Data Science for Migration and Mobility

Migration is a complex phenomenon with multi-dimensional factors. With ever-expanding data storage and processing capabilities, new data sources (such as social media, mobile call data records, and satellite imaging) are becoming available to study migration from both qualitative and quantitative perspectives. Data Science for Migration and Mobility addresses the needs of both migration scholars who stand to benefit from the analysis of these new sources but lack the computational tools, as well as data scientists who have practical and theoretical knowledge in dealing with these data sources but have no familiarity with the relevant questions of migration research. It describes the main conceptual frameworks, explains techniques of data collection and processing, provides case studies, discusses the strengths and limitations of each data source, and critically discusses the ethical, legal, and privacy-related issues specific to each data source.

Albert Ali Salah, Utrecht University,Emre Eren Korkmaz, University of Oxford,Tuba Bircan, Vrije Universiteit Brussel

Albert Ali Salah is Professor of Affective and Social Computing at Utrecht University, Department of Information and Computing Sciences, and Adjunct Professor at Bo?aziçi University, Department of Computer Engineering. He has held research positions at Centrum Wiskunde & Informatica (CWI), University of Amsterdam, and Nagoya University before. His work focuses on computer analysis of human behaviour. He was the scientific coordinator of the Data for Refugees Challenge, and leads a work package in the HumMingBird EU project. He is a senior member of IEEE and ACM, and a research affiliate of Datapop Alliance.


Emre Eren Korkmaz is a Departmental Lecturer in Migration and Development at the University of Oxford's Department of International Development, where he teaches on the MSc in Migration Studies. From October 2016 to September 2018, he was a British Academy Newton International Fellow at ODID. He was also a junior research fellow at St Edmund Hall (2017-20) and a Research Associate at the Centre for Technology and Global Affairs of the Department of Politics and International Relations (2018-20). In recent years, he has been driven by a passion for examining the social and political impact of new digital and frontier technologies on migration.


Tuba Bircan is an assistant professor at the Department of Sociology and the research coordinator of Interface Demography at the Vrije Universiteit Brussel. She currently the scientific coordinator of the Enhanced Migration Measures from a Multidimensional Perspective (HumMingBird), funded by European Commission Horizon 2020. Her major research interests are migration, gender, equal opportunities, evidence-based policymaking, quantitative and mixed methods, Big Data and AI applications for social research. She has served as an external expert and reviewer for several international funding institutes and as a reviewer for many international journals.

Section 1 - Introduction
1. New data sources and computational approaches on migration and human mobility
Section 2 - Data Sources
2. Leveraging mobile phone data for migration flows
3. Analysing refugees' secondary mobility using mobile phone Call Detail Records (CDR)
4. Remote sensing data for migration research
5. Using Facebook and LinkedIn Data to Study International Mobility
6. Twitter data for migration studies
7. Issues about analysing multilingual communication in immigrant contexts
8. Indicators and survey data to understand migration and integration policy frameworks and trends in the EU
9. Financial datasets: Leveraging transactional big data in mobility and migration studies
Section 3 - Tools
10. CESSDA Data Catalogue: opportunities and challenges to explore mobility and migration
11. Conflict and forced migration: Social media as event data
12. Visual Exploration of Large Multidimensional Trajectory Data
13. Voyage Viewer: A Multivariate Visualisation Tool for Migration Analysis
14. Ethical and Legal Aspects on Data Science for Large Scale Human Mobility
Section 4 - Case studies
15. Combining Mobile Call Data and Satellite Imaging for Human Mobility
16. Predicting Support for Refugees in Europe: Using Machine Learning and Synthetic Populations to Predict Support for Acceptance of Asylum Seekers in European Regions
17. Applying Computational Linguistic and Text Analysis to Media Content about Migration: Opportunities and Challenges for Social Scientific Domains
18. Exploring digital connectivities in forced migration contexts: digital 'making do' practises
Section 5 - A Final Word
19. Eight Theses on Migration Studies and Big Data