Welcome to our project webpage. We are team Data Vizards and we are exploring Turkey’s internal migration data.

Please stay tuned to follow our project activities.


  1. Şadiye Öztürk

  2. Melike Nur Kutlusan

  3. Zeynep Tuncaboylu

  4. Beyzanur Nas

  5. Duygu Eken

  6. Melek Er


We will carry out our project by considering 2021 internal migration data across Turkey. Our main focus is In Migration of internal cities. In this regard, migration trends between different regions such as Eastern Anatolia, Southeastern Anatolia, Aegean, Marmara will be determined and migration relations between big cities and other cities will be analyzed in detail. The relationship between population density and migration will be determined by examining migration rates, especially in cities with large populations. Migration trends will be evaluated according to age groups, gender, educational status and reasons for migration. (retirement, appointment, etc.) Also, the economic development level of the economic region will be interpreted according to the labor force, household disposable income, number of enterprises, number of housing sales, etc. This comprehensive analysis will guide our project in understanding Turkey’s migration situation.


Migration Data


Data of 26 Region


The aim of this project is to analyze internal migration data in Turkey and investigate the factors such as migration reasons, age, education, and other variables that influence migration. We conducted our research using the internal migration and population datasets available on the Turkish Statistical Institute (TÜİK) website. The conclusions drawn from this project are outlined in the following points:

  1. We had assumed that the highest migration due to retirement would be to the Central Anatolia Region or the Aegean Region. To validate this assumption, we created a migration value and cause graph. When looking at this graph, we observed that the highest migration is to the Marmara Region. To obtain more accurate results, we created a migration rate and cause graph. In this graph, we see that the highest migration is to the Black Sea and Aegean regions. So, we realized that part of our assumption is correct. Understanding the importance of creating a Rate graph, we concluded that we should make decisions based on this graph.

  2. The age range of the people who migrate the most is seen in the graph as 20-24. The age group of the people who migrated the second most is seen in the graph as 15-19. In the graph of reasons for migration, the most common reason for migration was observed to be education. We can reconcile this incident with this situation.

  3. The visual representation of migration numbers across different educational backgrounds highlights a compelling inverse relationship between education and migration. Doctoral and primary school-educated individuals show lower migration rates compared to high school and university-educated individuals. Individuals with higher education levels tend to migrate less. The scatter plot analysis reveals a lack of a straightforward correlation between the average education level and fertility rates across regions in the dataset. While some general trends emerge—like higher education levels in certain regions coinciding with lower fertility rates—outliers such as Central Anatolia and the Mediterranean Region defy these patterns.

  4. TÜİK has divided provinces into 26 regions based on geographical proximity, simplifying regional oversight and data collection as cities in these regions are closely clustered. The graph comparing workforce to usable income generally shows an increase in usable income as the workforce increases, yet this trend varies among regions. Additionally, the Usable Income data is calculated based on the Gini coefficient. The Gini coefficient, in turn, represents the distribution of usable income within a region. For example, according to 2021 TÜİK data, Istanbul’s Gini coefficient of 0.43 compared to Izmir’s 0.37 indicates a higher income inequality in Istanbul.

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