Lecture 6 – David McCandless: The beauty of data visualisation (TEDx talk)

According to data journalist and information designer, David McCandless, “data visualisation are like flowers blooming from mediums, when you look at it directly it is just numbers and disconnected facts, but if you start working with it or playing with it – interesting things can appear, and different patterns can be revealed” (2010).

McCandless described data visualisation as “the new soil” (2010) and is a fertile, creative medium.

Screenshot 2016-09-23 20.34.58.png
(McCandless, 2010)

He displayed this image as seen above and asked his audience, what rises twice a year, one in Easter and then two weeks before Christmas, has a mini peak every Monday and flattens out over the Summer?

Screenshot 2016-09-23 20.38.34.png
(McCandless, 2010)

McCandless (2010) and information guru, Lee Byron, searched 10,000 Facebook status updates for the phrase, ‘brake up’ and ‘broken up’ and here are the patterns found:

(McCandless, 2010)

McCandless didn’t study design but after many years of being exposed to the media, he believed to have instilled “a design literacy” (2010) in him. In addition, he believes many people today are blasted by information design and demand a visual aspect to all information. Further, McCandless (2010) believes what’s data visualisation is effortless and is literally poured into our senses and understanding.

Screenshot 2016-09-23 20.54.50.png
(McCandless, 2010)

The image above displays how much is poured into our senses while on the computer. As you can see, majority is visual and this is often an unconscious act (McCandless, 2010) . McCandless (2010) explains that the eye is exquisitely sensitive to patterns in variations, colour, shape and pattern, it is the language of the eye. Further, if you combine the language of the eye, with the language of the mind, which is words, numbers and concepts, you start speaking two different languages simultaneously, each enhancing the other (McCandless, 2010).

Screenshot 2016-09-23 21.10.49.png
(McCandless, 2010)

McCandless (2010) displayed an image of all the evidence regarding nutritional supplements. This diagram is called a balloon race, therefore the higher up the image, the more evidence there is for each supplement and the bubbles respond to popularity as far as Google hits. With this, you can engrave the evidence and create a ‘worth it’ line (as seen in the image above), and so the supplements above the line are worth investigating, while the supplements below the line are perhaps not worth investigating (McCandless, 2010).
This data visualisation took a huge amount of time and research, which according to McCandless, “visualising information like this is a form of knowledge compression. It’s about pulling an enormous amount of information into a small space but once it’s there, you can convert that data into an interactive app, and a viewer is able to search for specific heath issues, such as, supplements for heart health, as seen below.

Screenshot 2016-09-23 21.24.47.png
(McCandless, 2010)

McCandless ends the TED talk by stating that, “design is about solving problems and providing elegant, quick solutions to those problems” (2010).

I found this video really interesting. McCandless spoke about data visualisation as a beautiful art that helps clarify the understanding of individual’s everyday, even unknowningly. He proved that data visualisation can be used in various ways such as, for serious issues, for example, by displaying how much evidence is out there regarding nutritional supplements and for humourising issues for example, the peek ‘break up periods’ within a year discovered through Facebook updates. McCandless also expressed how an individual’s sight is the largest sense used when looking at a computer. In addition, the language of the eye and the mind each enhance one another to draw connections through variations in shapes, colour, words and concepts, which help us to reveal interesting patterns. Further, enhancing the value of data visualisation.


McCandless, D. (2010, August 23). The beauty of data visualization Retrieved 23 October 2016, from http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization


Lecture 5 – Part 3: Data Journalism in action – The London Olympics 2012

As we know, during the olympics there’s an obsession between each country to compare the amount of medals won.

With this, Rogers (2013) and his team thought, “What if you could see those medal tables with other types of data, what if you could weigh those medals by population or team size? Because if a small, poor country wins five gold medals, it’s got to be worth more than a rich country winning five gold medals.”

Screenshot 2016-08-22 15.19.45
(The Guardian, 2013)

Screenshot 2016-08-22 17.06.09
(The Guardian, 2013)

With the help of Professor Anagonostopoulos (academic statistician) and Blight (interactive designer), the team created a data blog with the results of the medal tally and designed it into a data visualisation which ran through a Google spreadsheet. This meant the visualisation could update live according to the change in medals won each day. Here is an example of what it looked like.

