Forecasting Migration Patterns and Land Border Encounters
This paper leverages open source “big data” intelligence to develop predictive models that can provide timely, relevant and accurate indications, warning, and tracking of migration flows / movements of large groups (> 100 persons) through South and Central America to the southwest border of the United States. We describe experiments with a live forecasting setup, development and refinement of predictive models, and how machine learning models can yield insight into the factors underlying mass migration.
Authors
- Raquib Bin Yousuf
- David R. Mares (ORCID: https://orcid.org/0000-0001-6299-0829)
- Naren Ramakrishnan (ORCID: https://orcid.org/0000-0002-1821-9743)
- Shengzhe Xu (ORCID: https://orcid.org/0009-0008-8589-6526)
- P. J. Butler (ORCID: https://orcid.org/0000-0003-0468-6794)
- Brian Mayer (ORCID: https://orcid.org/0000-0002-9925-8276)
- Nathan Self (ORCID: https://orcid.org/0000-0001-8075-9866)
Institutions
- University of California San Diego (US)
- Virginia Tech (US)
Publication Details
- Published
- 2024-12-15
- DOI
- https://doi.org/10.1109/bigdata62323.2024.10825714
- Primary Topic
- Migration and Labor Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.00