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2008-2022 Yılları Arasında Van İlindeki Net Göç Eğilimlerini Belirleyen Unsurlar: İstatistiksel Bir Yaklaşım

Yıl 2025, Cilt: 7 Sayı: 1, 1 - 33, 30.06.2025

Öz

Türkiye'nin Doğu Anadolu Bölgesi'nde yer alan Van ili, bölgenin önemli bir göç merkezi olarak kritik bir rol oynamaktadır. Bu çalışma, çeşitli istatistiksel analizler kullanılarak 2008-2022 yılları arasında Van'daki göç modellerini incelemektedir. Çalışma, Van ilinin net göç örüntülerinin temel belirleyicilerini ve net göçü etkileyen faktörleri istatistiksel yöntemlerle belirlemeyi ve ilin gelecekteki göç eğilimlerini tahmin etmeyi amaçlamaktadır.
Veriler, Türkiye İstatistik Kurumu (TÜİK) ve diğer kurumlar tarafından sağlanan yıllık nüfus ve göç istatistikleri ile sosyo-ekonomik, demografik ve çevresel göstergeler kullanılarak elde edilmiştir. Analizlerde tanımlayıcı istatistikler, korelasyon analizi, regresyon analizi, coğrafi bilgi sistemleri (CBS) analizi ve kümeleme analizi yöntemleri kullanılmıştır.
Çoklu korelasyon analizi, Van ilinin net göçünü etkileyen en önemli faktörler olarak nüfus artışı ve dışarıya göçü gösterirken, içeriye göç, ekili tarım alanları ve konaklama tesislerine gelişlerin net göç üzerinde orta düzeyde bir etkiye sahip olduğunu göstermiştir. Çoklu doğrusal regresyon analizi, pozitif net göç artışının nüfus artış hızı, okuryazarlık oranı, sıcaklık, tarım alanları, hava kirliliği (PM10) ve yıllık ziyaretçiler ile ilişkili olduğunu ortaya koymuştur. Bununla birlikte bölgesel istihdam ise net göçü olumsuz etkilemektedir. Hiyerarşik küme analizi sonuçlarına göre 2008 ve 2013 yılları arasında benzer göç eğilimleri görülmektedir. Dışa göç ve turizmin 2014, 2015, 2016 ve 2018 yıllarında arttığı görülmüştür. İklim ve tarım açısından 2017, 2019 ve 2022 yılları benzerlik gösterirken, 2019 yılındaki göç örüntüleri diğer yıllardan farklılaşmıştır. Bu çalışmada, net göçü etkileyen faktörleri belirlemek için Van ilinden alınan iç göç verileri istatistiksel olarak analiz edilmiştir. Bulgular, göç eğilimlerini daha iyi anlamak ve politikalar geliştirmek için önemli bilgiler sağlamaktadır. Model, gelecekteki il göç dinamikleri konusunda bölgesel planlamacılara ve karar alıcılara rehberlik edebilir ve politika kararlarını bilgilendirebilir.

