Abstract

The features of the development of the tourism sector in the regions of the Russian Federation, which have an impact on the socio-economic development of the country, have been investigated. Analysis of the current state of the tourism sector, classified as the main types of economic activity, is relevant and important for increasing the competitiveness of the regions of the Russian Federation and ensuring the economic security of the state. The study is aimed to model and analyze tourist cluster formations in Russia. The study of tourist activity in the regions of Russia based on the indicators of the database of the Federal State Statistics Service was carried out using a new promising approach - cluster analysis using the scientific and methodological apparatus of artificial neural networks. The distribution of Russian regions into five tourist clusters has been obtained as a result of clustering multidimensional data using neural networks - self-organizing Kohonen maps, which are focused on self-study, and modern information technologies. In neural network modeling, the six-dimensional space of tourism development indicators was mapped, taking into account the topology, into a two-dimensional space, which made it possible to visualize the results of grouping regions by tourist clusters. The features of the development of the tourism sector in the regions of the Russian Federation have been revealed by the totality of the considered indicators The obtained results state that there is a strong variation in the number of regions by tourist clusters and the ametric nature of the development of tourist activity in the regions of Russia. The results of the study are of practical significance for the strategic planning of the tourism sector development, which ensures the development of domestic and inbound tourism. Analysis of the functioning of the tourism sector in the regions of the Russian Federation allows concluding the necessity to take a set of measures to stimulate effective investment activity in a number of tourism clusters, harmonizing the strategies of the state and business, which will contribute to the renewal and competitiveness of this type of economic activity.

Highlights

  • Ensuring sustainable economic growth in each region of the Russian Federation and the country as a whole corresponds to one of the main directions in solving the key tasks of economic policy [1]

  • The study of tourist activity in the regions of Russia based on the indicators of the database of the Federal State Statistics Service was carried out using a new promising approach cluster analysis using the scientific and methodological apparatus of artificial neural networks

  • The features of the development of the tourism sector in the regions of the Russian Federation have been revealed by the totality of the considered indicators The obtained results state that there is a strong variation in the number of regions by tourist clusters and the ametric nature of the development of tourist activity in the regions of Russia

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Summary

Introduction

Ensuring sustainable economic growth in each region of the Russian Federation and the country as a whole corresponds to one of the main directions in solving the key tasks of economic policy [1]. The development of the tourism sector [2, 3] is a significant factor contributing to the sustainable development and competitiveness of the regional economy. The tourism sector is associated with various industries that integrate and form the provision of the tourism industry and travel: hotel business, transport, catering, arts and crafts, utilities and other activities. This industry is significant for the sustainable development of the economy taking into account the number of organizations, entrepreneurs, and the population involved in the tourism industry, as well as the associated social and economic effect. In the countries of the Organization for Economic Cooperation and Development, the tourism sector accounts for 4.4% of GDP, 6.9% of employment and 21.5% of exports of services [5]

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