Without any indication, your audience tends to process all the information you show. To make a graph effective, you must guide the audience. Preattentive attributes, such as size, colour and position on the page are interesting artifacts to signal what is important and direct your audience’s attention.
- Leverage the preattentive attributes to give the expected focus on graphs
- Avoid information overload
Preattentive attributes
Preattentive attributes are visual features that our brains can process quickly and effortlessly, without the need for conscious thought. These attributes include characteristics such as color, shape, and size, and are often used in data visualization to draw the viewer’s attention to important information. By utilizing preattentive attributes, we can effectively communicate the key findings of our data and help our audience to quickly and easily understand the information being presented.
Recommended reading Chapter four from the book "Storytelling with Data: A Data Visualization Guide for Business Professionals", by Cole Nussbaumer Knaflic (Editor: John Wiley & Sons, Nov 18, 2015)
Recommended video "Storytelling with Data" by Cole Nussbaumer Knaflic, Talks at Google, 2015. If you want to focus on how to focus your audience's attention, watch from 17:05 to 26:22.
Color
Color is an important aspect of visualizing data, as it can be used to draw attention to specific elements, convey meaning, and improve the overall aesthetic of a visualization. Studies have shown that color can affect people’s emotions, so it is important to choose colors carefully in order to effectively communicate the intended message. In addition, color can be used as a preattentive attribute, which means that it can help the viewer quickly and unconsciously perceive patterns in the data. This can be particularly useful in large and complex datasets, as it allows the viewer to easily identify trends and relationships.
Recommended reading Choosing Colors for Data Visualization, by Maureen Stone. This 10 pages document gives some basic principles of color design, then the problem of legibility (readibility), and conclude with some guidelines for picking colors based on these principles.
Recommended tool Encyclopedia Colorpedia is a website that offers information about color palettes and their use in design, including pre-designed palettes and the ability to create custom ones. One advantage of the site is that it offers color names in multiple languages, useful for designers and developers working with clients or users who speak different languages. It is a valuable resource for anyone interested in creating visually appealing and accessible designs.
Size
Size is an important factor to consider in data visualization, as it can significantly impact how the audience perceives the information being presented. Large objects are more likely to attract attention and be perceived as more important, while smaller objects may be overlooked. It is important to use size effectively to communicate the relative importance of different elements in the visual.
Additionally, the interaction between size and color can be used to convey information. When working with small areas, it can be effective to use high saturation colors to make them more noticeable. On the other hand, when dealing with large areas, using low saturation colors can help to create a sense of balance and hierarchy. Overall, understanding the impact of size on perception and using it effectively in combination with color can help to create more effective and engaging data visualizations.
Recommended video Effect of Size in Coursera course "Information Visualization: Applied Perception", New York University Tandon School of Engineering
Recommended reading Influence of size on color perception, by Maureen Stone. Quite technical. Focus on Figure 1 (and the author's comments about it) to understand the difference in colour perception with different sizes.
Position
People usually start reading the top of a page first. Obviously, the main recommendation is to write the most important thing there. Don’t forget the title, put it on top, and of course make this title informative.
Eyes are naturally going everywhere-anywhere-if they are not guided. The second recommendation is to draw viewers’ eyes to the part of the figure where you expect them to retrieve the important information: use size, color, but also text to verbalize what must be read and retain.
Recommended video 5 Eye-Tracking Discoveries - 5 design tips for creating optimal column charts. From a study conducted at the University of Applied Sciences Upper Austria.
Accessibility
In data visualization, accessibility is crucial to ensure that all audiences, including those with visual impairments, can understand and interpret the information being presented. There are several types of visual impairments that can affect a person’s ability to perceive and interpret visual information, including Deuteranomaly, Deuteranopia, Protanomaly, Protanopia, Tritanomaly, and Tritanopia.
Deuteranomaly and Protanomaly are forms of color blindness that cause difficulty distinguishing between certain shades of red and green. Deuteranopia and Protanopia are more severe forms of color blindness that cause difficulty distinguishing between all shades of red and green. Tritanomaly and Tritanopia are forms of color blindness that cause difficulty distinguishing between certain shades of blue and yellow.
In order to make data visualizations accessible to people with visual impairments, it is important to choose color palettes that are easy to distinguish for those with these types of color blindness. One option is to use color palettes designed specifically for accessibility, such as the ColorBrewer or David Nichols’ tools. Another option is to use high contrast color combinations to ensure that the visual information is easily distinguishable.
Overall, considering accessibility in data visualization is essential to ensure that all audiences can understand and interpret the information being presented. By choosing color palettes that are easy to distinguish for those with visual impairments, we can make sure that our visualizations are accessible to all.
To go further
Preattentive attributes are important visual cues that allow us to quickly and easily interpret and understand data visualizations. By utilizing color, size, and position effectively, we can draw the viewer’s attention to the most important information and effectively communicate our message.
To further explore this topic, we recommend researching the work of Stephen Few, who has written extensively on the subject, as well as Maureen Stone’s website.
Recommended reading The Encyclopedia of Human-Computer Interaction, Data Visualization for Human Perception, by Stephen Few.
Recommended reading More here on Maureen Stone's website: https://research.tableau.com/user/maureen-stone