Poster Presented at the American Astronomical Society 227th Meeting

1. Figure 3. Navigation bar example.
Figure 4. Details on demand example.
Figure 5. Small multiples example.
Figure 6. Additional visualization options example.
Contact Information:
• Matthew C. Doyle: mdoyle@oswego.edu
• Roger S. Taylor: roger.taylor@oswego.edu
Overview
Problem:
• Challenge of interpreting large astronomical data sets
(e.g., Large Synoptic Sky Survey).
Potential Solution:
• Utilization of data visualization software.
This Study:
• Critique Caltech/JPL iViz data visualization software.
Method
Participants
• Participants for the walkthrough consisted of 14 SUNY
Oswego undergraduate students.
Procedure
• Cognitive walkthroughs of scenarios and tasks for
goals of researchers investigating large synoptic
surveys.
• Specific metrics (accuracy, duration, non-critical errors)
of interface and visualization capabilities.
Interface Cognitive Walkthrough
• Scenarios
• Supernovae Identification, Kepler Object
Identification, RR Lyrae Classification
• Task
• Users identified different types of stellar objects
(e.g., RR Lyraes) and retrieve specific attributes
(e.g., amplitude).
Visualization Cognitive Walkthrough
• Scenario
• Eclipsing Binary & RR Lyrae Identification
• Task
• Users created visualization to help classify the data
points into Eclipsing Binary or RR Lyrae.
Evaluation of Data Visualization Software for Large Astronomical Data Sets
Matthew C. Doyle, Roger S. Taylor, Shashi Kanbur, Damian Schofield
SUNY Oswego
S. G. Djorgovski, Ciro Donalek
California Institute of Technology
Scott Davidoff
Jet Propulsion Laboratory
Critique: Visualization
Figure 2. Visualization to distinguish between RR Lyrae & W UMa.
Flexibility of Use
• Heuristic: system should satisfy range of skillsets.
• Recommendation: Providing wider range of visualizations
can assist needs of both novice and expert users.
Filtering
• Heuristic: Users should have efficient means of interactive
filtering.
• Recommendation: Relocating search function for more
efficient filtering.
Details on Demand
• Heuristic: Providing details on demand regarding a
specific data point allows user to make informed decisions
about the possible grouping of data.
• Recommendation: Details on demand should show users
descriptive statistics of the data point and allow users to
create lists of outliers.
Small Multiples
• Heuristic: Small multiples allow user to visualize same
data across multiple linked visualizations.
• Recommendation: Users would benefit from ability to
visualize linked datasets of outliers for additional insights
into relationships and trends in data.
Critique: Interface
Figure 1. Screen for assigning data variables to specific
visualization parameter slots.
Spatial Organization
• Heuristic: Important buttons or icons that drive core
functionality should be visible to users when necessary.
• iViz: Buttons, search function, and system status were
hard to locate for users.
Aesthetics & Minimalistic Design
• Heuristic: Unnecessary design items should be eliminated
because they may distract the user from core functionality.
• iViz: Application displays some unnecessary information
when prompting the user to visualize variables (see Figure
1).
Visibility of System Status
• Heuristic: User should be able to view the system status
at all times.
• iViz: System status should be moved to the top of screen.
Consistency & Standards
• Heuristic: Interface should conform to current design
standards and be consistent with other platform
conventions.
• iViz: Sprocket icon has two different functions (see Figure
2).
Mock-up of Recommended
Visualizations