Abstract: Tracking
group membership dynamics over time is a persistent challenge in visual analytics,
particularly when dealing with complex, multidimensional datasets. Existing
tools often struggle to visualize dynamic group transitions while preserving
attribute relationships and maintaining consistent group definitions. We present
GroupView, a visual framework designed to explore temporal data and group
dynamics to address this. GroupView enables users to slice data into time-based
segments and create dynamic groupings, facilitating the identification of
trends and patterns that may otherwise remain hidden. Its features include
automated grouping based on data similarities, combinatorial grouping for
richer insights, and custom grouping for tailored analysis. A heuristic user
study involving visualization experts provided feedback on usability and analytical
value, highlighting the strengths of GroupView in intuitive exploration and
insight discovery. These features position GroupView as a valuable tool for
analysts and researchers working with evolving datasets, offering new avenues
for uncovering trends and tracking group-level changes over time.
Teaser: GroupView’s analysis of U.S. county-level poverty data from 2012 to 2022 highlights significant patterns and trends in poverty dynamics. By leveraging automated and custom grouping methods, the tool provides a detailed exploration of how poverty status evolves across different socio-economic and geographic dimensions.
Shown here is a GroupView visualization for the year 2017, illustrating attributes such as Percent Rural, S1701_C01_001E (Total Population in Poverty), S1701_C02_001E (Male Population in Poverty), S1701_C03_001E (Female Population in Poverty), Total Population, and Group Size. The target column Poor Status is represented with color-coded categories: Poor Status Never (green), Poor Status Lost (red), Poor Status Gained (orange), and Poor Status Remained (yellow). This visualization highlights temporal and regional disparities in poverty, socio-economic factors such as gender differences, and rural-urban divides, alongside group size variations and their impact on poverty trends.
Video: Watch it to get a quick overview of the GroupView interface:
Paper: M. Singh, K. Mueller, "GroupView: A Visual Framework for Exploring Group Membership Dynamics over Time," Data, 10(8): 133-161, 2025. PDF
Funding: SUNY
System Administration under SUNY Research Seed Grant Award 23-01-RSG