###### Abstract  
Accurate identification of urban agglomeration spatial range is essential for scientific regional planning, optimal resource allocation, and sustainable development, forming the basis for regional development policy. To improve the accuracy of identifying urban agglomeration boundaries, this study fuses nighttime light data, which reflects urban economic levels, with LandScan data representing population distribution and heatmap data indicating population mobility. This fusion allows for identification from a differentiated perspective of population distribution and mobility. We propose a new method for identifying the dynamic boundaries of urban agglomerations through multi-source data fusion. This method not only provides technical support for scientific regional planning but also effectively guides the functional positioning of edge cities and the optimization of resource allocation. The results show that the spatial range identified by NTL_LS has an accuracy of 80.37% and a kappa coefficient of 0.5225, while NTL_HM achieves an accuracy of 89.17% with a kappa coefficient of 0.7342, indicating that the fusion of economic level with population mobility data more accurately reflects the spatial range of urban agglomerations in line with real development patterns. By adopting a differentiated perspective on population distribution and mobility, we propose a new approach to identifying urban agglomeration spatial range. The research results based on this method provide more comprehensive and dynamic decision-making support for optimizing transportation layouts, allocating public resources rationally, and defining the functional positioning of edge cities. P 2025 15 945 1  
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have gradually emerged. These challenges not only hinder the sustainable development of urban agglomerations but also present obstacles to regional coordination [3,4]. Therefore, in order to address these problems more effectively, it is essential to accurately identify the spatial range of urban agglomerations, enabling the exploration of how precise identification of spatial boundaries can facilitate optimized resource allocation and regional collaborative development. Since the 1980s, China has undergone a large-scale urbanization process characterized by massive rural-to-urban migration and the high concentration of economic resources in urban areas. This rapid urbanization has elevated the status of China's urban agglomerations, such as the Pearl River Delta, Beijing-Tianjin-Hebei, and the Yangtze River Delta, within the global urban network [5,6]. The Pearl River Delta, in particular, has attracted significant inflows of population and capital due to its favorable geographic location and its role as a global manufacturing hub. This has resulted in a high-intensity economic interaction zone centered around the Guangzhou-Shenzhen corridor. This urbanization process has not only shaped the regional economic structure within China but also had profound implications for the East Asian and global economic systems. [PERSON] et al. highlight that one key feature of China's rapid urbanization is the dual driving forces of globalization and regionalization. Through the clustering of regional urban agglomerations, China has optimized resource allocation among regions domestically while also becoming an integral part of the global value chain. This dynamic characteristic serves as a crucial context for urban agglomeration studies [7]. The spatial range of an urban agglomeration refers to a functionally integrated area comprising a core city and its surrounding regions, closely linked in terms of geography, economy, and society [8]. This range is typically determined by factors such as population distribution, economic activity density, and transportation networks, and it continuously adapts in response to urban expansion and changes in functional zones [9,10]. The definitions of terms related to urban agglomerations may vary under different research contexts.