The Truthful Art: A Review

The very first thing I thought about Alberto Cairo’s brand new book The Truthful Art is that the title on the cover sports no capitalization. This shows gumption! This shows panache! This shows that the author may have some new, snazzy, and possibly risky design tricks to teach me!

And guess what? I am happy to report that the contents absolutely lives up to my first impression. And I’m not even saying this because I’m completely psyched that my name is in the index or that he graciously refers to Cartographer’s Toolkit as “A good book to have by your side when choosing styles for your maps.”*

I've been indexed!

I’ve been indexed!

This is Alberto Cairo’s third book on data visualization. It manages to be both entertaining and full of substance in its main goal of taking to task misleading data visualizations, telling us why they are sub-optimal or even downright lies, and how we can do better.

In one particularly important section he describes the idea of applying controls to your data. We cartographers are familiar with the idea that we should normalize by population in a map of, say, the numbers of people who go to graduate school in each state, but we may be less familiar with other methods of normalization. One of the examples in the book is that a visualization of traffic fatalities by state might be more beneficial if we also knew how much people commute in each state. If we don’t apply these controls then the reader can come to erroneous conclusions. That bit is from Chapter 3, The Truth Continuum, which is quite possibly my favorite chapter in the book as it really reminds us of all the ways our data can trip us up.

The Truthful Art, Interior

Another bit that really resonated with me was the motto that he says he shares with his students:

“It’s more complicated than that.” 

And also: “Good visualizations shouldn’t oversimplify information.” I’ve been saying that for years (see When is Complexity Okay? and Not to Complicate Things…But More on Complexity.) Yes, clutter is bad but taking away all the details is also bad. In practice this means reducing unnecessary visuals such as neatlines around map legends but leaving in supporting charts and graphs that further explain the map data, for example.

One quibble, which I need to think more about, is that the text equates isarithmic maps such as weather and temperature with kernel density maps. However, kernel density analyses display a measure of the highest concentration of points in a given dataset, not the connections between points of real data. So I don’t typically think of kernel densities as isarithmic even though on first appearance they seem like similar beasts. But like I said I need to think more about this and feel free to weigh in, because I suppose kernel density visualizations really are typically shown as areas with the same value, but the values themselves really shouldn’t be seen as indicating anything other than relative density in a dataset, which to me is very different from lines connecting discreet data points.

Back to the positive notes, as these should far outweigh any quibble that I may have just uttered and that I haven’t even spent enough time really thinking through (something that Cairo says is SO important for any kind of visualization–Think It Through!). For one thing, the infographics explaining map projections are superior to the ones I’ve put together for my books. Also, Cairo goes into great detail on classification schemes for choropleth maps, which I think is a very good thing considering this is where a lot of data journalists (his primary audience, perhaps) need to be more cognizant. 

In all, a wonderful addition to my library that I’m sure I will go return to again and again in the years to come!

*Disclaimer which turns out not to be much of a disclaimer: I was sent a free copy of this book. But! I was not asked to review it. Furthermore, since I had already purchased this book on pre-order before finding out that I was getting a free copy, I believe we could almost say that no disclosure is needed though clearly instead of that we now have one very complicated and drawn out run-on disclosure.

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