Design

Lecture Pod 6 – The Beauty of Data Visualisation

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*Image taken directly from the lecture.

The main point of the lecture is that Information is Beautiful. Data Visualisation helps us to hone in and focus only on the information that matters. Visualising data helps us to see patterns and connections that we would not have otherwise seen. Data can be massaged, shaped and compared to create new insights. Through being shaped it can be used to tell a story that no-one has noticed before. Data visualisation is really the combination of the languages of both the eye and the brain, the combination of images and words to create new meaning. It is also a form of data compression, making a ridiculous amount of data fit into a small space while also being understandable.

It can also just look cool.

 

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*Image taken directly from the lecture.

The main thing that I learn’t from this lecture was that for information to be understood, it needs a good visualisation. It needs to be able to be quickly understood and also able to be further interrogated. If people are scrolling on social media, they are not going to stop for a huge paragraph of text but they may stop for a visualisation.

References

McCandless, D. (2016). The beauty of data visualization. Ted.com. Retrieved 18 October 2016, from http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization

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Student Time Visualisation

The story here is just how much we sleep. The visualization shown here shows just how much we actually do sleep. It is amazing. The amount of time we, as a group of 48 students over the course of a week, slept for more time than we worked, attended university study, did university study and traveled with both public and private transport. That is just insane. It shows just how vital sleep is to us.

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Lecture Pod 4 – Data Presentation Styles: Why do we use graphs?

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*This image was taken from the lecture.

To put it simply, this lecture was about why we use graphs. I know, it’s an exciting topic, but an important one. We use graphs to make comparisons easier. That’s why bar charts are so good. They allow simple comparisons. Bubble charts not so great at this. They only give us a general idea. Readers of a graph often simply compare the height instead of the area, making a bubble chart less powerful, despite its aesthetic appeal. We always tend to underestimate the size difference.

We also looked at three common types of graphs. First was the bar chart which is easy to use and familiar to the audience. It is often used to compare data across categories. Next was a line chart which is just as common but is better used to display trends over time.  Last were pie charts which are commonly used and misused. They are best used to show relative percentages of information.

But the most important point made here was that designers often choose how to display their data incorrectly. They don’t think about what type of data they are presenting and what would be best for it. They tend to pick their charts based on other things like aesthetics which, while important, need to be balance with actually communicating your information clearly. Designers can also be influenced by data visualisation trends at the time, which can work out terribly.

References

Cmielewski, L. (2016). leonGraphsPod720p. Retrieved from https://vimeo.com/177306425

Data Visualisation Analysis -National Flag Colours

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What is the story? 

This image represents the most commonly used colours on national flags around the world.

How is it being told?

A colour grid is used to display each colour. A variance in size also reflects the volume of each colours use.

The colours are grouped with similar colours to make it clearly identifiable which tones are the most popularly used.

As you can see, the main colour we see is red followed by blue/white, green, yellow, black and orange. With the subdivisions of each colour having their own hierarchy.

Does it allow for different levels of interrogation that can be seen or used on the part of the reader? eg can they drill down to discover more detail?

It allows the reader to look into the basic colours, then the colour variations of the main colour groups. It can be hard to read with the colours in some areas blending together but its detail is lost as it is only colour. There isn’t any real percentage or idea on number figure of the visualisation.

Viewers must take the face value of the visual, as there is no additional information leading them to which flags each colours may be from, or what the actual percentages are etc.

No written facts or data support the visualised data.

Are you able to create multiple stories from it? If so what are they?

Not really, the data visualisation only gives a visual representation of colour use without displaying where the colours specifically come from, number figures, differences between each country’s flag etc.

What can you say about the visual design- layout, colour, typography, visualisation style?

The layout is clean and clear. It shows us the message it needs to get across despite its lack of supporting typography. Its square layout represents 100%, although, a square to represent 100% is hard to dissect easily when there is such a variety of sections inside the square.

What improvements would you suggest?

Instead of a square to represent the whole data being displayed, a circle may have made it easier to section each colour into it, creating almost like a pie graph perhaps. If each colour had the flags, which used that colour labelled with it, may have given more dimension for readers to collect information.

