What is a visualisation?
A visualisation, at best, is a visual explanation. It uses the the brain's visual perception system to help us understand something - a large data set, a set of relationships or spatial relationships.
Visualising data allows us to:
- analyse and understand data better
- move past two dimensional flatland lists
- encourage the exploration of data complexity in engaging, interesting and compelling ways
- illustrate the stories behind the data.
Including data visualisations in your collections has the potential to increase the reuse, discovery and connectivity of your research data.
Tufte1 suggests that a good visualisation should:
- show the data
- induce the viewer to think about the substance rather than about the methodology
- avoid distorting what the data have to say
- present many numbers in a small space
- make large data sets coherent
- reveal the data at several levels of detail from a broad overview to the fine structure.
Tips on creating a great visualisation
- A really good visualisation conveys a message or story. Ask yourself, what I am trying to say with this data?
- Keep it simple. In particular avoid things like:
- three dimensional graphs for two dimensional data
- the gratuitous use of colour and other embellishments.
- Look for natural mappings. Time series often work best along the x-axis of a chart. Spatial data may work best on a map.
- Highlight relevant information. This can be done in many ways such as:
- subduing the colour of supporting information
- exploiting line thicknesses
- using size for emphasis.
- Make comparisons clear.
Examples of good visualisations
This visualisation presents wind on the earth and allows the user to zoom and pan the globe. It is quite hypnotic, but also very informative. It spawned a number of other weather visualisations.
After viewing this visualisation, it is really hard to argue about what's really warming the world.
Examples of bad visualisations
While it is always easy to criticise other peoples' work, it is not always productive. However, there is some value in looking at visualisations that have failed and learn from their mistakes. Here are some sites which collect such examples:
Resources for using colour in visualisations
Colorbrewer is a great resource for selecting palettes. It allows users to select the type of data (sequential, diverging or qualitative), specify the number of partitions and also to select attributes like 'colourblind safe'. The site is based on the research of Cynthia Brewer, a professor at Pennsylvania State University.
A Better Default Colormap for Matplotlib (YouTube 19 min) - It is about choosing the new default colourmap for Matplotlib and the difficult issues faced when choosing colours for a visualisation library.
Libraries for visualisation
Libraries for putting data on maps
- Visualising data at the Oxford Internet Institute has 23 examples of different ways to visualise data.
- During March/April 2018, Martin Schweitzer from ANDS presented two webinars about data visualisation.
- Periodic table of visualisation methods - mouse over each type of visualisation to see which ones might best 'visualise' your research data.
- Gapminder - ideas for presenting visualisations of data. This site includes links to the actual data and the visualisation.
- 39 studies about human perception in 30 minutes
- What I learned recreating one chart using 24 tools
- Few, S. Show Me the Numbers; Designing Tables and Graphs to Enlighten. Analytics Press, 2004
- A thousand words: visualising statistical data - 2018 Ihaka Lecture Series
1.Tufte, E. The Visual Display of Quantitative Information. 2nd ed. Graphics Press, Cheshire, 2001