Differentiating the Level of Territorial Development of the Transport and Logistics Infrastructure in Ukraine by Adapting the Cluster Analysis Methodology
Recently, much attention has been paid to the ideas and theories that highlight the cluster approach to the territorial organization of the economic system. Separation and development of transport and logistics cluster contributes to the development of competitive advantages, provides the leading position of the country in the world market and enhances the investment attractiveness of the region in which it operates.
The use of a multidimensional statistical method of cluster analysis to determine the homogeneity between the individual regions of Ukraine contributes to the identification of transport and logistics cluster. For this reason, the article analyzes the role of cluster analysis in the study of the development of transport and logistics services, shows that the transport and logistics cluster is formed on the basis of the real socio-economic situation in the conditions of a particular territory, taking into account its potential development. It is substantiated that the transport and logistics cluster, in addition to meeting the needs of the economy, performs a number of important functions: it creates opportunities for the development of sectors of the national economy; contributes to the development of the region's infrastructure; increases the investment attractiveness of the region. The conducted research of existing methods of cluster analysis of grouping of regions of Ukraine by the level and potential of development of the market of transport and logistic services has allowed to allocate the considered ways of estimation of efficiency of functioning of the cluster which differ in external and internal effects. A cluster analysis of the territorial level and potential of logistics development in the regions of Ukraine is carried out on the basis of three groups of indicators (socio-economic, transport performance indicators of the region and indicators characterizing the composition of the transport system of the region and its potential opportunities). As a result of the cluster analysis, the regions of Ukraine were grouped into three clusters by the level of logistics development, and into two clusters by the logistics development potential. It is determined that the classification of regions into homogeneous groups will allow to build in each cluster typologically regressive equations of interaction of market factors, which will increase the accuracy of the study of the dynamics of the development of the market environment of regions of potential placement of elements of transport and logistics infrastructure.
Holovach A. V. (2005). Statystychne zabezpechennia upravlinnia ekonomikoiu: prykladna statystyka [Statistical support to the economic management: applied statistics]. Kyiv: Kyiv National Economic University, p. 333 [in Ukrainian].
Yerina A. M., Yerin D. L. (2014). Statystychne modeliuvannia ta prohnozuvannia [Statistical modeling and forecasting]. Kyiv: Kyiv National Economic University, p. 348 [in Ukrainian].
Mantsurov I. H. 2006). Statystyka ekonomichnoho zrostannia ta konkurentospromozhnosti krainy [Statistics of the domestic economic growth and competitiveness]. Kyiv: Kyiv National Economic University, p. 392 [in Ukrainian].
Osaulenko O. H. (2008). Natsionalna statystychna systema: stratehichne planuvannia, metodolohiia ta orhanizatsiia [The national statistical system: strategic planning, methodology and organization]. Kyiv: “Information and analytical agency”, p. 415 [in Ukrainian].
Parfentseva N. O. (2007). Statystyka rynkiv [Statistics of markets]. Kyiv: “Information and analytical agency”, p. 863 [in Ukrainian].
Chala T. H. (2012). Statystychne doslidzhennia koniunktury rehionalnykh rynkiv iz zastosuvanniam metodiv klasyfikatsii bahatomirnykh sposterezhen [A statistical study of the regional market conjuncture using the methods of classification of multidimensional observations]. Prykladna statystyka: problemy teorii ta praktyky – Applied statistics: problems of theory and practice, 11, 178–184 [in Ukrainian].
Poliakova O. M. (2014). Klasternyi pidkhid do formuvannia intehrovanoi transportno-lohistychnoi systemy [The cluster approach to forming the integrated transport and logistics system]. Visnyk ekonomiky transportu i promyslovosti – Bulletin of transport and industry economics, 46, 239–244. Retrieved from http://nbuv.gov.ua/UJRN/Vetp_2014_46_28 [in Ukrainian].
Sych Ye. M., Boiko O. V. (2013). Lohistychno-klasternyi pidkhid do rozvytku transportnoho rynku [The logistics and transport approach to developing the transport market]. Visnyk Chernihivskoho derzhavnoho tekhnolohichnoho universytetu. Seriia: Ekonomichni nauky – Bulletin of Chernihiv State Technological University. Series: Economics, 1, 91–103. Retrieved from http://nbuv.gov.ua/UJRN/Vcndtue_2013_1_16 [in Ukrainian].
Waters D. (ed.) (2014). Global logistics: new directions in supply chain management. 6th ed. Kogan Page Publishers, 515 р.
Zhai D., Yu J., Gao F., Lei Y., Feng, D. (2014). K-means text clustering algorithm based on centers selection according to maximum distance. Appl. Res. Comput, 31, 713–719.
Amit Saxena, Mukesh Prasad, Akshansh Gupta, Neha Bharill, Om Prakash Patel, Aruna Tiwari, Meng Joo E., Weiping Ding, Chin-Teng Lin (2017). A Review of Clustering Techniques and Developments. Neurocomputing, vol. 267, issue 6. doi: 10.1016/j.neucom.2017.06.053
Rokach L., Maimon O. (2005). Clustering Methods. Data Mining and Knowledge Discovery Handbook. (pp. 331–352). Springer.
Jain A. K. (2010). Data Clustering: 50 years beyond k-means. Pattern Recognition Letters, vol. 31, issue 8, pp. 651–666.
Fraley C., Raftery A. E. (1998). How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. Technical Report, 329. Retrieved from https://www.stat.washington.edu/raftery/Research/PDF/fraley1998.pdf
Han J., Kamber M., Pei J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 740 р. Retrieved from http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf
Castro V. E., Yang J. A Fast and robust general purpose clustering algorithm. Retrieved from https://www.researchgate.net/publication/226564781_Fast_and_Robust_General_Purpose_Clustering_Algorithms
Urbankova E., Krizek D. (2020). Homogeneity of Determinants in the Financial Sector and Investment in EU Countries. Economies, 8(1), 1–17.
Sarstedt M., Mooi E. A concise guide to market research: The process, data, and methods using IBM SPSS Statistics. New York: Springer. Retrieved from https://www.researchgate.net/publication/260192478_A_concise_guide_to_market_research_The_process_data_and_methods_using_IBM_SPSS_Statistics_New_York_Springer
Khalafyan А. А. (2010). STATISTICA 6. Statisticheskiy analiz dannykh [Statistical analysis of data]. Moscow: Binom-press [in Russian].
Meloun M., Militký J., Hill M. (2012) Statistická analýza vicerozměrných dat v příkladech. Vyd. 1. Praha: Academia, 760 s.
Wang Q., Wang C., Feng Z., Ye J. (2102). Review of K-means clustering algorithm. Electron. Des. Eng, 20, 21–24.
Abstract views: 203 PDF Downloads: 114
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.