Time and Location Recommendation for Crime Prevention

摘要

In recent years we have seen more and more open government and administrative data made available on the Web. Crime data, for example, allows civic organizations and ordinary citizens to obtain safety-related information on their surroundings. In this paper, we study crime prediction as a recommendation problem, using fine-grained open crime data. A common issue in current crime prediction methods is that, given fine-grained spatial temporal units, crime data would become very sparse, and prediction would not work properly. By modeling crime prediction as a recommendation problem, however, we can make use of the abundant selection of methods in recommendation systems that inherently consider data sparsity. We present our model and show how collaborative filtering and contextual-based recommendation methods can be applied. Focusing on two major types of crimes in the city of San Francisco, our empirical results show that recommendation methods can outperform traditional crime prediction methods, given small spatial and temporal granularity. Specifically, we show that by using recommendation methods, we can capture 70% of future thefts using only 20% man-hour, 13% more than traditional methods.