Currently, there is a transition from analogue to digital technologies in all spheres of life. The pace of life is accelerating, and more and more digital technologies are found in key areas of life, such as education and healthcare. Therefore, it is important to look at how things are with the digitalization of knowledge-intensive spheres of life, such as science, education, healthcare, and telecommunications technologies in the context of the regions of the Russian Federation. The paper uses machine learning methods to classify regions according to a set of indicators of electronic services and services. A clear division of the regions into two large, almost equal groups according to this set of indicators has been obtained. The use of well-known statistical criteria has demonstrated the statistical significance of such a division. Scattering diagrams are constructed as an example of the relationship of such indicators. The multiple correlation coefficient between the indicators of electronic services and services is 0.71, which indicates a close relationship between the indicators of digitalization of services. In addition, the division of regions into clusters was obtained using hierarchical clustering, which implies that Moscow has significantly overtaken other regions of Russia in providing electronic services and services, and the remaining regions are heterogeneous in this indicator, taking into account the considered indicators of digitalization of regions according to Rosstat data for July 2024.
Borisova et al. (Thu,) studied this question.
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