# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 5 Experiment
### Ablation Experiment  
Building upon our foundational experiments, we performed ablation studies to rigorously assess the impact of various features on the model's predictive capabilities. This study integrates three primary feature representations. Spatial embeddings include the fundamental street characteristics, demographic data, socio-economic indicators (such as points of interest), and the topological structural features of the street network. Temporal embeddings capture the calendar-related features. External embeddings account for the weather conditions. Theoretically, the spatial embeddings are hypothesized to be the most critical, significantly influencing the accuracy of the prediction results.  
Specifically, we conducted four sequential ablation studies based on the existing DSTGAT model: first, by removing time-related features and components; second, by removing weather-related features and components; third, by removing both temporal and weather-related features and components; and finally, by removing spatial features and components. By analyzing the experimental results from each ablation study, we can evaluate the impact of each feature and component on the overall model performance.  
According to Table 3 and Figure 9, the ablation experiments reveal that the three embeddings we introduced have collectively enhanced the predictive performance. Although end-to-end deep learning models have inherently complex internal structures, making it challenging to isolate the specific contributions of each feature type, ablation studies provide a practical approach to evaluating their relative impact. The results clearly demonstrate that spatial features and components have the most substantial influence on the DSTGAT model.  
Figure 9: Ablation experiment result comparison for the 110 Call incidents: (**a**) all incidents; (**b**) crime-related incidents; (**c**) security-related incidents; and (**d**) assistance-related incidents.  
Specifically, using the ablation experiment on the crime-related incidents with the best predictive performance as an example, when only the temporal features were removed, the model's ACC, F1-score, and AUC decreased by 1.96%, 2.02%, and 1.83%, respectively, while the RMSE increased by 3.76%. When only the external features were removed, the model's ACC, F1-score, and AUC decreased by 3.84%, 3.56%, and 3.51%, respectively, while the RMSE increased by 7.26%. It can be observed that, in our model, the weather features contribute more significantly to the prediction compared to the features provided by calendar information. The removal of both temporal and weather-related features resulted in not greater changes, with the ACC, F1, and AUC decreasing by only 4.53%, 4.19%, and 4.06%, respectively, and the RMSE increasing by 8.57%.  
In contrast, when spatial features were removed, the model's ACC, F1-score, and AUC decreased by 24.34%, 15.29%, and 27.57%, respectively, while the RMSE increased by 28.96%. These findings underscore the effectiveness of using a GAT-based graph representation method for predicting events within street networks.