## 4 Discussion  
This study identifies the spatial range of the PRD urban agglomeration by fusing NTL data, Landscan residential population data, and heatmap data on the floating population. By combining the static distribution of the resident population with the dynamic characteristics of the floating population, this approach aims to overcome the limitations of traditional methods that rely solely on resident population or economic activity data. Data on the floating population capture the characteristics of short-term economic activities and dynamic changes in population mobility, which traditional methods relying on resident population data cannot reflect [73]. Observing high-mobility patterns in areas such as the Guangzhou-Shenzhen corridor, Dongguan, and Foshan reveals dynamic changes in economic functional zones and sub-core areas within the Pearl River Delta. Furthermore, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & & **Urban** & **Rural** & **Accuracy** & **Kappa** \\ \hline \multirow{2}{*}{NTL\_LS} & Urban & 1794 & 353 & \multirow{2}{*}{80.37\%} & \multirow{2}{*}{0.5225} \\  & Rural & 236 & 617 & \\ \hline \multirow{2}{*}{NTL\_HM} & Urban & 1978 & 169 & \multirow{2}{*}{89.17\%} & \multirow{2}{*}{0.7342} \\  & Rural & 156 & 697 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Verification of Identification Results. this method plays a significant role in analyzing cross-regional functional networks, such as logistics corridors and transportation hubs, and offers new research directions for the dynamic adjustment of urban agglomeration boundaries in the future [74]. The integrated identification process with multi-source data not only delineates the spatial boundaries of the PRD urban agglomeration but also highlights dynamic hotspots of population movement within the region. This approach provides a scientific basis and reference for accurately defining the spatial range of the PRD urban agglomeration. Introducing floating population data into the identification of urban agglomeration spatial range is of significant importance [26]. Traditional studies often rely on census data of the resident population or single-source economic activity data, which, while reflecting the long-term aggregation characteristics of urban agglomerations, fail to capture short-term economic and population dynamics. This limitation constrains the identification of urban agglomeration spatial range to a static model [75; 76]. Some studies have analyzed the spatial expansion of the PRD or other regional urban agglomerations through census or economic statistical data; however, these methods have limitations in identifying high-mobility areas and primarily reflect the stable core regions of economic and population concentration [77; 78]. Consequently, the boundaries of urban agglomerations tend to remain fixed, overlooking the identification of active economic corridors and high-mobility zones within the region [79]. In contrast, this study fuses the mobility characteristics reflected by heatmap data, enabling the identification results to encompass not only areas of stable population aggregation but also non-central urban areas with high economic activity and dense population movement. Consequently, the identified spatial range offers a more comprehensive representation of the expansion areas surrounding cities. Comparative analysis reveals that the spatial range identified by our NTL_HM data is broader, as urban agglomeration boundaries are inherently dynamic [80]. For example, the identified Guangzhou-Shenzhen corridor and its extended areas better capture the active elements within the PRD urban agglomeration compared to traditional methods. Conventional approaches based on resident population or economic activity often overlook or marginalize these high-mobility areas [18; 81], resulting in a relatively smaller, more fixed boundary [82]. In contrast, the NTL_HM data in this study, by incorporating dynamic data, more accurately reflects the actual dynamic expansion of urban agglomerations.