Screenshot 2016-08-22 17.11.31
(The Guardian, 2013)

It was received well by the public and initiated discussion around why certain countries were successful in certain sports which was especially interesting when comparing what was considered a rich or poor country.

The video ended up a quote from Roger (2013) who stated, “The nice thing about it, is that it allows you to explore and find certain countries that interest you and see its performance compared to other variables. That endless content is what marks out data journalism, whereas data visualisation allows people to [engage deeper] with the data itself.”

This last clip interested me most out of the three parts to this lecture. I like how it took data that was already successful in engaging an audience and gave it variables amount of new meaning. In turn, it created a larger audience and gave more recognition to poorer countries who were successful in gaining either a small or large amount of medals. This proves the amount of power data visualisation can give.


The Guardian. (Writer and Producer). (2013). Data journalism in action: the London olympics [Motion Picture]. London: The Guardian YouTube.

Lecture 5 – Part 2: History of Data Journalism at The Guardian

Part two discussed the evolution of data journalism. According to Rogers, it realise entirely on the the technologies of the environment and didn’t exist before 2009.

Screenshot 2016-08-22 15.45.34.png
(The Guardian, 2013)

He presented the very first bound edition of The Manchester Guardian from 1821. It held the first example of Guardian data journalism – a really long table of data which, consisted of topics that today, wouldn’t be consisted as controversial. Essentially, it was a list of every school in Manchester with how many children in each and how much it cost.

Screenshot 2016-08-22 15.51.08.png
(The Guardian, 2013)

In replace of using photographs from those days, they used graphics. The first graphic in the Guardian according to Rogers was the New South African tactics from the war which, was essentially a graphic made up of type. All of the elements were consisted of letters and circular shapes which represented the men at war.

Screenshot 2016-08-22 15.52.28
(The Guardian, 2013)

Then he represented a line chart from The Manchester Guardian Commercial, which was a regular commercial supplement that came out along side the paper. The chart consisted of various patterns and explanations to differentiate results – something that colour would do for visual designers today.

Screenshot 2016-08-22 15.57.43
(The Guardian, 2013)

Rogers also presented a graphic that was used as a reassurance technique. The icons in the second box conveyed icons of food that were being exported from America to the UK – this reassured people during the period of the war, they would be ok.

Screenshot 2016-08-22 16.01.21
(The Guardian, 2013)

In contrast, Rogers presented The Guardian Data Blog from 2013, which represents every meteor right on earth that was known at the time. The data was sourced from the meteorological site which, consisted of latitudes and longitudes in the data and meant they could map the whole design in roughly ten minutes. This interactive data blog allows people to zoom in and out of the image as well as share and search. According to Rogers (2013), “We have speed on our site now, in a way I think people would envy 20 or 30 years ago”.

This short YouTube video was informative in regards to the history of data journalism within a popular company such as, The Guardian. It portrayed how the technologies surrounding us are always inspiring the level of sophistication in the work of visual designers and how informative tools such as, interactive design and design elements such as, colour, have developed over the years. This is especially significant when comparing the very first bound Manchester edition of The Guardian from 1821 to The Guardian’s online Data Blog from 2013.


The Guardian. (Writer and Producer). (2013). History of data journalism at the guardian [Motion Picture]. London: The Guardian YouTube.

Lecture 5 – Part 1: What is Data Journalism?

So, what is data journalism according to the employees at The Guardian, London (one of their most popular newspaper company)?

Here are some of the key quotes I took away from the YouTube video.

Screenshot 2016-08-22 15.20.53
(The Guardian, 2013)

According to Rogers (2013),”Journalism is about telling a story using the power of data. He suggests that journalists are increasingly finding that the only way to get the best story now, is to involve techniques that statisticians would have used 10 years ago.”

Screenshot 2016-08-22 15.18.51.png
(The Guardian, 2013)

According to Professor Shadbolt (2013), “Data journalism is the use of key information sets, key data and key reference elements to inform a story.”

Screenshot 2016-08-22 15.19.17.png
(The Guardian, 2013)

Ball suggests (2013), “It allows you to create an interactive map and create a helpful, clear picture, which lets you tell a story in the way people receiving it will understand and enjoy.”

Screenshot 2016-08-22 15.19.45
(The Guardian, 2013)

According to Professor Anagnostopoulos (2013), “Data journalism is the recognition of the power of measurement in helping public conversations and public discourse in general.”

