Data science methods for comprehensive assessment of regional economic development
-
Author(s)Liubov ChagovetsLink to ORCID Index: https://orcid.org/0000-0003-4064-9712
,
Svitlana ProkopovychLink to ORCID Index: https://orcid.org/0000-0002-6333-2139
,
Viktor Kholod
-
DOIhttp://dx.doi.org/10.21511/dm.18(2).2020.05
-
Article InfoVolume 18 2020, Issue #2, pp. 43-56
- Cited by
- 1968 Views
-
159 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The paper deals with the assessment of the socio-economic development of Ukrainian regions using Data Science methods and multidimensional analysis, including taxonomy, n-dimensional classification, and ensemble decision trees methods. The methodological bases of economic regions devel¬opment by the economic and mathematical modeling methods were investigated. The necessity of improving and further developing estimation models of the regional economic development using business analytics tools and multidimensional scaling methods was investigated.
The ensemble decision trees methods was applied for the classification model of economic development of the Ukrainian regions according to the conceptual base of the research on regional econom¬ic development. It will increase the quality level of administrative decisions making on regional de¬velopment asymmetry equalization. It is determined that in Ukraine, there is a significant imbalance of regions clusters with high and low economic level. Here was investigated the relationship between the two groups of economic development indicators – the indicators of the economic development regional performance and the group of economic potential. The results of the classification model allow identifying the set of indicators that have significant impact on the overall economic development. The developed ensemble model allows carrying out qualitative recognition and prediction of the state probability of economic development. It will improve the quality of decision making pro¬cesses on equalization of regional development asymmetry.
The further research gives the possibility to develop the system of levers directions of regional development imbalance equalization, to determine priority vectors of sustainable development of both the regions and the country.
- Keywords
-
JEL Classification (Paper profile tab)C38, O18, R11
-
References34
-
Tables3
-
Figures7
-
- Figure 1. Концептуальна схема моделювання рівня ЕР регіонів
- Figure 2. Схема роботи алгоритмів бустингу та беггінгу
- Figure 3. Результати розрахунку рівня ЕР за регіонами
- Figure 4. Кореляційна матриця індикаторів
- Figure 5. Схема крос-валідації
- Figure 6. Графік впливу факторів
- Figure 7. Графік впливу факторів
-
- Table 1. Результати крос-валідації класифікатора на навчальній виборці
- Table 2. Результати валідації моделі на тестовій вибірці
- Table 3. Результати основних метрик валідації моделі
-
- Agishev, R., & Kapaeva, L. (2009). Problemy formirovaniya sistemy kompleksnykh pokazateley sotsialno-ekonomicheskogo razvitiya munitsipalnykh obrazovaniy [Problems of forming a system of complex indicators of socio-economic development of municipalities]. Regionology, 2. (In Russian).
- Arkhypenko, I. (2019). Foreign experience of formation and implementation of regional economic policy. Collection of scientific works «Public Administration and Local Government», 2(41), 32-42. (In Ukrainian).
- Boyko, A. (2014). Convergence and unevenness in regional development of Ukraine: risks, trends and prospects. Economics and region, 1, 72-78. (In Ukrainian).
- Communications Development Incorporated (2015). Human Development Report 2015. Work for human development (288 p.).
- Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (Eds.) (2016). The Global Innovation Index 2016: Winning with Global Innovation.
- Glushhenko, K. (2010). Metody analiza mezhregionalnogo neravenstva po dokhodam [Methods for analyzing interregional income inequality]. Region: Economics and Sociology, 1, 54-87. (In Russian).
- Grebenkin, A. (2005). Asimmetriya razvitiya regionov: faktory vozniknoveniya i regulirovaniye [Asimmetrija razvitija regionov: faktory vozniknovenija i regulirovanie]. Nalogi, Investitsii, Kapital - Taxes, Investments, Capital, 1-3, 182-189. (In Russian).
- Havkalova, N. (Ed.) (2015). Zabezpechennia rehionalnoho rozvytku ekolohichnoho suspilstva zasobom stvorennia systemnoho bazysu syntezovanoho kapitalu [Ensuring the regional development of ecological society by means of creating a systemic basis of synthesized capital] (304 p.). Kharkiv: HNEU. (In Ukrainian).
