SYBIS - Data Visualisation

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Iman Ef

Data Visualisation Tips

Technologie Business

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

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)

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

List of Recommended DV Tools
http://selection.datavisualization.ch

Q&A
Iman Eftekhari
iman.eftekhari@agilebi.com.au

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SYBIS - Data Visualisation

1. TIPS FOR BETTER DATA VISUALISATION Iman Eftekhari Principal Consultant iman.eftekhari@agilebi.com.au 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?

9. Colour is relative Same!

10. Basics & Principles

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

21. Chart Chooser http://labs.juiceanalytics.com/chartchooser/index.html

22. Colour Brewer http://colorbrewer2.org

23. List of Recommended DV Tools http://selection.datavisualization.ch

24. Q&A Iman Eftekhari iman.eftekhari@agilebi.com.au

Hinweis der Redaktion

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

SYBIS - Data Visualisation

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