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Overview
Migration, as an area of research, has attracted increased attention from different fields of science. There has been an increase in the number of scientific publications focused on migration, even though the share of migrants worldwide, relative to the population size, has stayed largely stable. Migration is, however, notoriously difficult to measure. There has been much emphasis on how to address data shortages in this area of research. With the increased digitization and the advent of social media, big data, and digital trace data, one of the widely used approaches in migration research is to repurpose data. Since such repurposed data has not been produced for research, researchers must make several decisions and overcome challenges to use it to answer migration research questions.
Computational approaches to migration research is a course designed to equip students with knowledge about migration theories, concepts, their operationalization and measurement, and methodological aspects of repurposing various sources of data from social media, online networks, geospatial data, metadata of scientific publications, and similar sources for migration research.
Learning Objectives
By the end of this course, students will be familiar with migration concepts and theories, different sources of data, and computational approaches to migration research. They will have knowledge of different research questions, data sources, and possible answers for the questions provided in Computational Social Science. Through hands-on lab sessions, they will acquire or advance their technical skills in R, Python, and SQL to use large-scale data. They will also learn about ethics in digital and computational migration research.
Reading Materials
Every week’s materials include required and recommended readings specific to that week’s topic, and some of the main ones are listed below.
Required Text
Drouhot, L. G., Deutschmann, E., Zuccotti, C. V., & Zagheni, E. (2022). Computational approaches to migration and integration research: Promises and challenges. Journal of Ethnic and Migration Studies, 0(0), 1–19. https://doi.org/10.1080/1369183X.2022.2100542
Recommended Texts
Akbaritabar, A., Danko, M. J., Zhao, X., & Zagheni, E. (2025). Global subnational estimates of migration of scientists reveal large disparities in internal and international flows. Proceedings of the National Academy of Sciences, 122(15), e2424521122. https://doi.org/10.1073/pnas.2424521122
Alessandretti, L., Aslak, U., & Lehmann, S. (2020). The scales of human mobility. Nature, 587(7834), 402–407. https://doi.org/10.1038/s41586-020-2909-1
de Haas, H. (2021). A theory of migration: The aspirations-capabilities framework. Comparative Migration Studies, 9(1), 8. https://doi.org/10.1186/s40878-020-00210-4
Kashyap, R., Rinderknecht, R. G., Akbaritabar, A., Alburez-Gutierrez, D., Gil-Clavel, S., Grow, A., Kim, J., Leasure, D. R., Lohmann, S., Negraia, D. V., Perrotta, D., Rampazzo, F., Tsai, C.-J., Verhagen, M. D., Zagheni, E., & Zhao, X. (2022, April). Digital and Computational Demography (tech. rep.). SocArXiv. https://doi.org/10.31235/osf.io/7bvpt
Massey, D. S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. E. (1993). Theories of International Migration: A Review and Appraisal. Population and Development Review, 19(3), 431. https://doi.org/10.2307/2938462
Salah, A. A., Bircan, T., & Korkmaz, E. E. (2022). New data sources and computational approaches on migration and human mobility. In Data Science for Migration and Mobility Studies. British Academy.
Tjaden, J. (2021). Measuring migration 2.0: A review of digital data sources. Comparative Migration Studies, 9(1), 59. https://doi.org/10.1186/s40878-021-00273-x
More information and the course’s syllabus
https://akbaritabar.github.io/Courses_Syllabus.html
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