Using Palm Trees to Visualize Performance Across Multiple Dimensions — Greenbook

Using Palm Trees to Visualize Performance Across Multiple Dimensions

Palm trees are great looking, easy visualizations to show multiple dimensions in your data.

by Tim Bock

By Tim Bock

Palm trees are my favorite visualization. They look great. They are easy to understand. There is no other visualization that is as effective at decomposing performance across multiple dimensions.

Palm trees perfectly align visual elements with the core underlying structure of the data. This encourages our brain to separate out additive effects from interactions – without our brains even needing to know what these terms mean!

A great looking, easy visualization to show multiple dimensions in your data

The palm trees below represent the concerns American travelers have about different destinations. The tallest palm tree is for Egypt. It has the worst performance (i.e., the most concerns). Hover your mouse over the fronds of the palm tree, and you will see the breakdown of these concerns.

Safety is clearly the top concern, although there are lots of other important concerns. Compare China. China has many issues in common with Egypt, but Not being understood is a bigger concern, and Safety is less of a concern.

If you want to play around with the examples used in this post, or create Palm Tree Plots of your own, you can do so by clicking here.

There is no better way to visualize data with multiple dimensions

I am making a bold claim here. However, it is a claim I can back up.

There is a standard best-practice visualization for data with multiple dimensions: a line chart (I explain the logic of this below). Check out the line chart version of the same data, shown below. Yes, if you dig it is the same data and shows the same patterns. However, your brain has to work really hard to extract them.

Perfect alignment between visual elements and underlying structure of data

The visual elements of a palm tree plot mirror the structure of a two-way analysis of variance, and allow us to visualize the type of conclusions typically obtained via formal statistical testing: separation of the two main effects, and the interaction.

If you want to play around with the examples used in this post, or create Palm Tree Plots of your own, you can do so by clicking here.

Acknowledgement

The OECD’s wonderful Better Life Index inspired the visualizations in this post.

This article was originally posted here.
data visualization