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Year 2022, Volume: 6 Issue: 2, 162 - 168, 30.12.2022

Abstract

References

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  • [2] Zimmermann, K. F. (2014). Circular migration. IZA World of Labor.
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  • [7] McAuliffe, M., Blower, J., & Beduschi, A. (2021). Digitalization and Artificial Intelligence in Migration and Mobility: Transnational Implications of the COVID-19 Pandemic. Societies, 11(4), 135.
  • [8] Azizi, S., & Yektansani, K. (2020). Artificial intelligence and predicting illegal immigration to the USA. International Migration, 58(5), 183-193.
  • [9] Beduschi, A. (2021). International migration management in the age of artificial intelligence. Migration Studies, 9(3), 576-596.
  • [10] Lindström, N., Koutsikouri, D., Stier, J., & Arvidsson, M. (2020, June). Migrant Employment Integration and Artificial Intelligence (AI). The 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS) will be held as an online conference in June (pp. 16-17).
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  • [12] Tarasyev, A. A., Agarkov, G. A., & Hosseini, S. I. (2018, July). Machine learning in labor migration prediction. In AIP Conference Proceedings (Vol. 1978, No. 1, p. 440004). AIP Publishing LLC.
  • [13] Aoga, J., Bae, J., Veljanoska, S., Nijssen, S., & Schaus, P. (2020). Impact of weather factors on migration intention using machine learning algorithms. arXiv preprint arXiv:2012.02794.
  • [14] Harrison, E. (2020). Modeling Movement: A machine-learning approach to track migration routes after displacement.
  • [15] Günay, E., Atılgan, D., & Serin, E. (2017). Migration management in the world and Turkey. Kahramanmaraş Sütçü İmam University Journal of the Faculty of Economics and Administrative Sciences, 7(2), 37-60.
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  • [29] Ravenstein, E. G. (1889). The laws of migration. Journal of the royal statistical society, 52(2), 241-305.
  • [30] Sirkeci, İ., Deniz, U. T. K. U., & YÜCEŞAHİN, M. M. (2019). An evaluation of the migration conflict model through participation, development and mass gaps. Journal of Economy Culture and Society, (59), 157-184.
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  • [37] Le, C. T. (1984). Logistic models for cross-over designs. Biometrika, 71(1), 216-217.
  • [38] Bonney, G. E. (1987). Logistic regression for dependent binary observations. Biometrics, 951-973.
  • [39] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
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Predicting the Income Groups and Number of Immigrants by Using Machine Learning (ML)

Year 2022, Volume: 6 Issue: 2, 162 - 168, 30.12.2022

Abstract

Migration is one of the biggest problems in the history of mankind. It is important to predict human migration as accurately as possible in terms of many aspects such as urban planning, trade, pandemics, the spread of diseases, and public policy development. With the help of Artificial Intelligence (AI), which is now used in almost all areas of life, it is possible to make predictions about migration. The purpose of this study is to predict the income groups and the number of immigrants by using ML algorithms. Two different applications were carried out in the study. The first one was about predicting the income groups of immigrants and the second one was about predicting the number of immigrants. Data used in the study was obtained from the World Bank. In the first application of the study, Support Vector Machines (SVM), Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbors (KNN) were used. In the second application of the study, Random Forest (RF), and Xgboost algorithms were used. As a result of the experiments conducted in the study, 98.37% success rates were obtained with Xgboost, 96.42% with RF, 86.04% with LR, 83.72% with SVM, 83.72% with KNN, and 69.76% with NB. The results of the study reveal that the highest success in the applications was achieved with the LR and Xgboost algorithms. In general, the predictive machine learning models of human migration used in this study will provide a flexible base with which to model human migration under different what-if conditions.

