Movers: A Visual Analytics System for Exploring Refugee Movement with Social Media Data
Knowledge of refugee movements can help NGOs and governments better target and provide services to refugees. However, currently available data about refugee movements are sparse and coarse from both spatial and temporal perspectives. We consider social media data such as Twitter, though very “noisy”, having potential to provide useful information about refugee movements. We thus design and build a web-based visual analytics system, Movers, to support analysts to systematically explore geo-referenced Twitter data and identify refugees. The identification of individual Syrian refugees who fled to Germany is used as a case study. The system can also be extended to filter other movement related data for identifying specific type of users.
with Feng Sun, Fridolin Linder, Mark Simpson, Yanan Xin, Ying Xu, Alexander Savelyev, and Alan MacEachren