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OECD Ülkelerinin Sürdürülebilir Kalkınma Değişkenlerine Göre Kendi Kendine Öğrenen Haritalar Yaklaşımı ile Kümelenmesi

Yıl 2023, Cilt: 12 Sayı: 5, 2850 - 2869, 31.12.2023
https://doi.org/10.15869/itobiad.1370419

Öz

Sürdürülebilir kalkınma kavramı gelecek nesiller için daha iyi bir yaşam sunmayı amaçlamaktadır. Ancak, COVID-19 pandemisi insanların yaşamında pek çok alanda muazzam etkilere neden olmuş, ülkelerin SK değişlenlerinin incelenmesi, ülkelerin politikalarını belirlemek için önemli hale gelmiştir. Bu nedenle, bu çalışmanın amacı, OECD ülkelerinde COVID-19 pandemisinin bazı SK değişkenleri üzerindeki etkisini Kendi Kendine Düzenleyen Haritalar kullanarak araştırmaktır. Yapay sinir ağlarının bir türü olan kendi kendine düzenleyen haritalar, değişkenler arasındaki doğrusal olmayan ilişkileri bulabilen etkili bir kümeleme analizidir. Veri 2019-2021 yıllarında 38 OECD ülkesine ait 11 sürdürülebilir kalkınma değişkenini içermektedir. Her bir sürdürülebilir kalkınma değişkeninin öncelikle ortalama, minimum, maksimum değerleri ve değişkenler arasındaki korelasyonu bulmak için parametrik olmayan Spearman sıra korelasyonu hesaplanarak yorumlanmıştır. Yıllar içerisinde ülkelerin birbirine göre gösterdiği farklılık, yakınsama katsayısı olarak kullanılan değişim katsayısı kullanılarak hesaplanmıştır. Sonrasında, iki aşamalı kümeleme analizi, kendi kendine düzenleyen haritalar ve hiyerarşik kümeleme analizleri kullanılarak uygulanmıştır. İdeal küme sayısı Silhouette indeksi ve Davies–Bouldin Indeksi kullanılarak üç elde edilmiştir. Gayri Safi Milli Hasıla yakınsama katsayısı yıllar içinde kademeli olarak artması, 2019’da %40,33, 2020’de %42.01 ve 2021’de %43.69, OECD ülkeleri arasındaki bağıl değişkenliğin arttığını göstermektedir. İncelenen çalışma yıllarında, ortalama yaşam süresi azalırken, kişi başına düşen sağlık harcamaları, sağlık harcamalarının payı, devlet sağlık harcamaları, ceptan yapılan sağlık harcamaları ortalaması artmıştır. Kümeleme analizine göre ise, Kolombiya hariç tüm ülkeler incelenen üç yıl için benzer özelliklere sahip olduğu bulunmuştur. Ayrıca ABD, OECD ülkelerinden çok farklı özellikler göstermektedir. Sonuç olarak, incelenen üç yıl içerisinde değişkenlerin ortalamaları pandeminin etkisi ile değişse de neredeyse bütün OECD ülkeleri kendi içerisinde benzer özellikler göstermektedir.

