Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/91555
Title: Методика анализа демографического потенциала российских регионов на основе нечеткой кластеризации данных
Other Titles: Methodology for analyzing the demographic potential of Russian regions using fuzzy clustering
Authors: Shubat, O. M.
Bagirova, A. P.
Akishev, A. A.
Шубат, О. М.
Багирова, А. П.
Акишев, А. А.
Issue Date: 2019
Publisher: Institute of Economics, Ural Branch of the Russian Academy of Sciences
Институт экономики Уральского отделения РАН
Citation: Шубат О. М. Методика анализа демографического потенциала российских регионов на основе нечеткой кластеризации данных / О. М. Шубат, А. П. Багирова, А. А. Акишев. — DOI 10.17059/2019-1-14. — Текст : электронный // Экономика региона. — 2019. — Том 15, выпуск 1. — С. 178-190.
Abstract: The research is aimed at developing and testing a methodology for analyzing demographic potential of Russian regions. The initial data are the regional official Russian statistics indicators. We proposed an approach for assessing the demographic potential based on a differentiated analysis of its quantitative and qualitative components. The paper presents the developed methodology for estimating the demographic potential, combining multidimensional data classification (fuzzy clustering) and expert assessments. Application of the proposed methodology revealed five specific models in the demographic space of Russia. The first model combines a low level of quantitative components of the demographic potential with a high level of its quality. The second model is characterized by average levels of both components. In the third model, an average level of the quantitative component is accompanied by a rather low level of the demographic potential’s quality. The fourth model combines a high level of quantitative component of the demographic potential with an imbalance of its quality indicators, and the fifth — a high level of both components. We have obtained estimates for the quantitative and qualitative components of the demographic potential for each region and rated them. This has allowed identifying “anchor”-regions and “driver”-regions, as well as regions with the most and least balanced assessments of the two components. The paper shows the potential application of the developed methodology. In particular, this methodology allows identifying groups of regions, which need the implementation of specific measures for increasing the quantity and improving the quality of the demographic potential. The most significant limitation of the developed methodology is the lack of a complete set of indicators in the official Russian statistics for assessing the demographic potential. Future research will be aimed at applying fuzzy clustering methods to various demographic phenomena, since this approach takes into account the natural uncertainty, which is typical for such processes and, therefore, makes the results of demographic analysis more formalized and valid. © 2019 The Linguistic Association of Finland. All Rights Reserved.
Предложена методика оценки демографического потенциала, сочетающая метод многомерной классификации данных и метод экспертных оценок. Рассмотрено понятие демографического потенциала. Представлен топ-лист регионов по степени сбалансированности количественной и качественной компонент демографического потенциала.
Keywords: ALGORITHM OF FUZZY C-MEANS
ASSESSMENT OF DEMOGRAPHIC POTENTIAL
CLUSTERS OF REGIONS
COMPONENTS OF DEMOGRAPHIC POTENTIAL
DEMOGRAPHIC POLICY
DEMOGRAPHIC POTENTIAL
EXPERT ASSESSMENTS
FUZZY CLUSTERING
MODEL OF DEMOGRAPHIC SPACE
RUSSIAN REGIONS
URI: http://hdl.handle.net/10995/91555
metadata.dc.rights: info:eu-repo/semantics/openAccess
SCOPUS ID: 85064174694
WOS ID: 000461754400014
ISSN: 2072-6414
2411-1406
DOI: 10.17059/2019-1-14
metadata.dc.description.sponsorship: The article has been prepared within the research project Fertility and parenting in Russian regions: models, invigoration strategies, forecasts, supported by the President of Russian Federation (the grant No. NSh-3429.2018.6).
Origin: Экономика региона. 2019. Том 15, выпуск 1
Appears in Collections:Economy of Region

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