A selection of my favourite visualisations – January 2016
So, we are at the start of a new year and it will surely be an interesting year of visualisation. But, before I present some newly created visuals, I would like to present some outstanding contribution from last year, 2015.
2015 – Visualise and Compare
The first visualisation of the year is courtesy of the blog informationisbeautiful allows you to compare you at your current age to people who had significant milestones at some point in their lives, for example when Coco Chanel opened her first shop at 27 or when Usain Bolt won his first Olympic Gold Medal at the tender age of 22. The reason why I like this visualisation is that you can instantly compare data across a variety of categories, it is colour coordinated of course, which allows you to draw fast but accurate conclusions. Or you can make them a little more far-fetched just because. For example, if I was a supermodel I would have peaked last year, but, luckily, I was not born with much of a supermodel body and rather focus my efforts on becoming the US President as their peak age is not until 56!
The next visualisation if from the Guardian, following the Ebola outbreak in 2015, and is a rather more serious comparison visualisation. This one is looking at contagiousness of different diseases versus deadliness. And even though that in itself is an interesting comparison, it is the quality of the visualisation itself that struck me. The data is not only colour coordinated, it is also categorised with shapes and labelled in a very informative manner where the scale of contagiousness goes from “quite contagiousness” to “vaccinate now!”.
2015 – Visualising sound
So, this is an older visualisation but one that recently came up as a favourite in Flowingdata’s review of the visualisation year 2015. The New York Times conducted an interview with Skrillex and Diplo regarding their collaboration with Justin Bieber (yes, I know, just ignore that part) on their hit “Where Are U Now”. The reason why I am mentioning it here is because I find it eloquently supports the interview with a dynamic visualisation, which was hugely helpful to someone as non-musical as me.
2016 – Policy making
The first for this year visually shows how policy making in the US directly affects immigration. Alvin Chang at Vox put together a dynamic view of how different countries emigrate to the US according to immigration restrictions, but the reason I’ve included it here is because he has also included comments about the wider political and economic climate, thereby wrapping the visualisation with context for greater understanding of the underlying reasons behind the data. Just think about it, without the comments the visualisation would not make much sense and therefore by default be seen as a poor example.
2016 – Star Wars
I would like to end this blog post on a little bit of a geeky note, because I can. We already know by now that Star Wars Episode 7 is the highest grossing film of all times in the US (not adjusted for inflation) and the third highest grossing film worldwide. So, you know, quite a significant feat. This visualisation from Duelling Data looks at the sentiment of the dialogue throughout episode 4, taking into account the characters involved and the location. It is not a perfect visualisation, but an interesting look at how sentiment analysis can be used, visualised and then put into context.