# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 6 Conclusions  
This study presents a deep spatio-temporal model leveraging the graph attention network (GAT) within a graph neural network framework to predict the likelihood of future 110 Call incidents in specific street segments. The model is designed to extract pertinent features from a diverse set of spatio-temporal data, including the street networks, population density, points of interest (POIs), and calendar and weather conditions. The architecture of the model comprises three primary components, each dedicated to learning spatial, temporal, and external environmental features relevant to the street segments. Empirical evaluations using real-world data underscore the model's efficacy. We envision integrating our model into an interactive dashboard that law enforcement can use to visualize high-risk streets in real time, with color-coded alerts highlighting incident probabilities. This tool would allow officers to monitor evolving risk patterns across the street network, enabling dynamic resource allocation based on both real-time data and historical trends. Leveraging the GAT's multi-head attention mechanism, the model identifies and prioritizes critical streets, supporting more targeted and efficient patrol strategies. The factors contributing to incidents' risk are highly complex, encompassing not only criminal activities that threaten personal and property security but also historical conflicts between neighbors, among other issues. Consequently, future research should integrate the professional expertise of law enforcement to refine the incident classification, thereby enabling more precise and effective responses by the police. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature and Component**} & \multicolumn{4}{c}{**All Incidents**} & \multicolumn{4}{c}{**Crime-Related Incidents**} \\ \cline{2-9}  & **ACC** & **F1** & **AUC** & **RMSE** & **ACC** & **F1** & **AUC** & **RMSE** \\ \hline DSTGAT & 0.7922 & 0.8007 & 0.87 & 0.3811 & 0.7992 & 0.8066 & 0.8786 & 0.3746 \\ \(w/o\) T & 0.7769 & 0.7852 & 0.8547 & 0.4062 & 0.7835 & 0.7903 & 0.8625 & 0.3605 \\ \(w/o\) W & 0.7626 & 0.7741 & 0.8407 & 0.4067 & 0.7685 & 0.7779 & 0.8478 & 0.4018 \\ \(w/o\) T and W & 0.7573 & 0.7690 & 0.8355 & 0.4118 & 0.763 & 0.7728 & 0.8429 & 0.4067 \\ \(w/o\) S & 0.5970 & 0.6699 & 0.6223 & 0.4851 & 0.6047 & 0.6833 & 0.6364 & 0.4831 \\ \hline \multirow{2}{*}{**Feature and Component**} & \multicolumn{4}{c}{**Security-Related Incidents**} & \multicolumn{4}{c}{**Assistance-Related Incidents**} \\ \cline{2-9}  & **ACC** & **F1** & **AUC** & **RMSE** & **ACC** & **F1** & **AUC** & **RMSE** \\ \hline DSTGAT & 0.7896 & 0.7980 & 0.8697 & 0.3817 & 0.7818 & 0.7897 & 0.8578 & 0.3911 \\ \(w/o\) T & 0.7738 & 0.7830 & 0.8536 & 0.3674 & 0.7674 & 0.7754 & 0.8434 & 0.3758 \\ \(w/o\) W & 0.7595 & 0.7715 & 0.8399 & 0.4075 & 0.7586 & 0.7687 & 0.8342 & 0.4121 \\ \(w/o\) T and W & 0.7545 & 0.7647 & 0.8337 & 0.4133 & 0.7537 & 0.7633 & 0.8285 & 0.4172 \\ \(w/o\) S & 0.5950 & 0.6704 & 0.6149 & 0.4853 & 0.5821 & 0.6667 & 0.5928 & 0.4913 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the ablation experiment. **Author Contributions**: Methodology, [PERSON]; Software, [PERSON]; Validation, [PERSON]; Data curation, [PERSON]; Writing--original draft, J.S. ; Supervision, P.C. ; Funding acquisition, J.S. ; [PERSON], Ph. D candidate at PPSUC; P.C., Professor at PPSUC; H.G., Ph. D candidate at PPSUC. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was funded by the \"Double First-Class\" innovative research project in criminology at the People's Public Security University of China (2023 SYL03). **Data Availability Statement:** The datasets, models, and codes that underpin the conclusions drawn in this research are accessible upon request to the corresponding author, subject to reasonable conditions. Due to privacy concerns, these data are not available for public access.