We describe a practical approach for visualizing multiple relationships defined on the same dataset using a geographic map metaphor, where clusters of nodes form countries and neighboring countries correspond to nearby clusters. Our aim is to provide a visualization that allows us to compare two or more such maps (showing an evolving dynamic process, or obtained using different relationships ). In the case where we are considering multiple relationships, e.g., different similarity metrics, we also provide an interactive tool to visually explore the effect of combining two or more such relationships. Our method ensures good readability and mental map preservation, based on dynamic node placement with node stability , dynamic clustering with cluster stability, and dynamic coloring with color stability.

Here is the link to the project webpage.

Research funded in part by NSF grants 1053573 "ImageQuest: Citizens Advancing Biology with Calibrated Imaging and Validated Analysis".

We study the use of games and crowd sourcing to capture and analyze scientific trajectory data. We design games to employ citizen scientists to trace (i) static objects in images and (ii) moving objects such as ants or bees in video data. We study the challenging data validation problem of selecting for each object, one accurate trajectory from the several (possibly inaccurate) trajectories which are submitted by the citizen scientists playing our game. For numerical values finding the consensus of several estimates is easily done by taking an average . Computing the consensus of traces is not as straight forward. We design algorithm which use clustering, geometric alignment, sampling, as well as other methods for this purpose.

Here is the link to the project webpage.

Research funded in part by NSF grants 1053573 "ImageQuest: Citizens Advancing Biology with Calibrated Imaging and Validated Analysis".