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Introduction to Visualizing Uncertainties

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Introduction to Visualizing Uncertainties

  1. 1. Visualizing Uncertainties Kai Li IST 719 “Information Visualization” Nov. 11, 2013
  2. 2. The world is uncertain in nature
  3. 3. What is uncertainty? or Wh?t ?s unc?rt??nty?
  4. 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. 5. (Pang et al., 1997)
  6. 6. Why should be care about uncertainties?
  7. 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. 8. Source: (Wainer, 2009)
  9. 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. 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. 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. 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. 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. 14. Adding Extra Layers
  15. 15. Color (Hengl, 2003)
  16. 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. 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. 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. 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. 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. 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. 22. Thank you!

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