- Helliwell, J., Layard, R., Sachs, J., & De Neve, J. (Eds.) (2020). World Happiness Report 2020.
- Hnativ, B., Tokarchuk, R., Kostrobij, P., & Tokarchuk, M. (2011). Mathematical modeling of economic processes by methods of nonequilibrium statistical mechanics. Journal of Lviv Polytechnic National University. Series of Physical and mathematical sciences, 696, 93- 100. (In Ukrainian).
- Hrechana, S., Kharytonova, O., & Klius, I. (2012). Research disproportions in development economy region. Efektyvna ekonomika - Efficient economy, 11. (In Ukrainian).
- ILO (2013). Resolution concerning statistics of work, employment and labour underutilization.
- Klebanova, T., & Kizima, N. (Eds.) (2011). Modeli otsenki neravnomernosti i tsiklicheskoy dinamiki razvitiya territoriy [Models for assessing the unevenness and cyclical dynamics of the development of territories] (352 p.). Kharkov: INZHEK. (In Russian).
- Kozyrieva, O., & Heiman, O. (2015). An Analysis of Inequality in the Socio-Economic Development of the Regions of Ukraine. Business Inform, 12, 93-104. (In Ukrainian).
- Makarova, O. (2015). Sotsialna polityka v Ukraini [Social policy in Ukraine] (244 p.). Kyiv: Ptoukha Institute for Demography and Social Studies of the National Academy of Sciences of Ukraine. (In Ukrainian).
- Mokij, A., & Daczko O. (2018). Implementatsiia instytutsionalnoho bazysu rehionalnoi polityky YeS v Ukraini [Implementation of the institutional basis of EU regional policy in Ukraine] (22 p.).
- Muzychuk, V. (2011). Indeks rozvytku liudskoho potentsialu yak indykator efektyvnosti sotsialnoi oriientatsii ekonomiky [Human resources development index as indicator of the economies social orientation efficiency].
- NISS (2009). Instytutsiini zasady derzhavnoho rehuliuvannia rozvytku depresyvnykh terytorii [Institutional framework of state regulation of depressed areas].
- Pilko, A., & Harda, T. (2018). The models of assessment and analysis of the regional development asymmetry. Economics of Development, 2(86), 24-35. (In Ukrainian).
- Ponomarenko, V., & Malyarets, L. (2009). Multivariate analysis of the socio-economic systems: study guide (384 p.). Kharkiv: KHNEU. (In Ukrainian).
- QuantDare (2016). What is the difference between Bagging and Boosting?
- Romanyuk, S. (2013). Rozvytok rehioniv u vidkrytii ekonomitsi: teoriia, polityka, praktyka [Development of regions in an open economy: theory, policy, practice] (375 p.). Kyiv: NADU. (In Ukrainian).
- Rudenko, L. (2005). Kontseptsiia zbalansovanosti rozvytku i yakist zhyttia naselennia [The concept of balanced development and quality of life]. Ukrainian Geographic Journal, 2, 44-50. (In Ukrainian).
- Samarina, V. (2008). Osobennosti otsenki neravnomernosti sotsial’no-ekonomicheskogo razvitiya regionov [Features of assessing the uneven socio-economic development of regions]. Problems of modern economics, 1(25), 300-303. (In Russian).
- Savchuk, T., & Luzhenetskyi, R. (2011). Vykorystannia klasternoho analizu dlia vyrishennia zadach tsilovoho marketynhu [Using cluster analysis to solve targeted marketing problems]. Measuring and computing devices in technological processes, 2, 144-148. (In Ukrainian).
- Scikit learn (n.d.). A random forest classifier.
- Scikit Learn (n.d.). Density-Based Spatial Clustering of Applications with Noise.
- Shitikov, V., & Mastickij, S. (2017). Klassifikatsiya, regressiya i drugiye algoritmy Data Mining s ispolzovaniyem R [Classification, Regression, and Other Data Mining Algorithms Using R]. (In Russian).
