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

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Abstract

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, is regularly 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 United Nations (UN), humanitarian non-governmental organisations (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 around the use of big data; and addressing political narratives. We advocate the need for careful consideration of the challenges faced by the use of big data, as well as 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

Journal

Journal ID (nlm-ta): UCL Open Environ

Journal ID (publisher-id): UCLOE

Title: UCL Open Environment

Abbreviated Title: UCL Open Environ

Publisher: UCL Press (UK )

ISSN (Electronic): 2632-0886

Publication date (Electronic, pub): 27 October 2021

Publication date (Electronic, collection): 2021

Volume: 3

Electronic Location Identifier: e027

Affiliations

[1 ]Institute for Global Health, University College London, London, UK

[2 ]Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK

[3 ]Institute of Environment, Health and Societies, Brunel University, London, UK

[4 ]Centre of Public Health Data Science, Institute of Health Informatics, University College London, London, UK

[5 ]United Nations’ Displacement Tracking Matrix, International Organization for Migration, International Organization for Migration, Juba, South Sudan

[6 ]CU Population Center, Institute of Behavioral Science, University of Colorado Boulder Campus, Boulder, CO, USA

[7 ]Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, Germany

[8 ]Health Management BD Foundation, Sector 6, Uttara, Dhaka, Bangladesh

[9 ]Adjunct Faculty, Department of Public Health, North South University, Dhaka, Bangladesh

[10 ]Department of Information Studies, University College London, London, UK

[11 ]GMV Innovating Solutions Ltd, HQ Building, Thomson Avenue, Harwell Campus, Didcot, UK

[12 ]WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK

[13 ]United Nations’ Displacement Tracking Matrix, International Organization for Migration, United Nations, London, UK

Author notes

*Corresponding author: E-mail: lydia.franklinos.16@ 123456ucl.ac.uk

Author information

Lydia H. V. Franklinos https://orcid.org/0000-0002-5766-3986

Rebecca Parrish https://orcid.org/0000-0003-3105-7969

Rachel Burns https://orcid.org/0000-0002-9559-2867

Bishawjit Mallick https://orcid.org/0000-0002-9492-1059

Vasileios Routsis https://orcid.org/0000-0001-8275-4284

Andrew J. Tatem https://orcid.org/0000-0002-7270-941X

Article

DOI: 10.14324/111.444/ucloe.000027

PMC ID: 10171412

SO-VID: 589cd812-be6a-441f-81ba-fb4668d81feb

License:

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