As urban centers become more diverse, detailed data on the distribution of different populations and their interrelationships are needed for effective planning and forecasting, while addressing segregation and inequality. In the Horizon 2020 Future Migration Scenarios for Europe (FUME) project, we focused on understanding migration patterns at multiple geographic levels, and specifically explored the complex dynamics of migrant distribution in urban areas using gridded population data. We used analytical approaches to understand the underlying patterns and advanced machine learning (ML) methods to make projections for the future.
In this talk, we will present the methods explored in the project, their potential applications, and share results from the FUME project. We will also suggest possible directions for further research. This presentation is a great opportunity to better understand the intersection of geospatial data science with migration dynamics through data-driven research and compelling visualizations.