Crime information improvement for situation awareness based on data mining

Abstract

Crime records hold critical information about crime situations including the offender actions, stolen objects description, characteristics of victims, the location of the crime situation, individuals involved and more. To consume crime data, police forces and other security analysts use risk management systems which process and organize data, and summarize it into relevant and useful information on criminal situations. This type of system depends on promoting Situation Awareness to stimulate the users understanding of the crime situations and consequently the decision-making assertiveness. In this work the goal is to contribute with the typification of crime situations by machine learning driven techniques, applied in conjunction with pre-processing and transformation. Results showed that the use of pre-processing techniques improved data quality and algorithms precision. In addition, the transformation technique with the best results found was Bag of Words Binarization. Finally, the Logistic Regression algorithm presented the best results for mining the crime data.