Screenshot 2016-08-22 15.20.04.png
(The Guardian, 2013)

According to McCandless (2013), “The Guardian had the first ever data blog and have a strong history in data visualisation. He suggests that data is becoming increasingly important because there is not only huge amounts of it, but also we have the and ability to analyse it, find patterns and reveal trends.”


This brief YouTube video was interesting to watch and listen to the different definitions around data visualisation from such professionals within the field.



The Guardian. (Writer and Producer). (2013). What is data journalism? [Motion Picture]. London: The Guardian YouTube.


Lecture 4 – Data Presentation Styles: Why we use graphics

This lecture discussed presentation styles and why we use them.

So, why do we use graphics?

Essentially, it is to make comparisons easier to understand. However, often graphic designers choose the wrong way to present information due to aesthetics what is most fashionable at the point of time. An example is bubble charts, as seen below.

Screenshot 2016-08-15 12.58.06

(Cairo, 2013).

This was a chart that was recreated by Alberto Cairo, The Functional Art. It’s displaying the change in market capitalisation in various banks between 2007 – 2009 and from analysing the image, it is clear that there was a decrease in market capitalisation.

Screenshot 2016-08-15 13.08.23

(Cairo, 2013).

The lecture then focused on one of the banks. On the left hand side, the bubble chart makes the the inner circle and the outer circle appear roughly the same size however, on the right hand side, the bar chart is telling the viewer a different story. The market capitalisation is 2009 looks roughly 1/3 of the size as it does in 2007. The lecture explained that while some visual designers create graphs that wants their readers to compare areas, readers will automatically compare heights and widths. Further, using circles make accurate comparisons harder and lead viewers to underestimate size difference.

Screenshot 2016-08-15 13.21.05
(McCandless, 2010)

In this image we can instantly see that squares are much easier to compare more accurately. Through height and width we can see that the Americans (yellow square) spent a huge amount of money – $7800 billion – to get themselves out of the financial crisis.

Screenshot 2016-08-15 13.45.29
(Cairo, 2013).

This is a ranking of different graphic approaches to comparing data, which is based on visual perception. The more accurate the judgement is for readers to make, is the more likely they will take away the correct perception. A map for example (which is situated next to ‘more generic judgements’) uses shading and colour saturation to show height, though there is other more important things on the map, the shading and colour saturation portray a realistic view of the image and make it easier for readers to make relevant comparisons. Whereas, when comparing things such as, dollar values, we need a more accurate judgement, therefore is is likely designers would use length to create clear comparisons for readers (as seen at the top next to ‘more accurate judgements’).

Screenshot 2016-08-15 13.49.50
(Cairo, 2013).

Above are the three most common charts used.

  1. Time series chart – often seen in stock market movements
  2. Bar chart – makes comparisons between things and is usually one dimensional
  3. Scatter plot – which as a variable on each axis and makes for an interesting way to compare a set of results.

Greater Insight can Resolve Big Issues

If a graph is designed well, they can lead to greater insight and a famous case of a disastrous use of poorly designed charts is a major launch accident.

The lecture explained the story:
Leading up to launch day, it had been unusually cold weather at the Kennedy Space Centre, Florida. The night before the launch their was a discussion regarding the safety of the o-rings (sealed sections of the rocket) and the possibility of them becoming damaged due to the cold weather. The booster rocket engineers made a number of launch recommendations and faxed thirteen graphics to support this. Here is one of them.

Screenshot 2016-08-15 14.11.59(Tufte, 2002)

In this graphic, there is nothing regarding the temperature and degree of damage, instead they ordered their information by time (that is the order of launch). Overall, any pattern is difficult to see.
After this skeptical image the rocket manager said the engineer managers evidence was inconclusive and they recommended the launch. The following morning the rocket launches and blew up a minute later.

Screenshot 2016-08-15 14.18.54
(Tufte, 2002)

Edward Tufte took that same data and re-created the rocket diagram as a simple scatter-plot graphic showing the relationship between the two variations that were of interest – temperature and o-ring damage. It reveals a clear pattern of damage, severity as temperature drops and shows there is always damage below 65 F.
This is a clear case of the mode of data presentation effecting the way we think about an issue.

