Multiscale network modelling of migration flows in Austria

The aim of this project is to exploit a unique combination of datasets of individual-level migration events in Austria with high resolution socioeconomic maps to reveal the nationwide multiscale, hierarchical flow of people over a period of more than two decades, together with its latent social and economic driving factors. Using state-of-the-art data science methodology, we will combine longitudinal neighborhood-level resettlement information with individualized data on income, employment, and demographic factors (e.g. race, gender, nationality). Using customized network science methods, we construct a higher-order dynamic generative model able to identify the most relevant spatial and temporal patterns of migration, and establish their causal structures. Our data-driven model will allow for the assessment of the impact of policy interventions on the dynamics of social mobility, segregation, urbanization, immigration, and socioeconomic development.