- Social Progress Index (n.d.). Official web-site.
- State Statistics Service of Ukraine (n.d.). Official web-site.
- Tomareva-Patlakhova, V. (2013). Model of regional development in the context of economic reforms. State and regions. Series: Economics and Business, 1(70), 76-80. (In Ukrainian).
- Vershygora, Yu., & Vershygora, V. (2017). Unevenness of development of the regions of Ukraine and ways of its overcoming. International Humanitarian University Herald. Economics and Management, 2(23), 25-28. (In Ukrainian).
- World Intellectual Property Organization (n.d.). Official web-site.
- XGBoost (n.d.). XGBoost Documentation.
-
Optimal control of continuous life insurance model
Viktor Oliynyk ,
Fedir Zhuravka 
,
Tetiana Bolgar ,
Olha Yevtushenko
doi: http://dx.doi.org/10.21511/imfi.14(4).2017.03
Investment Management and Financial Innovations Volume 14, 2017 Issue #4 pp. 21-29 Views: 1959 Downloads: 217 TO CITE АНОТАЦІЯThe problems of mixed life insurance and insurance in the case of death are considered in the article. The actuarial present value of life insurance is found by solving a system of differential equations. The cases of both constant effective interest rates and variables, depending on the time interval, are examined. The authors used the Pontryagin maximum principle method as the most efficient one, in order to solve the problem of optimal control of the mixed life insurance value. The variable effective interest rate is considered as the control parameter. Some numerical results were given.
-
Modern technologies of detection and prevention of corruption in emerging information society
Problems and Perspectives in Management Volume 16, 2018 Issue #1 pp. 58-67 Views: 1931 Downloads: 418 TO CITE АНОТАЦІЯWith the development of the information society, there has been rapidly growing number of international research on the role of various (information, innovation, intelligent) technologies as a catalyst to fight corruption. As you know, the problems posed by corruption are economic, social and political consequences. In recent years economists and various researchers have shown increasing interest in studying the phenomenon of corruption. Many researchers in the field of studying corruption phenomenon are of theoretical nature, which studied different behaviors without having developed effective methods and technologies to identify and prevent corruption in the various spheres of government. In this regard, there is a need to study the world experience of application of technologies in the prevention of corruption and anti-corruption platform in Ukraine. This will allow to adapt them to positive experience in the implementation of mechanisms to identify and prevent corruption in Ukraine. The authors believe that to obtain a more accurate picture of the corruption situations that provide characteristics and its quantitative description is possible only by means of modelling the corruption phenomenon. In this regard, the analysis of theoretical models of corruption was made: the modelling made by the mathematical notation of the models and conclusions on the effectiveness of their application in management. Using simulation, the authors came to the conclusion that the detection and prevention of corruption should be carried out with the use of technologies and the system approach: from the study of statistics and use of anti-corruption platforms, public registers and databases to logical and probabilistic (LP) risk models of corruption.
-
Modeling of diversification of foreign economic interactions
Ihor Hroznyi ,
Oleh Kuzmak ,
Olena Kuzmak ,
Olha Rusinova
doi: http://dx.doi.org/10.21511/ppm.16(1).2018.15
Problems and Perspectives in Management Volume 16, 2018 Issue #1 pp. 155-165 Views: 1547 Downloads: 162 TO CITE АНОТАЦІЯThe article developed a basic simulation model of diversification of foreign economic interactions, which makes it possible to assess the mutual influence of key economic indicators and their reaction to changes in the equilibrium in world markets. The peculiarity of the developed model of diversification of foreign economic interactions is its functioning as a complex dynamic system in which many factors are interrelated, and the connections are dynamic. This makes it possible to calculate a simulation model that takes into account hidden and subtle connections. While modeling foreign economic interactions and evaluating the influence of various regulators on them, in addition to the stage of model development itself, mandatory parameterization of this model is proposed, that is, the determination of specific types of dependence between the factors included in the model and the parameters of these dependencies.
The implementation of this model can be carried out in any simulation package.
