## 6 Conclusions  
Population data, as a fundamental dataset capable of reflecting the socio-economic conditions of a region, can provide important information for regional resource allocation, disaster prevention, and emergency management. With the proliferation of grid-based research and applications, traditional methods are no longer sufficient to meet the spatial data accuracy requirements in this field. Therefore, it is necessary to spatially grid population statistics at the county, city, and national levels to obtain population distribution information at the kilometer grid level or even higher spatial resolutions.  
In this article, we explored and conducted research on population spatialization from two aspects: data enhancement and model parameter optimization. In terms of data, we incorporated innovative social perception data, such as Points of Interest (POI), and high-precision building height data as auxiliary data. Additionally, we further processed and corrected data, such as nighttime light data, to enhance the quality of auxiliary data. Meanwhile, the feature importance analysis results indicated that high-precision socio-economic data plays a crucial role in the population spatialization.  
In terms of model construction, numerous researchers have explored various methods, but there has been relatively little research on model parameter tuning. Hence, this article placed a special focus on the optimization of model parameters. To ensure model interpretability, we chose the popular Random Forest algorithm as the base model and investigated the impact of six different parameter optimization methods on model performance. The results demonstrated that Bayesian optimization algorithms significantly improved model parameter optimization, effectively enhancing model accuracy.  
In the end, this article takes Sichuan Province as an example and constructs a spatial population distribution dataset at 1 km resolution based on the best parameters. It has been validated against township-level population census data and is found to closely match real-world conditions, demonstrating the effectiveness of the model. When compared to other international open-source datasets, the dataset created in this article better aligns  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **R\({}^{2}\)** & **MAE** & **RMSE** \\ \hline LandScan & 0.3915 & 17,805.7163 & 33,634.8154 \\ GPW & 0.5384 & 13,401.9731 & 29,293.9095 \\ WorldPop & 0.6094 & 12,707.6707 & 26,947.1740 \\ RF-BOA & 0.6628 & 12,459.3926 & 25,037.1139 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy assessment.  
with the actual population distribution at the township level, exhibiting higher accuracy and showcasing the advantages of the modeling approach.  
In conclusion, the population spatialization model constructed using the method proposed in this paper effectively predicts population distribution. It can provide detailed population spatialization information for urban planning and natural disaster prevention and control, enabling more rational resource allocation and efficient disaster response.  
Conceptualization, Y.C. and S.W.; Methodology, Y.C. and S.W.; Validation, Y.C. and S.W.; Formal analysis, Y.C. and S.W.; Writing--original draft, Y.C.; Writing--review and editing, S.W., Z.G. and F.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"National Natural Science Foundation of China, grant number 42271090\", \"Fundamental Research Funds of the Institute of Earthquake Forecasting, China Earthquake Administration, grant number CEAIEF2022050504 and CEAIEF20230202\", \"National High-Resolution Earth Observation Major Project, grant number 31-Y30F09-9001-20/22\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.