introduction to visualizing uncertainties

22
Visualizing Uncertainties Kai Li IST 719 “Information Visualization” Nov. 11, 2013

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Page 1: Introduction to Visualizing Uncertainties

Visualizing Uncertainties

Kai LiIST 719 “Information Visualization”

Nov. 11, 2013

Page 2: Introduction to Visualizing Uncertainties

The world is uncertain in nature

Page 3: Introduction to Visualizing Uncertainties

What is uncertainty?or

Wh?t ?s unc?rt??nty?

Page 4: Introduction to Visualizing Uncertainties

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)

Page 5: Introduction to Visualizing Uncertainties

(Pang et al., 1997)

Page 6: Introduction to Visualizing Uncertainties

Why should be care about uncertainties?

Page 7: Introduction to Visualizing Uncertainties

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?

Page 8: Introduction to Visualizing Uncertainties

Source: (Wainer, 2009)

Page 9: Introduction to Visualizing Uncertainties

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)

Page 10: Introduction to Visualizing Uncertainties

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

Page 11: Introduction to Visualizing Uncertainties

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

Page 12: Introduction to Visualizing Uncertainties

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.

Page 13: Introduction to Visualizing Uncertainties

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.)

Page 14: Introduction to Visualizing Uncertainties

Adding Extra Layers

Page 15: Introduction to Visualizing Uncertainties

Color

(Hengl, 2003)

Page 16: Introduction to Visualizing Uncertainties

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)

Page 17: Introduction to Visualizing Uncertainties

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)

Page 18: Introduction to Visualizing Uncertainties

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)

Page 19: Introduction to Visualizing Uncertainties

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?

Page 20: Introduction to Visualizing Uncertainties

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_using_the_HSI_colour_model_computations_with_colours

Page 21: Introduction to Visualizing Uncertainties

ReferenceMahoney, D. P. (1999). The picture of uncertainty. Retrieved November 11,

2013, from http://www.cgw.com/Publications/CGW/1999/Volume-22-Issue-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-confidence-area-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.

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Thank you!