Visualisations (week 9) ((very late))



The visualisation of raw data can bring understanding to concepts that may have been difficult to perceive beforehand. By making the “invisible visible” through visual representation of difficult concepts, we are able to more easily disseminate information to a wider audience.

Sometimes infographics can be created like the one above to represent data in a visually appealing way and present the data closer to its statistical nature. However sometimes it is just as effective to represent data in a symbolic way.


From this article from Metro UK about the decline in population in Polar Bears and the US Government’s position on climate change, the concept of a critically endangered species is conveyed. The lone polar bear perched precariously on a small piece of ice is more effective in communicating the concept of climate change and its effects on the species than the raw statistics of climate change data juxtaposed with the the numbers of polar bears over the years.

This image may not be representationally accurate, out of frame there might be large masses of ice with more polar bears but the image has been framed and used in a way that it is symbolic of the nature of the concept of climate change. It is emotive rather than accurate but still serves as the same effect as perhaps an infographic would.


Metrowebukmetro, “strugglung polar bears put on endangered list”, metro, may 15th 2008 [] last accessed 26/10/14


Culture and Data

Data is an essential part of culture, culture cultivates data and uses this data to inscribe meaning. Again I use the word meaning as I did in my last blog but it is intrinsic to understanding culture. The term Data Friction can be the processes that can make obtaining and culminating data difficult. Paul Edwards uses the example of scientists using global temperatures to track climate change. The difficulties they have faced has been the fact that there is not one standard for this collection of data, they would have to assimilate data from different metrics, different methods, or change platforms in which the data was collected. Climate change experts have been discredited by skeptics because of the nature of data friction, their argument being that they cant possibly conclude conclusive data as the methods around the would conflict.

This example calls for a standardization of data collection around the world, a change in the scientific culture that could lead to a better understanding of data itself and therefore science but that also effects the wider culture outside of the science world as the general public’s knowledge of climate is effected and can change publics behaviour.


Edwards, Paul N. (2010) ‘Introduction’ in A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Cambridge, MA: MIT Press: xiii-xvii

Distribution Aggregation and the Social

Because we live in flows, how we distribute and aggregate effects the social. As Dannah Boyd said “This idea suggests that you’re living in the stream: adding to it, consuming it, redirecting it” (2009). With an abundance of information to consume, it becomes vital to sort the information that is most relevant to us. More traditional forms of aggregation may be filing away important documents into a labelled folder, with each type of document – doctors letters, bills, receipts – in separate sections so that all the information is readily accessible but organised in a meaningful way. As the web becomes our most prevalent source of information these systems have to be put in place as otherwise information loses meaning as it can not be defined in its proper context. The consequences of having so much information available is also the demands of attention. Danah Boyd explains that while more people are able to add to the assemblage of information, attention is not divided equally. Hierarchies of attention are involved and one of the ways this is done is through aggregation. An example of online aggregation in the simplest forms is Facebook. The platform provides the ability to prioritise certain friends though categorizing them as aquaintences, friends, close friends or compiling other custom catagories. Notifications can then be received for those close friends and less notifications for those who are only aquaintences. Thereby directly affecting the social of our everyday lives. This example then becomes more complicated as Facebook algorithms try to predict what is deemed more relevant to you and what you want to consume on your feed, showing only what has the most likes, or showing things that can be monetized. This automatic aggregation can be slightly modified by choosing the “most recent” tab on the newsfeed options but it does serve as a possible issue of the future. By having our information aggregated for us ethics could be called into question as distributors have the ability to censor information.

What Is Implied by Living in a World of Flow, Hubert Guillaud, Truthout January 2010
last accessed 16/10/2014


Originally we thought we should choose to create a visualisation around ‘how’ a colourblind person would see the world day to day by comparing photoshopped images. However, we realised that this is a fairly common visualisation to find via a simple google search. Instead we chose to visualise the statistics around the prevalence of colourblindness, and we were shocked at how common it is.
The core of this assessment is to make the invisible, visible. Our research on colourblindness led us to this website provided an abundance of statistics from which we could construct our visualisation. The form which we thought our visualisation aligned with was a point of debate amongst our group. On one hand, our visualisation does represent scientific data. However we concluded that the form concerning the visualisation of science within the public sphere is more suited, as you do not need a scientific understanding to engage with our visualisation.
We chose this form specifically so that our visualisation would act as a point of engagement but also a medium through which we would inform the public about awareness of this condition- which effects more people than expected.

8% of men world wide are colour blind, to put this into context colourblindawareness,org uses an analogy : At an all-boys school with 1000 pupils would have approximately 100 colour deficient students. 12-13 would be deuteranopes, 12-13 would be protanopes, 12-13 would have a form of protanomaly and 62 would have a form of deuteranomaly.

This analogy does not explain that these percentages focus on the two different types of colour blindness.  Those with deuteranomaly and proanomaly have Anomalous Trichromacy, where the colour receptors are damaged and Deuternopes and Protanopes have a form of Dichromacy, in which a colour receptor is missing . So our diagram includes this information to avoid confusion.

100 in 1000 are effected.

About 25% will suffer from Dichromacy and about 75% will suffer from Anomalous Trichomacy

The percentages of those with Anomalous Trichomacy are:

62 would have a form of deuteranomaly (can see blue)

12-13 would have a form of protanomaly (reds can be mistaken for black)

The percentages of those who suffer from dichromacy are:

12-13 would be deuteranopes (can confuse blues with purples),

12-13 would be protanopes (everything seems red)

We chose to represent the analogy rather than raw statistics as the numbers were simplified but the scale and concept was still difficult to understand. To make this image we thought it would be best to represent the statistics with male figures as colour blindness is most prevalent in men. To better represent the sub divisions of colour blindness we chose to animate the transition of our images rather than try to make it all one image as there was a lot of information to visualize.

Unfortunately, as we made it ourselves in photoshop and prezi, scale was sacrificed so that the image was more representational.

When researching, designing and constructing our visualisation we came across a few issues, which placed limitations upon our visualisation. Firstly, as a result of extensive research we were left with an abundance of information and statistics- which made narrowing down what we wanted to include difficult. We though we had done well when we designed our first visualization, which included far more detailed statistics that we thought were necessary to increase the information our visualization communicated. To test the impact of our visualisation we showed it to a friend and received feedback concerning our visualisations complexity. It was then, that we decided to narrow it down even further by only focusing on the prevalence and the variations colour blindness. By simplifying the information we avoided confusion around that statistics, and stuck to our original goal of awareness as opposed to overloading information.

Prezi slides here: