Mesleki Eğitim ve Ortaöğretim Okullaşmasının Mekânsal Örüntüleri: Türkiye İl Düzeyinde Moran I, LISA Ve Coğrafi Ağırlıklı Regresyon İle Mekânsal Durağanlık Sınaması

Öz

Bu çalışma, MEB 2024-25 Örgün Eğitim İstatistikleriyle 81 ilde mesleki eğitim oranının mekânsal dağılımını ve ortaöğretim okullaşmasıyla ilişkisini incelemektedir. Global Moran I, LISA, Lagrange Çarpanı (LM) testleri, mekânsal gecikme modeli ve Coğrafi Ağırlıklı Regresyon (CAR) uygulanmıştır. Mesleki eğitim oranı (I=0,179, p<0,01) ve ortaöğretim okullaşması (I=0,611, p<0,001) anlamlı pozitif mekânsal otokorelasyon göstermektedir. LISA, okullaşmada Güneydoğu ve Doğu Anadolu’da on iki illik bir düşük-düşük kümesi saptamıştır; ortalaması %74,5 ile ülke ortalamasının 14,5 puan altındadır. LM hiyerarşisi mekânsal gecikme modelini işaret etmekte; gecikme katsayısı 0,505 (p<0,001) ve artıklardaki otokorelasyon kaybolmaktadır. CAR uyumu iyileştirse de (R²: 0,603'ten 0,662'ye) mesleki eğitim katsayısı hiçbir ilde anlamlı değildir ve değişkenliği de anlamsızdır (Monte Carlo p=0,849). Buna karşılık cinsiyet uçurumu katsayısı 39 ilde anlamlıdır ve değişkenliği anlamlı çıkmaktadır (p=0,002). Bulgular iki metodolojik sonucu birlikte ortaya koymaktadır: model uyumundaki iyileşme tek başına mekânsal heterojenlik kanıtı değildir, heterojenliğin yokluğu da her katsayı ayrı sınanmadan ilan edilemez.

Anahtar Kelimeler

• Mesleki eğitim
• Ortaöğretim okullaşma
• Mekânsal otokorelasyon
• LISA
• Coğrafi ağırlıklı regresyon
• Mekânsal durağanlık

Etik Beyan

Bu çalışmanın hazırlanma sürecinde bilimsel araştırma ve yayın etiği ilkelerine riayet edilmiş; yararlanılan tüm kaynaklar eksiksiz biçimde kaynakçada belirtilmiştir.

Spatial Patterns of Vocational Education and Upper-Secondary Enrollment in Turkey: Testing Spatial Stationarity with Moran's I, LISA, and Geographically Weighted Regression

Öz

This study examines the spatial distribution of the vocational education ratio and its relationship with upper-secondary enrollment across 57 Turkish provinces using Ministry of National Education (MoNE) Formal Education Statistics for 2024-25. Global Moran's I and Local Indicators of Spatial Association (LISA) identify spatial clustering patterns; Lagrange Multiplier (LM) tests guide model selection; Geographically Weighted Regression (GWR) tests the spatial stationarity assumption. Both the vocational education ratio (I=0.171, p<0.01) and upper-secondary enrollment (I=0.210, p<0.01) exhibit significant positive spatial autocorrelation. LISA analysis identifies a low-low (LL) cluster of eight provinces located in the transition zone between the Mediterranean and Central Anatolia regions. Although GWR improves model fit (R²: 0.452 to 0.573), none of the local coefficients exceed the corrected significance threshold, and Monte Carlo tests indicate that coefficient variability is not statistically significant (p=0.485 for vocational education). This finding constitutes a methodological caution: improvement in model fit alone cannot be treated as evidence of spatial heterogeneity.

Anahtar Kelimeler

• Vocational education
• Upper-secondary enrollment
• Spatial autocorrelation
• LISA
• Geographically weighted regression
• Spatial stationarity

Etik Beyan

The author confirms that this study was conducted inaccordance with the principles of academic research andpublication ethics, and that all sources used areappropriately cited.

Kaynakça

1. Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-7799-1
2. Anselin, L. (1995). Local indicators of spatial association (LISA). Geographical Analysis, 27(2), 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
3. Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77-104. https://doi.org/10.1016/0166-0462(95)02111-6
4. Bivand, R., Pebesma, E., & Gómez-Rubio, V. (2013). Applied spatial data analysis with R (2nd ed.). Springer. https://doi.org/10.1007/978-1-4614-7618-4
5. Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281-298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
6. Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1998). Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3), 431-443. https://doi.org/10.1111/1467-9884.00145 Cliff, A. D., & Ord, J. K. (1981). Spatial processes: Models and applications. Pion.
7. Comber, A., Brunsdon, C., & Radburn, R. (2011). A spatial analysis of variations in health access: Linking geography, socio-economic status and access perceptions. International Journal of Health Geographics, 10(1), 44. https://doi.org/10.1186/1476-072X-10-44
8. da Silva, A. R., & Fotheringham, A. S. (2016). The multiple testing issue in geographically weighted regression. Geographical Analysis, 48(3), 233-247. https://doi.org/10.1111/gean.12084

9. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. John Wiley & Sons.
10. Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. https://doi.org/10.1080/24694452.2017.1352480
11. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
12. Gültekin-Karakaş, D., & Yıldız, S. (2025). Multifaceted regional educational inequality: Evidence from Türkiye. Compare: A Journal of Comparative and International Education, 55(4), 603-622. https://doi.org/10.1080/03057925.2023.2292530
13. Gümüş, S., & Chudgar, A. (2016). Factors affecting school participation in Turkey: An analysis of regional differences. Compare: A Journal of Comparative and International Education, 46(6), 929-951. https://doi.org/10.1080/03057925.2015.1095073
14. Hanushek, E. A., & Woessmann, L. (2015). The knowledge capital of nations: Education and the economics of growth. MIT Press. https://doi.org/10.7551/mitpress/9780262029179.001.0001
15. Karahasan, B. C., & Uyar, E. (2009). Spatial distribution of education and regional inequalities in Turkey (MPRA Paper 30130). Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/30130/
16. Lu, B., Harris, P., Charlton, M., & Brunsdon, C. (2014). The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo-Spatial Information Science, 17(2), 85-101. https://doi.org/10.1080/10095020.2014.917453
17. Millî Eğitim Bakanlığı. (2025). Millî eğitim istatistikleri: Örgün eğitim 2024-2025. Strateji Geliştirme Başkanlığı. https://sgb.meb.gov.tr
18. Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1-2), 17-23. https://doi.org/10.2307/2332142
19. Özer, M. (2019). Background of problems in vocational education and training and its road map to solution in Turkey's education vision 2023. Journal of Higher Education and Science, 9(1), 1-11. https://doi.org/10.5961/jhes.2019.304
20. Rodríguez-Pose, A., & Tselios, V. (2011). Mapping the European regional educational distribution. European Urban and Regional Studies, 18(4), 358-374. https://doi.org/10.1177/0969776411399345
21. Ryan, P. (2001). The school-to-work transition: A cross-national perspective. Journal of Economic Literature, 39(1), 34-92. https://doi.org/10.1257/jel.39.1.34
22. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(Suppl. 1), 234-240. https://doi.org/10.2307/143141
23. Wheeler, D., & Tiefelsdorf, M. (2005). Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2), 161-187. https://doi.org/10.1007/s10109-005-0155-6