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The main purpose of this research is to understand and explain the clustering and/or segregation patterns of the rapid, massive and unexpected flow of Syrian refugees to Türkiye.

The analytical framework we use consists of three parts. In the first stage, we created a two-group model in which utility is assumed to be a function of consumption and the proportion of the population belonging to the same group living in their neighborhood. We show that equilibrium utility can be reduced to a univariate function that is the population proportion of the group to which one belongs. Then, with the help of a Python-based simulation, we analyze the redistribution dynamics of the groups and clustering patterns. In the final stage, we compute the dissimilarity index to determine the degree of clustering.

Although the dilution policy has been implemented, a relatively high spatial segregation of refugees still exists. The dissimilarity index we compute using the latest data shows that almost half of Syrians need to be displaced for a homogeneous geographical distribution.

By obtaining neighborhood-based housing data, it will be possible to conduct a more detailed analysis at the city level. This will improve policymakers' understanding of refugee policy at both local and national levels.

It is the first study in which a dissimilarity index is computed for Syrian refugees in Türkiye.