## 3 Results
### Distribution Characteristics of Main Influencing Factors  
The dependency relationship between the feature values of address information, access facilities, and dwellings, along with their corresponding SHAP values, was explored to investigate the impact of each feature on the final population estimation output of the XGBoost model, as shown in Figure 4. The SHAP value's zero line (horizontal red line) demarcates the positive and negative influences exerted by the main features on population estimation. Overall, the influence of these three main feature variables on population estimation is non-monotonic, indicating that the influence of a single feature variable varies regionally. Among them, the address information feature mainly exerts a positive influence; as the address information feature value increases, its SHAP value also increases, with most points positioned above the zero line. When the access facilities feature value exceeds 2.5 \(\times\) 10\({}^{-5}\), the SHAP value increases rapidly, and most points are again above the zero line, indicating a positive influence. The dwelling feature also exhibits a trend of increasing SHAP values alongside increasing feature values, with the majority of points remaining above the zero line. In summary, all three key POI features demonstrate a tendency to promote population increase.  
The spatial distribution characteristics of SHAP values for address information, access facilities, and dwellings at the town and street scales were analyzed to explore their influence on population estimation, as shown in Figure 5. Towns and streets with higher SHAP values for their address information are mainly located in the surrounding areas of Baoshan, Xuhui, Yangpu, Putuo, and Minhang, positively impacting the population spatial distribution. In contrast, towns and streets with low SHAP values are located in Chongming, Pudong New Area, Fengxiant, Qingpu, and other suburbs, which negatively  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**RE Range (\%)**} & \multicolumn{2}{c}{**WorldPop Estimation Results**} & \multicolumn{2}{c}{**XGBoost Model Estimation Results**} \\ \cline{2-5}  & **Town (Unit)** & **Proportion (\%)** & **Town (Unit)** & **Proportion (\%)** \\ \hline
[0, 10] & 33 & 15.4 & 194 & 90.9 \\ \hline
(10, 20] & 36 & 16.8 & 7 & 3.0 \\ \hline (20, 50] & 89 & 41.6 & 5 & 2.2 \\ \hline (50, 100] & 40 & 18.7 & 3 & 1.3 \\ \hline \textgreater{}100 & 16 & 7.5 & 5 & 2.6 \\ \hline Total & 214 & 100 & 2 impacts the population spatial distribution. The towns and streets with high SHAP values for access facilities are concentrated in Yangpu, Hongkou, Jing'an, Putuo, Changning, Huangpu, and Xuhui in the urban centers. The infrastructure in these urban areas is more developed, leading to a clear positive effect on the population spatial distribution. Conversely, the negative impact is mainly observed in the suburbs, especially in the outer suburbs, where it is more pronounced. The influence of SHAP values for dwellings shows a significant gradient change from urban centers to suburbs. In urban centers, dwellings have a greater positive impact on population spatial distribution, which gradually diminishes, and the negative impact increases towards the suburbs. Overall, the population spatial distribution in urban centers is significantly positively influenced by various features, while the negative impact increases progressively when transitioning to the suburbs.