# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 2 Related Work  
### Crime Prediction  
The theory of spatial criminology research primarily focuses on two aspects. The first is the theory of criminal opportunities, which examines the factors influencing the spatio-temporal distribution and concentration pattern of crime. The second is social control theory, which explores how structural factors shape neighborhood crime rates by affecting the emergent attributes of the community [3]. Within the realm of opportunity theory, the most well-known frameworks are crime pattern theory [14; 15] and routine activity theory [16; 17]. These theories conceptualize an urban environment where motivated offenders navigate the area and identify suitable targets in the absence of effective guardians. Specific locations within the built environment are identified as crime attractors and generators, with numerous studies validating these theoretical assumptions. On the other hand, social control theory [18] emphasizes the impact of a region's socio-economic characteristics, such as the population size, income, education level, and unemployment rate, on crime occurrence. Early criminological research observed that the occurrence of crime is not randomly distributed across time and space but instead exhibits a pattern of repeat victimization [19]. Consequently, fitting specific regression models to the trends and patterns of crime occurrence became the initial method and means of crime prediction. At this stage, the focus was primarily on the spatio-temporal characteristics of crime incidents, specifically the periodic patterns of their occurrence and the proximity of the locations involved. [PERSON] et al. proposed the prospective mapping approach to predict and map the future locations of crime [20]. [PERSON] et al. tested the near repeat hypothesis by using the contagion model [21]. [PERSON] et al. applied the kernel density estimation model to detect criminal hotspots [22; 23], [PERSON] et al. implemented the self-exciting point process to model crime [24]. [PERSON] et al. introduced the neural network to crime prediction [25]. Subsequent studies revealed that, in addition to the spatio-temporal characteristics of crime incidents, the socio-economic factors of the area and the distribution of the built environment also exhibit a significant correlation with crime. Accordingly, these corresponding indicator features were also incorporated into the crime prediction models to improve the accuracy of the forecasts. [PERSON] et al. demonstrated that the risk terrain model can improve crime hotspot mapping [26]. [PERSON] et al. explored Bayesian spatio-temporal methods for crime pattern analysis [27]. [PERSON] et al. applied the discrete choice framework to analyze burglary location choice [28]. [PERSON] et al. introduced the tensor decomposition method into crime modeling [29]. [PERSON] et al. presented a graph-based deep model to forecast sparse crime [30]. [PERSON] et al. proposed the flexible search window approach to detect crime hotspots [31]. With the fast development of mobile internet technology, the patterns and characteristics of people's travel further enriched the scope of opportunity theory research, which helped improve the crime prediction. Through the analysis of human movement patterns, including daily commuting behaviors and the influx of individuals during specific events,researchers seek to enhance their understanding of crime hotspots and trends. [PERSON] et al. leveraged Foursquare, subway, and taxi data to improve crime prediction [32]. [PERSON] et al. conducted a comparative analysis of four indicators associated with theft: the residential, subway, taxi, and mobile phone activity [33]. [PERSON] et al. discovered that communities with higher levels of connectivity to the offender's residence, as indicated by population flow patterns, are more likely to be targeted for theft [34]. Technological advancements, along with data improvements, have significantly advanced crime prediction research.