Kaynakça

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  • Arunachalam, D., & Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications, 111, 11–34. https://doi.org/10.1016/j.eswa.2018.03.007
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  • Brida, J. G., Disegna, M., & Osti, L. (2012). Segmenting visitors of cultural events by motivation: A sequential non-linear clustering analysis of Italian Christmas market visitors. Expert Systems with Applications, 39(13), 11349–11356. https://doi.org/10.1016/j.eswa.2012.03.041
  • Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). ClValid: An R package for cluster validation. Journal of Statistical Software, 25(4), 1–22. https://doi.org/10.18637/jss.v025.i04
  • Brodowicz, D. P., & Stankowska, A. (2021). European Union’s Goals Towards Electromobility: An Assessment of Plans’ Implementation in Polish Cities. European Research Studies Journal, XXIV(Issue 4), 645–665. https://doi.org/10.35808/ersj/2613
  • Bruwer, J., Prayag, G., & Disegna, M. (2018). Why wine tourists visit cellar doors: Segmenting motivation and destination image. International Journal of Tourism Research, 20(3), 355–366. https://doi.org/10.1002/jtr.2187
  • Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2022). Package ‘ NbClust ’. Içinde https://cran.r-project.org/web/packages/NbClust/NbClust.pdf (C. 3, ss. 1–9). https://sites.google.com/site/malikacharrad/research/nbclust-package
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  • Coscieme, L., Mortensen, L. F., Anderson, S., Ward, J., Donohue, I., & Sutton, P. C. (2020). Going beyond Gross Domestic Product as an indicator to bring coherence to the Sustainable Development Goals. Journal of Cleaner Production, 248, 119232. https://doi.org/10.1016/j.jclepro.2019.119232
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  • Diffenbaugh, N. S., Field, C. B., Appel, E. A., Azevedo, I. L., Baldocchi, D. D., Burke, M., Burney, J. A., Ciais, P., Davis, S. J., Fiore, A. M., Fletcher, S. M., Hertel, T. W., Horton, D. E., Hsiang, S. M., Jackson, R. B., Jin, X., Levi, M., Lobell, D. B., McKinley, G. A., … Wong-Parodi, G. (2020). The COVID-19 lockdowns: a window into the Earth System. Nature Reviews Earth and Environment, 1(9), 470–481. https://doi.org/10.1038/s43017-020-0079-1
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Self-Organizing Maps Approach for Clustering OECD Countries Using Sustainable Development Indicators

Yıl 2023, Cilt: 12 Sayı: 5, 2850 - 2869, 31.12.2023
https://doi.org/10.15869/itobiad.1370419

Öz

Sustainable Development concept (SD) aims to better life for future generations. However, the COVID-19 pandemic has caused tremendous effects on people’s life in several areas. Therefore, the study aimed to investigate the impact of COVID-19 on the selected part of SD indicators in the OECD countries using Self-Organizing Map (SOM). SOM is a kind of artificial neural network (ANN) method, which is an effective clustering method to find hinder non-linear relationships between indicators. The data contained 38 OECD member countries for 11 variables for each country, covering three years (2019-2021). Firstly, descriptive statistics and Spearman rank correlation analysis were used for bivariate analysis. The coefficient of variation was also used to measure the convergence of indicators. Then, it was a two-stage clustering method using SOM and hierarchical clustering methods—the optimal cluster found according to the Silhouette Index and Davies–Bouldin Index, and as three. The convergence of gross domestic product increased gradually to 40.33% in 2019, 42.01% in 2020, and 43.69% in 2021, meaning increasing relative variability of OECD countries. While the mean of the life span was decreased, the share of health expenditure, health expenditure per capita, out-of-pocket health expenditure, and government health expenditure were increased in the study period. According to clustering analysis, the countries had similar characteristics within three years, except Colombia. Also, the USA distinguished very different characteristics from other OECD countries. Although the mean of study indicators varies due to the effect of the pandemic, the change within each OECD country showed mostly similar characteristics within three years.

