https://doi.org/10.1109/pacificvis53943.2022.00024

Wang, S., Mondal, D., Sadri, S., Roy, C. K., Famiglietti J.S., and Schneider, K. A.: SET-STAT-MAP: Extending Parallel Sets for Visualizing Mixed Data. In Proceedings of the 15th IEEE PacificVis symposium, 2022.

Multi-attribute dataset visualizations are often designed based on attribute types, i.e., whether the attributes are categorical or numer- ical. Parallel Sets and Parallel Coordinates are two well-known techniques to visualize categorical and numerical data, respectively. A common strategy to visualize mixed data is to use multiple infor- mation linked view, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this pa- per, we design visualizations for mixed data, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution SET-STAT-MAP is a harmonious combination of three in- teractive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a geospatial map view (visualizes the spatial information). We augment these com- ponents with colors and textures to enhance users’ capability of analyzing distributions of pairs of attribute combinations. To im- prove scalability, we merge the sets to limit the number of possible combinations to be rendered on the display. We demonstrate the use of SET-STAT-MAP using two different types of datasets: a me- teorological dataset and an online vacation rental dataset (Airbnb). To examine the potential of the system, we collaborated with the meteorologists, which revealed both challenges and opportunities for SET-STAT-MAP to be used for real-life visual analytics.

Computer Science Core Team, Refereed Publications

https://doi.org/10.1109/pacificvis53943.2022.00024
https://www.cs.usask.ca/faculty/dmondal/Papers/Mondal_Set_Stat_Map.pdf