###### Abstract
This approach provides a more holistic view of urban agglomeration analysis [47]. Currently, many studies employ data fusion methods, combining NTL data with LandScan data [48]. NTL data reflect the intensity of economic activity, while LandScan data display resident population distribution, making it relatively more accurate to delineate urban agglomeration boundaries by combining both sources [49]. However, it is important to note that the increasing mobility of population factors within urban agglomerations complicates reliance on resident population data alone for boundary identification, especially as migrant populations have an increasingly significant impact on urban economic and social dynamics [50]. Overall, urban agglomeration boundary identification methods based on remote sensing imagery have been widely applied, with NTL data being a common source due to their ability to visually reflect the distribution of economic activity [51]. However, NTL data suffer from spillover effects, making it difficult to precisely identify economic activities in peripheral areas [52]. In comparison, LandScan data provide a static perspective on urban agglomeration through high-resolution population distribution information, but their lack of dynamic features limits their ability to capture short-term changes [53]. In recent years, heatmap data have become an important source for identifying the dynamic boundaries of urban agglomerations due to their ability to capture real-time population mobility. This study fuses NTL, LandScan, and heatmap data, combining the dynamic characteristics of economic activity, population distribution, and mobility. It proposes an innovative method to identify urban agglomeration boundaries from a multi-dimensional perspective. With the advancement of big data, the collection of population mobility data has become increasingly feasible. Data sources such as mobile device location data, social media sign-ins, and transit card swipe records are now used to analyze population flows and spatial structures [54; 55; 56]. To further enhance the accuracy of urban spatial boundary identification, researchers are increasingly incorporating various big data sources, such as Points of Interest (POI) data and transportation data, which provide detailed information on the distribution of functional zones and patterns of human activity within cities [57]. However, static data often fail to fully capture real-time population mobility patterns, leading to increased attention to heatmap data that can reflect real-time population flows [58]. Heatmap data, based on geolocation information, visually depict the intensity and distribution of human activity within specific areas. Observing macro-level mobility trends helps identify functional areas and boundaries between cities, effectively capturing dynamic interactions within a region [59]. Heatmap data allow for an intuitive analysis of high-frequency population mobility paths and hotspot areas, revealing the actual intensity of connections across city boundaries. For example, in the Pearl River Delta region, the intensity of population mobility along the Guangzhou-Shenzhen corridor reflects not only economic activities but also serves as an important basis for defining functional areas. Compared to static administrative boundaries, boundary identification methods based on dynamic mobility data offer greater flexibility. By integrating heatmap data, researchers can better represent dynamic population flows within urban agglomerations, offering a more accurate and flexible approach to identifying urban agglomeration boundaries, especially in areas with dense migrant populations. This method significantly improves the precision and reliability of urban agglomeration boundary identification [60; 61], thereby providing a more scientific basis for urban development and policymaking.