Analysis of migration of Van province from neighboring provinces and Iran with geographic weighted regression

Migration is a complex and dynamic feature of modern societies and is influenced by many factors. Analysing migration using geographically weighted regression contributes to a better understanding of the influencing factors behind migration. Geographically weighted regression takes into account the spatial relationships between variables associated with migration and helps to determine how these relationships affect migration. Therefore, the study of migration by geographically weighted regression method plays a critical role in better understanding the social, economic and geographical structures of societies and in future planning and policy-making processes. In this thesis, the migration of Van province from the first and second degree border neighbouring provinces and Iran is analysed by Geographically Weighted Regression method. In the thesis, the variables of "migration due to social environmental pressure" in local surveys and "adaptation to my new life in a short time after migration" in foreign surveys and the factors affecting these variables were analysed by Geographically Weighted Regression method. Least Squares and Geographically Weighted Regression methods were used in the analysis and which method gives stronger results was analysed according to AIC, AICc, BIC, RSS, 𝑅2 and 𝐴𝑑𝑗.𝑅2 values. According to the results of the analysis, it was observed that the Geographically Weighted Regression method gave stronger results. In addition, the variable of "my job had a positive contribution to the post-migration adaptation process" in the local surveys and the factors affecting this variable were analysed by Geographically Weighted Poisson Regression method. Poisson Regression and Geographically Weighted Poisson Regression methods were used in the analysis and which method gave stronger results was analysed according to AIC, AICc, 𝑅2 and 𝐴𝑑𝑗.𝑅2 values. According to the results of the analyses, it is observed that the Geographically Weighted Poisson Regression method gives stronger results. At the same time, the effects and significance of independent variables according to provinces, districts and regions are presented in detail on the maps. Keywords: Geographically Weighted Regression, Geographically Weighted Poisson Regression, Least Squares, Migration, Poisson Regression.