The problem with pie chartsÂ and how this relates to data visualisation as a whole.
Many visualization types have cropped up just in the past two decades, riding the growth of the internet. But they nevertheless share many characteristics with the garden-variety pie chart, including some of its primary weaknesses and a slew of new ones. Recognizing them will move science closer to tools that work for users, rather than the other way around. [â€¦]
Unfortunately, the pie chart incorporates tasks that we humans systematically fail to perform accurately, all those exercises that come at the bottom of the hierarchy of perceptual tasks [â€¦]. So although we’re good at comparing linear distances along a scaleâ€‰â€”â€‰judging which of two lines is longer, a task used in bar graphsâ€‰â€”â€‰and we’re even better at judging the position of points along a scale, pie charts don’t bring those skills to bear. They do ask us compare angles, but we tend to underestimate acute angles, overestimate obtuse angles, and take horizontally bisected angles as much larger than their vertical counterparts. The problems worsen when we’re asked to judge area and volume: Regular as clockwork, we overestimate the size of smaller objects and underestimate the size of larger ones, to a much greater degree with volume than with area.
Newer visualizations can have these defects and more.
via Link Banana
There’s not much I can say about this collection other than giving you its accurate title: 50 great examples of infographics.
By crunching data from more than a billion user interactions on scholarly databases, Los Alamos National Laboratory researchers produced a high-resolution map of the relationships between different fields of science.
That’s from WiredÂ where they display the ‘Map of Science‘ that was produced, in part, to “help researchers frame discipline-hopping questions and identify neglected cooperative opportunities”.
For those interested PLoS ONE has the original research article.
The New York Times Developer Network is the media outlet’s “API clearinghouse” offering details of how you can get access to the extensive data they have released (from stories dating back to 1981).
Using this API, Jer Thorp has created some visualisations of NYT trends using Processing (a language I keep promising to take a serious look at). Two of my favourites: the frequency of the words ‘communism’ and ‘terrorism’ in NYT articles since 1981, and an abstract visualisation of the occurrence of the term ‘organic’.
Hot on the heels of The New York Times, The Guardian announced yesterday the release of their Open Platform which allows access to data from full-text articles back to 1999. The Open Platform consists ofÂ the Content API and the Data Store. Jer Thorp has already compiled a brief introduction to The Guardian’s API and has created a few early visualisations.
Also: Carl Morris on what this is all about and why it’s exciting and important.
Flickr user 802.11 has created a lovely flowchart depicting the evolution of art and design between 1845 and 1980. The chart depicts art movements and design groups and how each are connected.
You should take a look at some of her other visualisations, too. I particularly like the depiction of character interactions throughout Shakespeare’s Romeo & Juliet, act 1 scene 1.