Exploration Strategies for Discovery of Interactivity in Visualizations

Area of Science:

• Human-Computer Interaction
• Information Visualization
• Data Accessibility

Background:

• Interactive visualizations are common but their public discoverability is poorly understood.
• Effective design of public-facing visualizations requires insight into user interaction patterns.
• Making data accessible to the general public is a key goal in data science.

Purpose of the Study:

• To investigate how the general public discovers functionality in interactive visualizations.
• To identify user exploration strategies for interactive data visualizations.
• To inform the design of more discoverable and accessible data visualizations.

Main Methods:

• A lab study was conducted with participants interacting with public energy data visualizations.
• Data collection included eye-tracking, interaction logs, video, and audio recordings.
• Analysis focused on extracting and categorizing user exploration strategies.

Main Results:

• Distinct exploration strategies were identified among participants.
• Observed strategies varied in efficiency for discovering visualization functionality.
• Participant behavior provided insights into common interaction patterns.

Conclusions:

• Understanding user-driven exploration is crucial for designing effective interactive visualizations.
• Identified strategies offer actionable directions for enhancing visualization discoverability.
• Improved discoverability can lead to greater public engagement with data.