The ZingChart team recently acquired a copy of Design for Information by Isabel Meirelles. It is “an introduction to the histories, theories, and best practices behind effective information visualizations.” From the introduction to the appendix, we found interesting tidbits in this book that would be helpful to anyone trying to improve their data visualizations and charts.

design for information book cover

This 2013 book is full of some amazing visuals.

Want to know what this mesmerizing chart is about? You’ll just have to pick up a copy of Design for Information to find out. (Hint, it uses data from Flickr).

Data visualization skills

In discussing the type of people who are usually tasked with creating data visualizations, there are designers and technical/scientific personnel. Meirelles tries to bridge the gap between these two skill sets by including additional readings and sources for readers to learn more. It is a nuanced task, to create a data visualization. It “requires both analytical and visual/spatial methods of reasoning.” You can see how the ZingChart team used these methods in our Fantasy Football Dashboard explainer.

Dataviz terms used by the graphic design community

Early on in the book, Meirelles makes clear the difference between infographics and information design.

“Infographics stand for visual displays in which graphics (illustrations, symbols, maps, diagrams, etc) together with verbal language communicate information that would not be possible otherwise.”

“Information design, on the other hand, is broadly used to describe communication design practices in which the main purpose is to inform, in contrast to persuasive approaches more commonly used in practices such as advertising.”
Basically, information design can cover a lot of ground, including systems and all types of visualizations.

 page from Design for Information book

Meirelles also shows many dataviz examples from some experts whose work we admire, including Alberto Cairo, the NYT Graphics team, Stamen Design, and Mike Bostock. Plus, she gets historical with Florence Nightingale’s 1800’s dataviz and Beck’s iconic London tube map.

Most common types of network dataviz layouts

After some exhaustive case studies, Meirelles examines some other common types of network dataviz layouts. They include:

These examples of the types of dataviz listed were created with the ZingChart JavaScript charting library.

Textual structures… word clouds

Towards the end of the book, Meirelles devotes significant time to some case studies concerning word clouds.

A word cloud dataviz from search engine queries in January 2014

She sets them apart from other dataviz options for their strength in textual analysis. She lays out some challenges of this type of dataviz:

  • No inherent ordering
  • High dimensionality
  • Information linked to text
  • Long words give the impression of greater weight
  • Color encoding can confuse

Data types

At the very end, Meirelles gives some comprehensive definitions to the three data types so they can be effectively encoded in dataviz:

  • Nominal Data

  • Ordinal Data

  • Quantitative Data

We wrote about some of these types of data when we discussed color selection on the ZingChart dataviz blog a while back.

What are you reading?

Have you read any good data visualization books lately? Share your favorite titles with us in the comments section below.