1. Visualising Knowledge:
Why? What? How?
Ben O‟Steen

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“Data Visualisation” is a terrible term
http://www.theguardian.com/news/datablog/2012/sep/20/women-participation-peace-deals
http://www.fastcodesign.com/1672161/infographic-how-people-died-in-the-20th-century

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Why?
• Data Visualisation:
– the "main goal of data visualization is to communicate
information clearly and effectively through graphical means. It
doesn‟t mean that data visualization needs to look boring to
be functional or extremely sophisticated to look beautiful.
Vitaly Friedman (2008) "Data Visualization and
Infographics" in: Graphics, Monday
Inspiration, January 14th, 2008.

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Why?
• Data Visualisation:
– the "main goal of data visualization is to communicate
information clearly and effectively through graphical means.
It doesn‟t mean that data visualization needs to look boring to
be functional or extremely sophisticated to look beautiful.
Vitaly Friedman (2008) "Data Visualization and
Infographics" in: Graphics, Monday
Inspiration, January 14th, 2008.

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Where do we want to start then?
• Expression of a set of ideas or knowledge in order to
share or explore that knowledge, clearly and effectively.

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Where do we want to start then?
• Expression of a set of ideas or knowledge in order to
share or explore that knowledge, clearly and effectively.
• The “language of expression” you choose will direct affect
how you are interpreted and by whom.

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Where do we want to start then?
• Expression of a set of ideas or knowledge in order to
share or explore that knowledge, clearly and effectively.
• The “language of expression” you choose will direct affect
how you are interpreted and by whom.
From http://archives.nypl.org

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Language of Expression?
• A „shorthand‟, a shared vocabulary.
– Graphical, verbal, contextual or implied.

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Language of Expression?
• A „shorthand‟, a shared vocabulary.
– Graphical, verbal, contextual or implied.
• All expressions have an origin. They are all inventions.

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Language of Expression?
• A „shorthand‟, a shared vocabulary.
– Graphical, verbal, contextual or implied.
• All expressions have an origin. They are all inventions.
– Acronyms

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Language of Expression?
• A „shorthand‟, a shared vocabulary.
– Graphical, verbal, contextual or implied.
• All expressions have an origin. They are all inventions.
– Acronyms
– „0‟ for the concept of zero (976 Persian or 1598 for first
English use) and „=„ for equality (1557 by Robert Recorde:)

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Language of Expression?
• A „shorthand‟, a shared vocabulary.
– Graphical, verbal, contextual or implied.
• All expressions have an origin. They are all inventions.
– Acronyms
– „0‟ for the concept of zero (976AD or 1598 for first English
use) and „=„ for equality (1557)
– Abstraction of numbers (eg we can use the same word for
three fish as for three bales of hay. The concept of „three‟ is
abstract.)

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Charts and graphs
• Easy to forget that these are all inventions too.
Florence Nightingale‟s flower diagrams

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Basic tools of Design
• Must read: “The Non-Designer‟s Design Book”
• Examples it uses are a little dated, but the core tenets of the
book are solid and based on how we interpret information.

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Four main principles of design:
• Contrast:
– If things are not the same, make them very different
• Repetition:
– Repeat visual elements to direct attention on the things you
want expressed.
• Alignment:
– Nothing should be placed arbitrarily. Align like concepts on
the same lines.
• Proximity:
– Elements related to one other should be close together.

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Consider the bar chart:
• Contrast:
– Differ heights, colours and textures

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Consider the bar chart:
• Contrast:
– Differ heights, colours and textures
• Repetition:
– „Chart Junk‟ is not helpful. It should be obvious that there is
nothing out of the ordinary, apart from the data.

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From http://wtfviz.net/

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Consider the bar chart:
• Contrast:
– Differ heights, colours and textures
• Repetition:
– „Chart Junk‟ is not helpful. It should be obvious that there is
nothing out of the ordinary, apart from the data.
• Alignment:
– Make it easy to „read‟ the axes, with regard to the bars by
visually alignment. (See last image!)

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Consider the bar chart:
• Contrast:
– Differ heights, colours and textures
• Repetition:
– „Chart Junk‟ is not helpful. It should be obvious that there is
nothing out of the ordinary, apart from the data.
• Alignment:
– Make it easy to „read‟ the axes, with regard to the bars by
visually alignment.
• Proximity:
– Do not order the bars randomly along the bottom axis.

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Look at wtfviz.net for „anti-patterns‟
• A staggering list of examples of data visualisations NOT to
copy.
• wtfviz.net

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You can‟t please everyone!
• You will have to rely on your audience having some
understanding of the language of expression you choose
• The most important things to keep in mind are:
– how much they will need to know before they can
understand it and
– how much effort they are willing to put in to do so. (This part
is often overlooked.)

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Non-graphical forms of expression
• Titanic: The Artifact Exhibition
– Each attendee was given a boarding pass with a passenger‟s
name on it. They were told at the end of the experience if
they were one of the few who survived.

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Non-graphical forms of expression
• Titanic: The Artifact Exhibition
– Each attendee was given a boarding pass with a passenger‟s
name on it. They were told at the end of the experience if
they were one of the few who survived.
• Games:
– SOS Titanic: aim is to get passengers to lifeboats, and gives
you a score which you can compare to the actual event. Time
of sinking represented visually.
– Tulipmania 1637: A gamified „simulation‟ of the driving forces
behind the first huge stock market bubble.

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A „Classic‟ example

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“The Chromatographic Chronicle of English
History…”, 1864

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Exploring unknown data
• Example random data Experimental Data

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How to create data visualisations?
• It never needs to be „fancy‟ or require a special tool.
• The goal is to be “clear and effective”
• Sometimes, a small table of figures is all it needs.

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Data Quantisation – turning ideas into
numbers
• Personal interpretation always plays a part.
– Author: choice of metrics, view and perspective.
– Audience: “Misinterpretation” and misunderstanding
• NB sometimes that misinterpretation is not by accident…

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Data Quantisation – turning ideas into
numbers
• Personal interpretation always plays a part.
– Author: choice of metrics, view and perspective.
– Audience: “Misinterpretation” and misunderstanding
• NB sometimes that misinterpretation is not by accident…

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Misinterpretation
• Round things versus Rectangles
– Human beings are generally bad at judging relative sizes of
round things, compared to rectangular.
– If you want to hinder judgement of relative
proportions, represent the data in a pie, conic or circular
chart, rather than in a rectangular representation.

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Hidden Subjective Choices
• Grouping data together by a subjective, ill-defined or
controversial metric.
• Hard to spot but crucial to understanding the nature of the
data.
• Try to be aware of these choices!

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From: http://iwasnteventhere.tumblr.com/post/7882377942/reply-to-mondrian-vs-rothko-footprints-and-evolution

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Checklist:
• Understanding of what information you want to share or
explore.
• A quantitative measure of that information, in a useful form
like a spreadsheet.
• Inspiration for how you want to display it!
• Create a visualisation and assess how well it fits.
– Try and try again!

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Tools and resources
• Mia Ridge‟s excellent list of tools, documentation and ideas:
– https://chasegoingdigital.wordpress.com/2013/02/19/links-for-
data-visualisation-for-humanities-researchers/
• Bamboo DiRT‟s list of visualisation tools: (Big list, not
thematically grouped as Mia‟s list is)
– http://dirt.projectbamboo.org/categories/visualization
• For data visualisation inspiration:
– http://d3js.org/ - remember the effort required by your
audience!

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Questions?