SYBIS - Data Visualisation
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SYBIS - Data Visualisation

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Data Visualisation Tips

Data Visualisation Tips

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  • Challenge: pick the best encoding (or mapping) from many possibilities

SYBIS - Data Visualisation SYBIS - Data Visualisation Presentation Transcript

  • TIPS FOR BETTER DATA VISUALISATION Iman Eftekhari Principal Consultant iman.eftekhari@agilebi.com.au www.agilebi.com.au
  • Agenda • What is DV? • Tips for more effective DV • Q&A
  • What is Data Visualisation?
  • A Picture is Worth a Thousand Numbers
  • 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
  • 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
  • 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)
  • Colour is relative Same or different?
  • Colour is relative Same!
  • Basics & Principles
  • 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
  • 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)
  • Importance Ordering of Perceptual Properties
  • Effective Design • Mapping data to visual attributes: • Faster to interpret • More distinctions • Fewer errors
  • 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
  • Cannot express the facts • Which colour is greater than the other?
  • Expressing facts not in the data • Length is interpreted as a quantitative value • Length of bar says something untrue about data
  • Effective Design • Importance Ordering • Expressiveness • Consistency
  • 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
  • Bertin’s Retinal Variables Jacques Bertin, a French cartographer, Semiology of Graphics
  • Chart Chooser http://labs.juiceanalytics.com/chartchooser/index.html
  • Colour Brewer http://colorbrewer2.org
  • List of Recommended DV Tools http://selection.datavisualization.ch
  • Q&A Iman Eftekhari iman.eftekhari@agilebi.com.au