CRII: CHS: Visualizing Data Relationships Across Multiple Views

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In an increasingly data-rich world, effective design of tools for analyzing and visualizing data in domains from health to national security can have great social and scientific value. A common situation for analysts is needing to manage multiple views of a dataset at the same time, raising questions of how visual analysis tools can help analysts see relationships between separate views. Current tools require extensive effort, such as repetitively selecting individual nodes in a graph and manually following information highlighted in a histogram, scatterplot, and map to identify abnormal behaviors. This project will investigate methods for analytics tools to flexibly and unobtrusively display relationships across multiple visualizations for supporting data analytics. Doing this will deepen our scientific understanding of how multiple visualizations support sensemaking of data and expand the design space of multi-view visualizations. The work will also enrich educational materials in data science and human-centered computing courses.This project includes four major lines of work. First, the team will design a usable overview of cross-view data relationships, supported by computations, for helping users to relate information from multiple visualizations. This design highlights a core concept of "context separation", which creates stand-alone views to plot computed, cross-view data relationships instead of relying on displayed visual elements. Second, the researchers will create a novel layout method for spatially organizing multiple visualizations by considering data relationships among them. Third, the team will develop novel user interactions for manipulating displayed cross-view data relationships, including merging and splitting, to support both analysis and navigation. Fourth, the team will conduct user experiments to study the effectiveness and cognitive load of these cross-view data relationship visualizations. In addition, techniques developed in this project will be open-sourced, so researchers, data scientists, practitioners, and educators can use, extend or modify them for data analysis, tool development, and teaching.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.