Global warming – a hot topic (excuse the pun) by all means! It’s one of those issues that constantly appears in the media. But how do we know about the concept of global warming? It all comes down to data – data that has been collected from various and multiple studies that have looked at past and present climate and weather patterns. Paul Edwards (2010) delves into this very issue of global warming in “A Vast Machine”, where he looks at the reliability of the sources and basis of information from where global warming theories have derived. Edwards (2010) acknowledges the significance of models in collecting weather information, clearly stating that “without models, there is no data”. What intrigued me about this reading was that it allowed me to gain an understanding of how weather forecasts are predicted. In saying that, to predict future weather, examination and investigation of past weather patterns must be conducted. As Edwards explains, “models we use to project the future of climate are not pure theories, ungrounded in observation. Instead, they are data – data that bind models to measurable realities.”
Data to the everyday person (like myself) can be boring, more so it can be confusing – facts and statistics on topics you may or may not know. This is where the media dive in and (I believe) create a beautiful love affair with data, because they “act as the bridge between the data and the people” (Rogger, 2011) – they research, clarify and explain the data in terms that can be understood by the general public. This is known as “data journalism”, and as explained by Rogger it has been around as long as data itself. I’m particularly intrigued by the vast amount of analysis this form of journalism requires, as explained by Rogger’s (2011) it can take anywhere from weeks of investigative data management to grab an incredible scoop. However, due to the incredibly fast pace that news clocks over, a new short-form of data journalism demands journalists to swiftly analyse key data, and present it to readers in a comprehensive manner while the story is still current.
Of course the issue of ‘anyone’ being able to take data and transcribe it through free tools such as Google Fusion Tables, Google Charts, Many Eyes and Timetric conveys the flexibility of ‘storytelling’ (Rogger 2011) and can be viewed as increasing competition against data journalists because it is no longer a specialised procedure. I argue, however, that many (not all) amateurs would bypass what the journalists do – thoroughly investigate and analyse the data in order to visually present the correct translation of data. Not only do data journalistic have to be spot on with their data analysis but they have to tell the story in the best way possible, “sometimes that will be a visualisation or a map” or even a number/s (Rogger 2011).
The Guardian has a section on its website dedicated to data journalism – The Guardian Datablog. This is a good example of the various ways that data can be visually translated, mapped or put into easy to understand numbers. The image below is an example of one of The Guardians visual interactive guides. The visualisation displays data based on government spending.
The simple, minimalistic approach to this visualisation makes it easy to comprehend and attractive to the user, in which a simple click on any circle provides further information on the topic of government spending (go here to see this visualisation in action). This data journalism example supports the idea of how data on government spending is being released on a much greater scale (Quilty-Harper 2010). As expressed by Quilty-Harper (2010) the more that data about how our Governments operate are published in an easy to understand fashion, will in turn inevitably put pressure on Governments to change – because data journalism allows more and more people to understand and see what was previously (as I mentioned) unknown, boring data through a diverse light – that is, through easy to comprehended visualisations, mapping or numbering.
Blight, G and Rogers, S 2012, ,Public spending by UK government department 2011-12: an interactive guide’, data visualisation image, the Guardian, December 4, accessed 16 April 2013, <http://www.guardian.co.uk/news/datablog/interactive/2012/dec/04/public-spending-uk-2011-12-interactive>.
Edwards, P 2010, ‘Introduction’ in A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Cambridge, MA: MIT Press: xiii-xvii.
Quilty-Harper, C 2010, ’10 ways data is changing how we live’, The Telegraph, August 25, accessed 16 April 2013, <http://www.telegraph.co.uk/technology/7963311/10-ways-data-is-changing-how-we-live.html>.
Rogers, S 2011, ‘Data journalism at the Guardian: what is it and how do we do it?’, The Guardian, Datablog, July 28, accessed 16 April 2013, <http://www.guardian.co.uk/news/datablog/2011/jul/28/data-journalism>.