## 3 Results
### Model Results  
#### 3.2.1 Hyper-Parameter Tuning  
In our modeling process, we conducted hyperparameter tuning using a grid search approach to optimize key parameters such as max_depth, learning_rate, and n_estimators. Additionally, to ensure model reliability and prevent overfitting, we applied a three-fold cross-validation, evaluating different parameter combinations based on the RMSE in the validation set. \begin{table}
\begin{tabular}{l l l c c c c c c} \hline \hline  & \multirow{2}{*}{**Data**} & \multicolumn{3}{c}{**Total**} & \multicolumn{3}{c}{**Match**} & \multicolumn{3}{c}{**Mismatch**} \\ \cline{3-8} \multicolumn{1}{c}{} & & \multicolumn{2}{c}{**N = 60,600**} & \multicolumn{2}{c}{**N = 48,431**} & \multicolumn{2}{c}{**N = 12,169**} \\ \cline{3-8} \multicolumn{1}{c}{} & & **Avg** & **Std. Dev** & **Avg** & **Std. Dev** & **Avg** & **Std. Dev** \\ \hline \multirow{8}{*}{Land uses/ land covers} & \multirow{2}{*}{Grassland} & Natural grassland area & 0.01 & 0.05 & 0.01 & 0.05 & 0.01 & 0.05 \\ \cline{3-8}  & & Artificial grassland area & 0.10 & 0.20 & 0.09 & 0.20 & 0.11 & 0.19 \\ \cline{3-8}  & \multirow{2}{*}{Bareland} & Natural bareland area & 0.01 & 0.08 & 0.01 & 0.08 & 0.00 & 0.01 \\ \cline{3-8}  & & Artificial bareland area & 0.03 & 0.12 & 0.03 & 0.13 & 0.02 & 0.10 \\ \cline{2-8}  & \multirow{2}{*}{Water} & Inland water area & 0.05 & 0.21 & 0.06 & 0.23 & 0.02 & 0.10 \\ \hline \multirow{8}{*}{Urban facilities} & \# of bus stop & 0.18 & 0.54 & 0.14 & 0.48 & 0.38 & 0.71 \\ \cline{3-8}  & \# of subway station & 0.01 & 0.09 & 0.01 & 0.08 & 0.01 & 0.12 \\ \cline{3-8}  & \multirow{2}{*}{\# of school} & Elementary school & 0.01 & 0.09 & 0.01 & 0.09 & 0.01 & 0.08 \\ \cline{3-8}  & & Middle school & 0.01 & 0.07 & 0.01 & 0.07 & 0.00 & 0.05 \\ \cline{3-8}  & & High school & 0.01 & 0.07 & 0.01 & 0.07 & 0.00 & 0.05 \\ \cline{3-8}  & \# of hospital & 0.30 & 1.45 & 0.24 & 1.36 & 0.54 & 1.77 \\ \cline{3-8}  & \# of traditional market & 0.00 & 0.06 & 0.00 & 0.05 & 0.01 & 0.09 \\ \cline{3-8}  & \# of tourist attraction & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \multirow{8}{*}{Road infrastructures} & \# of crosswalk & 0.61 & 1.30 & 0.46 & 1.17 & 1.19 & 1.59 \\ \cline{3-8}  & \# of crossing & 0.16 & 0.39 & 0.11 & 0.33 & 0.33 & 0.51 \\ \cline{1-1} \cline{2-8}  & \# of traffic light & 0.16 & 0.39 & 0.11 & 0.33 & 0.33 & 0.51 \\ \cline{1-1} \cline{2-8}  & \# of speedbump & 0.26 & 0.73 & 0.22 & 0.69 & 0.41 & 0.88 \\ \cline{1-1} \cline{2-8}  & \# of child protective zone & 0.03 & 0.19 & 0.03 & 0.19 & 0.03 & 0.21 \\ \cline{1-1} \cline{2-8}  & \# of elderly and silver protective zone & 0.00 & 0.05 & 0.00 & 0.04 & 0.00 & 0.06 \\ \hline \multirow{8}{*}{S sociodemographic features} & \# of total population & 155.91 & 240.93 & 147.36 & 240.50 & 189.94 & 239.62 \\ \cline{1-1} \cline{2-8}  & \# of elderly population & 28.25 & 46.20 & 26.57 & 45.52 & 34.94 & 48.20 \\ \cline{1-1} \cline{2-8}  & \# of working population & 88.81 & 347.13 & 70.71 & 309.52 & 160.84 & 460.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The hyper-parameters are the configuration settings or parameters that are set before the training process begins to control the learning process [39]. Poor hyper-parameter choices can result in overfitting or underfitting problems [40]. In the case of XGBoost, the model's performance depends on the careful tuning of various hyperparameters, which control aspects like how trees are built, how to handle overfitting, and how the model is regularized [41]. Table 4 summarizes the tuned hyper-parameters of the XGBoost models that are used in this study. Here, the 'colsample_bytree' parameter refers to the fraction of the features sampled for constructing each tree, helping reduce overfitting and enhancing computational efficiency [42]. And'max_depth' controls the maximum depth of the trees, balancing the model's ability to capture complex patterns with its generalization to new data [43].