Picking up where I have left, I have a nice addition to my collection of colour tools for visualisation experts: http://color.hailpixel.com
What you see below is the whole interface, when you open the website by Devin Hunt.
Choosing colours is easy as pie: Move your mouse pointer around and the area changes its colour. Once you like what you see, click, and the colour will be saved to a part in the browser window, together with its hex code. In the remaining area you can continue to create new colours until you’re satisfied with your palette. Colours can also be selectively deleted again and the URL of the page keeps track of your palette and allows for easy sharing (and, if you will, “export” of the colour values).
These are too many words, best enjoy the simplicity of http://color.hailpixel.com yourself! (Also, visit that frontpage and pull on the thread!)
When designing a map or a visualisation, sooner or later there is the point where you have to choose a range of colours (except in very specific circumstances which may require you to produce a black-and-white or greyscale visualisation). What is there to consider in such a situation?
Appropriate use of colours
According to Bertin‘s (1918–2010) seminal work, Semiologie Graphique, colour (defined as hue with constant value) as a visual variable is both selective and associative. These mean, respectively, that an object with slightly differing hue can be selected with ease out of a group of objects and that objects with identical colour but differing values for other visual variables (e.g., in the case of shape as the other variable: a red circle, a red square and a red triangle) can easily be grouped mentally. Continue reading
Another New York find: If the evolution of the NYC skyline and street grid interested you, you might also fall for this time-lapse movie by H. Caesar of his arrival in NYC by ship (click on “vimeo” in below player to watch the video at bigger size):
A nice idea, to shoot time-lapse from a moving yet quite stable plat-form.
I remember my early days of computing: There has been a lot of command line stuff going on (and QBASIC programming). Then came Windows 3.1 et al. on our home computer and Macs at school. Later, at university with Unix there has been more command line action again. Nevertheless, computing has clearly become more graphical (a bit more recently also haptic) and much less textual.
When the Mac was being developed, artist Susan Kare was one of the early hires of the team, her first assignment being font design for Mac OS. She came up with the first proportionally spaced (rather than monospaced/typewriter) digital font for the Mac. Later she assumed the task of designing visual GUI elements for Mac OS, using a $ 2.50 graphing book. Head over to PLoS for the full story (and numerous sketches) about how some of the iconic elements of the Mac GUI came into being (and also which sketches didn’t make the cut).
After the last post I have to report about a movie again already: Part of the Off Book series by PBS Arts, the short documentary gives a glimpse into computer generative art. Computer generative art in the words of Luke Dubois (starring in the documentary) is
[art] where you surrender control over some aspect what’s going down to some [computer] process.
Generative Art: Computers, Data, and Humanity portrays three artists and their work.
In Turning Data Into Music and Stories Luke Dubois tells how he turned casualties, missing and refugees of 8 years of war in Iraq into an 8 minute musical piece. Dubois says the reason for him to do that was, that the Iraq war is the first conflict of the U.S. where we have more data than information. I am not sure whether this is true from the information side (i.e. whether people were better informed about other wars), but modern gizmos and equipment certainly do produce heaps of data and thus maybe, in fact, make us know or feel less about what is going on.
I also found myself agreeing to Dubois saying:
This century is the century of data. That’s gonna be the defining thing.
I would add to that: and how we approach that heap of data. We amass such amounts of data that turning it into valuable, actionable information is getting harder and harder. In some fields, where data was hard or expensive to get, the situation has changed and we now seek for ways how to filter and intelligently assess incoming data streams. This is certainly true for many fields in Geography.
Wired has a gallery of ten iconic images of our planet from space, three of which are displayed below – enjoy!
Earth from ESA's Rosetta spacecraft 2009 swing-by
Sun-facing view of tiny Earth from behind Saturn, captured by Cassini spacecraft
First-ever view of the Earth (1966) from around the moon captured by USA's Lunar Orbiter spacecraft
Nathan Yau of Flowing Data has a blogpost about the many terms floating around visualization. It’s worth reading! But, in my opinion, not complete without Robert Kosara’s sometimes constrasting view.
Terminology is often flourishing in thriving disciplines where people seek to differentiate themselves and find their niche. It’s probably not that bad, but while potentially adding differentiation it also introduces confusion (as above posts/lists show), especially when talking to people with a slightly different angle.