Kaynakça

  • Anarkooli, A. J., Hosseinpour, M. and Kardar, A. (2017), Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model, Accident Analysis and Prevention, 106, 399-410.
  • Antonio, K. and Beirlant, J. (2007), Actuarial statistics with generalized linear mixed models, Insurance: Mathematics and Economics, 40(1), 58-76.
  • Antonio, K. and Valdez, E. A. (2012), Statistical concepts of a priori and a posteriori risk classification in insurance, AStA Advances in Statistical Analysis, 96, 187-224.
  • Bakhshi, A. K. and Ahmed, M. M. (2021), Practical advantage of crossed random intercepts under Bayesian hierarchical modeling to tackle unobserved heterogeneity in clustering critical versus non-critical crashes, Accident Analysis and Prevention, 149, 105855.
  • Balusu, S. K., Pinjari, A. R., Mannering, F. L. and Eluru, N. (2018), Non-decreasing threshold variances in mixed generalized ordered response models: A negative correlations approach to variance reduction, Analytic Methods in Accident Research, 20, 46-67.
  • Barua, S., El-Basyouny, K. and Islam, M. T. (2015), Effects of spatial correlation in random parameters collision count-data models, Analytic Methods in Accident Research, 5, 28-42.
  • Barua, S., El-Basyouny, K. and Islam, M. T. (2016), Multivariate random parameters collision count data models with spatial heterogeneity, Analytic Methods in Accident Research, 9, 1-15.
  • Chen, F., Chen, S. and Ma, X. (2018), Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data, Journal of Safety Research, 65, 153-159.
  • Davis, J. and Goadrich, M. (2006), The relationship between Precision-Recall and ROC curves, In: Proceedings of the 23rd International Conference on Machine Learning – ICML ‘06, 233-240.
  • De Jong, P. and Heller, G. Z. (2008), Generalized Linear Models for Insurance Data, In: International Series on Actuarial Science, Cambridge University Press.
  • Dong, C., Clarke, D. B., Yan, X., Khattak, A. and Huang, B. (2014), Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections, Accident Analysis and Prevention, 70, 320-329.
  • Eluru, N., Bhat, C. R. and Hensher, D. A. (2008), A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes, Accident Analysis and Prevention, 40(3), 1033-1054.
  • Embrechts, P. and Wüthrich, M. V. (2022), Recent challenges in actuarial science, Annual Review of Statistics and Its Application, 9, 119-140.
  • Frees, E. W. (2010), Regression Modeling with Actuarial and Financial Applications, In: International Series on Actuarial Science, Cambridge University Press.
  • Fountas, G. and Anastasopoulos, P. C. (2017), A random thresholds random parameters hierarchical ordered probit analysis of highway accident injury-severities, Analytic Methods in Accident Research, 15, 1-16.
  • Fountas, G., Pantangi, S. S., Hulme, K. F. and Anastasopoulos, P. C. (2019), The effects of driver fatigue, gender, and distracted driving on perceived and observed aggressive driving behavior: A correlated grouped random parameters bivariate probit approach, Analytic Methods in Accident Research, 22, 100091.
  • Garrido, J., Genest, C. and Schulz, J. (2016), Generalized linear models for dependent frequency and severity of insurance claims, Insurance: Mathematics and Economics, 70, 205-215.
  • Gong, H., Fu, T., Sun, Y., Guo, Z., Cong, L., Hu, W. and Ling, Z. (2022), Two-vehicle driver-injury severity: A multivariate random parameters logit approach, Analytic Methods in Accident Research, 33, 100190.
  • Haberman, S. and Renshaw, A. E. (1996), Generalized linear models and actuarial science, Journal of the Royal Statistical Society: Series D (The Statistician), 45(4), 407-436.
  • Hedeker, D. (2005), Generalized linear mixed models, In: B. Everitt, D. Howell (Eds.), Encyclopedia of Statistics in Behavioral Science, John Wiley & Sons, New York, 729-738.
  • Hossin, M. and Sulaiman, M. N. (2015), A review on evaluation metrics for data classification evaluations, International Journal of Data Mining and Knowledge Management Process, 5(2), 1-11.
  • Kaas, R., Goovaerts, M., Dhaene, J. and Denuit, M. (2008), Modern Actuarial Risk Theory: Using R, Second Edition, Springer Berlin, Heidelberg.
  • Khamis, H. (2008), Measures of association: How to choose?, Journal of Diagnostic Medical Sonography, 24(3), 155-162.
  • Kim, M., Kho, S. Y. and Kim, D. K. (2017), Hierarchical ordered model for injury severity of pedestrian crashes in South Korea, Journal of Safety Research, 61, 33-40.
  • Lord, D. and Mannering, F. (2010), The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives, Transportation Research Part A: Policy and Practice, 44(5), 291-305.
  • Mannering, F. L., Shankar, V. and Bhat, C. R. (2016), Unobserved heterogeneity and the statistical analysis of highway accident data, Analytic Methods in Accident Research, 11, 1-16.
  • McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, In: Monographs on Statistics and Applied Probability 37, Second Edition, Chapman and Hall, London, New York.
  • Miao, G. M. (2018), Application of hierarchical model in non-life insurance actuarial science, Modern Economy, 9(3), 393-399.
  • Nelder, J. A. and Wedderburn, R. W. M. (1972), Generalized linear models, Journal of the Royal Statistical Society: Series A (General), 135(3), 370-384.
  • Ohlsson, E. and Johansson, B. (2010), Non-life Insurance Pricing with Generalized Linear Models, In: EAA Series Textbook, Springer Berlin, Heidelberg.
  • Pai, J. S. and Walch, A. H. (2020), ACTEX Study Manual for Exam MAS-II, ACTEX Learning/SRBooks, Inc., Greenland, NH.
  • Pantangi, S. S., Fountas, G., Sarwar, M. T., Anastasopoulos, P. C., Blatt, A., Majka, K., Pierowicz, J. and Mohan, S. B. (2019), A preliminary investigation of the effectiveness of high visibility enforcement programs using naturalistic driving study data: A grouped random parameters approach, Analytic Methods in Accident Research, 21, 1-12.
  • Portet, S. (2020), A primer on model selection using the Akaike Information Criterion, Infectious Disease Modelling, 5, 111-128.
  • Saito, T. and Rehmsmeier, M. (2015), The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PloS One, 10(3), e0118432.
  • Tran, V., Liu, D., Pradhan, A. K., Li, K., Bingham, C. R., Simons-Morton, B. G. and Albert, P. S. (2015), Assessing risk-taking in a driving simulator study: Modeling longitudinal semi-continuous driving data using a two-part regression model with correlated random effects, Analytic Methods in Accident Research, 5, 17-27.
  • Yau, K., Yip, K. and Yuen, H. K. (2003), Modelling repeated insurance claim frequency data using the generalized linear mixed model, Journal of Applied Statistics, 30(8), 857-865.

