What Makes a Good Visualization?

This post has been on my backlog for about a year, but I’m finally getting to it. That’s the power of backlogs!

Xenographics

The Xenographics website provides a number of creative ways of visualizing data. Some of them are great. Others are…not. So I decided to review a few and try to explain my reasoning along the way. I definitely won’t review every one, but let’s look at some of these.

Bracket Probabilities

Let’s start with one I like: bracket probabilities.

What I like about this is that it’s a really good “newspaper” graphic. In other words, this is a visual which works well in explaining probabilities to the general public. It shows the probability of victory for each team across each game, includes a few notes on key events, and explains a lot of information, though in a fairly large amount of space.

Manhattan Plots

Next up is the Manhattan Plot:

This is another plot I like, though it’s more of a “business” graphic than a “newspaper” graphic. This is sort of like a histogram with a large number of categories.

Multi-Class Hexbins

Now we’re going to look at one I’m not as fond of: multi-class hexbins.

Because it’s something I don’t like, let me dive into a bit about why. The idea here is to show differences over an area, such as voting for one of two political candidates by district. It does so with a series of hexagonal pie charts. Pie charts are typically not a very good type of visual for a few reasons:

• They take up a good amount of space
• Humans have a difficult time comparing angles, especially angles with small differences
• Once you get past 2 or 3 elements, you greatly increase the likelihood that you’ll end up with tiny slivers of pie chart which become almost impossible to read with a legend

Whenever you want to use a pie chart, there is usually a better visual available. In this case, I’d prefer a heatmap with gradients from orange to purple. It’d be hard to tell exact numbers with a heatmap as well, but this gives you a feeling of false precision that a heatmap doesn’t.

A Few More I Like

I’m a big fan of UpSet plots, which provide a lot of information in a relatively compact space.

The problem with UpSet plots is that it takes some getting used to. Laura Ellis has a great post on how UpSet plots work, but the short version of this is taht the bar chart on the left indicates number of Twitter followers per person. The dot-and-line plot at the bottom indicates combinations and the column chart at the top tells how many people fall into each category. For example, 634 people follow all of the Twitter accounts, and 10,864 follow @dataandme but none of the others.

There are a couple of dot-style plots that I like as well: the Raincloud plot and the Dot-boxplot. These provide you the relevant information in a dot plot (including jitter, which is really important as your dataset gets dense) along with a broader signifier of density.

Defining Important Characteristics

In an episode of Shop Talk a year ago, I talked in detail about six characteristics that I think are important when choosing a visual. They are as follows:

• Intuitive — A visual should be easy for a person to understand despite not having much context. In some cases, you have the opportunity to provide additional context, be it in person or in a magazine. That lets you increase the complexity a bit, but some visuals are really difficult to understand and if you don’t have the luxury to provide additional context, it makes your viewer’s job difficult.
• Compact — Given two visuals, the one which can put more information into a given space without losing fidelity or intuitiveness is preferable. This lets you save more screen real estate for additional visuals and text. There are certainly limits to this philosophy, so consider it a precept with diminishing marginal returns.
• Concise — Remove details other than what helps tell the story. This fits in with compactness: if you have unnecessary visual elements, removing them lets you reclaim that space without losing any fidelity. Also, remove unnecessary coloration, changes in line thickness, and other things which don’t contribute to understanding the story. Please note that this doesn’t mean removing all color—just coloration which doesn’t make it easier for a person to understand what’s happening.
• Consistent — By consistency, what I mean is that the meaning of elements on the visual does not change within a run or between runs. Granted, this is more relevant to dashboards than individual visuals, but think about a Reporting Services report which uses default colors for lines on a chart. If you refresh the page and the colors for different indicators change, it’s hard for a person to build that mental link to understand what’s happening.
• Glanceable — Concise and consistent visuals tend to be more glanceable than their alternatives. Glanceable means that you are able to pick out some key information without needing to stare the the visual. Ideally, a quick glance at a visual tells you enough of what you need to know, especially if you have seen the same visual in prior states.
• Informative — This last consideration is critical but often goes overlooked. The data needs to be useful and pertinent to users, describing the situation at the appropriate grain: it includes all of the necessary detail for understanding while eschewing unnecessary detail.

If you want to see that episode, here it is below:

Conclusion and Hand-Waving

As I wrap up this post, I do want to mention that context means a lot. If you’re dealing with an audience which is intimately familiar with UpSet plots, they might not think the plot difficult to understand at all. But if that’s the first time you’ve seen the plot in your life, it’s not going to be easy to figure out. If I’m in a situation in which I can provide additional information, either by explaining it in person or adding informative notes in a presentation deck, then I don’t have a problem moving ahead with it. But if I won’t get that opportunity to explain the chart in detail, I think I might try to pick something simpler to ensure that my audience will get it. There’s nice value in some of the complex charts, but the most important thing is to remember your audience and have a good understanding of their capabilities and experience.