# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 5 Experiment
### Experimental Results  
Table 2 presents the results of the various models, highlighting the highest-performing model in bold and the second highest in underline. As shown in Table 2 and Figure 7, our study yields several key findings. The models based on GNNs outperform the others, followed by deep learning models and then traditional machine learning models, reflecting technological advancements. On the general test metrics, the traditional machine learning and deep learning models show similar performance, with deep learning being slightly ahead. However, for the specific incident types, the gap widens, with the traditional models, particularly XGBoost, showing more performance fluctuations. The GNN-based models maintain stable, superior performance across all the incident types compared to the other baseline models. Figure 7: Result comparison for the 110 Call incidents: (**a**) all incidents; (**b**) crime-related incidents; (**c**) security-related incidents; and (**d**) assistance-related incidents. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**All Incidents**} & \multicolumn{4}{c}{**Crime-Related Incidents**} \\ \cline{2-9}  & **ACC** & **F1** & **AUC** & **RMSE** & **ACC** & **F1** & **AUC** & **RMSE** \\ \hline RF & 0.6654 & 0.6645 & 0.7276 & 0.4981 & 0.6458 & 0.6163 & 0.7174 & 0.5066 \\ XGBoost & 0.6641 & 0.6608 & 0.7280 & 0.4999 & 0.5719 & 0.5718 & 0.6262 & 0.5749 \\ AFM & 0.6473 & 0.6899 & 0.7229 & 0.4601 & 0.655 & 0.6936 & 0.7324 & 0.4571 \\ DCN & 0.6600 & 0.6944 & 0.7395 & 0.4533 & 0.6671 & 0.7063 & 0.7516 & 0.4511 \\ NFM & 0.6478 & 0.6850 & 0.7248 & 0.4591 & 0.6643 & 0.6890 & 0.7420 & 0.4522 \\ DeepFM & 0.6630 & 0.6877 & 0.7410 & 0.4524 & 0.6712 & 0.6952 & 0.7488 & 0.4503 \\ DSTGCN & 0.7645 & 0.7713 & 0.8369 & 0.4014 & 0.7657 & 0.7682 & 0.8413 & 0.4081 \\ DSTGAT & **0.7922** & **0.8007** & **0.8700** & **0.3811** & **0.7992** & **0.8066** & **0.8786** & **0.3746** \\ \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Security-related incidents**} & \multicolumn{4}{c}{**Assistance-related incidents**} \\ \cline{2-9}  & **ACC** & **F1** & **AUC** & **RMSE** & **ACC** & **F1** & **AUC** & **RMSE** \\ \hline RF & 0.6320 & 0.6007 & 0.6974 & 0.5184 & 0.6280 & 0.5922 & 0.6965 & 0.5222 \\ XGBoost & 0.5688 & 0.5406 & 0.6277 & 0.5862 & 0.5619 & 0.5407 & 0.6126 & 0.5823 \\ AFM & 0.6387 & 0.6756 & 0.7135 & 0.4637 & 0.6301 & 0.6612 & 0.7040 & 0.4672 \\ DCN & 0.6520 & 0.6879 & 0.7326 & 0.4571 & 0.6369 & 0.6694 & 0.7136 & 0.4631 \\ NFM & 0.6443 & 0.6793 & 0.7197 & 0.4612 & 0.6242 & 0.6696 & 0.6973 & 0.4701 \\ DeepFM & 0.6525 & 0.6708 & 0.7274 & 0.4592 & 0.6337 & 0.6464 & 0.7059 & 0.4681 \\ DSTGCN & 0.7461 & 0.7518 & 0.8166 & 0.4249 & 0.7290 & 0.7342 & 0.8001 & 0.4355 \\ DSTGAT & **0.7896** & **0.7980** & **0.8697** & **0.3817** & **0.7818** & **0.7897** & **0.8578** & **0.3911** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for the 110 Call incidents. Despite these efforts, the experimental results indicate that even the best-performing model in the deep learning group, DeepFM, falls short when compared to the second group of models based on graph neural networks (GNNs). Specifically, for all the incidents, the DSTGCN model surpasses DeepFM by 15.31%, 12.16%, and 12.94% in terms of the ACC, F1-score, and AUC, respectively, while achieving an 11.27% reduction in the RMSE. Our proposed model, DSTGAT, demonstrates superior performance for all the incidents, exceeding DeepFM by 19.49%, 16.43%, and 17.41% in the ACC, F1, and AUC, respectively, and achieving a 15.76% reduction in the RMSE. These findings corroborate prior research, which has established that the topological structural features of street networks significantly enhance the accuracy of spatio-temporal event prediction tasks [55; 56; 57].