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      Key opportunities and challenges for the use of big data inmigration research and policy


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          Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, has been proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the UN, humanitarian NGOs, policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns; and addressing political narratives. We advocate the need for increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities.

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          Author and article information

          UCL Open: Environment Preprint
          UCL Press
          18 June 2020
          [1 ] Institute for Global Health, University College London, London, UK; Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK
          [2 ] Institute for Global Health, University College London, London, UK; Institute of Environment, Health and Societies, Brunel University, London, UK
          [3 ] Centre of Public Health Data Science, Institute of Health Informatics, University College London, London, UK
          [4 ] United Nations’ Displacement Tracking Matrix, International Organization for Migration, International Organization for Migration, Juba, South Sudan
          [5 ] CU Population Center, Institute of Behavioral Science, University of Colorado Boulder Campus, Boulder, CO, USA; Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, Germany
          [6 ] Health Management BD Foundation, Sector 6, Uttara, Dhaka, Bangladesh; Adjunct Faculty, Department of Public Health, North South University, Dhaka, Bangladesh
          [7 ] Department of Information Studies, University College London, London, UK
          [8 ] GMV Innovating Solutions Ltd, HQ Building, Thomson Avenue, Harwell Campus, Didcot, UK
          [9 ] WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
          [10 ] United Nations’ Displacement Tracking Matrix, International Organization for Migration, United Nations, London, UK

          This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY) 4.0, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

          UCL Grand Challenges, UCL, London, UK 506002-100-156425


          Decision Date: 19/06/2020

          Handling Editor: Ben Milligan

          This article is a preprint article and has not been peer-reviewed. It is under consideration following submission to UCL Open: Environment Preprint for open peer review.

          2020-09-17 13:09 UTC

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