## 4 Results and Discussion
### Machine Learning Models
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & **Variables** & \multicolumn{4}{c}{**Mean**} & \multicolumn{4}{c}{**S.D.**} & \multicolumn{4}{c}{**Min.**} & \multicolumn{4}{c}{**Max.**} \\ \cline{3-14}  & **2009** & **3009** & **2001** & **2009** & **2009** & **2002** & **2001** & **2009** & **2009** & **2001** & **2009** & **2009** & **2001** \\ \hline \multirow{3}{*}{Dependent variable} & Commercial crime & 0.00134 & 0.00137 & 0.00130 & 0.00112 & 0.00112 & 0.00111 & 0.00004 & 0.00005 & 0 & 0.01176 & 0.127 & 0.0111 \\ \cline{2-14}  & Livering prediction & 2.84194 & 2.84198 & 2.7424,500 & 1.5334,707 & 1.8583,381 & 1.6313,706 & 85.950 & 60.597 & 56.408 & 16.9000,00 & 12.8000,000 \\ \cline{2-14}  & Youth sales ratio & 37.55 & 36.91 & 35.5 & 10.99 & 10.77 & 10.61 & 0.76 & 0 & 0 & 78.40 & 79.65 & 78.62 \\ \cline{2-14}  & Middle-aged sales ratio & 40.98 & 41.41 & 41.0 & 8.05 & 8.14 & 7.78 & 12.74 & 0 & 0 & 64.93 & 65.84 & 64.98 \\ \cline{2-14}  & Elderly sales ratio & 40.32 & 1.301 & 41.99 & 6.64 & 4.99 & 5.46 & 14.23 & 0 & 0 & 64.81 & 37.17 & 39.77 \\ \cline{2-14}  & Land appraisal value & 36.0746 & 3.9986 & 4.3064,645 & 2.3270,870 & 2.253,799 & 2.8554,455 & 158,00 & 158.030 & 158.470 & 22.700,000 & 24.3000,000 & 27.5000,000 \\ \cline{2-14}  & Commercial crime & 5.37 & 5.17 & 5.14 & 15.62 & 15.52 & 15.60 & 0 & 0 & 0 & 99.98 & 99.98 & 99.98 \\ \cline{2-14}  & Land area index & 0.33 & 0.13 & 0.33 & 0.20 & 0.23 & 0.20 & 0 & 0 & 0 & 0.84 & 0.84 & 0.84 \\ \cline{2-14}  & Commercial-basedness ratio & 86.83 & 83.28 & 88.01 & 30.91 & 26.04 & 297.61 & 0 & 0 & 0 & 79.062 & 4005.10 & 5105.10 \\ \hline \multirow{3}{*}{Park/ frequency features} & California facilities & 12.28 & 12.28 & 9.58 & 7.67 & 7.67 & 6.52 & 0 & 0 & 0 & 73 & 73 & 39 \\ \cline{2-14}  & Neighborhood peak & 0.25 & 0.25 & 0.25 & 0.58 & 0.58 & 0.58 & 0.58 & 0 & 0 & 0 & 4 & 4 & 4 \\ \cline{2-14}  & Observer park & 2.26 & 2.86 & 2.86 & 2.37 & 2.37 & 2.37 & 0 & 0 & 0 & 14 & 14 & 14 \\ \cline{2-14}  & Small park & 0.09 & 0.09 & 0.09 & 0.99 & 0.39 & 0.99 & 0 & 0 & 0 & 5 & 5 & 5 \\ \cline{2-14}  & CIV & 17.47 & 17.47 & 17.47 & 3.72 & 3.72 & 3.72 & 9.70 & 9.70 & 9.70 & 38 & 38 & 38 \\ \cline{2-14}  & Store density & 0.13 & 0.13 & 0.33 & 0.08 & 0.08 & 0.08 & 0.01 & 0.01 & 0.07 & 0.78 & 0.63 \\ \cline{2-14}  & Closure rate & 2.76 & 2.34 & 2.80 & 2.81 & 2.56 & 3.13 & 0 & 0 & 0 & 23.05 & 19.70 & 20 \\ \cline{2-14}  & Conventional index & 74.9146 & & 30.566,99 & & & 10.593,18 & & 387.05,621 & \\ \cline{2-14}  & Moveover & 2.74 & 774 & 0.67 & 0.67 & 0.67 & 3.76 & 3.76 & 3.76 & 10.25 & 10.25 & 10.25 \\ \cline{2-14}  & Down sales ratio & 6.45 & 5.33 & 2.09 & 3.63 & 3.10 & 1.82 & 0 & 0 & 0 & 27.87 & 21.70 & 12.65 \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics. Overall, the optimal model for 2019 and 2020 was the Extra Trees Regressor, and the Random Forest Regressor was the optimal model for 2021. However, using a different model for 2021 alone would likely reduce the reliability of year-to-year comparisons. The Extra Trees Regressor model, which continuously performed well across all years, is an extreme randomized machine learning method that is constructed on similar principles to the Random Forest Regressor but is comparatively superior in terms of speed and model performance [44]. Therefore, we selected it as our final model to better compare three different years.