Kaynakça

  • Aburto, J. M., Schöley, J., Kashnitsky, I., Zhang, L., Rahal, C., Missov, T. I., Mills, M. C., Dowd, J. B., & Kashyap, R. (2022). Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: A population-level study of 29 countries. International Journal of Epidemiology, 51(1), 63–74. https://doi.org/10.1093/ije/dyab207
  • Adrangi, B., & Kerr, L. (2022). Sustainable Development Indicators and Their Relationship to GDP: Evidence from Emerging Economies. Sustainability (Switzerland), 14(2). https://doi.org/10.3390/su14020658
  • Akal, M., & Bayram, E. (2022). Koronavirüs H astalığının Türkiye ’ de Temel Makroekonomik ve Sektörel Etkileri. Journal of Business and Trade (JOINBAT), 3(2), 169–194.
  • Arunachalam, D., & Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications, 111, 11–34. https://doi.org/10.1016/j.eswa.2018.03.007
  • Bloom, J. Z. (2004). Tourist market segmentation with linear and non-linear techniques. Tourism Management, 25(6), 723–733. https://doi.org/10.1016/j.tourman.2003.07.004
  • Bollyky, T. J., Castro, E., Aravkin, A. Y., Bhangdia, K., Dalos, J., Hulland, E. N., Kiernan, S., Lastuka, A., McHugh, T. A., Ostroff, S. M., Zheng, P., Chaudhry, H. T., Ruggiero, E., Turilli, I., Adolph, C., Amlag, J. O., Bang-Jensen, B., Barber, R. M., Carter, A., … Dieleman, J. L. (2023). Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis. The Lancet, 401(10385), 1341–1360. https://doi.org/10.1016/S0140-6736(23)00461-0
  • Brida, J. G., Disegna, M., & Osti, L. (2012). Segmenting visitors of cultural events by motivation: A sequential non-linear clustering analysis of Italian Christmas market visitors. Expert Systems with Applications, 39(13), 11349–11356. https://doi.org/10.1016/j.eswa.2012.03.041
  • Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). ClValid: An R package for cluster validation. Journal of Statistical Software, 25(4), 1–22. https://doi.org/10.18637/jss.v025.i04
  • Brodowicz, D. P., & Stankowska, A. (2021). European Union’s Goals Towards Electromobility: An Assessment of Plans’ Implementation in Polish Cities. European Research Studies Journal, XXIV(Issue 4), 645–665. https://doi.org/10.35808/ersj/2613
  • Bruwer, J., Prayag, G., & Disegna, M. (2018). Why wine tourists visit cellar doors: Segmenting motivation and destination image. International Journal of Tourism Research, 20(3), 355–366. https://doi.org/10.1002/jtr.2187
  • Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2022). Package ‘ NbClust ’. Içinde https://cran.r-project.org/web/packages/NbClust/NbClust.pdf (C. 3, ss. 1–9). https://sites.google.com/site/malikacharrad/research/nbclust-package
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  • Coscieme, L., Mortensen, L. F., Anderson, S., Ward, J., Donohue, I., & Sutton, P. C. (2020). Going beyond Gross Domestic Product as an indicator to bring coherence to the Sustainable Development Goals. Journal of Cleaner Production, 248, 119232. https://doi.org/10.1016/j.jclepro.2019.119232
  • Das, R. C., Das, A., & Martin, F. (2016). Convergence analysis of households’ consumption expenditure: A cross country study. Handbook of Research on Global Indicators of Economic and Political Convergence, 1–28. https://doi.org/10.4018/978-1-5225-0215-9.ch001
  • Diffenbaugh, N. S., Field, C. B., Appel, E. A., Azevedo, I. L., Baldocchi, D. D., Burke, M., Burney, J. A., Ciais, P., Davis, S. J., Fiore, A. M., Fletcher, S. M., Hertel, T. W., Horton, D. E., Hsiang, S. M., Jackson, R. B., Jin, X., Levi, M., Lobell, D. B., McKinley, G. A., … Wong-Parodi, G. (2020). The COVID-19 lockdowns: a window into the Earth System. Nature Reviews Earth and Environment, 1(9), 470–481. https://doi.org/10.1038/s43017-020-0079-1
