The Application of Clustering as a Statistical Analysis Method in Assessing The Socio-Economic Development of Regions
DOI:
https://doi.org/10.31150/ajshr.v6i8.3866Keywords:
Region, Socio-Economic Development, Cluster Analysis, Euclidean Metric, Similarity Matrix, Set, ElementsAbstract
The application of the clustering statistical analysis method in assessing the socio-economic development of regions has gained significant relevance amidst global economic instability and disparities in resource distribution. This study examines the clustering of cities and districts in the Khorezm region of the Republic of Uzbekistan based on socio-economic development indicators from 2017 to 2024. The clustering of cities and districts was conducted using the "Cluster Analysis" method. According to the research findings, the cities and districts of the Khorezm region were divided into five clusters. This clustering was performed based on the "Euclidean distance" measure. The Euclidean distances reflect the degree of similarity, where a larger distance indicates a lower level of similarity, and a smaller distance signifies a higher level of similarity. Elements identified as the most similar were grouped into the same cluster.
References
[1] International Monetary Fund, World Economic Outlook: Policy Pivot, Rising Threats, Washington, DC, Oct. 2024, pp. 9–10.
[2] Sh. M. Mirziyoyev, Yangi Oʻzbekiston Strategiyasi, Toshkent: Oʻzbekiston, 2021, pp. 131–170.
[3] D. Cui, Y. Yu, and Z. Song, "Cluster Analysis of Regional Economic Development in Hebei Province," in Engineering Education and Management, L. Zhang and C. Zhang, Eds., Lecture Notes in Electrical Engineering, vol. 111, Springer, Berlin, Heidelberg, 2012.
[4] I.-A. Popescu and M. Neagu, "Cluster analysis of regional research and development disparities in Europe," Studies in Business and Economics, vol. 15, no. 3, pp. 303–312, 2020.
[5] S. K. V. Krishnan and M. F. C. Mohammed, "Assessment of regional environmental quality using cluster analysis," Environmental Development, vol. 45, 2023.
[6] R. R. Stough and J. Yu, "Industrial Cluster Analysis, Entrepreneurship and Regional Economic Development," in Food Security and Industrial Clustering in Northeast Asia, L. Kiminami and T. Nakamura, Eds., New Frontiers in Regional Science: Asian Perspectives, vol. 6, Springer, Tokyo, 2016.
[7] M. Çağlar and C. Gürler, "Sustainable Development Goals: A cluster analysis of worldwide countries," Environment, Development and Sustainability, vol. 24, no. 6, pp. 8593–8624, 2022.
[8] L. M. Akimova, N. L. Khomiuk, I. M. Bezena, I. L. Lytvynchuk, and O. Petroye, "Planning of socio-economic development of the territories (experience of European Union)," International Journal of Management, vol. 11, no. 4, 2020.
[9] M. C. Nogueira and M. Madaleno, "Are international indices good predictors of economic growth? Panel data and cluster analysis for European Union countries," Sustainability, vol. 13, no. 11, p. 6003, 2021.
[10] S. E. Barykin, A. A. Mikheev, E. G. Kiseleva, Y. E. Putikhin, N. S. Alekseeva, and A. Mikhaylov, "An empirical analysis of Russian regions’ debt sustainability," Economies, vol. 10, no. 5, p. 106, 2022.
[11] Z. Arshad, M. Robaina, and A. Botelho, "The role of ICT in energy consumption and environment: an empirical investigation of Asian economies with cluster analysis," Environmental Science and Pollution Research, vol. 27, no. 26, pp. 32913–32932, 2020.
[12] R. Huseynov, N. Aliyeva, V. Bezpalov, and D. Syromyatnikov, "Cluster analysis as a tool for improving the performance of agricultural enterprises in the agro-industrial sector," Environment, Development and Sustainability, vol. 26, no. 2, pp. 4119–4132, 2024.
