TIPS FOR BETTER

DATA VISUALISATION

Iman EftekhariPrincipal 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

Nominalscale

Ordinalscale

Intervalscale

Ratioscale

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 EstimationMost 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