Led by Lisa Singh, Katharine Donato, Ali Arab, and Susan Martin
This project is an interdisciplinary collaborative across universities, NGOs, and national labs. The core work takes place at Georgetown University and York University.
While there are a number of theories and frameworks explaining the drivers of movement, or more specifically forced migration, many are very linear and do not adequately account for shifts in patterns of displacement. This makes it difficult to understand when, where and how displacement will take place. The Big Data and Displacement project proposes a new theoretical model for capturing the drivers of movement within the decision-making process.
This Big Data and Displacement project aims to develop methods and approaches for using big data in conjunction with traditional administrative and survey data to understand and eventually forecast mass movement of people who are forced to migrate. The project’s initial case study is Iraq, a case that will be used to highlight both the theoretical and data driven methodologies used to advance research in this space.
Both indirect indicators of movement (big data variables) and exploratory tools are available to social scientists studying forced migration. This project creates both new visual analytic tools and a data portal to support research in this area. These interactive visualizations attempt to highlight the relationship between chatter and buzz from 100s of millions of tweets in both English and Arabic on Twitter and ISIS movement through Iraq.
Since 2014, the interdisciplinary Big Data & Displacement team has published results about forced migration, algorithms for extracting signals from unstructured data and our project methodology. This work has been supported by the National Science Foundation (NSF), Social Sciences & Humanities Research Council, Canada, and the Massive Data Institute (MDI) at Georgetown University.
Globally Unified Air Quality Project
The Digital Service Collaborative at the Beeck Center for Social Impact & Innovation
Use of Facial Recognition and Surveillance Technologies