As already discussed when it comes to visualisation, it can be used in a myriad of ways for a myriad of purposes. The thing with visualisations, and at its core, data sets, is that they can be interpreted, altered and taken out of context in order to prove whatever you want to prove. I think that is something we must always keep in mind; that there is a reason behind the creation of every text, and even though something may seem ‘scientific’, that the way the science is conveyed, or visualised, is done in a way to persuade. I’m not saying that this is necessarily a bad thing; sometimes people need to be persuaded in order to understand the truth of certain matters. However, we cannot discount the fact that history has proven that ‘science’ and the presentation and visualisation of science can be used negatively or exploitatively. This could be a major difference between data in a private, scientific sphere, vs data that is broadcast into the public sphere with intention.
For example if we look at modern medicine, we can see that there is some contention around the use of data sets in order to sell products. As Torres writes, “what passes for “science” today is a collection of myths, half-truths, dishonest data, fraudulent reporting and inappropriate correlations passed off as causation. Correlational studies can NOT prove causation, yet the end result of most scientific studies in mainstream medicine make a causal claim without any proof and then pass those suggestions to the public to sell the medical model to the public.”
It’s not only in the world of medicine where data can be used to exploit and manipulate consumers. Here are some more examples where regular conventions of graphing and visualisations have been broken in order to manipulate those viewing the data.
Look at the graph above. What do you think it says at a glance? This line graph that aims to demonstrate the level of gun deaths in Florida at a first glance appear to show a decline in these types of incidents. However, if we closely analyse the graph, we can see that the y-aixs, or the one running vertically, is counting down as apposed to counting up. By breaking this convention, it gives the appearance that gun violence is going down when in reality it is on the rise. It seems fairly obvious, but when it comes to data visualisations, we really only look at the information presented to us for a brief period; six seconds if we are to believe the theory of our attention spans (The Guardian, 2014) or just think of ‘Vines’ (kidding…sort of). So therefore, the creation of such a convention-breaking graph was done in order to trick, or fool those who saw it.
Here’s another way we can see visualisations created in order to trick a responder. By truncating a y-axis, you can alter the way information is perceived. For example the graph on the left gives the illusion that interest rates are increasing astronomically. However, if we set the y-axis at zero, and use an appropriate scale, then we see the information in a completely different light.
The way we perceive visuals is based on preconceived ideas of what certain symbols and signs mean. When we see a line moving up, we automatically assume something is on the rise. It is by subverting these assumptions that people who create fraudulent visualisations off the back of real data sets try to convince us of what they want to convince us. That is why it is important to know basic ideas behind data, graphing and visualisation so that you can remain aware, and protected to the pitfalls of data visualisation that are out there.
Torres, M. (2014) False Science – How Bought And Paid For Propaganda Masquerades As Scientific Progress, Prevent Disease, http://preventdisease.com/news/14/022414_False-Science-Propaganda-Masquerades-As-Scientific-Progress.shtml, date accessed 25/9/2014
Weatherhead, R. (2014) Say it quick, say it well – the attention span of a modern internet consumer, The Guardian, http://www.theguardian.com/media-network/media-network-blog/2012/mar/19/attention-span-internet-consumer, accessed 25/9/2014