Forecasting Migration Patterns and Land Border Encounters

Raquib Bin Yousuf, Shengzhe Xu, Patrick Butler, Brian Mayer, Nathan Self, David Mares, Naren Ramakrishnan

Abstract: 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.

External IDs:dblp:conf/bigdataconf/YousufXBMSMR24