  Street-level visual appearances play an important role in studying social
systems, such as understanding the built environment, driving routes, and
associated social and economic factors. It has not been integrated into a
typical geographical visualization interface (e.g., map services) for planning
driving routes. In this paper, we study this new visualization task with
several new contributions. First, we experiment with a set of AI techniques and
propose a solution of using semantic latent vectors for quantifying visual
appearance features. Second, we calculate image similarities among a large set
of street-view images and then discover spatial imagery patterns. Third, we
integrate these discovered patterns into driving route planners with new
visualization techniques. Finally, we present VivaRoutes, an interactive
visualization prototype, to show how visualizations leveraged with these
discovered patterns can help users effectively and interactively explore
multiple routes. Furthermore, we conducted a user study to assess the
usefulness and utility of VivaRoutes.
