This document discusses visualizing uncertainties in data. It defines types of uncertainty like statistical variation and errors. It provides examples of traditional uncertainty visualization techniques like error bars, box plots, and confidence intervals. It also discusses using other visual cues like color, blur, glyphs, and amplitude modulation to represent uncertainty. The document raises questions about how to integrate uncertainty visualization into the design process and how it can challenge modern storytelling with data. It provides references for further research on visualizing and communicating uncertainties and unknowns.

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!