Crime in Urban Areas: A Data Mining Perspective
Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging demographic data, which is insufficient to capture the complexity and dynamics of urban crimes. 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. The availability of big urban data provides unprecedented opportunities to enable us to construct advanced urban crime research. Meanwhile, environmental and social crime theories from criminology provide better understandings about the behaviors of offenders and complex patterns of crime in urban. 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. 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.
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