Determinants of Net Migration Trends In Van Province, Türki̇ye From 2008 To 2022: A Statistical Analysis

Yıl 2025, Cilt: 7 Sayı: 1, 1 - 33, 30.06.2025

Öz

In Türkiye's Eastern Anatolia region, Van province plays a crucial role as an important migration hub in the area. This study examines migration patterns in Van between 2008 and 2022 using a variety of statistical analyses. The study aims to identify key determinants and affecting factors of net migration patterns of Van province with statistical methods and to predict future migration trends. The data were obtained using annual population statistics, migration movements, and socio-economic and environmental indicators provided by the Turkish Statistical Institute (TURKSTAT) and other institutions. Descriptive statistics, correlation analysis, regression analysis, geographic information systems (GIS) analysis, and cluster analysis methods were used in the analysis. Multiple correlation analysis (MCA) showed population growth and out-migration as the top factors influencing Van province's net migration, while also demonstrating in-migration, cultivated agricultural areas, and arrivals to accommodation facilities had a moderate impact on net migration. The multiple linear regression (MLR) analysis found positive net migration growth is associated with population growth rate, average temperature, in-migration and annual number of visitors. However, regional employment negatively impacts net migration. The hierarchical cluster analysis (HCA) results showed similar migration trends between 2008 and 2013. Out-migration and tourism were observed to increase in 2014, 2015, 2016, and 2018. While the years 2017, 2019, and 2022 exhibited climatic and agricultural similarities, the migration patterns differed in 2019 compared to the other years. This study statistically analyzes internal migration data from Van province to identify factors affecting net migration. The findings provide key insights to better understand migration trends and develop policies. The model can inform policy decisions by guiding regional planners and decision-makers on future provincial migration dynamics.

