## 4 Discussion  
With the advancement of population spatialization research, methodologies have evolved from traditional population density models and spatial interpolation techniques to machine learning approaches [41]. Machine learning methods demonstrate significant advantages in population spatial estimation, particularly in terms of accuracy and interpretability. However, when processing large-scale datasets and addressing the complexity of population distribution factors across diverse regions, certain models like random forest algorithms may encounter challenges that include overfitting, limited applicability, and reduced interpretability [42; 43]. This study employs the XGBoost-SHAP method, effectively integrating the strong learning and high-precision prediction capabilities of the XGBoost model with the interpretability of the SHAP method. Our results indicate that the XGBoost model successfully extracts valuable 20-dimensional feature information from our established multidimensional population spatial feature database, achieving a superior estimation accuracy. The comparative analysis with WorldPop data demonstrates that our multi-scale grid estimations, using official statistics as reference, yield significantly improved precision. The WorldPop data are constructed based on national census data from various countries, incorporating multi-dimensional geographic covariates such as land cover, nighttime light data, and terrain elevation, with random forest modeling. In contrast, the high-precision XGBoost population model developed in this study benefits more from finer-grained local data features like those of Shanghai's POI data. Additionally, the SHAP method provides a deeper understanding and better interpretation of the model's feature information. In this study, the SHAP values quantitatively reveal the influence of various modeling features on Shanghai's population distribution estimation. The spatial analysis of the top three influential features shows that POI characteristics and DN values exhibit particularly high SHAP values. As illustrated in the POI kernel density and DN value distribution maps (Figure 8), both features display spatial patterns strongly correlated with estimated population density. Existing research confirms that POIs and nighttime light data show a high level of correspondence with human and socioeconomic activities [44; 45], with more intense activity areas typically exhibiting a greater population concentration. These findings collectively demonstrate the substantial impact of human geographical features on Shanghai's population distribution patterns. The XGBoost-SHAP method proposed in this study not only establishes a high-precision population estimation model but also ensures the interpretability of the model's prediction results. It quantitatively identifies the key influencing variables in population spatialization and reveals their spatial characteristics, thereby providing methodological insights for future research on population spatialization estimation and its influencing factors. In population spatialization studies, the choice of grid scale significantly influences the representation of population distribution patterns [46]. Excessively fine grid scales may introduce difficulties in converting multi-source data and capturing the broader characteristics of population distribution, while overly coarse scales may reduce model accuracy and obscure variations in spatial distribution. Consequently, selecting an appropriate grid scale represents a critical step that enhances both the model's estimation accuracy and practical applicability while better reflecting true population distribution characteristics. Datasets like GPW, GRUMP, LandScan, and WorldPop typically employ 5 km and 1 km grids to represent the global or intercontinental population spatial distribution [47; 48; 49; 50], though their suitability for regional studies remains debatable.