Difference between revisions of "Responsible visualization"

Revision as of 21:12, 13 October 2015

What is a Visualisation?

A visualisation is defined as a visual presentation of data (http://dictionary.reference.com/browse/visualisation). That covers a lot of things, from maps (visual representations of where things are, typically with shapes, labels, icons and colours all meaning something) to data-based drawings (e.g. XKCD data-based comics), graphs (line graphs, bar charts, pie charts etc), infographics and dashboards.

Types of Visualisation

There are many types of visualisation, in as many forms as the human imagination can devise, but you’ll see some types a lot more than others. Some of these are:

• Line graphs
• Bar charts
• Column charts
• Maps
• Choropleths
• Sankey diagrams
• Network visualisations

Case Study: Human Rights Funding Research

1. How do you show the findings?

2. How would you should who is funding where?

First, do no harm

We create visualisations because they usually have more persuasive power and are more accessible to more people than columns of numbers. With that power comes responsibility, especially when those visualisations are used to make decisions that affect vulnerable people.

There's an RDF blogpost series and [event on responsible visualisation https://responsibledata.io/forums/data-visualization/], which will cover these issues in depth. Until then, here are some things to consider when you create visualisations.

Do you have to redact data?

• Aggregate data at different levels.

Make sure the data actually represents the comparison in the true form

• Aggregate and quantify using statistics.

Be true to the data.

• Stay away from assumptions
• Infographics are propositions

How do people interact with your visualisation?

What is the story you are trying to tell

• Clear, concise story-telling strategy.

Interaction

• How do people interact with your data.
• Is it overwhelming?

Understand how people see visualisations

Disconnect about the text and the visual if done by two different individual.

• Annotations are important so make it part of the visual in a way that's not separable even while someone is remxing.

Choosing the right colors.

• Make sure it looks good on print
• Consider colorblindness
• The human eye can see more shades of grey

Annotation

• Labels
• Titles
• Tweets
• Description

Communicate uncertainty in the visual, if possible

Pie charts

1. Do not do 3D, pretty please! 2. Do not show more than 3 data points. 3. Good for quick prototypes.

Reading List

• Mushon Zer-Aviv, [Disinformation Visualisation: How to Lie with Dataviz https://visualisingadvocacy.org/blog/disinformation-visualization-how-lie-datavis]
• Sajjad Anwar, [You can't always/just throw a map at a problem https://github.com/geohacker/maps-mayhem/blob/master/README.md]
• Pandey et al, [The Persuasive Power of Data Visualisation http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474695]