Two takeaways from “Pulling Strings”

The Pulling Strings project visualizes ten metrics, from freedom and governance scores to happiness and wellbeing indexes, for every country. The visualization offers a readable result despite the number of indicators and lets the readers both learn and explore about the world population situation. A mobile version provides a different experience associating both horizontal and vertical scrollings.

Think visual encodings

When I first began my journey into data visualization, I had no idea I had to build a chart according to the data in my disposal and the message I wanted to convey. With practice, welcomed feedbacks and self-learning, I realized I “had to pick” a graphical representation mainly depending on those two.

Like me, most of us have heard some “do not use pie charts to compare values” or other “prefer lines for showing trends”. And it is good. It is one of the first step to a better use of data visualization to explore or expose your data. For a while then, I thought the basic component of a graphical representation was the chart types.

And I heard about Jacques Bertin, Cleveland, McGill, or Mackinley. I heard about perceptual properties of visual variables. It changed everything to me. I did not think about chart types anymore but about position, color, shape and other variables that are defined as visual encodings. It was like discovering the grammar and the roots of a foreign language after having repeated already well-formed sentences to survive.

MacKinley APT (A Presentation Tool), 1986

Bars were not bars anymore but mostly lengths, pie charts were angles, lines were directions. And better, I could combine those attributes with each other. Mixing colors, textures, positions to build a well-formed graphical representation of my own. Now, everytime I develop something, I feel “in control”.

You can see Pulling Strings as an experiment to use those visual encodings to show several metrics and to reveal insights in one graphical representation without using the regular chart types.

Graphical representation of the United States In Pulling Strings

  • Positions for the freedom and governance metrics
  • Directions for the difference between the two agregate metrics
  • Area for the population and wellbeing score
  • Color saturation for the happiness index

In my humble opinion, it is ok to experiment, to not use regular chart types, to be innovative and creative in our field. It is one of the best way for our practice to evolve, to adapt, to not become an extinct language. But let’s not forget about the roots, the history and the basic components of our media.

Help me understand what I see

With innovative forms of data visualization, it is our duty to help people interpret the representation correctly. It is our duty to minimize intellectual gymnastics. It is our duty to not let our graphical representation misguide our audience.

And it is the main role of legends. As data visualization practitioners, the legend already takes a great place in our projects. But too often it’s missing. Too often it’s not as well crafted as the rest. Do not underestimate your legends. The legend can be a great opportunity to interact with your audience or add context. I have already mentioned evolution and adaptation, I am sure legends will have a role to play.

In Pulling Strings, a full chapter is dedicated to explain what we see, how it is build and how interpret it. It was important as the visual is unique and specific to the project. I also let my legend endorse one more role here: introducing the data sources and the metrics.

Legend In Pulling Strings

It was my two takeaways from Pulling Strings. I hope it will help you in your own data visualization journey.

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