Consider the data table below. It contains numbers on the yearly imports and exports between England on the one hand, and Denmark and Norway on the other hand. Now look at the table and try to answer the following questions:
| Year | Import | Export |
|---|---|---|
| 1700 | 71,1 | 32,8 |
| 1705 | 74,5 | 40,9 |
| 1710 | 82,6 | 59 |
| 1715 | 87,2 | 77,9 |
| 1720 | 96,8 | 75,2 |
| 1725 | 102,6 | 71,3 |
| 1730 | 96,4 | 64,7 |
| 1735 | 93,7 | 60,5 |
| 1740 | 92,9 | 65,1 |
| 1745 | 92,5 | 74,3 |
| 1750 | 90,1 | 77,4 |
| 1755 | 79,9 | 82,8 |
| 1760 | 76,6 | 117,5 |
| 1765 | 79,6 | 151,8 |
| 1770 | 83,8 | 163,8 |
| 1775 | 90,4 | 175,7 |
| 1780 | 92,7 | 185,4 |
With some mental effort, you are probably able to answer the questions posed above by scanning the table and comparing the numbers horizontally and vertically. But finding the answers is not straightforward, and does require some mental brain work.
When the same data is represented in a visual way, answering these questions suddenly becomes much easier. Check the chart below: finding the year with the highest imports is as easy as finding the location where the yellow line is at its peak (around 1725), and finding the moment the trade balance shifts is just a matter of finding the location were the yellow and red line cross each other (around 1755).
Source: William Playfair, public domain
The chart above was published in 1786 by William Playfair in his book Commercial and Political Atlas. Playfair was a Scottish engineer and he was the first to represent time series as a line chart (in the same book, he also invented the bar chart). In the introduction of the book he writes the following:
As knowledge increases amongst mankind, and transactions multiply, it becomes more and more desirable to abbreviate and facilitate the modes of conveying information from one person to another, and from one individual to the many.
So Playfair understood that publishing numbers in tables is not the optimal way for people to find and understand trends and proportions in numbers: turning the numbers into visual lines and shapes makes it much easier for people to makes sense of numerical (and other) data.
So, in a way, data visualisation is a form of assistive technology: by mapping numbers and data to the colour, size and position of visual elements, the trends and proportions in the numbers are made more accessible. Without it, making sense of the numbers would take a lot more time and effort, and may not even be possible at all for some people.
By its nature, data visualisation is only accessible to sighted people: blind persons do not enjoy the benefits of data visualisation. While we are already having a hard time determining what the optimal way is to present numbers to sighted people, making data and numbers understandable to blind people is especially challenging, and requires converting numbers and the patterns in data into information that can be perceived by other senses than sight.
But next to people who lack sight completely, there is a much bigger group of people who suffer from some kind of partial loss of sight. These conditions include daltonism (different kinds of colour blindness), myopia, farsightedness and glaucoma. People suffering from these conditions can still have access to the advantages of data visualisation, but they require data visualisations to have an optimal design and digital and analog tools to gain access.
However, in order to make sense of data visualisations, sight alone is not enough. People need to understand what rules were used to translate numbers into the visual elements presented to them, how to decode the data in a chart, and finally be able to take away the main message of a visualisation. This requires cognitive capacities, familiarity with charts and numeracy from your audience. Ignoring this will lead to a mismatch between the interface (= the data visualisation) and the people using it to get access to the data.
Making data visualisation accessible to people with visual impairments and making visualisations understandable for as many people as possible, is the main topic of this training. To do so, we can apply the POUR principles introduced in the previous a11y? module to data visualisation. But because the POUR principles were not specifically developed with data and data visualisation in mind, 3 other principles are added to POUR: Compromising, Assistive and Flexible.
The so called POUR-CAF principles were developed by the designers of the Chartability framework, a set of tests to ensure the accessibility of visualisations and interfaces. The rest of this module is based on these principles and the tests that are part of the framework. The Chartability framework is properly introduced and covered in the Chartability: a framework for auditing accessibility module.
In order to be understood, a data visualisation and its components need to be perceived by the viewer. Assuming that some people in your audience might suffer from visual impairments, or might even lack sight completely, this means that the information contained in a visualisation needs to be presented in a way that other senses can perceive it.