Kaynakça

  • Anarkooli, A. J., Hosseinpour, M. and Kardar, A. (2017), Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model, Accident Analysis and Prevention, 106, 399-410.
  • Antonio, K. and Beirlant, J. (2007), Actuarial statistics with generalized linear mixed models, Insurance: Mathematics and Economics, 40(1), 58-76.
  • Antonio, K. and Valdez, E. A. (2012), Statistical concepts of a priori and a posteriori risk classification in insurance, AStA Advances in Statistical Analysis, 96, 187-224.
  • Bakhshi, A. K. and Ahmed, M. M. (2021), Practical advantage of crossed random intercepts under Bayesian hierarchical modeling to tackle unobserved heterogeneity in clustering critical versus non-critical crashes, Accident Analysis and Prevention, 149, 105855.
  • Balusu, S. K., Pinjari, A. R., Mannering, F. L. and Eluru, N. (2018), Non-decreasing threshold variances in mixed generalized ordered response models: A negative correlations approach to variance reduction, Analytic Methods in Accident Research, 20, 46-67.
  • Barua, S., El-Basyouny, K. and Islam, M. T. (2015), Effects of spatial correlation in random parameters collision count-data models, Analytic Methods in Accident Research, 5, 28-42.
  • Barua, S., El-Basyouny, K. and Islam, M. T. (2016), Multivariate random parameters collision count data models with spatial heterogeneity, Analytic Methods in Accident Research, 9, 1-15.
  • Chen, F., Chen, S. and Ma, X. (2018), Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data, Journal of Safety Research, 65, 153-159.
  • Davis, J. and Goadrich, M. (2006), The relationship between Precision-Recall and ROC curves, In: Proceedings of the 23rd International Conference on Machine Learning – ICML ‘06, 233-240.
  • De Jong, P. and Heller, G. Z. (2008), Generalized Linear Models for Insurance Data, In: International Series on Actuarial Science, Cambridge University Press.
  • Dong, C., Clarke, D. B., Yan, X., Khattak, A. and Huang, B. (2014), Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections, Accident Analysis and Prevention, 70, 320-329.
  • Eluru, N., Bhat, C. R. and Hensher, D. A. (2008), A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes, Accident Analysis and Prevention, 40(3), 1033-1054.
  • Embrechts, P. and Wüthrich, M. V. (2022), Recent challenges in actuarial science, Annual Review of Statistics and Its Application, 9, 119-140.
  • Frees, E. W. (2010), Regression Modeling with Actuarial and Financial Applications, In: International Series on Actuarial Science, Cambridge University Press.
  • Fountas, G. and Anastasopoulos, P. C. (2017), A random thresholds random parameters hierarchical ordered probit analysis of highway accident injury-severities, Analytic Methods in Accident Research, 15, 1-16.
  • Fountas, G., Pantangi, S. S., Hulme, K. F. and Anastasopoulos, P. C. (2019), The effects of driver fatigue, gender, and distracted driving on perceived and observed aggressive driving behavior: A correlated grouped random parameters bivariate probit approach, Analytic Methods in Accident Research, 22, 100091.
  • Garrido, J., Genest, C. and Schulz, J. (2016), Generalized linear models for dependent frequency and severity of insurance claims, Insurance: Mathematics and Economics, 70, 205-215.
  • Gong, H., Fu, T., Sun, Y., Guo, Z., Cong, L., Hu, W. and Ling, Z. (2022), Two-vehicle driver-injury severity: A multivariate random parameters logit approach, Analytic Methods in Accident Research, 33, 100190.
  • Haberman, S. and Renshaw, A. E. (1996), Generalized linear models and actuarial science, Journal of the Royal Statistical Society: Series D (The Statistician), 45(4), 407-436.
  • Hedeker, D. (2005), Generalized linear mixed models, In: B. Everitt, D. Howell (Eds.), Encyclopedia of Statistics in Behavioral Science, John Wiley & Sons, New York, 729-738.
  • Hossin, M. and Sulaiman, M. N. (2015), A review on evaluation metrics for data classification evaluations, International Journal of Data Mining and Knowledge Management Process, 5(2), 1-11.
  • Kaas, R., Goovaerts, M., Dhaene, J. and Denuit, M. (2008), Modern Actuarial Risk Theory: Using R, Second Edition, Springer Berlin, Heidelberg.
  • Khamis, H. (2008), Measures of association: How to choose?, Journal of Diagnostic Medical Sonography, 24(3), 155-162.
  • Kim, M., Kho, S. Y. and Kim, D. K. (2017), Hierarchical ordered model for injury severity of pedestrian crashes in South Korea, Journal of Safety Research, 61, 33-40.
  • Lord, D. and Mannering, F. (2010), The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives, Transportation Research Part A: Policy and Practice, 44(5), 291-305.
  • Mannering, F. L., Shankar, V. and Bhat, C. R. (2016), Unobserved heterogeneity and the statistical analysis of highway accident data, Analytic Methods in Accident Research, 11, 1-16.
  • McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, In: Monographs on Statistics and Applied Probability 37, Second Edition, Chapman and Hall, London, New York.
  • Miao, G. M. (2018), Application of hierarchical model in non-life insurance actuarial science, Modern Economy, 9(3), 393-399.
  • Nelder, J. A. and Wedderburn, R. W. M. (1972), Generalized linear models, Journal of the Royal Statistical Society: Series A (General), 135(3), 370-384.
  • Ohlsson, E. and Johansson, B. (2010), Non-life Insurance Pricing with Generalized Linear Models, In: EAA Series Textbook, Springer Berlin, Heidelberg.
  • Pai, J. S. and Walch, A. H. (2020), ACTEX Study Manual for Exam MAS-II, ACTEX Learning/SRBooks, Inc., Greenland, NH.
  • Pantangi, S. S., Fountas, G., Sarwar, M. T., Anastasopoulos, P. C., Blatt, A., Majka, K., Pierowicz, J. and Mohan, S. B. (2019), A preliminary investigation of the effectiveness of high visibility enforcement programs using naturalistic driving study data: A grouped random parameters approach, Analytic Methods in Accident Research, 21, 1-12.
  • Portet, S. (2020), A primer on model selection using the Akaike Information Criterion, Infectious Disease Modelling, 5, 111-128.
  • Saito, T. and Rehmsmeier, M. (2015), The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PloS One, 10(3), e0118432.
  • Tran, V., Liu, D., Pradhan, A. K., Li, K., Bingham, C. R., Simons-Morton, B. G. and Albert, P. S. (2015), Assessing risk-taking in a driving simulator study: Modeling longitudinal semi-continuous driving data using a two-part regression model with correlated random effects, Analytic Methods in Accident Research, 5, 17-27.
  • Yau, K., Yip, K. and Yuen, H. K. (2003), Modelling repeated insurance claim frequency data using the generalized linear mixed model, Journal of Applied Statistics, 30(8), 857-865.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistiksel Analiz, Uygulamalı İstatistik
Bölüm Makaleler
Yazarlar

Olgun Özdemir 0000-0002-8454-136X

Zafer Ağyar 0009-0000-2319-2920

Mehmet Şaban Ucari 0009-0004-3037-8538

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 9 Ağustos 2024
Kabul Tarihi 27 Ocak 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: 1

Kaynak Göster

APA Özdemir, O., Ağyar, Z., & Ucari, M. Ş. (2025). Determinants of Net Migration Trends In Van Province, Türki̇ye From 2008 To 2022: A Statistical Analysis. Nicel Bilimler Dergisi, 7(1), 1-33.