Category: Visualization

Rob Farley continues a thread:

The basic concept is that once you have a greenscreen application background in place, you can easily show text or drawings or whatever on the screen, superimposed over your webcam feed. I show how to do this in this post: http://blogs.lobsterpot.com.au/2021/01/30/presentation-trickery-online-glassboard-like-lightboard-but-using-just-free-software/. Scott Hanselman also made a video about it at https://youtu.be/-oaikJCR6ec, and used the idea to make scrolling credits, which is a really neat idea that everyone loves.

Click through to see how it works.

Comments closed

Alex Velez explains the difference between a histogram and a bar (or column) chart:

Consider the above illustration of two data visualizations. 

A histogram is on the left, and to the right is a bar chart (also known as a bar graph). Histograms and bar charts look almost identical, yet they are dramatically different. Understanding their differences is important, so you know when to use each one and accurately convey—or consume—the insights they contain. 

Let’s take a closer look. 

Click through for that closer look.

Comments closed

Meagan Longoria has contributed to a series:

Jon Schwabish over at PolicyViz has created great initiative called the One Chart at a Time Video Series. It’s an effort to expand readers’ graphic literacy through short videos explaining how to read and use different charts. Each video is from a different person in the data visualization industry. Participants include people I admire such as Andy Kirk, Ben Jones, and Cole Nussbaumer Knaflic. Jon releases a new video each weekday. The initiative started January 11 and will continue through mid-March.

Click through for Meagan’s contribution on waterfall charts.

Comments closed

Elizabeth Ricks completes a series on designing visuals. Question 4 involves the audience:

When communicating with data, always consider what broader purpose it serves. Too often, we don’t pause and think about the context (understandable in the real-world maelstrom of deadlines, constraints and organizational politics!). Rather, we force the visuals that we used to analyze the data upon our audience, hoping they’ll come to the same conclusion. 

Question 5 looks at the text on a visual:

In data visualization, words can be more powerful than we realize. When designing graphs, the non-negotiable text includes explicit axes labels, descriptive chart titles, supporting annotations, and footnotes with relevant methodology and assumptions. Never assume it’s apparent what’s being shown—make it your default to include this important context so your audience doesn’t have to guess what they’re seeing. 

Be sure to read both.

Comments closed

Elizabeth Ricks continues a series on data visualization techniques:

Data can be visualized many different ways, creating an often daunting task to select the best chart to use. We also sometimes complicate our audience’s experience, packing too much information into a single graph, in the hopes that it will answer every possible question. Instead of making the Swiss Army Knife of charts, we might instead create different views of the same data, examine what each iteration allows a viewer to see, and then decide which version(s) to share with our audience. 

Read on for more.

Comments closed

Elizabeth Ricks continues a series:

To create more impactful explanatory visuals, develop the habit of using color in a purposeful way. When applied thoughtfully, color is arguably the most important decision you make when focusing your audience’s attention where you want it. 

One way to start is by overriding the default color palette of your data viz tool and start with nothing emphasized.

Using color for emphasis is important. Color is a pre-attentive attribute, so it’s great to take advantage of that powerful implicit signal. And that means not using lots of different colors.

Comments closed

Elizabeth Ricks has started a series on improving a particular visual:

I empathize with the plight of this anonymous creator. In previous roles, I frequently created visuals that looked like this, and was left frustrated when requests came back for “more data.” I slowly came to realize that I was assigning my audience the tedious task of figuring out for themselves what the takeaways were. My visuals should have been highlighting the interesting things to those seeing them for the first time. The five questions we’ll be discussing in this series will help us to do just that.

The first question in the series is, “What elements can I eliminate?” I think that’s a really good idea—with data visualization, less is more.

Comments closed