Parallelspaces: Simultaneous Exploration of Feature and Data for Hypothesis Generation
Deokgun Park, Jungu Choi, Niklas Elmqvist
We present ParallelSpaces, a novel method to explore bipartite datasets in both feature and data dimensions. This dyadic data is displayed as weighted bipartite graphs using scatterplots in two separated visual spaces, where each entity is positioned according to multi-dimensional properties of each entity or similarity in preferences. Selecting or navigating in one space is reflected in the other space, so that organic visual patterns can be formed to facilitate the characterization of underlying groupings. To aid visual pattern recognition we also overlay a contour plot based on kernel density estimation. We have implemented two instantiations of ParallelSpaces for (a) movie preferences, and (b) business reviews as Web-based visualizations. To validate the method, we performed a qualitative user study involving eleven participants using these Web-based tools to explore data and collect deep insights.