The key defining points of datavisualisation, as I established from the lecture, would have to be:
- a visual representation of abstract information
- an exploitation of the ability of human beings to see things
- having the purpose to translate abstract, numerical and so on facts or statistics into something visual
- quantity translated into scale, colour, shape, position, movement, and so on.
These four things, I think, basically sum up datavisualisation. At first I thought "Oh, okay, you mean like putting stuff into a graph so we can see it and the trends and shiz". But as Michael showed a few more examples, I realised I've come across examples of datavisualisation many times before. The Oakland police department was a classic example, using a map to visually represent the location of various crimes throughout the city, but on top of this representing the crimes committed by use of colour-coded dots and abbreviations.
The map showed a simple way to display multiple pieces of data simultaneously. If this data was in its raw form, it would be a massive list. But at a glance all of the information needed is there, and is easy to digest.
This, I think, is something that is really important to dataviz. It makes it all the more nifty, because you can get much more with half the work. Lovely.
The dataviz delicious links were really interesting. It gave me some inspiration as to not only the different ways I could create a datavisualisation in a way that isn't a graph, but different subjects.
Take the Charting the Beatles concept, for example. I was interested because it is something I love (Les Beatles). I was initially worried that I would have to make something more informative and formal for Production Project B, because I couldn't think of any way I could think of something cool to dataviz about. The Beatles examples Michael Deal puts forward are intriguing, not only because the way it incorporates one of my favourite bands, but because one of the graphs attempt to mathematically represent something quite un-mathematical.
The specific example I am talking about is the Authorship and Collaboration graph that was published in William J Dowdling's book Beatlesongs. In this graph, which you can see for yourself HERE, Dowdling charts the degree of collaboration on all Beatles songs, with the time they were released ordered horizontally. What is unusual is that on songs Lennon and McCartney co-wrote, they are not simply distinguished as 50/50 input; the graph represents more uneven collaboration levels such as 70-30, 80-20 and so on.
I guess this graph highlights the possibility for data representation - it is perfect to notice and reveal trends and correlations, such as how towards the end of their career the collaboration between the four members, particularly Lennon and McCartney, is much less, and George Harrison's songwriting took a definite increase. I am just interested to know how Dowdling got his data exactly, as to me, it is hard to put a percentage on artistic collaboration. Do they do this numerically by the number of words or chords one of the members put in? Or did he ask the members or producer(s) of the respective songs for their estimates as to how the credit should be distributed?
At this stage, I'm still trying to come up with an idea for my Project B datavisualisation that will carry me through and be specific enough not to have necessarily been done before. The Beatles example has shown what kind of spin I can take on traditional statistic representations.
I would be interested in something a little bit beyond a simple chart or graph. While although I've never used Flash before, perhaps I could come up with an animation or video such as this example. Seeing a Hashtag Spread represented its findings quite niftily, especially seen in the second video, where they used people's Twitter icons lighting up to represent how the tag spread. It would be cool to do something like this.
... and I guess I'll have to make sure I keep these five things in mind:
- exploration
- layering
- interactivity
- availability of data
- comparison of datasets
Especially the interactivity one. Nobody wants something they're just forced to stare at.
Also, I'm pretty devo'd that we don't get any more lectures for this subject. It's such a relevant subject and was pretty damn epic. So thanks Michael for all your hard work making the internet more than just Facebook, I found a picture for you as a present should you ever read this.
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