(untitled) (Research, Society and Development, 2020)
Context: Crime is a common and complex social problem that affects a nation’s quality of life, economic growth and reputation. Governments and society in general have had enormous problems caused by this phenomenon. Each year, governments spend millions of dollars fighting violence and, consequently, crime prevention and control are issues of great concern to public security agencies. Objective: To apply fundamentals of Data Science and provide an automated model, constantly updated, to analyze open government data related to crimes occurred in Minas Gerais. Method: We have performed an experiment to discover associations between municipalities, Integrated Public Security Regions (IPSRs), crimes, robbery targets, and theft targets. Additionally, we have developed rankings with the most dangerous municipalities. Results: From a general point of view, with scores for crimes, Belo Horizonte, Confins and Contagem were always among the five most dangerous. In addition, it became evident that there are dependencies between: crimes and municipalities, crimes and IPSRs, robbery targets and municipalities, and robbery targets and IPSRs. Conclusion: Data Science enables the execution of more accurate and faster diagnoses, helping strategic planning and decision making in Public Security. With some peculiarities and going beyond homicides, Minas Gerais partially follows the national trend of having lower crime rates in areas around regions with greater economic development.