###### Abstract
In recent years, the Global Human Settlement Layer (GHSL), as a global approach based on satellite imagery and spatial modeling, has been widely applied in urban spatial identification. GHSL provides a unified global perspective that allows for the analysis of spatial changes in urban morphology across regional differences. It also effectively reflects the boundaries and structural characteristics of cities in different areas. Compared to traditional methods based on economic or population data, GHSL integrates multi-source remote sensing data and a global analytical framework, offering significant advantages in capturing the dynamic evolution of urban agglomerations, cross-regional connections, and morphological distribution patterns [27]. Studies using GHSL data have analyzed the boundary dynamics of major urban agglomerations in Europe, revealing the interaction between urban morphology and transportation networks [28]. Furthermore, the application of GHSL in functional area division provides a useful reference framework for the rapidly developing East Asian region [28]. Previous approaches for identifying the spatial range of urban agglomerations from an economic perspective include spatial econometric models, multi-level regression analysis, and urban growth boundary models [29; 30]. These approaches reveal the spatial characteristics of urban agglomerations by analyzing inter-city economic interactions, spillover effects, and the impact of economic activities on land use and spatial structure [31]. Some scholars have applied spatial autocorrelation models and Spatial Durbin Models (SDMs) to analyze economic linkages and spillover effects among cities [32], while others have employed Hierarchical Linear Models (HLMs) to explore economic growth factors at different scales [33]. The data used in these studies mainly include macroeconomic indicators, spatial economic data, transportation and logistics data, and regional policy data, which help delineate the spatial range of urban agglomerations through insights into economic activity, industrial division, transportation infrastructure, and policy influence [34; 35]. Some studies use land use and housing price data to analyze urban agglomeration boundaries [36], while others assess the impact of infrastructure using transportation data [17]. However, these traditional data sources lack sufficient temporal and spatial precision, as well as real-time accuracy, making it difficult to accurately capture the dynamic changes in urban agglomerations. Nighttime light (NTL) data, which reflects economic activity and spatial development patterns, has been widely applied to identifying urban spatial boundaries [37]. Nevertheless, limitations such as spillover effects and limited spatial resolution of NTL data still restrict their precision in defining urban boundaries [38]. From a demographic perspective, methods for identifying the spatial range of urban agglomerations include analyses of population density and spatial distribution, migration patterns, age structure, occupational distribution, and housing demand [39]. Researchers utilize data on population density, migrant populations, age structure, and housing demand to reveal the population dynamics and characteristics that inform the spatial delineation of urban agglomerations [40]. Some studies analyze housing demand data to define the spatial boundaries of urban agglomerations, while others explore the effects of population concentration [41]. Among the widely used population distribution datasets is LandScan, a global population dataset that enables the rapid identification of densely populated areas within urban agglomerations, thus helping to define their spatial boundaries [42; 43; 44]. However, LandScan data also face limitations, such as insufficient resolution and low update frequency, which constrain its applicability in dynamic change analysis. In recent years, scholars have begun to fuse economic and demographic perspectives to identify urban spatial boundaries more comprehensively [45; 46].