Data Visualization

In “The Mind’s Eye – A Look at Data Visualization Psychology,” Tetiana Donska writes:

“People have used visuals to help tell stories and illustrate answers to essential questions for thousands of years. The earliest example of data visualization is probably a map from around 27,000 years ago, and for a long time, it was rare to see data visualizations for anything other than geography.

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“Data visualization has a long history and made significant advances between the 17th and 19th centuries. The idea of presenting quantitative data graphically came about in the 18th century when Rene Descartes invented a two-dimensional coordinate system to display values for mathematical operations. That system was improved when William Playfair pioneered graphical forms as we know them today. He is credited with having invented line and bar charts, and later the pie chart and circle graph.

“Over the years, using quantitative graphs became more widespread. Their methodology and effectiveness increased significantly in the second half of the 20th century with the publication of Jacques Bertin’s book The Semiology of Graphics. His work was crucial because he found that in order to present information intuitively, clearly, and efficiently, visual perception operated according to rules and patterns that could be followed.

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“Understanding the key elements of human perception and the cognitive process is an essential part of designing excellent data visualizations. When working on products with data visualization needs—be it a B2B dashboard or a financial app—designers need to be mindful of the human brain’s visual perception process and fundamental data visualization design principles.”

She writes an interesting article, in which she discusses and illustrates visual variables involved in data visualization: position along a common scale, length, direction, angle, area, volume color saturation, color.

Read also “Data Visualization – Best Practices and Foundations” by Mayra Magalhaes Gomes. She discusses some principles of good data visualization, and give some examples of the good and the bad. She concludes with:

“Good data visualization should communicate a data set clearly and effectively by using graphics. The best visualizations make it easy to comprehend data at a glance. They take complex information and break it down in a way that makes it simple for the target audience to understand and on which to base their decisions.

“As Edward R. Tufte pointed out, ‘the essential test of design is how well it assists the understanding of the content, not how stylish it is.’ Data visualizations, especially, should adhere to this idea. The goal is to enhance the data through design, not draw attention to the design itself.

“Keeping these data visualization best practices in mind simplifies the process of designing infographics that are genuinely useful to their audience.”

Some nice examples:

And in “A Complete Overview of the Best Data Visualization Tools,” Cameron Chapman provides some nice examples of data visualizations, for example: