planning and climate adaptation research. However, existing studies are often limited to a single dimension and fail to fully reveal the spatiotemporal impact mechanisms of multi-dimensional urban morphology on thermal environments and their connection to regional planning policies. This study focuses on the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), combining quantitative data from landscape pattern indices, land use expansion patterns, and local climate zones (LCZs) derived from 2000 to 2020. By using geographically weighted regression and spatial autocorrelation analysis, we systematically explore the spatiotemporal effects and mechanisms of multi-dimensional urban morphology characteristics on regional thermal effects. We found the following points. (1) Built-up land patch density is significantly positively correlated with LST, with the urban heat island (UHI) effect spreading from core areas to the periphery; this corroborates the thermal environment differentiation features under the “multi-center, networked” spatial planning pattern of the GBA. (2) Outlying expansion mitigates local LST rise through an ecological isolation effect, and infill expansion significantly exacerbates the UHI effect due to high-intensity development, reflecting the differentiated impacts of various expansion patterns on the thermal environment. (3) LCZ spatial distribution aligns closely with regional planning, with the solar radiation shading effect of high-rise buildings significantly cooling daytime LSTs, whereas the thermal storage properties of traditional building materials and human heat sources cause nighttime LST increases; this reveals the deep influence of urban morphology mechanisms, building materials, and human activities on thermal environments. The findings provide scientific support for achieving a win–win goal of high-quality development and ecological security in the GBA while also offering a theoretical basis and practical insights for thermal environment regulation in high-density urban clusters worldwide.