The lecture then ended with an explanation of a line and pie chart, which hadn’t been discussed throughout the lecture as yet.

Screenshot 2016-08-15 14.28.04
(Google Ngram Viewer, 2016)

  1. Line chart – this image is taken from Google Books. Google books have been scanning books from the 1800s to the present and under ‘books.google.com/ngrams’, you can type in words and make comparisons of thousands of books that have been scanned for the occurrence of these words. The above image is an example of the rise and plato of commercial art and graphic design.

Screenshot 2016-08-15 14.37.23.png
(Cmielewski, 2016)

2. Pie chart – these are commonly used to show the relative proportions or perceptions of information such as, a percentage of a budget that is spent on different departments. It is important to limit the number of wedges on the pie to six or seven – more than this becomes too difficult for a reader to analyse and should be created into a bar chart instead. The above image is a good example of a pie chart – it is clear that the comparison between the electronics (in red) and the other subjects portray a large difference in price value.

Overall this lecture clearly explained why we use graphs, how to best use graphs and what graphs suit certain information more than others. I learnt five things:

  1. That using circles make making accurate comparisons harder and lead viewers to underestimate size difference.
  2. That readers automatically compare heights and width more so then an area of a shape, therefore using a square is a more successful approach then using a circle to convey comparisons.
  3. I learnt about the ranking of different graphic approaches to comparing data based on visual perception by understanding what ‘allows more accurate judgements’ and what ‘allows more generic judgements’.
  4. That if a graph is designed well it can lead to greater insight and resolve issues large or small.
  5. That a time series chart, bar chart, scatter plot, line chart and pie chart are the most successful and commonly used graphs today.


Cairo, A. (2013). The functional art: An introduction to information graphics and visualisation. (1st ed.). Berkeley, CA: New Riders

Cmielewski, L. (Speaker). 2016. Lecture Pod 04: Data Presentation Styles: Why we use graphics [Vimeo video]. Western Sydney University

Google Ngram Viewer. (2016). Books.google.com. Retrieved 15 August 2016, from https://books.google.com/ngrams/graph?content=commercial+art%2Cgraphic+design&year_start=1800&year_end=2000&corpus=15&smoothing=2&share=&direct_url=t1%3B%2Ccommercial%20art%3B%2Cc0%3B.t1%3B%2Cgraphic%20design%3B%2Cc0

McCandless, D. (2016). Information is Beautiful. Information is Beautiful. Retrieved 15 August 2016, from http://www.informationisbeautiful.net/

Tufte, E. (2002). O-ring Damage Index: Scatter Plot. Retrieved from http://motherboard.vice.com/read/how-mistakes-were-made

Lecture 3 – Historical & Contemporary Visualisation Part 2

Continuing on from part one we can ask the question, why do we visualise?
Essentially, we visualise to gain an insight and understanding to complex issues.

The lecture discussed a book by Alberto Cairo, which is called ‘The Functional Art’. Cairo talks about reading an article about the worlds population and the conflicting ideas about the fertility rate. This articles author discussed that on average, fertility in rich countries is very low but in the last few years has trended slightly upwards, however on the other hand, poor countries are beginning to show a decrease in average fertility. Therefore, the author suggests that because of these two complimentary trends, fertility rates everywhere will converge around 2.1 in a few decades and the worlds population will stabilize at 9 billion people.

Screenshot 2016-08-05 14.59.12
(Cmielewski, 2016)

This discussion is supported by this graphic seen above, which shows how much world population increases compared to the previous year. Despite this, the graphic impedes our ability to see the multiple patterns that the author discusses… Where are those rich countries? Where is the evidence that developing countries are stabilizing their populations? Due to this, Cairo went to the United Nations website and downloaded data on fertility rate, children per women where he challenged himself to discover interesting patterns through a spreadsheet program, like the one seen below.

Screenshot 2016-08-07 17.46.34
(Cairo, 2013).

If we look closer, we can see that Spain started at 1950 with an average number of children per women higher than Sweden’s, but then fertility in Spain fell drastically after 1979 and only recovered partially in the last five years. We can also see that Sweden’s fertility rate has remained stable in the last sixty years besides the drop in the 70s.

Screenshot 2016-08-05 20.16.29
(Cairo, 2013).

