SYBIS - Data Visualisation
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This document provides tips for effective data visualization (DV). It discusses how the human visual system works differently than cameras, with selective attention and constant adaptation. Effective DV designs consider visual perceptual properties like color, position, length, area and encode data dimensions accordingly. Designs should also follow principles like expressing only the facts in the data set and maintaining consistency. Overall, the document aims to explain how understanding human visual cognition can help create visualizations that facilitate insight from data.
SYBIS - Data Visualisation
- 1. TIPS FOR BETTER DATA VISUALISATION Iman Eftekhari Principal Consultant [email protected] www.agilebi.com.au
- 2. Agenda • What is DV? • Tips for more effective DV • Q&A
- 3. What is Data Visualisation?
- 4. A Picture is Worth a Thousand Numbers
- 5. Thinking With Our Eyes • 70% of body’s sense receptors reside in our eyes • “The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centres” Colin Ware, Information Visualization, 2004 • Important to understand how visual perception works in order to effectively design visualisations
- 6. How the Eye Works • The eye is not a camera! • Attention is selective (filtering) • Cognitive processes • Psychophysics: concerned with establishing quantitative relations between physical stimulation and perceptual events
- 7. Eyes vs. Cameras • Cameras • Good optics • Single focus, white balance, exposure • Full image capture • Eyes • Relative poor optics • Constantly scanning • Constantly adjusting focus • Constantly adapting (white balance, exposure) • Mental reconstruction of image (sort of)
- 8. Colour is relative Same or different?
- 11. Classification of Data Types • N Nominal (labels) • Fruits: Apples, Oranges, … • O Ordinal • Quality Rating: A, AA, AAA • Q Quantitative • Interval (location of zero arbitrary) • Date, geometric point • Ratio (zero fixed) • Physical measurements, counts, amounts
- 12. Pyramid of Scales Nominal scale Ordinal scale Interval scale Ratio scale Logical/ math operations × ÷ N N N Y + - N N Y Y < > N Y Y Y = ≠ Y Y Y Y S. S. Stevens, On the Theory of Scales of Measurement (1946)
- 13. Importance Ordering of Perceptual Properties
- 14. Effective Design • Mapping data to visual attributes: • Faster to interpret • More distinctions • Fewer errors
- 15. Mackinlay’s Expressiveness Criteria • A set of facts is expressible in a visual language if: The sentences (i.e. the visualisation) in the language express all the facts in the set of data, and only the facts in the data. Mackinlay, APT (A Presentation Tool), 1986
- 16. Cannot express the facts • Which colour is greater than the other?
- 17. Expressing facts not in the data • Length is interpreted as a quantitative value • Length of bar says something untrue about data
- 18. Effective Design • Importance Ordering • Expressiveness • Consistency
- 19. Relative Magnitude Estimation Most accurate Least accurate Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Color (hue/saturation/value) Spring 2010 I 247 19
- 20. Bertin’s Retinal Variables Jacques Bertin, a French cartographer, Semiology of Graphics
- 23. List of Recommended DV Tools http://selection.datavisualization.ch
Editor's Notes
- #15: Challenge: pick the best encoding (or mapping) from many possibilities