Big Data' and Data Mining' Role in Fight Against Drug Trafficking

Abstract

The subject of the research is the ways of using modern Big Data and Data Mining methodologies to improve the process of lawmaking and law enforcement practice in the field of illicit trafficking of new psychoactive substances. Based on the experience of European countries and international experience in general, the author proves the need to create a methodology for early detection of the spread of new psychoactive substances that pose a significant threat to public health. This technique, according to the author, can be based on the use of methods of applied statistical analysis of the Google Trends database, which, in turn, can be used to combat drug crime in terms of detecting and predicting crimes. The practical significance of the conducted research lies in the development of a methodology for early detection of the spread of new psychoactive substances, which can be used to optimize the operational legislative response and improve law enforcement practice in the field of combating illicit drug trafficking. The scientific novelty of the conducted research consists in the creation of a predictive rule in the form of a linear regression equation that allows predicting the spread of such a new psychoactive substance as mephedrone in various regions of the Russian Federation, as well as planning operational investigative measures taking into account the identified risk zones. This study may be of interest to law enforcement officials, legal scholars, students and teachers of law schools.