Maybe even dividing each colour shade into separate graphs based on continents the flags come from etc, maybe also give a deeper dimension to the info graphic.

Where does the data came from, and comment on it’s source.

It was an award winning data visualisation project by designers Jeppe Morgenstjrne and Birger Morgenstjrne. It was taken from the UN Flag Data of all 193 countries. Coming from UN information, the data behind the info graphic must be somewhat reliable.

Reference

Morgenstjerne, J. & Morgenstjerne, B. (2016). Interesting Facts About Flag Colors And Design That You Probably Didn’t Know. Digital Synopsis. Retrieved 6 August 2016, from http://digitalsynopsis.com/design/flag-stories-colors-symbols-data-infographics/

Data Visualisation Analysis – Brazilian Flag

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What is the story?

The Brazilian flag graphic tells the story of how money is dispersed amongst the Brazilian population in categories. It does this by using each different colour featured on the Brazilian flag to represent one statistical group of people. The green (the largest area) being people who live with ten dollars a month and the white (smallest area) being those who live with more than 100,000 dollars a month.

How is it being told?

The story is being told through the clever use of the Brazilian national flag. Each colour on the flag represents one group of people. The data visualisation is the flag. There is no bar graph of pie chart used here to show the data, only an already recognisable symbol of Brazil. The use of colour, shape and varying size is how each section is compared.

Does it allow for different levels of interrogation that can be seen or used on the part of the reader? eg can they drill down to discover more detail?

No. This Data Visualisation does not allow you to interrogate it at all. Each statistic is only alluded to through the size of the area of each colour, which you cannot easily tell anyway. There are no numbers used at any point in this data visualisation.

The data visualisation is only displaying one dimension of the money figures- how it is dispersed. It does not allow different aspects of the information to be interpreted such as, what the employment rate is, where these people live in each category around brazil, etc.

Are you able to create multiple stories from it? If so what are they?

Not really. All you can really tell from this visualisation is that there is a huge divide in the income with most being very poor.

What can you say about the visual design- layout, colour, typography, visualisation style?

Honestly, there is not much actual design involved in this visualisation. All that has been done here is to stylize the Brazilian flag to give it a little more depth and add a key to show the statistics. The small amount of type used is just a normal sans serif typeface used for body text, easy to read and simple, but it works well. The actual yellow and blue parts of the visualisation also look to be about the same size in terms of area, despite the intent to have the yellow bigger.

By using the Brazilian flag to layout each section, it does make it harder for the reader to compare as the distance is further away, the sections are dispersed into smaller sections as well.

What improvements would you suggest?

I would suggest a clearer or more organized comparison of data against each other (The visual style of the flag makes it hard to really compare the size and shape of each colour against the others when they are placed abstractly amongst each other in the shape of a flag).

Perhaps including a different dimension of information to create a stronger story about the topic. So instead of just talking about each group and how much money they live off each month, maybe also compare where around brazil majority of those people live in each money bracket.

I would also suggest the use of some actual statistics in this visualisation to give it more credibility.

Where does the data came from, and comment on it’s source.

It is doubtful that there were any actual statistics used in this visualisation at all. We could not find any actual data source cited at all throughout this visualisation or anywhere on the websites we found it. It is likely that this “data” was simply anecdotal observations used to make a point about the living conditions in Brazil. Raw statistics is not what this visualisation is about, it is about making a point.

Feedback and comments on this visual on the website we found it, commented on how powerful the visual was, although they did also question just how credible the scale was in terms of representing real data.

Reference

Icaro Doria. Meet the World, Brazil [Image] (2007, February 16). Retrieved July 28, 2016, from http://www.davidairey.com/flags-of-contemplation/

Lecture Pod 3 – Visualisation: Historical and Contemporary Visualisation Methods

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*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.

References

Cmielewski, L. (2016). Visualisation: Historical and contemporary visualisation methods- Part 1. Retrieved from https://vimeo.com/176255824

Cmielewski, L. (2016). Visualisation: Historical and contemporary visualisation methods- Part 2. Retrieved from https://vimeo.com/176255825

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.

References

Waterson, S. (2016). DataVis POD02- Data Types. Retrieved from https://vimeo.com/176274669