Mining location-based social networks for criminal activity prediction

The problem of finding critical features for different crime types has been the focus in the field of environmental criminology because crime would lead to bad zoning in urban areas. However, conventional analysis ignores social dynamics of human beings. With the increasing growth of location-based social networks, the fine-grained data associated with social connections and the geographical information of users are available for representing the spatio-social dynamics of people. In this work, we devise a series of features to characterize an urban climate by data obtained from Foursquare and Gowalla in San Francisco. As for crime, we take use of crime data provided by the authorities. The features we mined are based on two general signals: geographical features that capture the distribution of various types of venues in urban areas, and social features that model the topological interactions between people in a region. We use these features to analyze and detect urban areas with high crime activities. The experimental results show the effectiveness of the proposed features on five different crime types and encourage future advanced criminal analysis using location-based social network data.