  • Elsamadony, M., Fujii, M., Ryo, M., Nerini, F. F., Kakinuma, K., & Kanae, S. (2022a). Preliminary quantitative assessment of the multidimensional impact of the COVID-19 pandemic on Sustainable Development Goals. Journal of Cleaner Production, 372(March), 133812. https://doi.org/10.1016/j.jclepro.2022.133812
  • Elsamadony, M., Fujii, M., Ryo, M., Nerini, F. F., Kakinuma, K., & Kanae, S. (2022b). Preliminary quantitative assessment of the multidimensional impact of the COVID-19 pandemic on Sustainable Development Goals. Journal of Cleaner Production, 372(March), 133812–133825. https://doi.org/10.1016/j.jclepro.2022.133812
  • Freeman, T., Gesesew, H. A., Bambra, C., Giugliani, E. R. J., Popay, J., Sanders, D., Macinko, J., Musolino, C., & Baum, F. (2020). Why do some countries do better or worse in life expectancy relative to income? An analysis of Brazil, Ethiopia, and the United States of America. International Journal for Equity in Health, 19(1), 1–19. https://doi.org/10.1186/s12939-020-01315-z
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  • Gue, I. H. V., Ubando, A. T., Tseng, M. L., & Tan, R. R. (2020). Artificial neural networks for sustainable development: a critical review. Clean Technologies and Environmental Policy, 22(7), 1449–1465. https://doi.org/10.1007/s10098-020-01883-2
  • Halkos, G., & Gkampoura, E. C. (2021). Where do we stand on the 17 Sustainable Development Goals? An overview on progress. Economic Analysis and Policy, 70, 94–122. https://doi.org/10.1016/j.eap.2021.02.001
  • Hansmann, R., Mieg, H. A., & Frischknecht, P. (2012). Principal sustainability components: Empirical analysis of synergies between the three pillars of sustainability. International Journal of Sustainable Development and World Ecology, 19(5), 451–459. https://doi.org/10.1080/13504509.2012.696220
  • Haykin, S. (2008). Neural Networks and Learning Machines. Içinde Pearson Prentice Hall New Jersey USA 936 pLinks (C. 3). https://doi.org/978-0131471399
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  • Kassambara, A., Mundt, F., & Kassambara, A.; Mundt, F. (2017). Factoextra: extract and visualize the results of multivariate data analyses. URL http://www.sthda.com/english/rpkgs/factoextra BugReports, 1–76. https://rdrr.io/github/kassambara/factoextra/%0Ahttps://github.com/kassambara/factoextra/issues%0Ahttp://www.sthda.com/english/rpkgs/factoextra%0ABugReports
  • Khan, J. R., Awan, N., Islam, M. M., & Muurlink, O. (2020). Healthcare Capacity, Health Expenditure, and Civil Society as Predictors of COVID-19 Case Fatalities: A Global Analysis. Frontiers in Public Health, 8(July), 1–10. https://doi.org/10.3389/fpubh.2020.00347
  • Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69. https://doi.org/10.1007/BF00337288
  • Kohonen, T. (2013). Essentials of the self-organizing map. Neural Networks, 37, 52–65. https://doi.org/10.1016/j.neunet.2012.09.018
  • Marmot, M. (2005). Social determinants of health inequalities. Lancet-Public Health, 365, 1099–1104. https://doi.org/10.1249/00005768-199411000-00015
  • Marois, G., Muttarak, R., & Scherbov, S. (2020). Assessing the potential impact of COVID-19 on life expectancy. PLoS ONE, 15(9 September), 1–12. https://doi.org/10.1371/journal.pone.0238678
  • Mebratu, D. (1998). Sustainability and sustainable development: Historical and conceptual review. Environmental Impact Assessment Review, 18(6), 493–520. https://doi.org/10.1016/S0195-9255(98)00019-5
  • Megyesiova, S., & Lieskovska, V. (2018). Analysis of the sustainable development indicators in the OECD countries. Sustainability (Switzerland), 10(12), 1-22 (s.11-12). https://doi.org/10.3390/su10124554
  • Moraci, F., Errigo, M. F., Fazia, C., Campisi, T., & Castelli, F. (2020). Cities under pressure: Strategies and tools to face climate change and pandemic. Sustainability (Switzerland), 12(18), 1–31. https://doi.org/10.3390/su12187743
