  Real-time tweets can provide useful information on evolving events and
situations. Geotagged tweets are especially useful, as they indicate the
location of origin and provide geographic context. However, only a small
portion of tweets are geotagged, limiting their use for situational awareness.
In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning
model for city-level geolocation prediction, and integrate it with a visual
analytics system tailored for real-time situational awareness. We provide
computational evaluations to demonstrate the superiority and utility of our
geolocation prediction model within an interactive system.
