# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 2 Related Work
### Street Network Modeling of Crime  
Since the beginning of the 21 st century, a growing number of criminologists have shifted their focus to street networks, as this spatial unit more closely aligns with people's daily lives and travel patterns. Consequently, research on crime prediction based on street networks has gradually become a prominent area of interest. Research on crime within the context of street networks can generally be divided into two main approaches. The first approach focuses on the spatial attributes and socio-economic characteristics of street networks, as well as their correlation with and impact on crime. Early studies suggested that streets offer a more natural and efficient framework for policing activities. For instance, [PERSON] et al. proposed a mathematical modeling method based on street networks for predicting burglaries [38]. [PERSON] et al. demonstrated that street network indicators derived from graph theory play a crucial role in the spatial decision-making of burglars [39]. [PERSON] et al. investigated the influence of the street network's physical layout on severe outdoor violent crimes, finding that higher degrees of integration and choice are associated with an increased likelihood of crime [40]. Additionally, [PERSON] discovered that structural features rooted in social disorganization and criminal opportunities theories significantly influence crime patterns [41]. Subsequently, scholars began incorporating built environment characteristics into crime research based on street networks, particularly utilizing Google Street View (GSV) to provide extensive, objective data for quantitative analysis. For example, [PERSON] et al. collected environmental data through field survey and found that the functions of street buildings and the escape paths significantly impact street theft and robbery [42]. [PERSON] et al. were pioneers in integrating GSV into street-based crime prediction, combining it with data from Twitter and Foursquare to obtain visual, textual, and human activity information, resulting in significant improvements in crime prediction accuracy [4]. Since then, the use of streetscape data has become a prominent area of focus in street-based crime analysis, leading to numerous research developments. For instance, [PERSON] et al. developed a zero-inflated negative binomial regression model to investigate the relationship between different geographic environmental variables and the risk of street crime [5]. [PERSON] et al. performed a multi-scale analysis to examine how the built environment influences crime occurrence, utilizing data obtained from street view imagery [6]. [PERSON] et al. extracted 11 features across four dimensions and employed machine learning to demonstrate their influence on five types of crimes [7]. [PERSON] et al. computed eight indicators and employed a Poisson regression model to explore the relationship between streetscape data and crime patterns [8]. The aforementioned research has undoubtedly advanced street-based crime studies to a new level. However, these studies primarily focus on projecting variables such as socio-economic characteristics and built environment attributes onto streets for correlation analysis, with limited attention paid to the topological structure of street networks. Therefore, some researchers have sought to treat the street network as an integrated whole, applying graph theory to analyze and forecast crime by modeling the spatial relationships and interactions within the street network. For example, [PERSON] et al. introduced the first predictive crime mapping method based on street networks, known as NTKDE. This approach adapted KDE, originally used in prospective mapping, to the network space. It demonstrated superior prediction accuracy for property crime compared to traditional grid-based methods [9]. The development of GLDNet, a deep learning model for network-based predictive mapping, marked a significant advancement over NTKDE.