Date Thesis Awarded


Access Type

Honors Thesis -- Open Access

Degree Name

Bachelors of Science (BS)


Data Science


Dan Runfola

Committee Members

Anthony Stefanidis

Alex Nwala


Being able to predict migratory flows is important in ensuring political, social, and economic stability. In the wake of violence, unrest, natural disasters, and social pressures, millions of mi- grants have fled Central America in search of a better life. However, due to the infrequent nature and high cost of census data, there is a need for a more remote and up to date approaches. Con- volutional Neural Networks offer a computer vision based approach that is cheaper and with significantly less lag. In this study, we seek to evaluate the effectiveness of different convolu- tional neural networks in predicting migratory patterns in Guatemala. Using a combination of open source satellite images and census data, we implement a variety of network architec- tures that seek to predict migration both through regression and classification techniques. We find that while regression and classification models do not prove to be an effective tool, there is an opportunity for additional research into the spatial nature of migratory prediction. Our preliminary results affirm the need for continued research and advancement in deep learning algorithms to predict migratory flows.

Included in

Data Science Commons