SiAPP: An Information System for Crime Analytics Based on Logical Relational Learning

The growing of criminality in Brazilian cities is a common theme addressed by media as well as by the legal authorities. To effectively reduce the criminality, people and infrastructure must be carefully involved to not only punish who had committed crimes, but also predict and prevent it. Since acquiring official data about crimes is far from trivial, citizens have become important data sources through Web-based collaborative systems. These systems provide a huge volume of data that has to be analyzed. How to analyze this volume of data and identify patterns in crimes is an important, yet open, issue. Thus, this work presents a system called SiAPP. Its main objective is to support the analysis and prediction of crime patterns using a machine learning algorithm. SiAPP automatically acquires data from collaborative sources, generate logical rules and visualizes the found patterns. Experimental analysis shows that SiAPP is a promising solution tool to assist crimes prevention.