  • Nerini, F. F., Henrysson, M., Swain, A., & Swain, R. B. (2020). Sustainable Development in the Wake of Covid-19. Research Square, 17. https://www.researchsquare.com/article/rs-63414/latest.pdf OECD. (2023). OECD Statistics. https://stats.oecd.org/
  • Peng, S., Yang, X., Lu, H., & Guo, K. (2022). COVID-19 Impact on Global Electricity Generation Structure-Based on Sustainable Development Perspective. Procedia Computer Science, 214(C), 1206–1213. https://doi.org/10.1016/j.procs.2022.11.297
  • Preston, S. H. (1975). The Changing Relation between Mortality and level of Economic Development. Population Studies, 29(2), 231–248. https://doi.org/10.1080/00324728.1975.10410201
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  • Ranjbari, M., Shams Esfandabadi, Z., Zanetti, M. C., Scagnelli, S. D., Siebers, P. O., Aghbashlo, M., Peng, W., Quatraro, F., & Tabatabaei, M. (2021). Three pillars of sustainability in the wake of COVID-19: A systematic review and future research agenda for sustainable development. Journal of Cleaner Production, 297, 126660. https://doi.org/10.1016/j.jclepro.2021.126660
  • Sachs, J., Schmidt-Traub, G., & Lafortune, G. (2020). Speaking truth to power about the SDGs. Nature, 584(7821), 344. https://doi.org/10.1038/d41586-020-02373-7
  • Schöley, J., Aburto, J. M., Kashnitsky, I., Kniffka, M. S., Zhang, L., Jaadla, H., Dowd, J. B., & Kashyap, R. (2022). Life expectancy changes since COVID-19. Nature Human Behaviour, 6(12), 1649–1659. https://doi.org/10.1038/s41562-022-01450-3
  • T.C Sağlık Bakanlığı. (2023). T.C. SAĞLIK BAKANLIĞI SAĞLIK İSTATİSTİKLERİ YILLIĞI 2021. Içinde T.C. Sağlık Bakanlığı.
  • The World Bank. (2023). From Double Shock to Double Recovery: Health Financing in a Time of Global Shocks. https://www.worldbank.org/en/topic/health/publication/from-double-shock-to-double-recovery-health-financing-in-the-time-of-covid-19
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  • United Nations. (2015b). Transforming our world: the 2030 Agenda for Sustainable Development. Içinde United Nations General Assembly Resolution adopted by General Assembly on 25 September 2015 (Sayı October). https://doi.org/10.4324/9781843146575-59
  • United Nations. (2020). World Economic Situation And Prospects: July 2020 Briefing, No. 139 | Department of Economic and Social Affairs. https://www.un.org/development/desa/dpad/publication/world-economic-situation-and-prospects-july-2020-briefing-no-139/
  • United Nations. (2023a). Goal 8 | Department of Economic and Social Affairs. https://sdgs.un.org/goals/goal8
  • United Nations. (2023b). The Sustainable Development Agenda -United Nations Sustainable Development. https://www.un.org/sustainabledevelopment/development-agenda/
  • Vesanto, J., & Alhoniemi, E. (2000). Clustering of self-organizing map. IEEE TRANSACTIONS ON NEURAL NETWORKS, 11(3), 586–600.
  • WCED, U. (1987). Our Common Future. https://doi.org/10.1080/07488008808408783
  • Wehrens, R., & Kruisselbrink, J. (2018). Flexible self-organizing maps in kohonen 3.0. Journal of Statistical Software, 87(7). https://doi.org/10.18637/jss.v087.i07
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  • Wright, K., YiLan, L., & RuTong, Z. (2023). Package ‘ clustertend ’ (1.7; ss. 1–3). CRAN. https://doi.org/10.1093/oxfordjournals.aob.a083391>.License
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonometrik ve İstatistiksel Yöntemler
Bölüm Makaleler
Yazarlar

Pakize Yıgıt 0000-0002-5919-1986

Erken Görünüm Tarihi 24 Aralık 2023
Yayımlanma Tarihi 31 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 5

Kaynak Göster

APA Yıgıt, P. (2023). Self-Organizing Maps Approach for Clustering OECD Countries Using Sustainable Development Indicators. İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, 12(5), 2850-2869. https://doi.org/10.15869/itobiad.1370419
İnsan ve Toplum Bilimleri Araştırmaları Dergisi  Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.