Proactive Models for Information Support of the Judicial System

Abstract

In the modern dynamic world, crime is becoming increasingly technologically supported and transnational. This demands that the justice systems of different countries apply innovative approaches and state-of-the-art information technologies to effectively combat it. The presented research proposes a new proactive approach to identifying crime trends, which combines associative rule models and geospatial models. The article aims to develop an information model and implement a new predictive approach to studying crime trends to uncover complex patterns and strong relationship in crime data. The research utilized a comprehensive methodology, incorporating experimental procedures, synthesis methods, associative rule mining for pattern recognition, and geospatial predictive modeling for spatial trend identification and forecasting. Applied models were constructed using real data on criminal cases in the Ternopil region (Ukraine) from 2013 to 2023. Types of crimes and periods with elevated levels of criminal activity, as well as indicators of involvement by organized groups, were identified. Zones of concentrated offenses within the region were visualized. The higher prevalence of crime in urban areas was confirmed. These models can be easily adapted to new data and integrated into a unified information system to support decision-making within the judicial systems of Ukraine and the European Union.