Data Visualisation For Dummies

So in my last blog, I briefly explored the proliferation of data, and examined how humans interact and process all this information. Going on to explore how data visualization has emerged as people seek to find ways to interpret complex information and translate it into a format that is easily understood.

This week I dove into the topic of data visualization more methodically, in the aim to answer this question: “What is data visualization, and why does it matter?” In this I examined the historical and academic origins of the practice; examine its use in society and lastly explored and analyzed key examples of data visualization.

While some might think that data visualization is a new concept, and only emerged with the introduction of Web 2.0, computers and developments in statistics. In actuality, graphic representation of quantitative information has deep roots. According to historian Michael Friendly (2006 & 2008) it dates back historically to “early map making and visual depiction, later into thematic cartography, statistics and statistical graphics, medicine and other fields.” Friendly goes on to state that due to developments in technologies (namely printing and reproduction), mathematical theory and practice, and empirical observation and recording, has enabled a wider use of graphics thus leading to new advancements in form/content analysis and interpretation.

While it’s longstanding historical documentation and application in multiple fields might suggest its importance, academics suggest its value lies in the medium’s ability to summarise large amounts of complex data in a format that is universal and easy to understand (Few 2007, p. 9). Just how do we end up with beautiful pieces of data art, what process do these visualisation experts go through to create these data masterpieces. Data visualisation expert Ben Fry in his book ‘Visualizing Data: Exploring and Explaining Data with the processing Environment’ breaks down the process into a series of steps:

Acquire, Parse, Filter, Mine, Represent, Refine, Interact (Fry 2008, p.7)

I aim to follow these steps during my own creation of a piece of data visualisation, and document the challenges and my thinking at each stage. So look for the next blog where I explore the ‘Acquire’ step and share some amazing resources that I have found, while exploring the issues such as data storage, access and privacy.

In the meantime, feast your eyeballs on some of the diamonds that has emerged from the data mining and refining process. Click (hyperlinked title above image) and explore. Your welcome.

Day in the live of an American 


How Americans Came to Except Gay Marriage


3D View of Our Economic Future


2016 US Federal Budget proposals


A Year in Google Search: 2015


Diseases in the 20th Century and the Effects of Vaccinations 


Oscar Acknowlegements


Uber’s Effects on Cab Usage



Friendly, M 2006, ‘A Brief History of Data Visualization’ in Handbook of Computational Statistics: Data Visualization, Vol. 1, no. 3, pp. 1 – 43, Toronto, York University Press, Canada


Friendly M & Denis, D.J 2008, ‘Milestones in History of Data Visualization’, in Milestones in the History of Thematic Cartography, Statistical Graphics and Data Visualization, pp. 50 -73, York University Press, Canada

Few, S 2007, Data Visualization: Past, Present, and Future, accessed 20 March 2015, pp. 1 – 12

Fry, B 2008, ‘The 7 stages of Visualizing Data’ in Visualizing Data: Exploring and Explaining Data with the processing Environment, pp, 1 – 18, O’Reilly Media



4 thoughts on “Data Visualisation For Dummies

  1. I have had to do some data visualization for classes before and I have to say it can be quite tricky determining how it should look and what you need to represent each point of data. it is an artform and getting it right can be hard, but when it is done right it really does allow us to process and visual just how much each bit of data is represented.

    Data visualisation can also be misleading sometimes where the margins in data aren’t that great but visually they have been purposely blown out of proportion in order to make something either look better or worse than it really is.

    as you can see here:


  2. Data visualisation is always so satisfying. I’ve found the best data visualisations (to me anyway), are heatmaps, colour charts and ones similar to your first example. It was great to see in real time, the average American day. They’re so detailed, but so simple to follow. I do feel however that the exact type of visualisation can differ depending on the data and topic you’re showing, similar to how you wouldn’t use a Pie Chart for every piece of data.


    1. I completely agree with you, you cannot make all the data fit into one format so for my project, consideration has gone into which method would best highlight and showcase the information. I keep thinking of the game little kids play where they have to put the circle into the cut out circle shape in the box, using another shape won’t work. Thats the approach I am taking to the project. 🙂

      Liked by 1 person

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