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)
Most, if not all stories are more complicated than it
• 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
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.
Examples of uncertainty visualization
• Traditional plots:
– Error bar
– Box plot and Violin plot
– Confidence/Prediction Intervals
• Visual cues that may be used:
• Error bars are a graphical
representation of the
variability of data and are
used on graphs to indicate
the error, or uncertainty in a
• Effective way to present
errors and uncertainties
in the data
• Not appealing
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
• Violin Plot is one of the
extensions to Box Plot, in
that it adds density of the
values to the x-axis in each
• 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
• Prediction interval is the
range where you can expect
the next data point to
– A model is needed for
– Blur is a preattentive visual
– It is also a perfect visual
metaphor for uncertain data.
– It’s hard to quantify blurry
• Adding glyph to vector field
to present uncertainty
information is common
especially for GIS information
• Can be used to present
• Will save more common
visual cues (color)
• Vector glyph can be visually
• A. Cedilnik and P. Rheingans
used the density of
amplitude modulation in
annotation lines to mark the
uncertainty in each area.
(Cedilnik & Rheingans,
• 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
• 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
• Deconstructs data/information in a meaningful way?
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).
Gelman, A. (2004). Bayesian data analysis. Boca Raton, Fla.: Chapman &
Hengl, T. (2003). Visualisation of uncertainty using the HSI colour model:
computations with colours. Retrieved November 10, 2013,
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.
StackOverflow. (n.d.). creating confidence area for normally distributed
scatterplot in ggplot2 and R. Retrieved November 10, 2013,
Wainer, H. (2009). Picturing the Uncertainty world: How to understand,
communicate, and control uncertainty through graphical display.
Princeton: Princeton University Press.