My local AIGA chapter was hosting a portfolio review for young professionals last month, and I had just redone the theme and some of the structure for this site, so I decided to sign up. It was a really intense speed-dating session with 2 hours of 15 minute blocks, which gave me feedback from a…
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Marrying UX and Data Vis: UXPA Boston, May 2019
My coworkers and I presented a talk at the UXPA Boston conference last week. We had a great turnout (300+ people, with standing room only), and got some enthusiastic responses to our discussions of how UX, UI and data visualization can work together to make charts clearer and easier to read. Presentation abstract:Join us as…
Properties of Visual Variables
Visual variables can be used to represent two kinds of information: identity channels that tell us what an object is or what kind it is, and value channels that give us additional information about the object itself. Each visual variable does these jobs more or less well, depending on how humans perceive visual stimuli.
Information channels
Sometimes, a single mark can be used to encode multiple pieces of information. The main purpose of a chart is to show relationships between data points in some visual way. To support more sophisticated analysis, we often need to show that data points belong to multiple categories at one time, so that we can understand relationships between different groups.
Visual Variables
This post is part of a larger series focused on exploring the fundamental principles of data visualization. Eventually, the collection may grow into something larger and more coherent. For now, each post simply picks up and plays with one idea related to how we represent data visually. Other posts in this series can be found…
Gestalt principles
When we look at a chart, several things happen at once. First, the rods and cones in our eyes detect light. This is called a visual stimulus: an outside influence stimulates our eyes to record what’s happening. Next, our brains whir into action, trying to make sense of that stimulus.
Marks and encodings
This post is part of a larger series focused on exploring the fundamental principles of data visualization. Eventually, the collection may grow into something larger and more coherent. For now, each post simply picks up and plays with one idea related to how we represent data visually. Other posts in this series can be found…
How to integrate Data Visualization and UX into a Product team
The SHYFT design team presented at the Boston Data Visualization and UX Meetup in Boston at Cambridge Innovation Center. Our talk was titled “Designing for Data Visualization: Hierarchy, Color, and Interaction,” and was presented by Alex Sitbon, Pouya Shabanpour, and myself. An outline of the talk we presented is given below. How to integrate Data…
Inspired by Escher
I went to see an exhibit of work by M.C. Escher at the MFA last weekend. As a former crystallographer, tesselation is a subject near and dear to my heart, and it was really fun to see some of his original prints and woodcuts up close. This one in particular caught my eye: It’s a…
Creating color systems for data visualization
We have been working on a product re-design at work lately, and I’ve been thinking a lot about what goes into making a good color system for analytical applications. The purpose of color in our application UI can be broken down into a few distinct (and often competing) themes: Color to direct attention. Color can…