  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, which enable us to conduct
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 can not only 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 guide us to build computational
models for crime. In this article, we give an overview to key theories from
criminology, summarize crime analysis on 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 crime research into a
new frontier.
