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HumMingBird is a Horizon 2020 research that aims to improve the mapping and understanding of changing migration flows. The research analyses patterns, motivations and new geographies, and tests new methods to forecast emerging and future trends.

Migration has become a matter of significant social, political and broader public concern. It has therefore shifted very high on the agenda of national and EU authorities. Well-informed, evidence-based migration policies should be based on reliable evidence, beginning with a thorough understanding of existing data as well as the demographic, economic, environmental and political drivers of migration.  There is a need for tools to forecast migration flows in order to minimise shocks and tensions. 

The project’s objectives can be summarised as follows:

  • to identify key uncertainties and reappraise the migration concepts through a profound review of migration theories and measures;
  • projections based on the analyses of the patterns, motivations and modalities of migration and the changing nature of flows and factors;
  • to widen the EU’s viewpoint on the policy migration nexus as to possible future implications of today’s policy decisions and suggesting areas for future policy initiatives;
  • qualitative scenario building on the stories of migrants en route to complement quantitative scenarios;
  • to validate big data technologies to provide dynamic and novel rich evidence on various  aspects/factors that might help estimate stock migration and migration flows;
  • to merge knowledge of the quantitative and qualitative approaches to provide a holistic view of migration and migration processes, to delineate some of the major global developments around migration in the future, and to draw out policy implications as to preparedness for possible migration futures.

Thanks to the high-level partnership and wide skillset of the consortium, HumMingBird’s ambitions are but not limited to identify the uncertainties and reappraise, to explore the reasons why migration predictions may not hold,  to demonstrate non-traditional data sources for migration research including various types of big data (such as social media or telecommunication).