## 1 Introduction  
China's rapid urbanization has triggered massive rural-to-urban migration, creating significant governance challenges, particularly in megacities. Consequently, acquiring high-resolution spatial population distribution data has become crucial for urban planning, emergency management, public service provision, and economic development [1, 2]. Traditional population distribution data are typically collected based on administrative divisions through surveys and statistical methods. However, this approach requires significant human and material resources, involves lengthy cycles, and suffers from coarse spatial granularity. As a result, it struggles to capture the spatial heterogeneity of population distribution within administrative regions and fails to accurately reflect population distribution patterns [3, 4]. To address these limitations, scholars have explored various population spatialization estimation methods to improve both the accuracy and spatial resolution of population estimates [5]. The development of models and methods is central to research on urban population spatialization. In the early stages, various methods were used for population spatialization, including population density models, spatial interpolation methods, and statistical regression methods [6]. Population density models, including the Clark model, normal density model, Smeed model, and Gamma model [4; 7; 8; 9], are grounded in urban geography theory and describe the decline in population density from the city center to the periphery. However, their estimation accuracy and spatial scale often fall short of contemporary demands for refined population management. Spatial interpolation methods, which assume that adjacent locations share similar characteristics, convert regional demographic data from a source spatial scale to a target scale, typically using downscaling techniques like point interpolation and area interpolation [10]. While these methods enable scale conversion, they often neglect boundary effects and fail to incorporate multiple factors influencing population distribution, leading to inaccurate population estimates in heterogeneous regions [11; 12]. Statistical regression methods, such as geographically weighted regressions [13], multiple linear regressions [14], and support vector regressions [15], construct quantitative models of population spatial distribution using multi-source geographic data. Although these methods can capture the complex relationships between various factors and population spatial distribution, they struggle to reveal nonlinear relationships. Additionally, it is difficult to integrate multi-source heterogeneous data in the modeling process [16]. With the development and application of technologies such as geographic information systems, remote sensing, and big data, numerous new data sources have emerged. These sources, including nighttime light data, social network data, and mobile phone data, provide rich contextual information, detailed attributes, and a fine spatial resolution. The effective integration and utilization of multi-source data provide a new paradigm for population spatialization methods [17; 18]. Among these approaches, machine learning has become increasingly prominent and is now the dominant methodology. Models such as random forest [19] and XGBoost [20] have been widely adopted. [PERSON] [21] and [PERSON] [22] constructed a random forest model based on multi-source data to achieve population spatialization in Tibet and Beijing. [PERSON] et al. [23] employed an XGBoost model to estimate population distribution at a 100 m grid scale in Shenzhen. Both models are based on decision tree algorithms. The random forest model demonstrates strong noise resistance and can effectively simulate complex nonlinear relationships between population distribution and influencing factors through factor weighting. However, its predictive capability is limited to the range of the training dataset in regression problems, which can lead to data overfitting.