Modeling push-and-pull factors in cross-border migration with deep learning
The challenge of securing the safe, orderly, and humane processing of migrants who arrive at our Southwest border is renderedincreasingly complicated due to the growing volume and substantial variations of this influx of migrants. Advancing our understanding of why,how, and where migration occurs across U.S. borders will assist the Department of Homeland Security (DHS) in its effort to better respond tothese challenges and increase the security of our expansive border.
The objective of this research is to investigate the extent to which strategic decision-making influences countries that currently do not possess a robust chemical weapons (CW) program. The research team will conduct a systematic examination of relevant dynamics surrounding CW using robust and replicative red teaming exercises. The detailed data captured will explore the decision elements that might precipitate changes in current strategic CW perceptions and identify early warnings of nations and groups pursuing CW weapons and the type and timing of those activities. The project will also indicate if any identified tactics may be replicated, across multiple countries and regions.
Human migratory decisions are the result of a complex range of interacting factors, including economic, social, and environmental vulnerabilities. This project is harnessing the power of data science to develop innovative analytical capabilities and software solutions to support DHS in its efforts to respond to migration-related challenges at the border. More specifically, we are developing a deep learning-based approach to model the interplay of a broad spectrum of parameters, ranging from socioeconomic data conveyed through census surveys to environmental conditions as they are captured in satellite imagery, and advance our ability to understand migration patterns and their variations
The framework and analytical capabilities developed in this project will assist DHS in its planning for resource allocation, to optimally respond to migration-related stress on our national security along the border. The same solutions can also support policy-making aimed at relieving future migratory stresses by directly addressing some of the conditions that affect patterns of migration. Transition of software solutions has already begun.
Research Leadership Team
Principal Investigator: Anthony Stefanidis, College of William and Mary
Co-PI: Daniel Miller Runfola, College of William and Mary