There are many reasons why I am always keen to explore the world of data visualisation. I’ve probably covered most of them here before:
- The enormous amount of information sometimes conveyed in a simple chart, diagram or cleverly constructed sequence of pictures;
- The analytical effort made more valuable by a visual story that can be understood without the reader needing to understand the technical detail;
- The scale of data collection projects and algorithm creation projects underpinning the final visualisation;
- The brilliance of some of the tools available for creating data visualisation, their constant development and oft-times open source availability;
- Sheer beauty
This month, I’d like to focus my favourites selection on visualisation projects which are simply beautiful. To me, at least (don’t they say that beauty is in the eye of the beholder?).
Change over time
The glacier on the front page of this website is somewhere I’ve actually been. I was there in 2010, when it was obviously static to my naked eye, so these time-lapsed photos showing how it has been shrinking over time since 2005 were fascinating.
The website holds a 5 minute video showing changes to landscapes in a variety of locations, some of the sequences showing seasonality, others man-made change, others, like the glacier, environmental change. It’s a fascinating stream of photos.
A couple of months ago I linked to “Dear Data”, a postcard project by two data journalists, selecting aspects of their lives to send to one another with visual description week after week. There’s also now “Dear Data Two”, by Andy Kriebel, who is similarly choosing aspects of his own life to visualise onto the web. These are small examples of “data art”. I’m linking here to an intriguing article by Jacoba Urist of NBC News, which looks at how data visualisation projects have developed with the available technologies, from hand-print “selfies” in prehistoric caves through increasingly creative media.
A month ago, Peter Kerpedjiev of the university of Vienna, inspired by an isochrone map of travel times from Vienna to the surrounding towns, cities and countries, set up a series of assumptions around known journey lengths around Europe to estimate how far you could get from major European cities in given time periods.
The weather-map-esque style is beautiful as, to me, is the ability to write algorithms that can make calculations like this. I wonder how much effort went into the originals that inspired Kerpedjiev and which were created at the turn of the century and would have been made by hand?
Journeys as a chess piece
In a totally different kind of journey analysis, see here for a view of where chess pieces go. As typical for data visualisations which are appealing to me, this takes information from millions of data points (in this case, moves in over 2 million chess games) and highlights the most frequent journeys made by each piece. The stronger the lines, the more common the journeys.
The image above shows how the white knight moves, you can see how it rarely makes it across to the far side of the board. For me, the beauty is in the simplicity.