Agglomerative Approach to Assessing the Degree of Criminalization of the Subjects of the Russian Federation: the Methodology of Statistical Analysis and Criminological Description

Abstract

Alongside a wide range of analytical crime research methods in criminology, the method of assessing the degree of criminalization of the subjects of the Russian Federation through an agglomerative approach to the features characterizing the indicators of regional crime is of special research interest. This method includes three stages: 1) sampling and processing empirical statistical indicators of regional crime; 2) clustering of regions by groups of criminological characteristics; 3) criminological analysis and summary of obtained results. In order to assess the criminalization of subjects and Districts of the Russian Federation, the authors use such indicators as the categories of crimes, as they are most informative ones regarding the degree of crime prevalence in the population. The calculations are based on crimes registered by category per 100 thousand residents in the corresponding subject or District. The authors use three classes of criminal activity of population to present the results of clustering which reflects the minimal number of elements necessary for the discrete covering of all the variety of manifestations of criminal social prevalence and makes it possible to characterize groups of regions with high, medium and low levels of such prevalence. Calculation results allowed the authors to interpret classes as groups of subjects with a low (first class), medium (second class) and high (third class) level of criminalization of their population. The authors also present the clustering of subjects and Federal Districts of the Russian Federation by coefficients of registered crimes of the corresponding categories. This clustering encompasses information in 2010–2020. The dynamics of the distribution of the subjects of the Russian Federation by class showed a wave-like transition of the specific weight of the corresponding classes and a stable growth of criminal prevalence in the population in 2019–2020. The scattering diagrams make it possible to use a trendline with linear parameters to show either a positive or a negative correlation of crime indicators. The authors use the clustering of subjects to show that, among the Federal Districts, the specific weight of the third class is prevalent in the Siberian Federal District in 2010–2020. The presented methodology can be applied to create a probability model of predicting that a subject or a District will be in a certain class.