Data Visualisation Tips

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