Visualisation of the populations of the European Union countries by age groups using correspondence analysis

Öz

Particularly since the early 2000s, the European Union (EU) has undergone significant expansion through the accession of numerous countries, and it presently comprises 27 member states. There exists a multifaceted demographic change occurring within the member states. The primary factors influencing this demographic change include the ageing of the population, intra-member state population mobility, and immigration from countries outside the Union. These components exert varying effects on each member state, and the 27 member states exhibit significantly diverse demographic structures in relation to one another. This study aims to visually illustrate the similarities and differences in the populations of EU countries by age groups. To that end, four age groups (0-14, 15-24, 25-64, and 65+) were determined in the study. The populations of the EU countries by age groups as of January 1, 2023, were sourced from EUROSTAT. The populations were subsequently analysed, followed by the establishment of the appropriate number of dimensions and the visualisation of the populations of the EU member states categorised by age groups using correspondence analysis, a dimensionality reduction technique that allows researchers to visualise the results graphically without losing significant information. The study’s findings indicate that the demographic structures of the EU member states differ from one another, revealing various groupings among them. The study thus assists decision makers in comprehending the demographic composition of member states and developing a strategic roadmap, as well as in taking various measures or steps if required.

Anahtar Kelimeler

• Correspondence Analysis
• European Union
• Population
• Age Group

Kaynakça

1. Bilir, N. (2018). Yaşlılık Tanımı, Yaşlılık Kavramı, Epidemiyolojik Özellikler. Yaşlılık ve Solunum Hastalıkları. Ertürk A, Bahadır A, Koşar FSerisi (ed): TÜSAD Eğitim Kitapları, Istanbul, 13-31. google scholar
2. Chatzipetrou, E., & Moschidis, O. (2016). QualitY Costing: A survey in Greek Supermarkets using Multiple Correspondence Analysis. International Journal of Quality & Reliability Management, 33(5). https://doi.org/10.1108/ijqrm-01-2014-0004 google scholar
3. Cozma, O. M., & Pricop, A. I. (2024). Economic Security and Migration within the European Union. Sivas Interdisipliner Turizm Araştırmaları Dergisi, 7(2), 18-38. google scholar
4. Cox, P. R. (1976). Demography. Cambridge University Press. google scholar
5. Dieguez, F. J., Zau, Y., Viegas, I., Fragoso, S., Turner, P. V., & Da Graça-Pereira, G. (2020). An Evaluation of Portuguese Societal Opinion towards the Practice of Bullfighting. Animals, 10(11), 2065. https://doi.org/10.3390/ani10112065 google scholar
6. EUROSTAT (2024a). Demography of Europe-2024 Edition. Retrieved from https://ec.europa.eu/eurostat/web/interactive-publications/ demography-2024 google scholar
7. EUROSTAT (2024b). EU population diversity by citizenship and country of birth. Retrieved from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=EU_population_diversitY_bY_citizenship_and_countrY_of_birth google scholar
8. EUROSTAT (2025). Population by age group. Retrieved from https://ec.europa.eu/eurostat/databrowser/view/TPS00010/default/table google scholar

9. Greenacre, M. (1994). Correspondence analYsis and its interpretation. In M. Greenacre, & J. Blasius (Eds.), Correspondence Analysis in the Social Sciences (pp. 3-22). San Diego, CA: Academic Press. google scholar
10. Idrissou, Y., Korir, D., Seidou, A. A., Worogo, H. S. S., Baco, M. N., & Traore, I. A. (2023). Adapting cattle farming to climate change in the dry and sub-humid tropical zones in Benin: how adaptation strategies affect productivity. Revue D'elevage Et De Medetine Veterinaire Des Pays Tropicaux, 76, 1-10. https://doi.org/10.19182/remvt.37118 google scholar
11. Kakai, H., Maskarinec, G., ShumaY, D. M., Tatsumura, Y., & Tasaki, K. (2003). Ethnic differences in choices of health information bY cancer patients using complementarY and alternative medicine: an exploratorystudY with correspondence analYsis. Social Science & Medicine, 56(4), 851-862. https://doi.org/10.1016/s0277-9536(02)00086-2 google scholar
12. Kassambara, A. (2017). Practical Guide To Principal Component Methods in R (Multivariate Analysis II). PCA, M(CA), FAMD, MFA, HCPC, factoextra. STHDA. google scholar
13. Lana, R. M., Riback, T. I. S., Lima, T. F. M., Da Silva-Nunes, M., Cruz, O. G., Oliveira, F. G. S., Moresco, G. G., Honorio, N. A., & Codeço, C. T. (2017). Socioeconomic and demographic characterization of an endemic malaria region in Brazil bY multiple correspondence analYsis. Malaria Journal, 16(1). https://doi.org/10.1186/s12936-017-2045-z google scholar
14. Lutz, W., Amran, G., Belanger, A., Conte, A., GaileY, N., Ghio, D., Grapsa, E., Jensen, K., Loichinger, E., Marois, G., Muttarak, R., Potancokovâ M., Sabourin P., & Stonawski, M. (2019). Demographic scenarios for the EU: Migration, population and education. Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/590301 google scholar
15. Özer, A., Totan, T., & Atik, G. (2011). Individual Correlates of Bullying Behaviour in Turkish Middle Schools. Australian Journal of Guidance and Counselllng, 21 (2), 186-202. https://doi.Org/l0.1375/ajgc.21.2.186 google scholar
16. Silva, V. S. D., Souza, I., Silva, D. A. S., Petroski, E. L., & Fonseca, M. D. J. M. D. (2015). Correspondence between overweight and socioeco-nomic and demographic indicators in the adult Brazilian population. Revlsta Brasllelra De Epldemlologla, 18(2), 476-489. https:// doi.org/10.1590/1980-5497201500020015 google scholar
17. Stupariu, M. I. (2023). Demographic Policies in the European Union. Revlsta Românâ De Geografie Polltlcâ, 25(2), 97-122. https://doi.org/10.30892/rrgp.252105-368 google scholar
18. Szûcs, P. (2024). Demographic Shifts and Economic Challenges in the European Union. Euröpaı’ Tükör, 27(i), 87-106. https://doi.org/10.32559/et.2024.1.5 google scholar
19. Yates, S. & Lockley, E. (2018). Social Media and Social Class. American Behavioral Scientist, 62(9), 1291-1316. https://doi.org/10.1177/0002764218773821 google scholar