Category: Visualization

Jennifer Lyons shows us a few places where we can use histomaps to good effect:

Histomaps can be a good option when we are looking to visualize qualitative trends over time. The trick is that you need to be using two mutually exclusive variables. In Spark’s case he used time and power. In the example below, I am using time and staff’s satisfaction with their work environment. Imagine you are collecting open-ended survey data every quarter during your grant term. You code a person’s response into the mutually exclusive category of met, partially met, and not met regarding their expressed satisfaction.

Read the whole thing.

Lisa Charlotte Rost uses election results to give us some tips on building map-based comparisons:

This map shows us that both parties received a higher vote share in the east than in the west. But it also artificially increases the polarisation: If the AfD gets just one more vote than the Linke, the whole district flips from pink to blue. And we would need to create a third category, “tied”, for the nine election districts in which there were exactly as many AfD voters as Linke voters. (The New York Times created that category for their “Extremely Detailed Map of the 2016 Election”.)

There is another option: We could show the percentage point difference between the two shares. To do so, we subtract the AfD votes from the Linke votes. If the result is positive, we show the district in blue. If it’s negative, we show it in pink.

This is a case where there’s not a huge difference between methods, but it can make a big difference in other situations.

Stephanie Evergreen explains why you don’t want diagonal or vertical text in your charts:

In languages based on the Latin alphabet, we read horizontally, from left to right. Reading on a diagonal produces cramped necks. Reading vertical text is just not going to happen. So, as much as possible, the text in our graphs should be horizontal.

Let’s walk through a demo. I was trying to combat my sense of hopelessness about the world by exploring the latest dataviz related to the Sustainable Development Goals (don’t we all?). The vast majority of their viz is pretty awesome, especially given how complicated the data can be. I saw this graph about how few developing countries have representation on international development councils.

Watch as Stephanie starts with a column chart with diagonal (and cut-off) text and converts it to a visual which is much easier to read.

Meagan Longoria has some advice for presentation templates:

The title text is 36pt Segoe UI Light, the subheading text is 24pt Segoe UI, and the speaker info text is 14 pt Segoe UI.

Those font sizes alone make it very hard to read from the back of even the smaller rooms at the conference.

In addition to being too small, the gray text for the speaker info doesn’t have enough contrast from the white background. We want to get a contrast ratio of at least 4.5:1 (but 7:1 would be better). The contrast ratio for these colors is 4.0.

While sans serif fonts are generally thought to be easier to read in presentations, it’s better to use fonts with a stroke width that is not too thin – not necessarily wider characters, but thicker lines that make up each letter. So Segoe UI Light would not be my first choice for a title font, but Segoe UI or Segoe UI Bold might be ok.

Also, the red used on the right half of the slide is VERY bright for an element that is purely decorative, to the point that it might be distracting for some people. And the reason we need to squish our title into two lines of too-small text is because that giant red shape takes up half the page. What is more important: a “pretty” red shape to make our slide look snazzy or being able to clearly read the title of the presentation?

There’s a lot along these lines, and it’s great food for thought.  Meagan includes a set of recommendations at the end, so be sure to catch those.