Uncertainty Visualization Study Group Notes – 09/17/2012
Discussion
Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty Boukhelifa et al., 2012 link
- Undefined uncertainty in the paper
- Is it qualitative? quantifiable?
- 2 categories of uncertainty: epistemic / epistemological (can be decreased with knowledge) vs aleatoric (exists on principle / does not decrease with knowledge)
- Does uncertainty need to be defined before it can be depicted?
- This paper does not make an attempt to fully operationalize uncertainty.
- User-study end of the paper: issues/comments on the conclusions of the tests
- Did they overreach? Certainly exhibited strong inclination to approve sketchiness as a potential representation of uncertainty
- Methodological issues with some of the experiments; however, multiple attempts at examination of sketchiness’ effectiveness as a representation of uncertainty
- Sketchiness increasing interaction by to nature (early design, wireframes) or simply b/c it’s novel?
- People’s reaction to a new approach certainly providing benefit
- Sketchiness “unprofessional” as opposed to “objective” and “authoritative”?
- Take away: No intuitive encoding? In a real-world scenario, you will necessarily be given some context.
- Is asking for intuition without context fair?
- Do we want to give people a user study communicating uncertainty explicitly?
- Intuitive understanding may be more useful when accompanied by an explicit legend
- Expressive rather than intuitive encoding channel for uncertainty?
- Different visualization methods for different kinds of uncertainty
- finer partitioning than epistemological/aleatoric?
- ordinal vs. categorical, geospatial vs. numeric, etc.
- Uncertainty as a separate variable: misleading, because it is often dependent on another variable
- Use primary attribute for data, secondary attribute for uncertainty
- Testable: extrinsic vs. intrinsic representations of uncertainty, and how effectively they communicate data
- Extrinsic: assumes that people choose hierarchically; intrinsic assumes a more simultaneous approach
- Geospatial representations are limiting: avenues of representation are already used
- 2D single representations may work better for some kinds of uncertainty data: data where primary concern is borders, e.g. weather pattern uncertainty
- Mechanical Turk: is it usable? Do they use it properly?
- There are some issues with data quality (reliability of self-reported demographics)
- More traditional methods safer in terms of reception, at least on the cognitive side
- less unknowns, more controlled
- rooted heavily in tradition where students required to participate in research
- Mechanical Turk is useful for gathering large numbers of subjects, in very short tasks
- Online studies using the participant pool vs. in-person studies with the participant pool have produced similar results.
- We can make our own online studies as well
- Should we look at the literature evaluating Mechanical Turk?
- Uncertainty quantification has been a topic of investigation in the mathematical community for 15 years; less investigated and recognized by the visualization community
Administrative Info
Next Week: Paper on 1D and 2D representations of uncertainty + short paper on extrinsic vs. intrinsic representation (if we have time)
A separate weekly meeting (immediately after this one?) will happen each week to generate a methodology for an experiment.