By visually encoding numbers saves a visual designer the time it takes to analyse numbers from a table, the graph creates an easier alternative to discover trends such as the comparison of Spain and Sweden’s fertility rates above.

Here is what happens if you highlight some rich countries and a few developing countries into a graph. In this case the author argued that the drop in fertility rates could be from a range of factors such as, women getting better access to education. This allows us to see the evidence supporting the authors discussion on the evolution of fertility.

Screenshot 2016-08-05 20.29.14

(Cairo, 2013).

Furthermore, from looking at the images of part one and two, we can see that of the more contemporary examples (as just explained) is that the amount of information presented now, is far greater than the earlier examples. Thus, does that mean today we have a more graphically sophisticated audience? Maybe.

I found this part of the lecture really interesting because it focused on a complex issue that involved a large amount of data. It encouraged productive ways to manage this amount of data, such as, downloading the information into a spreadsheet and looking closely at areas to discover patterns and develop trends before turning the information into a sophisticated, coherent graph. It expressed that one of the key requirements for visualisation is that readers should be given enough information to enable them to either follow a presented argument or use their own intelligence to extract their own meaning.


Cairo, A. (2013). The functional art: An introduction to information graphics and visualisation. (1st ed.). Berkeley, CA: New Riders

Cmielewski, L. (Speaker). 2016. Lecture Pod 03: Historical & Contemporary Visualisation Part 2 [Vimeo video]. Western Sydney University.

Lecture 3 – Historical & Contemporary Visualisation Part 1

Essentially, we use visualisation as a way to present complex data that enables our audience to grasp the complexities with the least amount of work possible.

The lecture began by discussing the story of the Napoleon Russian campaign.

Screenshot 2016-08-05 13.21.59
(Prianishnikov, 1812)

In short form, in the middle of 1812 Napoleons grand army of over 400,000 men headed towards Mascou. They found a city that had been completely evacuated and stripped with all its supplies, which gave them no choice but to retreat. However, supplying the army on the way back was nearly impossible, mainly because of the terrible weather conditions. Starvation and disease took a toll on these soldiers, in addition, roughly 10,000 of the 400,000 of Napoleon men survived the Russian campaign.

Screenshot 2016-08-05 13.48.11
(Minnard, 1860)

Here is another view of that same event, which was created by a French engineer – it was made 50 years after the event in the 1860s. There is a 900km difference between the start and end of the beige and black lines, which is the start and end of the soldiers journey. This diagram displays seven different variables in this 2D image. The thickness of the lines indicate the strength of the army as indicated by the red numbers, which display the disintegration in soldiers as the trip began and finished.
The lower portion of the image is a graph, which reads from right to left, this shows the temperature on this backward retreat that the army made through Russia. The vertical lines that are going from the temperature points are connecting the temperature to the location of the army at that certain time. It starts at zero degrees in Moscou to -30 degrees towards the end of the retreat. Overall, this image simply presents how the retreat went from bad to worse and is a great way for an audience to analyse and make comparisons.

The lecture then went on to discuss another war called the Crimean War, 1858. In short, there was a war between the Russians and alliance of Europeans including the British and the old ottoman empire. It was also famous for the work of Florence Nightingale, a nurse during that period. She helped the wounded of the war and during this time took records of the death toll in the hospitals as evidence of the importance of patient welfare. Eventually, she turned the evidence int graphs to put an argument against the British military commanders.

Here is the original version of Nightingale’s monographs:

Screenshot 2016-08-05 14.05.47
(Nightingale, 1858)

Here is a modified version:

Screenshot 2016-08-05 14.06.33
Modified version of Nightingale (1858)

They each display the causes of mortality in the army in the east as well as reveal that the major cause of death was caused not by the Russians but disease (as seen in blue).

Screenshot 2016-08-05 14.19.32

Modified version of Nightingale (1858)

With a closer look, we can understand that the green is 3x larger than the red and the blue is 32x larger – therefore, troops were dying from disease at 32x the rate they were dying from battle wounds. This allowed Nightingale to prove the importance of patient welfare. Overall, these graphs are great for presenting key variables. Here is an example of a bar chart made from Nightingale’s graphs, which, is another way (maybe better and clearer way) she could have presented her argument.

