An intelligent decision-support model using FSOM and rule extraction for crime prevention

In the recent era of increasing volume crimes, crime prevention is now one of the most important global issues, along with the great concern of strengthening public security. Government and community officials are making an all-out effort to improve the effectiveness of crime prevention. Numerous investigations addressing this problem have generally employed disciplines of behavior science and statistics. Recently, the data mining approach has been shown to be a proactive decision-support tool in predicting and preventing crime. However its effectiveness is often limited due to different natures of crime data, such as linguistic crime data evolving over time. In this paper, we propose a framework of intelligent decision-support model based on a fuzzy self-organizing map (FSOM) network to detect and analyze crime trend patterns from temporal crime activity data. In addition, a rule extraction algorithm is employed to uncover hidden causal-effect knowledge and reveal the shift around effect. In contrast to most present crime related studies, we target a non-Western real-world case, i.e. the National Police Agency (NPA) in Taiwan. The resultant model can support police managers in assessing more appropriate law enforcement strategies, as well as improving the use of police duty deployment for crime prevention.