Keywords
cluster analysis
regional typology
tourism management
Far Eastern Federal District
How to Cite
Abstract
This paper compares eleven regions of the Russian Far Eastern Federal District across four groups of conditions for ecotourism development: conservation base, protected-area resource structure, transport accessibility, and tourism readiness. The dataset combines Rosstat statistics for 2024–2025 with road-infrastructure indicators from RIA Rating. Eight indicators were standardized and aggregated into four equally weighted analytical blocks. Regions were classified using Ward’s hierarchical clustering and K-means. Robustness was assessed using the silhouette coefficient, Calinski–Harabasz and Davies–Bouldin indices, Adjusted Rand Index, a leave-one-region-out procedure, and sensitivity analysis of the aggregation scheme. Three regional profiles were identified. In the article, regions are categorized based on their level of ecotourism development. The labels “leading,” “intermediate,” and “peripheral” were assigned to the profiles following cluster formation – a process based on the analysis of average standardized indicators across four thematic blocks and a composite score. These labels reflect only the relative positioning of the groups within the adopted system of indicators; they do not characterize the overall level of socio-economic development of the regions, nor are they used to evaluate the performance of regional administrative bodies. The presented typology characterizes only the structural prerequisites for the development of ecotourism, and does not constitute an assessment of the level of utilization of the tourism potential or the degree of environmental sustainability of the Far Eastern Federal District.
References
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12. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218. DOI: 10.1007/BF01908075.
13. Koľveková, G., Liptáková, E., Štrba, Ľ., Kršák, B., Sidor, C., Cehlár, M., Khouri, S., & Behún, M. (2019). Regional tourism clustering based on the three Ps of the sustainability services marketing matrix: An example of Central and Eastern European countries. Sustainability, 11(2), Article 400. DOI: 10.3390/su11020400.
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15. Liu, C.-L., & Liu, W.-Y. (2025). Assessing tourism pressure on wildlife in a protected area: A social big data approach using photo-sharing data and species distribution modeling. Ecological Informatics, 92, Article 103441. DOI: 10.1016/j.ecoinf.2025.103441.
16. Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI: 10.1109/TIT.1982.1056489.
17. Maksanova, L., Bardakhanova, T., Budaeva, D., Mikheeva, A., Lubsanova, N., Sharaldaeva, V., Eremko, Z., Andreeva, A., Ayusheeva, S., & Khrebtova, T. (2023). Ecotourism development in the Russian areas under nature protection. Sustainability, 15(18), Article 13661. DOI: 10.3390/su151813661.
18. Maslovskaia, O., Kopeva, A., Petrova, E., Ivanova, O., & Khrapko, O. (2020). Development of ecotourism infrastructure at protected areas of Primorsky Krai. IOP Conference Series: Materials Science and Engineering, 890, Article 012002. DOI: 10.1088/1757-899X/890/1/012002.
19. Nuñez-Torres, A., Arones-Huarcaya, A., & Yarasca-Aybar, C. (2023). Strategies for territorial tourism planning in natural protected areas: Alto Mayo Protected Forest, Peru. City, Territory and Architecture, 10, Article 15. DOI: 10.1186/s40410-023-00200-w.
20. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. DOI: 10.1016/0377-0427(87)90125-7.
21. Shpak, N., Kulyniak, I., Novakivskyi, I., & Oleksiv, I. (2023). Clusterization in tourism development level’s assessment of regions: Example of Ukraine. Journal of Tourism and Services, 14(26), 45–56. DOI: 10.29036/jots.v14i26.444.
22. Skiniti, G., Lilli, M., Skarakis, N., Tournaki, S., Nikolaidis, N., & Tsoutsos, T. (2024). A holistic approach for tourism carrying capacity estimation in sensitive ecological areas. Environment, Development and Sustainability, 26, 31971–31995. DOI: 10.1007/s10668-024-04805-0.
23. Sobhani, P., Esmaeilzadeh, H., Sadeghi, S. M. M., Marcu, M. V., & Wolf, I. D. (2022). Evaluating ecotourism sustainability indicators for protected areas in Tehran, Iran. Forests, 13(5), Article 740. DOI: 10.3390/f13050740.
24. van der Zee, E., Gerrets, A.-M., & Vanneste, D. (2017). Complexity in the governance of tourism networks: Balancing between external pressure and internal expectations. Journal of Destination Marketing & Management, 6(4), 296–308. DOI: 10.1016/j.jdmm.2017.07.003.
25. Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. DOI: 10.1080/01621459.1963.10500845.
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