Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services
Digitalisation is important not only for the work of professionals in their workplaces, but also for the well-being of specialists and adaptation to changes in the information sphere as well as for providing high-quality educational, medical and social services to the population. The paper uses mach...
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| Format: | Article |
| Language: | Russian |
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State University of Management
2025-01-01
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| Series: | Цифровая социология |
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| Online Access: | https://digitalsociology.guu.ru/jour/article/view/345 |
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| author | L. A. Borisova |
| author_facet | L. A. Borisova |
| author_sort | L. A. Borisova |
| collection | DOAJ |
| description | Digitalisation is important not only for the work of professionals in their workplaces, but also for the well-being of specialists and adaptation to changes in the information sphere as well as for providing high-quality educational, medical and social services to the population. 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 digitalisation of services. In addition, the division of regions into clusters has been 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, considering the considered indicators of digitalisation of the subjects according to the Federal State Statistics Service data for May 2024. |
| format | Article |
| id | doaj-art-56ca90665e804b9f8a0c2fe50a5074be |
| institution | Kabale University |
| issn | 2658-347X 2713-1653 |
| language | Russian |
| publishDate | 2025-01-01 |
| publisher | State University of Management |
| record_format | Article |
| series | Цифровая социология |
| spelling | doaj-art-56ca90665e804b9f8a0c2fe50a5074be2025-02-04T16:32:35ZrusState University of ManagementЦифровая социология2658-347X2713-16532025-01-0174334310.26425/2658-347X-2024-7-4-33-43214Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic servicesL. A. Borisova0Financial University under the Government of the Russian FederationDigitalisation is important not only for the work of professionals in their workplaces, but also for the well-being of specialists and adaptation to changes in the information sphere as well as for providing high-quality educational, medical and social services to the population. 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 digitalisation of services. In addition, the division of regions into clusters has been 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, considering the considered indicators of digitalisation of the subjects according to the Federal State Statistics Service data for May 2024.https://digitalsociology.guu.ru/jour/article/view/345machine learning methodsdigitalisationstatisticsregressioncorrelationclusteringdendrogramclassification of regionselectronic services |
| spellingShingle | L. A. Borisova Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services Цифровая социология machine learning methods digitalisation statistics regression correlation clustering dendrogram classification of regions electronic services |
| title | Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services |
| title_full | Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services |
| title_fullStr | Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services |
| title_full_unstemmed | Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services |
| title_short | Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services |
| title_sort | comparative analysis of the russian regions using machine learning methods for a set of indicators of electronic services |
| topic | machine learning methods digitalisation statistics regression correlation clustering dendrogram classification of regions electronic services |
| url | https://digitalsociology.guu.ru/jour/article/view/345 |
| work_keys_str_mv | AT laborisova comparativeanalysisoftherussianregionsusingmachinelearningmethodsforasetofindicatorsofelectronicservices |