Oh my god. Percentages! Grids! Graphs! Numbers of graduate starting salaries organised by industry placed in a table! It’s all too much, I’m tearing up… give me some tissues!!!
I often see things presented in a jumble of statistics and cliche graphics (e.g. the classic pie graph whipped up in 2 minutes) that I simply don’t care for. We are too often used to old conventions in visual presentations, and a funny example can be found in this post parodying PowerPoint use.
But then I saw this visualisation: Periscopic’s ‘U.S Gun Deaths’
At first it looked like it was going to become another boring graph. Then I saw a solitary orange line weave its way across. Words emerged above it, identifying it as ‘Anthony Hopkins, killed at 29’. It then turned grey, depicting the years Anthony could have lived, and landed at a spot on the horizontal axis with more words – ‘could have lived to be 93’.
More lines shoot across the screen, gradually accumulating. The death count rose in the top left corner, the amount of ‘stolen years’ in the top right.
It accumulated more and more, so fast that it quickly became a blurred heap before my eyes.
For a quick moment, it felt creepy, distressing and overwhelming.
How did it accomplish this so effectively?
In the lecture, Andrew identified the basics of what a visualisation is, its aesthetics, why they are made, how they are made, and what resounding effects they have on society. I am going to explore these definitions and details using the aspects of this gun visualisation as a framework.
What a visualisation is
Most simply put, a visualisation is a visual representation of data. This can be a simple line graph, a diagram (see this reading) or it can be more interativem,complex or visually appealing (a really good website to see examples, Information is Beautiful, was provided in the lecture). It also presents data in a clear, friendly way.
We can see this with ‘U.S Gun Deaths’.
‘U.S Gun Deaths’ visually depicted the deceased’s name (if it was available), age (if available), race (if available), how they were shot (if available), where (if available), by who (if available) and other details such as if other people were shot or what situation it was in. It also gives a predicted outcome of that person’s death if they had died by natural causes, and when they would have passed away otherwise.
It also tallies up the amount of deaths and ‘stolen years’.
It also had a number of filters of which to sort the deaths: Sex, Age Group, Region, Time.
Things that Andrew said we should keep in mind include things like colour, contrasts, shapes, patterns and words. In ‘U.S. Gun deaths’ we had the contrast of orange and grey to show the difference between the ages they died at, and the ages they would have died at if they had not been shot.
Why make a visualisation?
To make the invisible, visible. By making and presenting a visualisation, people are able to discover and see trends that would have otherwise been hidden. Information has to be abstracted into a nice form for us to easily analyse. For example, if we filtered the deaths by sex we can see that it is mostly males being shot. If we filter by location, certain areas of the U.S. have more shooting deaths than others (particularly in the south).
It is also a good way to see patterns, outliers, and kind of connections there are. For example, it is mostly adults that are being shot. Not many young children/adolescents are dying from shootings – they are outliers.
To make a point!
It’s quite clear that this visualisation is demanding a focus on gun control by depicting the sheer amount of deaths by shootings in North America. It wants to persuade people to support more gun control in the U.S. It has huge agency to affect politics and social relationships. The U.S Senate recently rejected gun control measures. If this graphic was presented to multitudes of people, perhaps it would have been a different outcome.
It is quite effective by creating a new factor to visualise: ‘stolen years’ – although this was not part of their raw data, they extrapolated data of mortality rates from WHO and utilised it to create age and cause of death predictions. However, we need to be wary of visualisations. We have to remember that they are not accurate representations all the time, and although this one is thorough, that is not the case with every single one. Information can be presented such that data is distorted.
Visualisations can have amazing potential to change data into beautiful, meaningful, powerful and emotional experiences. We take for granted the strength the image carries. Its capacity to influence society is immense. I’ll be interested to see how visualisations continue to develop.
I’ll lay off all this emotional stuff. Visualisations aren’t always intense. They can be just entertaining, too. To end on a light note, check out a visualisation that lays out all your facebook profile data. You discover things you never have before. You can find it at http://create.visual.ly/graphic/your-complex-facebook-tale-by-amstel.
secret word: visible