  The goal of visual analytics is to create a symbiosis between human and
computer by leveraging their unique strengths. While this model has
demonstrated immense success, we are yet to realize the full potential of such
a human-computer partnership. In a perfect collaborative mixed-initiative
system, the computer must possess skills for learning and anticipating the
users' needs. Addressing this gap, we propose a framework for inferring focus
areas from passive observations of the user's actions, thereby allowing
accurate predictions of future events. We evaluate this technique with a crime
map and demonstrate that users' clicks appear in our prediction set 95% - 97%
of the time. Further analysis shows that we can achieve high prediction
accuracy typically after three clicks. Altogether, we show that passive
observations of interaction data can reveal valuable information that will
allow the system to learn and anticipate future events, laying the foundation
for next-generation tools.
