# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 1 Introduction  
Since its establishment in 1987, the 110 call for police service (hereinafter referred to as \"110 Call\") has been responsible for receiving information on potential illegal and criminal incidents. It dispatches the nearest police force to respond to and handle these incidents promptly based on the specific content [1; 2]. After nearly four decades of development, the platform has become deeply ingrained in the public consciousness and the most important focus for the local police. Although only a small proportion of the 110 Call incidents are ultimately referred to as crime cases, the local police have to respond to each one of them and dispatch police forces to the scene for handling any issues, thus becoming a core part of the police's daily work. Due to the continuous and rapid increase in 110 Call incidents in recent years, the existing police force has become overextended, highlighting the urgent need to implement predictive technologies. If it were possible to predict the 110 Call incidents through scientific methods, it could assist the police in better deploying their forces and improving the response efficiency.  
By analyzing historical 110 Call records, we find that their underlying principles, particularly in terms of the spatio-temporal distribution characteristics, can be informed by the relevant crime prediction literature. To date, the evolution of crime prediction has advanced from basic analyses to sophisticated deep learning methodologies that integrate crime data with various multimodal sources. The spatial unit has gradually shifted from a grid-based region to a street network, as street networks better reflect real-life scenarios and effectively guide police in timely responses and daily operations [3].  
While prior research has produced significant results in terms of crime and traffic analysis at the street level, many studies, such as those by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [4; 5; 6; 7; 8], primarily emphasize the impact of built environment factors on crime, overlooking the interrelationships among streets within the network. Others, like [PERSON] and [PERSON] [9; 10],explore crime diffusion in the street network but neglect the inherent properties of individual streets.  
Recent advancements in graph representation learning have treated street networks as graphs, such as the studies by [PERSON] and [PERSON] [11; 12], typically assigning equal weights to all the nodes (with streets represented as nodes). This method fails to consider the significant differences in street attributes and spatial structural relationships between nodes. This treatment leads to overlooking variations in the degree of mutual influence between nodes, which can impact the accuracy of the model in capturing the true dynamics of the street network. To solve this problem, we introduce an attention mechanism (graph attention network, GAT) that addresses these differences from the outset, employing stacked GAT layers to automatically learn the varying weights of streets, integrating temporal and external environmental features to improve the prediction outcomes [13].