Sonification turns data into sound, just like visualization turns data into pictures. Except it’s a lot more complicated and limited. Something about sonification has always bugged me, and I think I’ve finally figured out what: the crowding on the time axis. I’ve also recently discovered some of the powers of sonification, though. [Read more…] about Sonification: The Power, The Problems
There are many visualization techniques, or chart types. These articles describe what they are, how they work, and what they are good for.
Programming languages use words and symbols to represent structures like blocks and conditions. A visual representation of these structures seems useful to keep track of all the different cases, see the scope of variables, etc. Nassi-Shneiderman diagrams offer just such a representation. [Read more…] about Nassi-Shneiderman Diagrams
Bar charts are great. They always work. They’re always the safe choice. Right? Well, no. Stacked bar charts are deceiving because we think they work just like regular bars, when they’re really pretty terrible. [Read more…] about Stacked Bars Are the Worst
Communicating data visually is not only about perception and precision, but also understanding. ISOTYPE was developed to bridge the gap between showing data in a way that’s easy to read and at the same time easier to understand than unadorned bar charts. [Read more…] about The ISOTYPE
How do we know what we can do with things in the world or in user interfaces? What makes us push buttons, flip switches, or pick up objects that fit our hands? This guidance comes from affordances, a clever and intuitive theory that has been around for decades but is often misunderstood. [Read more…] about Affordances
Networks are usually drawn using a technique called node-link diagrams. While that works well for small graphs (the technical name for networks), it breaks down beyond a few dozen nodes. Better techniques exist, though these are currently focused on specific types of graphs or answer particular questions. [Read more…] about Graphs Beyond the Hairball
Venn diagrams are a great way to visualize the structure of set relationships. They’re also an example of a technique that works very well for a particular purpose, but that entirely fails outside its well-defined scope or when the number of sets gets too large.
The common wisdom in visualization is that to find periodicity in data, it should be displayed on a spiral whose period the user can control. Repeating patterns are easy to spot on a spiral, and its layout suggests repetition. But are spirals really the most effective way of finding periodic patterns? Here is an interactive version that lets you compare spirals against a rectangular layout to find out for yourself.
[Read more…] about Spirals for Periodic Data
When visualizing uncertainty in data, a common choice is to use blur. While that may seem natural, it is unfortunately ineffective. Blur has the effect of guiding attention, but is hard to quantify and annoying to look at. Uncertainty information, or any other data, cannot be shown effectively this way. [Read more…] about Blur and Uncertainty Visualization
Parallel coordinates are one of the most famous visualization techniques, and among the most common subjects of academic papers in visualization. While initially confusing, they are a very powerful tool for understanding multi-dimensional numerical datasets.
[Read more…] about Parallel Coordinates