Investigating internal migration with network analysis and latent space representations: An application to Turkey

TL;DR

This study uses network analysis and latent space representations to explore Turkey's internal migration patterns from 2008 to 2020, revealing stable, geographically bounded migration laws and unique insights into migration dynamics.

Contribution

It introduces a novel combination of network analysis methods and representation learning to study migration, applying these tools to Turkey for the first time.

Findings

Migration links are geographically bounded with notable exceptions.

Major migration flows often counterbalance in the opposite direction.

Migration system remains stable over the studied period.

Abstract

Human migration patterns influence the redistribution of population characteristics over the geography and since such distributions are closely related to social and economic outcomes, investigating the structure and dynamics of internal migration plays a crucial role in understanding and designing policies for such systems. We provide an in-depth investigation into the structure and dynamics of the internal migration in Turkey from 2008 to 2020. We identify a set of classical migration laws and examine them via various methods for signed network analysis, ego network analysis, representation learning, temporal stability analysis, community detection, and network visualization. The findings show that, in line with the classical migration laws, most migration links are geographically bounded with several exceptions involving cities with large economic activity, major migration flows are countered with migration flows in the opposite direction, there are well-defined migration routes, and the migration system is generally stable over the investigated period. Apart from these general results, we also provide unique and specific insights into Turkey. Overall, the novel toolset we employ for the first time in the literature allows the investigation of selected migration laws from a complex networks perspective and sheds light on future migration research on different geographies.