## 4 Results and Discussion
### Shap  
The Global Shapley Value (GSV) and Local Shapley Value (LSV), derived from the Extra Trees Regressor model, were used to determine the importance of each variable for the crime calls. In the subsequent visualization, mean absolute SHAP values (GSV) are used to delve deeper into the influence of each feature. As depicted in Figure 3a, the mean absolute SHAP value for every feature is presented and ranked by impact. Additionally, the average trend that each feature has on the outcome is indicated, with blue signifying an increase in the number of crimes reported and red denoting a decrease. For the interpretation of LSV, a position of 0.0 on the x-axis implies no substantial link between the outcome variable and the features. Data points to the left of this mark negatively influence the prediction, whereas those to the right enhance it [45]. The GSV results depict that all variables had a smaller impact on crime reports as we move into 2021, although their influence varied significantly (Figure 3a). Specific variables emerged as highly influential in predictions from 2019 to 2021. In particular, store density exhibited the most significant impact, followed by the dawn sales ratio, number of gathering facilities, Mu-score, elderly sales ratio and land appraisal value. Upon considering the most influential factors for each year, the order and direction of influential factors are slightly changed. The elderly sales ratio had a negative relationship with crime in 2019 but turned positive after 2020. In addition, the significance of the Mu-score slightly dropped in 2021 (from third place to fifth place). The importance of the middle-aged sales ratio also scarcely increased in 2021 by flipping positions with the land appraisal value. Moreover, land appraisal value and elderly/youth sales ratios had a negative influence in 2019, and only GVI and land appraisal value showed negative and moderate influences on crime report dynamics after 2020. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**2019**} & \multicolumn{3}{c}{**2020**} & \multicolumn{3}{c}{**2021**} \\ \cline{2-10}  & **R\({}^{2}\)** & **RMSE** & **MAPE** & **R\({}^{2}\)** & **RMSE** & **MAPE** & **R\({}^{2}\)** & **RMSE** & **MAPE** \\ \hline Extra Trees Regressor & 0.4626 & 0.0008 & 0.4715 & 0.4361 & 0.0008 & 0.479 & 0.4714 & 0.0008 & 0.4658 \\ \hline Random Forest Regressor & 0.4112 & 0.0008 & 0.4597 & 0.4033 & 0.0008 & 0.4675 & 0.464 & 0.0008 & 0.4273 \\ \hline Light Gradient Boosting Machine & 0.4091 & 0.0008 & 0.46 & 0.3916 & 0.0008 & 0.5591 & 0.4586 & 0.0008 & 0.4853 \\ \hline Ridge Regression & 0.3896 & 0.0009 & 0.5236 & 0.3913 & 0.0008 & 0.5644 & 0.4537 & 0.0008 & 0.4743 \\ \hline Bayesian Ridge & 0.3886 & 0.0009 & 0.5115 & 0.3902 & 0.0008 & 0.4421 & 0.4526 & 0.0008 & 0.4377 \\ \hline Linear Regression (benchmark) & 0.3831 & 0.0009 & 0.5156 & 0.3883 & 0.0008 & 0.5574 & 0.3812 & 0.0008 & 0.5671 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top five best-performing ML models. Figure 3: SHAP GSV (Absolute Mean) and LSV (Besswarm) plots. When delving into each feature, the living population under the socioeconomic characteristics continuously had a positive association with the commercial crime report density. This is consistent with earlier research that suggests that crime is related with population contact, with more crowded places having higher crime rates owing to increased interpersonal contact [46]. The result, however, differs from the findings of [33], which suggest that an excessive floating population leads to a surveillance effect that causes multiple people to monitor their surroundings. This disparity may be attributable to the unit of population employed in this study. The living population here includes both floating and resident populations. Interestingly, the importance of the living population increased after the outbreak, but its magnitude has not significantly changed over time.