To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. Such findings suggest that the future locations of crime should-to some extent at least-be predictable. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent a finding that Weisburd (Criminology 53:133–157, 2015) has recently termed the ‘law of crime concentration at places’. In this article, we give an overview to these theories from criminology, summarize crime patterns observed from urban data, review state-of-the-art algorithms for various types of computational crime tasks and discuss some appealing research directions that can bring the urban safety and security research into a new frontier.ĭecades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. They not only can help bridge the gap from what we have (big urban data) to what we want to understand about urban crime (urban crime analysis) but also can guide us to build computational models for crime. Meanwhile, environmental and social crime theories from criminology provide better understandings about the behaviors of offenders and complex patterns of crime in urban. The availability of big urban data provides unprecedented opportunities to enable us to construct advanced urban crime research. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources as well as rich environmental and social information.
Traditional urban crime research focused on leveraging demographic data, which is insufficient to capture the complexity and dynamics of urban crimes. Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban.
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