Screenshot 2016-08-05 14.23.30

(“Nightingale’s ‘Coxcombs’ | Understanding Uncertainty”, 2016)

The lecture then went on to discuss Otto Neurath who was a pioneer in social politics in Vienna. He had a museum there called the Museum for Society and The Economy, which had a mission to make social and economical relationships understandable through various visualisations. Neurath developed a system called ISOTYPE (International System Of Typographic Picture Education). This focused on multiple images such as the figures seen below which were to portray certain quantities in a linear formation.

Screenshot 2016-08-05 14.41.59
(Neurath, 1932)

The photos on the left give you an idea of the industrial scale of the operation producing these charts such as, in-grave letter press plates and a production worker cutting out the copies of the icon.

It was interesting to look back at the graphs used in the earlier days and see how their ideas, tools and designs have been modified to organise the complexity of data today. However, with the tools they had, the visualisation designers of that time did an excellent job in creating graphs to present an argument, state facts and allow their audience to analyse and make comparisons in a clever manner.


Cmielewski, L. (Speaker). 2016. Lecture Pod 03: Historical & Contemporary Visualisation Part 1 [Vimeo video]. Western Sydney University.

Nightingale’s ‘Coxcombs’ | Understanding Uncertainty. (2016). Understandinguncertainty.org. Retrieved 11 October 2016, from https://understandinguncertainty.org/coxcombs

Lecture 2 – Data Types

As visualisation designers, it is important to know what data types are and what can be done with each of them so that data can be understood appropriately. There are many ways to describe data measurements, but across the fourteen weeks of studying data visualisation, we will be primarily using levels of measurement known as, nominal, ordinal, interval and ratio.

Definition of Data Types

Nominal: Comes from the Latin word nomen and means, pertaining to names. It can be thought as, as being in a category.

Screenshot 2016-07-30 10.13.35
(Waterson, 2016)

It can also be counted and used to calculate per cents, as seen below.

Screenshot 2016-07-30 10.13.46
(Waterson, 2016)

Ordinal: Is based on order and this is primarily done with numbers which for example, can identify the level agreement to a certain issue such as one being ‘strongly disagree’ and 5 being ‘strongly agree’ as seen below. The numbers selected represent ordinal categories which change how the viewer interprets the end analysis.

Screenshot 2016-07-30 10.13.18
(Waterson, 2016)

Interval: Interval data is numeric, and you can do mathematical operations on it but it doesn’t have a meaningful zero point. That is, the value of zero doesn’t indicate the thing you are measuring.

Screenshot 2016-07-30 10.20.12

(Waterson, 2016)

Ratio: Ratio data is numeric, and a lot like interval data, except it does have a meaningful zero point. In ratio data the value of zero indicates an absence of what is being measured. Elements that could be counted as ratio data are height, weight, age and money.

Screenshot 2016-07-30 10.22.53
(Waterson, 2016)

Here is a clear example of how each of the data types can be used.

Screenshot 2016-08-01 13.13.06
(Waterson, 2016)

Here is an example of what data types can be measured (numerical) and what data types can be counted (categorical).

Screenshot 2016-07-30 10.24.05

(Waterson, 2016)

Qualitative and Quantitative Data

Lastly, the lecture briefly focused on qualitative and quantitative data. Qualitative refers to non-numeric data (description/information) while quantitative is typically numeric hence, quantifiable (numerical/information). They can be thought as ‘quality’ and ‘quantity’. The example below will help clarify this.

Screenshot 2016-07-30 10.24.48
(Waterson, 2016)

This lecture was good to get an understanding of the levels of measurement, nominal, ordinal, interval and ratio. While this at first sounded quite complicated, I believe I will grasp a better understanding of these data types as the semester goes on. However, I learnt that it is important as visualisation designers, the types of data used need to be understood.


Waterson, S. (Speaker). 2016. Lecture Pod 02: Data Types [Vimeo video]. Western Sydney University.

Lecture 1 – Introduction to Data Visualisation

Today, data visualisation is a mass medium. For graphic designers, data visualisation is an essential part of the communication process. We live in the most data-rich time, for example, according to a report by the School of Information Management and Systems (2003), 23 exabytes (23 billion gigabytes) of information was recorded and replicated in 2002, now we record and transfer that much every 7 days.

