Spatial Determinants of Internal Migration in Türkiye: An Empirical Evaluation with Geographically Weighted Regression Approach

Öz

This study aims to analyse the socioeconomic factors affecting internal migration movements at the provincial level in Türkiye from the perspective of spatial heterogeneity. Net migration rate is used as the dependent variable, and unemployment rate, educational attainment, export volume, and technological development index are used as independent variables. In the analyses, the classic multiple linear regression model and the geographic weighted regression model are applied comparatively. The findings reveal that the unemployment variable, which is not significant in the Multiple Linear Regression model, exhibits different effects according to regions in the Geographically Weighted Regression model, while the variables of duration of participation in education, export volume, and technological development index vary spatially. In particular, the duration of participation in education and the technological development index encouraged migration in some regions and discourage it in others. The high explanatory power of the Geographically Weighted Regression model ( = 0.725) shows that this method successfully reflects spatial differences in internal migration analysis. Moreover, the disappearance of significant spatial autocorrelation in the residuals of the Geographically Weighted Regression model proves that the model controls spatial dependence more accurately. These results show that internal migration movements are shaped by region-specific dynamics and that uniform policies may be insufficient. At the same time, these findings reveal that the migration phenomenon should be evaluated with economic indicators and spatial contexts. This study contributes significantly to the literature by examining internal migration at the provincial level in Turkey from a spatial heterogeneity perspective, mapping its varying effects at the local level using the GWR application, and presenting policy implications alongside comprehensive data for the year 2023.

Anahtar Kelimeler

• Internal Migration
• Spatial Analysis
• Geographically Weighted Regression
• Socioeconomic Factors

Türkiye’de İç Göçün Mekânsal Belirleyicileri: Coğrafi Ağırlıklı Regresyon Yaklaşımıyla Ampirik Bir Değerlendirme

Öz

Bu çalışma, Türkiye’de iller düzeyinde iç göç hareketlerini etkileyen sosyoekonomik faktörleri mekânsal heterojenlik perspektifiyle analiz etmeyi amaçlamaktadır. Bağımlı değişken olarak net göç hızı; bağımsız değişkenler olarak işsizlik oranı, eğitime katılım süresi, ihracat hacmi ve teknolojik gelişim endeksi kullanılmıştır. Analizlerde, klasik Çoklu Doğrusal Regresyon modeli ile Coğrafi Ağırlıklı Regresyon modeli karşılaştırmalı olarak uygulanmıştır. Elde edilen bulgular, Çoklu Doğrusal Regresyon modelinde anlamlı olmayan işsizlik değişkeninin Coğrafi Ağırlıklı Regresyon modelinde bölgelere göre farklı etkiler sergilediğini, eğitime katılım süresi, ihracat hacmi ve teknolojik gelişim endeksi değişkenlerinin ise mekânsal olarak değişkenlik gösterdiğini ortaya koymuştur. Özellikle, eğitime katılım süresi ve teknolojik gelişim endeksinin bazı bölgelerde göçü teşvik edici, bazı bölgelerde ise caydırıcı etkiler yarattığı görülmüştür. Coğrafi Ağırlıklı Regresyon modelinin açıklayıcılık gücünün yüksek olması (R^2= 0.725), bu yöntemin iç göç analizinde mekânsal farklılıkları başarılı bir şekilde yansıttığını göstermektedir. Ayrıca Coğrafi Ağırlıklı Regresyon modelinin kalıntılarında anlamlı mekânsal otokorelasyonun ortadan kalkması, modelin mekânsal bağımlılığı daha doğru biçimde kontrol ettiğini kanıtlamaktadır. Bu sonuçlar, iç göç hareketlerinin bölgelere özgü dinamiklerle şekillendiğini ve tek tip politikaların yetersiz kalabileceğini göstermektedir. Aynı zamanda bu bulgular, göç olgusunun sadece ekonomik göstergelerle değil, aynı zamanda mekânsal bağlamlarla birlikte değerlendirilmesi gerektiğini ortaya koymaktadır. Bu çalışma, Türkiye’de iller düzeyinde iç göçü mekânsal heterojenlik perspektifinden ele alarak GWR uygulamasıyla yerel düzeyde değişen etkileri haritalandırması ve 2023 yılına ait kapsamlı verilerle politika çıkarımlarını bir arada sunması açısından literatüre önemli bir katkı sağlamaktadır.

Anahtar Kelimeler

• İç göç
• Mekânsal Analiz
• Coğrafi Ağırlıklı Regresyon
• Sosyoekonomik Faktörler

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