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Using color in complex visualizations. Greg Trafton Naval Research Laboratory. Using color in complex visualizations. Greg Trafton Naval Research Laboratory. Complex Visualizations, Spatial Transformations, and Uncertainty. Greg Trafton Naval Research Laboratory.

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using color in complex visualizations

Using color in complex visualizations

Greg Trafton

Naval Research Laboratory

using color in complex visualizations2

Using color in complex visualizations

Greg Trafton

Naval Research Laboratory

complex visualizations spatial transformations and uncertainty

Complex Visualizations,Spatial Transformations,and Uncertainty

Greg Trafton

Naval Research Laboratory

collaborators reverse alphabetical
Collaborators (reverse alphabetical)
  • Susan Trickett (GMU, NRL postdoc)
  • Chris Schunn (LRDC, University of Pitt.)
  • Cara Stitzlein (GMU)
  • Lelyn Saner (University of Pittsburgh)
  • Raj Ratwani (GMU)
  • Paula Raymond (Center for Applied Research)
  • Farilee Mintz (ITT/NRL)
  • David Jones (APL, U. of Wash)
  • Funded by the Office of Naval Research
undisplayed uncertainty
Undisplayed Uncertainty
  • Notice these visualizations show no uncertainty
displaying uncertainty in this workshop
Displaying uncertainty in this workshop
  • Captain Knight’s COP visualization with color-coded dots showing nationality of each ship (lots of data, little uncertainty)
  • Data fusion examples (Duquet)
    • Uncertainty was not displayed at the visualization level, but rather calculated “under the hood” by various classifiers (e.g., 80% merchant, 20% unknown)
  • Col (Ret’d) Johansen’s comment: display that uncertainty so people can make the decisions!
problem
Problem
  • How do experts reason with uncertainty in these highly spatial domains that display little or no explicit uncertainty?
  • Is there a relationship between uncertainty and spatial reasoning? What is that relationship? What’s the process that experts go through to deal with uncertainty?
framework
Framework
  • Since uncertainty is critical in complex visual/spatial domains (Schunn, Kirschenbaum, & Trafton, under review-a, under review-b), people must understand how displayed uncertainty effects their task.
  • If uncertainty is not explicitly displayed, the person must add their own understanding of uncertainty to the visualization itself
  • Even if it is displayed, they may need to modify the displayed/represented uncertainty in some way if the person does not agree with it.
hypothesis
Hypothesis
  • Hypothesis: Experts must mentally add their own understanding of uncertainty to the visualizations by spatial transformations
    • A front may be displayed in one location but the forecaster is unsure where it will be. She may play with several possibilities (geographical or extent) and mentally move the front around until she’s happy with it.
    • This moving / extending / deleting of part of an onscreen object is a spatial transformation (Trafton et al., 2000; 2002; under review).
  • Specifically, experts use spatial transformations to create certainty out of uncertain data visualizations (qual vs. quant)
theoretical framework spatial transformations
Theoretical Framework:Spatial Transformations
  • A spatial transformation is a mental operation the user performs on a visualization or an image (happens inside the user’s head)
  • The visualization or image can be either internal (mental representation) or external (on-screen visualization) (for an excellent review of cognition on different diagrammatic representations, see Hegarty, 2004)
  • (Can be seen as a measure of spatial problem solving / work)
spatial transformations
Spatial Transformations
  • Mental rotation
    • Shepard & Metzler, 1985
  • Time series extrapolation
  • Object movement
  • Object animation
    • Bogacz & Trafton, 02; Hegarty, 94
  • 2D <-> 3D transformation
    • St. John et al., 2000; Trafton, 2002
  • Comparisons between images
    • Trafton et al., 2002
  • Other mental manipulations
spatial transformations and uncertainty
Spatial Transformations and Uncertainty
  • Difficulty of task will increase when uncertain and will show itself in increased number of spatial transformations (spatial reasoning)

Specific Hypothesis for this project:

  • When working with these complex visualizations, as people become uncertain, they will perform more ST than when they are certain.
sidebar
Sidebar
  • I’m interested in how experts deal with these issues.
  • All experts performing their own data analyses in their own domain with their own tools (Dunbar’s in vivo approach)
    • Requires protocol analysis, detailed examination of what people do and say (tons of rich data, not for the FOH)
    • Interviews after the analysis (retrospective protocols can be suspect!)
      • In general, believe what people do, not what they say they do and not what they remember
  • I’ll be examining two different domains (meteorology and fMRI) to show generalizability, etc.
study 1 in vivo method fmri and metoc
Study 1 (in vivo)Method (fMRI and METOC)
  • 14 experts examined while they were “thinking with their data” (took about an hour for each expert)
    • fMRI scientists were analyzing their data for the first time
    • Navy forecasters were predicting what the weather would be as part of their job
  • Talk-aloud protocols given
  • Coded spatial transformations of in vivo fMRI and in vivo METOC data [Trafton lab]
  • Coded (un)certainty of each in vivo minute [Schunn lab]
    • Certain
    • Mixed
    • Uncertain
predictions
Predictions
  • The more uncertain, the more spatial transformations
other times spatial transformations are used
Other times spatial transformations are used?
  • Are spatial transformations used when thinking about space?
  • YES!
summary
Summary
  • Uncertainty in these complex domains has a very strong spatial component
  • In these kinds of complex domains, when people are uncertain, they use more spatial transformations than when they are certain
  • Specifically, in order to understand and deal with the uncertainty in these complex, visual/spatial domains that do not show uncertainty, experts must use spatial transformations to create certainty out of uncertain data visualizations
  • People’s gestures also tell us what they’re thinking
so what
So what?
  • General info about collecting data from people
    • People’s memories aren’t that great – believe what they do, not what they remember doing
      • They may tell you how they are supposed to do a task (or how they think you want to hear it), not how they actually do it
      • They may not be able to describe how they solve problems in their domain of expertise (like driving a car)
    • Multiple converging experimental methods (protocol analysis, eye-tracking, gesture, etc.) are critical to triangulate
  • Specific info about these studies
    • Many military visualizations do not show uncertainty
    • Many COP systems emphasize data display, accuracy of data, etc. There should also be a strong emphasis on spatial cognition/spatial transformations
    • How you display uncertainty is a big question – minimize spatial transformations