What is data?
Data are values of qualitative and quantitative variables belonging to a set of items, typically, it is the result of measurements and can be visualised using graphs and images. The terms data, information and knowledge are used to overlap concepts. The main difference within these terms is the level of abstraction being considered. Here is an example of this (data being the lowest level of abstraction).

Screenshot 2016-07-26 16.21.28.pngInformation Interaction Design: A Unified Field Theory of Design (2014).

What is data visualisation?
Simply, the visualisation of data. According to Friendly (2003), it is “information that has been abstracted in some schematic form, including attributes or variables for the units of information”.
It is one of the steps in analysing data and presenting it to users and its primary goal is to communicate information clearly and efficiently through statistics. It makes complex data more accessible, understandable and usable. Further, viewers may have particular analytical tasks, such as making comparisons or understanding causality and the design principle (for example, showing comparisons) follows the task.
What is the difference between data visualisation and infographics?
Not all information visualisations are based on data, but all data visualisations are information visualisations. For example, infographics aren’t based on data but are based on a description of a process. In essence, majority are a list of images which visually displays a story and/or process, just like this one below.
(Wong, 2015).

The 4X4 Model for Winning Knowledge Content

Studies have shown that the majority of individuals who visit a website will leave it within 10 seconds. Further, it is typical that they will read only 20% of a page.
So, what is the solution to make people stay?
This is known to be the 4×4 Model for Knowledge Content and it consists of four key models and four critical components of those models. It is believed by following this format, your knowledge content had the ability to better engage your audience.
What are these models?

Screenshot 2016-07-27 10.03.45
(“The 4X4 Model for Winning Knowledge Content Online • Inspired Magazine”, 2011).

1. The Water Cooler: People gather around a water cooler to exchange in small
conversation as brief respite from work. This is direct and compelling and in the case of a website, can be thought as a headline, tweet or ad. They are used to engage the user and grab their attention to want more. ‘Upworthy’ is an example of a successful site to engage with their intended audiences.
2. The Cafe: A cafe gives individuals an opportunity to delve into a subject at some length, but still isn’t deep study. This could be a blog post, one or two page article or a three minute video. Cafe content needs to be crafted to tell a compelling story – a story that is easy to relate to.
3. The Research Library: If you have had read an interesting stat (water cooler) and chatted with a colleague about it (cafe) and it interests you – you will go to the library and dig deep. The library backs up the information found from the water cooler and cafe content. It is more scholarly, long form content.
4. The Lab: This is when users can interact with data found in the research library. In this content, you open the vaults and give users access to the data. They can twist the knobs and make it more about them and their interests.

What are these components?
1. Visualisation: This is not just about data, but also concept and geographic visualisation.
2. Story-telling: If you can tell a story, you are able to convert the abstract into something people can relate to.
3. Interactivity: Means offering interactive experiences such as lab moments and/or images that can be zoomed for more detail and/or communities to engage in.
4. Share-ability: The power of water cooler moments is strongest when you think about sharing. It leads more traffic back to your site content.

The lecture was really informative in regards to what data visualisation is, why we need it, what data is and the difference between data visualisation and infographics. Through it I was able to understand that an infographics consist of lists which describe a process and/or story through images, whereas data visualisation focuses on results and numbers. It was helpful to focus on what data is alone through the levels of abstraction, data, information, knowledge and wisdom.
The 4×4 approach was really interesting to learn about. It was simple, easy to understand and definitely relatable. This is an approach I will remember to engage audiences by stepping them into the right level of content based on their needs and improving outcomes from that content.


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Information Interaction Design: A Unified Field Theory of Design (2014). Retrieved 26 July 2016, from http://nathan.com/information-interaction-design-a-unified-field-theory-of-design/

Report by the School of Information Management and Systems, University of California, USA. (2003). Retrieved 26 July 2016, from http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/exescum.htm

The 4X4 Model for Winning Knowledge Content Online • Inspired Magazine. (2011). Inspired Magazine. Retrieved 27 July 2016, from http://inspiredm.com/winning-knowledge-content/

Waterson, S. (Speaker). 2016. Lecture Pod 01: Introduction to Data Visualisation: Infographics and Data Visualisation [Vimeo video]. Western Sydney University.

Wong, J. (2015). Flowchart: How Designers Work. Retrieved 26 July 2016, from http://designtaxi.com/news/371916/Flowchart-How-Designers-Work/?interstital_shown=1