Lecture Pod 3 – Visualisation: Historical and Contemporary Visualisation Methods


*This image was taken from the lecture.

This lecture pod was about why we use data visualisations at all and its various used throughout our history. This lecture showed various historical uses of data visualisation like the visualisation of Napoleon’s invasion of Moscow, Florence Nightingales charts of causes for death among British soldiers, Otto Neurath’s , and serialised charts all the way to the recent work by Alberto Cairo, The Functional Art.

Numerous key points made in this lecture which all related to the historical examples given. The first was the visualisation is used to an audience grasp complex ideas and difficult concepts quickly. Other points similar to this are made like extracting meaning from raw data is difficult, but a graphic makes it simple, saving us time and effort. Another point made is that a visualisations aim is for your eyes and you brain to perceive what lies beyond their natural reach. Another good point made in the lecture is that data visualisations are more complex today because we have access to much more data than ever before.

But I believe the most important point made in this lecture was really very simple, that what you show in a visualisation can be just as important as what you don’t. This point struck home for me because it made me realise that sometimes showing only a small amount of data can be more effective than showing all of it. But it also made me realise something else, just how easy it is to convince people of a statistic through the simple fact or omitting it from a visualisation.


Cmielewski, L. (2016). Visualisation: Historical and contemporary visualisation methods- Part 1. Retrieved from

Cmielewski, L. (2016). Visualisation: Historical and contemporary visualisation methods- Part 2. Retrieved from


Lecture Pod 2 – Data Types

lecture 2 image

*This image was taken directly from the lecture.

This lecture pod was about one thing and one thing only – Data Types. There are four different types of data listed in this lecture. First was Nominal, then Ordinal, Interval and lastly Ratio.

Nominal data (pertaining to names) is named categories that can be counted and used to calculate percentages but you cannot take averages from them. The best example of this would be a supermarkets section (e.g. dairy, produce, canned & frozen), each category would be classes as nominal data.

Ordinal data is all about order. This type of data can be counted and be used to calculate percentages. There is also a debate about whether this data can be used to take averages, but I won’t go into it (my opinion is yes, it can). This data has no true mathematical value with numbers being assigned to make data analysis easy. The numbers just need to be in order. The best example of this data would be calculating which line will get you out of the supermarket quickest (long line, medium line, short line etc.).

Interval data refers to data in which the interval between each point is fixed. This type of data is numeric. The best example of this would be time, there is always a set amount of seconds in each minute making it interval data. In this type of data, 0 does not mean that nothing is there (0˚C does not mean that there is not temperature).

Lastly there is ratio data. This type of data is numeric, like interval data but differs from it in one key aspect; it has a meaningful 0 point. To put it clearly, in this type of data, 0 is the absence of anything. Examples of this data would be height, age, weight and money.

I believe however that the most important point made in this lecture was made early on, about whether data types matter. The answer is obviously yes, otherwise we wouldn’t use them but the reason for this is exceptionally simple. We use data types to prevent mistakes, like a postcode being interpreted as a pin code or something like that. This is the most important point because it drives home one simple point to me, it is easy to make mistakes, but it is easier to avoid them if you label your data correctly.


Waterson, S. (2016). DataVis POD02- Data Types. Retrieved from

Lecture Pod 1 – Introduction to Data Visualisation

There were a few important points made throughout this first lecture. First was that there is more data now that at any point throughout history. 23 exabytes (1 exabyte = 1 billion gigabytes) of data was recorded and replicated in 2002 (UC Berkeley’s School of Information Management and Systems, 2003). We now do that in seven days . Richard Saul Wurman (1997) said “There is a tsunami of data that is crashing onto the beaches of the civilised world. This is a tidal wave or unrelated, growing data formed in bits and bytes, coming in an unorganised, uncontrolled, incoherent cacophony of foam. None of it is easily related, none of it comes with any organisation methodology…”. Another important point made is that data itself has no meaning. It does not become information until someone interprets it.

But out of all these I think the most important point was the first one made. Data Visualisation is a mass medium. It has millions of viewers, award shows and even celebrities. It is an essential part of the communication medium, a data driven story without some form of visualisation is like a fashion story without a photo. The reason I think this is the most important point is because it made me realise just how big data visualisation is. There is so much data around today that it now blends into the background and this point made me realise just how much data we consume every day without even registering it.


The featured image here is a data visualisation of the most popular running routes in major cities, this one is New York (Yu, 2014). The data was pulled from the workout app Runkeeper. Darker means there is more traffic, lighter is less travelled.


UC Berkeley’s School of Information Management and Systems,. (2003). How much Information?. University of California, USA. Retrieved from

Waterson, S. (2016). DataVis POD01- What is Data Vis?. Retrieved from

Wurman, R.S (1997) Information Architects, Graphis Inc; USA

Yu, N. (2014). 10 Cool Big Data Visualizations | Master’s in Data Science. Retrieved 25 July 2016, from