1. Visualizing Uncertainties
Kai Li
IST 719 “Information Visualization”
Nov. 11, 2013

2. The world is uncertain in nature

3. What is uncertainty?
or
Wh?t ?s unc?rt??nty?

4. Definitions of uncertainty
• A classification of statistical uncertainty:
–
–
–
–
Statistical variations or spread
Errors and differences
Minimum-maximum range values
Noisy or missing data (Pang, Wittenbrink, & Lodha, 1997)

5. (Pang et al., 1997)

6. Why should be care about
uncertainties?

7. Most, if not all stories are more complicated than it
looks
• The highest and lowest
kidney cancer death rates
happen in nearby counties,
which tend to be “rural,
mid-western, southern, and
western”. (Gelman, 2009)
• Is it because of any
geographical or
environmental factors?

8. Source: (Wainer, 2009)

9. We should, at least to some extent, expose the
complicity and uncertainties in the data
• Manuel Lima:
– Aspire for knowledge (Lima, 2009)
• Howard Wainer:
– Effective display of data must
• remind us that the data being displayed do contain some
uncertainty, and then
• characterize the size of that uncertainty as it pertains to the
inferences we have in mind, and in so doing
• help keep us from drawing incorrect conclusions through the
lack of a full appreciation of the precision of our knowledge.
(Wainer, 2009)

10. Examples of uncertainty visualization
• Traditional plots:
– Error bar
– Box plot and Violin plot
– Confidence/Prediction Intervals
• Visual cues that may be used:
–
–
–
–
Color
Blur
Glyph
Amplitude

11. Error Bar
• Error bars are a graphical
representation of the
variability of data and are
used on graphs to indicate
the error, or uncertainty in a
reported measurement.
– Pros:
• Effective way to present
errors and uncertainties
in the data
– Cons:
• Not appealing

12. Box Plot and Violin Plot
• Box Plot is a good way to
present groups of numerical
data through their quartiles
and outliers, thus to present
their variance and
uncertainty.
• Violin Plot is one of the
extensions to Box Plot, in
that it adds density of the
values to the x-axis in each
plot.

13. Confidence/Prediction Interval
• Confidence interval is a
range of values so defined
that there is a specified
probability that the value of
a parameter lies within it.
– A number of different models
to calculate confidence
interval.
• Prediction interval is the
range where you can expect
the next data point to
appear.
– A model is needed for
prediction.
(StackOverflow, n.d.)

14. Adding Extra Layers

15. Color
(Hengl, 2003)

16. Blur
• Pros:
– Blur is a preattentive visual
variable;
– It is also a perfect visual
metaphor for uncertain data.
• Cons:
– It’s hard to quantify blurry
areas.
(Kosara, 2001)

17. Adding glyph
• Adding glyph to vector field
to present uncertainty
information is common
especially for GIS information
visualization:
– Pros:
• Can be used to present
multi-facet uncertainty
information
• Will save more common
visual cues (color)
– Cons:
• Vector glyph can be visually
annoying
(Mahoney, 1999)

18. Amplitude modulation
• A. Cedilnik and P. Rheingans
used the density of
amplitude modulation in
annotation lines to mark the
uncertainty in each area.
(Cedilnik & Rheingans,
2000)

19. Questions
• 1. How can we integrate visualizing uncertainties into the
workflow of visualization design?
• 2. How to integrate uncertainty visualization to the bigger
graph to present meaningful information?
• 3. How can we evaluate the outcomes of uncertainty
visualization?
• 4. How can uncertainty visualization challenge the modernist
ways that stories are told using visualization?
– Is there a way to make visualization that
• Exposes the inaccuracy and discourse in the visualization per
se; or
• Deconstructs data/information in a meaningful way?

20. Reference
Andrej Cedilnik and Penny Rheingans (2000). Procedural Annotation of
Uncertain Information. Proceedings of IEEE Visualization '00, pp. 77-84.
Cedilnik, A., & Rheingans, P. (2000). Procedural annotation of uncertain
information. In Visualization 2000. Proceedings (pp. 77–84).
doi:10.1109/VISUAL.2000.885679
Gelman, A. (2004). Bayesian data analysis. Boca Raton, Fla.: Chapman &
Hall/CRC.
Hengl, T. (2003). Visualisation of uncertainty using the HSI colour model:
computations with colours. Retrieved November 10, 2013,
from http://www.academia.edu/1217951/Visualisation_of_uncertainty_u
sing_the_HSI_colour_model_computations_with_colours

21. Reference
Mahoney, D. P. (1999). The picture of uncertainty. Retrieved November 11,
2013, from http://www.cgw.com/Publications/CGW/1999/Volume-22Issue-11-November1999-/The-picture-of-uncertainty.aspx
Pang, A. T., Wittenbrink, C. M., & Lodha, S. K. (1997). Approaches to
uncertainty visualization. The Visual Computer, 13(8), 370–390.
doi:10.1007/s003710050111
StackOverflow. (n.d.). creating confidence area for normally distributed
scatterplot in ggplot2 and R. Retrieved November 10, 2013,
from http://stackoverflow.com/questions/7961865/creating-confidencearea-for-normally-distributed-scatterplot-in-ggplot2-and-r
Wainer, H. (2009). Picturing the Uncertainty world: How to understand,
communicate, and control uncertainty through graphical display.
Princeton: Princeton University Press.

22. Thank you!