The Complex Network Patterns of Human Migration at Different Geographical Scales: Network Science meets Regression Analysis

TL;DR

This paper combines network science and regression analysis to study human migration patterns across various geographical scales, revealing network traits that influence migration modeling and highlighting the need for integrated approaches.

Contribution

It introduces a comprehensive analysis of migration networks at city, national, and global levels, bridging the gap between network science and regression modeling in migration studies.

Findings

Identified distinct migration network traits at different geographical levels.

Demonstrated how network structures affect regression modeling of migration.

Highlighted gaps and opportunities for integrating network analysis with regression approaches.

Abstract

Migration's influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study delves into two distinctive substrates of Migration Studies: the "why" substrate, which deals with identifying the factors driving migration relying primarily on regression modeling, encompassing economic, demographic, geographic, cultural, political, and other variables; and the "how" substrate, which focuses on identifying migration flows and patterns, drawing from Network Science tools and visualization techniques to depict complex migration networks. Despite the growing percentage of Network Science studies in migration, the explanations of the identified network traits remain very scarce, highlighting the detachment between the two research substrates. Our study includes real-world network analyses of human migration across different geographical levels: city, country, and global. We examine inter-district migration in Vienna at the city level, review internal migration networks in Austria and Croatia at the country level, and analyze migration exchange between Croatia and the world at the global level. By comparing network structures, we demonstrate how distinct network traits impact regression modeling. This work not only uncovers migration network patterns in previously unexplored areas but also presents a comprehensive overview of recent research, highlighting gaps in each field and their interconnectedness. Our contribution offers suggestions for integrating both fields to enhance methodological rigor and support future research.