References

  • [1] Richard, P., & Jillyanne, R. C. (2011). Glossary on migration. Book Glossary on Migration, 2.
  • [2] Zimmermann, K. F. (2014). Circular migration. IZA World of Labor.
  • [3] Kelleher, J. D., Mac Namee, B., & D'arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.
  • [4] Micevska, M. (2021). Revisiting forced migration: A machine learning perspective. European Journal of Political Economy, 70, 102044.
  • [5] Iman, H. S., & Tarasyev, A. (2018). Machine learning methods in individual migration behavior. In Russian Regions in the Focus of Changes: Conference proceedings. Ekaterinburg, 2018 (pp. 72-81). LLC Publishing office EMC UPI.
  • [6] Hussain, N. H. M. (2021). Machine Learning of the Reverse Migration Models for Population Prediction: A Review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(5), 1830-1838.
  • [7] McAuliffe, M., Blower, J., & Beduschi, A. (2021). Digitalization and Artificial Intelligence in Migration and Mobility: Transnational Implications of the COVID-19 Pandemic. Societies, 11(4), 135.
  • [8] Azizi, S., & Yektansani, K. (2020). Artificial intelligence and predicting illegal immigration to the USA. International Migration, 58(5), 183-193.
  • [9] Beduschi, A. (2021). International migration management in the age of artificial intelligence. Migration Studies, 9(3), 576-596.
  • [10] Lindström, N., Koutsikouri, D., Stier, J., & Arvidsson, M. (2020, June). Migrant Employment Integration and Artificial Intelligence (AI). The 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS) will be held as an online conference in June (pp. 16-17).
  • [11] Robinson, C., & Dilkina, B. (2018, June). A machine learning approach to modeling human migration. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (pp. 1-8).
  • [12] Tarasyev, A. A., Agarkov, G. A., & Hosseini, S. I. (2018, July). Machine learning in labor migration prediction. In AIP Conference Proceedings (Vol. 1978, No. 1, p. 440004). AIP Publishing LLC.
  • [13] Aoga, J., Bae, J., Veljanoska, S., Nijssen, S., & Schaus, P. (2020). Impact of weather factors on migration intention using machine learning algorithms. arXiv preprint arXiv:2012.02794.
  • [14] Harrison, E. (2020). Modeling Movement: A machine-learning approach to track migration routes after displacement.
  • [15] Günay, E., Atılgan, D., & Serin, E. (2017). Migration management in the world and Turkey. Kahramanmaraş Sütçü İmam University Journal of the Faculty of Economics and Administrative Sciences, 7(2), 37-60.
  • [16] Hayakawa, T. (2020). Skill levels and inequality in migration: A case study of Filipino migrants in the UK. Asian and Pacific Migration Journal, 29(3), 333-357.
  • [17] Errichiello, G., & Nyhagen, L. (2021). “Dubai is a transit lounge”: Migration, temporariness and belonging among Pakistani middle-class migrants. Asian and Pacific Migration Journal, 30(2), 119-142.
  • [18] Sinha, V. N. P., Ataullah, M. D., & Ataullah, M. (1987). Migration: an interdisciplinary approach. Seema Publications.
  • [19] Rubin, R. L., & Melnick, J. (2007). Immigration and American popular culture: An introduction (Vol. 4). NYU Press.
  • [20] Castles, S., & Miller, M. J. (1998). The Age of Migration (Mcmillan, London).
  • [21] Massey, D. S. (1989). Economic development and international migration in comparative perspective (No. 1). Commission for the Study of International Migration and Cooperative Economic Development.
  • [22] Taylor, J. E., & Fletcher, P. L. (2001). Remittances and development in Mexico: the new labour economics of migration: a critical review. Rural Mexico Research Project, 2.
  • [23] Memisoglu, F., & Yiğit, C. International Migration and Development: Theory and Current Issues. Yildiz Social Science Review, 5(1), 39-62.
  • [24] Yusuf, G. E. N. Ç., Gündüz, D. U., & Çöpoğlu, M. The Relationship of Migration and Development. Avrasya Uluslararası Araştırmalar Dergisi, 7(18), 479-498.
  • [25] Özdemir, D. (2018). Determinants of interregional internal migration movements in Turkey. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 22(3), 1337-1349.
  • [26] Zlotnik, H. (1995). Migration and the family: The female perspective. Asian and Pacific Migration Journal, 4(2-3), 253-271.
  • [27] Çatalbaş, G. K., & Yarar, Ö. (2015). Determining the factors affecting interregional internal migration in Turkey with panel data analysis. Alphanumeric Journal, 3(1), 99-117.
  • [28] Schutte, S., Vestby, J., Carling, J., & Buhaug, H. (2021). Climatic conditions are weak predictors of asylum migration. Nature communications, 12(1), 1-10.
  • [29] Ravenstein, E. G. (1889). The laws of migration. Journal of the royal statistical society, 52(2), 241-305.
  • [30] Sirkeci, İ., Deniz, U. T. K. U., & YÜCEŞAHİN, M. M. (2019). An evaluation of the migration conflict model through participation, development and mass gaps. Journal of Economy Culture and Society, (59), 157-184.
  • [31] Nalbant, T. E. (2020). International Migration and Security. Avrasya İncelemeleri Dergisi , 9 (2) , 309-313 . DOI: 10.26650/jes.2020.020
  • [32] Betts, A. (2009). Forced migration and global politics. John Wiley & Sons.
  • [33] United Nations Department for Economic and Social Affairs. (2020). World economic situation and prospects 2020. UN. Available at: https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2020_FullReport.pdf
  • [34] Albu, D. (2019). UNHCR Global Trends Report: Forced displacement in 2018. Drepturile Omului, 114.
  • [35] Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.
  • [36] Cornfield, J. (1962, July). Joint dependence of risk of coronary heart disease on serum cholesterol and systolic blood pressure: a discriminant function analysis. In Fed Proc (Vol. 21, No. 4, pp. 58-61).
  • [37] Le, C. T. (1984). Logistic models for cross-over designs. Biometrika, 71(1), 216-217.
  • [38] Bonney, G. E. (1987). Logistic regression for dependent binary observations. Biometrics, 951-973.
  • [39] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
  • [40] World Development Indicators, 2018, Available at: https://www.kaggle.com/theworldbank/world-development-indicators. Date of access: 26.11.2021
There are 40 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Belgin Aydemir 0000-0002-0083-0050

Hakan Aydın 0000-0002-0122-8512

Ali Çetinkaya 0000-0003-4535-3953

Doğan Şafak Polat 0000-0003-0786-1789

Publication Date December 30, 2022
Submission Date June 15, 2022
Published in Issue Year 2022 Volume: 6 Issue: 2

Cite

IEEE B. Aydemir, H. Aydın, A. Çetinkaya, and D. Ş. Polat, “Predicting the Income Groups and Number of Immigrants by Using Machine Learning (ML)”, IJMSIT, vol. 6, no. 2, pp. 162–168, 2022.