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In order to quickly and effectively respond to a newly received criminal case, information regarding the type and severity of the case is crucial for authorities. This paper designs and develops a crime type and risk level prediction technique based on machine learning technology and verifies its performance. The designed technology can predict crime type and crime risk level using a text-based criminal case summary, which is criminal case receipt data. For the text-based criminal case summary data, the KICS data format is considered, which is actual policing data that contains information about criminal cases. For the crime type, 21 representative types of crimes are considered; therefore, the system can predict one of 21 types of crime for each criminal case. Furthermore, to predict the crime risk level, we developed a crime risk calculation formula. The developed formula calculates the crime risk level and outputs the risk score in numerical terms considering the severity and damage level of the criminal case. To predict the crime type and crime risk score, both DNN and CNN-based prediction models were designed and developed. The performance evaluation section shows that, in the case of crime type prediction, the proposed prediction models can achieve better performance than traditional classification algorithms such as na&#xef;ve Bayes and SVM. The performance of the CNN-based crime type prediction model is about 7% and 8% better than those of the SVM algorithm and the na&#xef;ve Bayes algorithm, respectively. The performance of the designed technology was comprehensively analyzed and verified through various performance measurement parameters. It is also developed in the form of a software platform with a GUI, allowing field personnel (e.g. police officers) to intuitively identify the type of criminal case and the level of risk from a text-based criminal case summary upon receipt of a new criminal case.

Smart Policing Technique With Crime Type and Risk Score Prediction Based on Machine Learning for Early Awareness of Risk Situation