## 4 Discussion
Therefore, our findings differ from traditional identification methods in some ways, yet they offer a distinct advantage in recognizing high-mobility areas, underscoring the importance of floating population data in identifying dynamic spatial range. This study proposes a dynamic identification method based on multi-source data fusion, combining NTL data with static resident population data and dynamic floating population data to enhance the accuracy and applicability of identifying the spatial range of the PRD urban agglomeration. This approach effectively addresses the limitations of relying on single-source data, revealing both the stable economic and population aggregation areas of the PRD and identifying hotspots of economic activity and population mobility. It offers a new approach to urban agglomeration spatial identification that integrates dynamic and static factors. Furthermore, this integrated method provides a more scientific basis for regional planning and policymaking, especially in highly economically active regions like the PRD, underscoring its strong practical value. The results of this study hold significant implications for regional policy planning. The edge cities identified through the NTL_HM method, such as Jiangmen and Zhaoqing, show lower economic activity intensity but higher population mobility density. This indicates their potential for growth within the regional economic network. Policymakers can gradually enhance the economic connectivity of these regions to the core cities by strengthening infrastructure development and industrial support policies in these regions, thereby realizing the bal anced development of the PRD region. Additionally, the identified high-mobility areas, such as the Guangzhou-Shenzhen corridor, can serve as priority regions for optimizing transportation hubs and public resource allocation. Specifically, in transportation planning, the population mobility hotspots identified by heatmap data provide a key basis for optimizing the layout of regional transportation hubs. By building efficient transportation networks, these efforts promote the rational flow of resources and labor within the urban agglomeration. In terms of public service allocation, identifying high-mobility and sub-core areas allows the government to more precisely allocate public resources such as healthcare and education, alleviating overcrowding in core cities. In economic planning, the dynamic population mobility trends observed in edge cities, such as Zhaoqing and Jiangmen, offer new directions for promoting regional economic integration. Relevant departments can formulate industrial policies targeting these areas to help them gradually integrate into the Pearl River Delta urban agglomeration network. However, this study has certain limitations, primarily in capturing spatiotemporal dynamics. Due to the limited time span and granularity of the data, this study is unable to explore in depth the dynamic impacts of population mobility on the urban agglomeration boundary across different time periods [83]. Additionally, there is a degree of ambiguity in identifying peripheral areas, which may lead to an underestimation of regions with low economic activity but high mobility [84]. Therefore, future research could enhance the capacity for dynamic monitoring of spatiotemporal changes in urban agglomerations by incorporating finer time scales and more diverse dynamic data sources (such as traffic flow and social media data). This would allow for a more accurate reflection of the spatiotemporal evolution patterns of urban agglomerations, providing a more precise basis for spatial range identification and future development predictions.