Alberto Cairo’s new book, How Charts Lie, takes readers on a tour of how charts are used and misused, and teaches them how to not be misled. It’s a useful book for both makers and consumers of charts, in the news, business, and pretty much anywhere else.
We all use data all the time, but what exactly is data? How do different programs know what to do with our data? How is visualizing data different from other uses of data? And isn’t everything inside a computer data in the end?
Prolific is a crowd-sourcing platform for running studies. In contrast to the widely-used Mechanical Turk, it’s specific to studies, has a much better interface, pricing that’s fair to participants, and useful filters to find the right people for your study.
How do we read pie charts? This seems like a straightforward question to answer, but it turns out that most of what you’ve probably heard is wrong. We don’t actually know whether we use angle, area, or arc length. In a short paper at the VIS conference this week I’m presenting a study I ran to answer this question – a study using 3D pie charts!
How we read pie charts is still an open question: is it angle? Is it area? Is it arc length? In a study I'm presenting as a short paper at the IEEE VIS conference in Vancouver next week, I tried to tease the visual cues apart – using modeling and 3D pie charts.
Charts usually show values as visual properties, like the length in a bar chart, the location in a scatterplot, the area in a bubble chart, etc. Unit charts show values as multiples instead. One famous example of these charts is called ISOTYPE, and you may have seen them in information graphics as well. They’re an interesting family of charts and they seem to have some unusual properties that most other charts don’t have.
This website is nw thirteen years old. While it has slowed down somewhat recently, it’s still alive and kicking. Now in its teens, it is looking for new experiences and trying out new things.
Take a JPEG image file and a CSV file. Which of these two is data? Is one of them more obviously data than the other? I think the key difference is the intent behind the data and its primary interpretation.
After writing about visualization for over a decade, it’s time to mix things up a bit and try a new medium: video. I just uploaded the first video to my new YouTube channel, which I’m calling eagereyesTV. Take a look and tell me what you think!
When dealing with large amounts of data, we often use summary statistics like average, median, standard deviation, sum, etc. They’re useful because they actually hide data, they reduce the amount of information we need to look at to give us a sense of the data. But the same averages and can describe datasets that look vastly different.