Author: dataviss

My Inspiration

1: Data Art

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DataArt is a project where the lines between information data and art are blurred to create stunning creations that tell a story.

2: Information Aesthetics

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Information aesthetics was formed in 2004 and serves to bring creative design and information together, collecting data projects that are both intriguing and informative.

3: Flowing Data

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Flowing data is a website that accumulates all different types of data visualisation, from musical data to medical data. This website is interesting as it brings together all sorts of interesting graphs that allows the user to explore.

4: Setosa

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Setosa is a website that features interactive, game-like data visualisation collections that tell stories through the user interacting with them.

5: After Babylon

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This is a website that caught my attention due to its minimalist yet bold data visualisations. The imagery and information focus on geographic linguistics.

6: Google Trends

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This website houses an interactive map that shows the planes that flew across america before thanksgiving. The user of the website can interact with the map by changing the time and see the changes live.

7: The Daily Routine of Creative PeopleScreen Shot 2016-10-18 at 6.05.43 PM.png

This visualisation looks like a traditional graph in aesthetics but showcases the lives of people in a linear form with great colour coding.

8: Good

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The ‘good’ website houses a collection of very different visualisations, topics ranging from gay rights to state laws.


Tutorial week 10: Study Time VS. Travel Time

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Today in class we compiled al of the data from the 48 students across data visualisation. We discovered by comparing university study time and travel time to see how much time is wasted by the average university student.

As you can see in the bar graph above, travel time is roughly more than half of the time at 6.6% (public and private transport) and time spent studying equaled to 11.1%. In addition we found out that public transport equaled to 1.3% and private transport equaled to 5.3% showing that private transport is used 5 times more than public transport, therefore better time use while on private transport could equate to more hours studying.

We worked this out by totaling the number of entries of study time and travel time:

step 1 – Public transport: 158 / 11761 (total number of entries by all 48 students) x 100 (to work out percentage) = 1.3%

step 2 – Private transport: 624 / 11761 (total number of entries by all 48 students) x 100 (to work out percentage) = 5.3%

step 3 – We added the public transport and private transport percentage, which = 6.6%

step 4 – Study Time: 1308 / 11761 (total number of entries by all 48 students) x 100 (to work out percentage) = 11.1%

Data journalism in action: the London Olympics

People are obsessed with counting medals in the olympics. Mashing the data from the medal tally with other types of data like population or team size. They need something visual because of the popularity and how it needs to be easily understood. The ability to update the data visualisation is important in breaking stories so the audience can always be up to date with the latest data. As soon as you represent something in numbers everyone seems to have an opinion on it and it’s harder to guide the conversation.

The amount of similarities between journalism and data visualisation became apparent through the medal tally graph. The objective is the same, “To tell a story using numbers”.

The ability to engage and explore the data yourself through the interactive graphic data visualisations.

History of Data Journalism at The Guardian

Data journalism is something new and relies on the technologies of the moment. Since the very first issue of the Guardian in 1821 and have been presenting that data in interesting ways to bring the story to life.

The first table of data in the Guardian showcased information about the amount of kids in school at that time before compulsory education. Unless we understand and know whats going on in the world through data then how can things improve.

Before the guardian utilised photographs they represented visual data through graphs made up of type. The use of cross hatching and textures through lines to show different categories within the graphs. Moving from just stating facts to visually reassuring people through iconography and graphs. Photography and illustrations were then used to further add to the story as well as using the data. Interactions through digital maps and data visualised on computers allowed for speed and accessibility to data in the 21st century.

What is data journalism?

Before reporting was just about words but now it’s much more, its about telling a story through data. It is not just about obtaining the information it’s about what the data tells you. It lets you tell story in a way people will understand and enjoy it while also recognising the power of measurement in helping public conversation.

Data has become increasingly important because we have the tools to really analyse it and find patterns and trends.

“so what is data journalism? … It’s just journalism”

Visualisation Styles: Graphs

Why do we use graphs?

“To make comparisons easier.”

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Overuse of bubble charts. This chart show the world’s biggest banks and their capitalization from 2007 to 2009. The data shows how the capitalization has decreased over the two year period through the light and dark green areas of the bubbles.

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The comparisons of the bubble chart and the bar chart shows how inaccurate bubble charts can be. Our brains think that the dark green bubble shows a 50% decrease but when the same data is shown in the form of a bar chart we can see how it actually about 1/3.

(Circle vs. Squares.)Screen Shot 2016-08-09 at 2.13.37 PM.pngSquares are easier to compare than circles.

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The use of colours and shading to represent heigh in a map is successful because there are more important things on the map that the reader needs to focus on. The bar graph in the top right shows data in a more accurate way, comparing the two data sets to clearly show the meaning extracted. On the scale, colours and shading is considered less accurate while position comparisons are more accurate (bar graphs etc).

Three most common charts.

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  • Time serious chart (left)
  • Bar chart (middle)
  • Scatter plot (right)

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Bar charts are useful and easy to use and are widely known. It makes it quick to compare info especially when using numbers.

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Line charts are as popular as bar charts, they conenct individual numeric data points. Simple way to visualise a sequence of values and to display trends over time.

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Pie charts are used to show the relative proportions of information.


Different graphs are used to show different types of information and data, if used incorrectly the information becomes blurred to the viewer.


Visualisation: Historical and contemporary visualisation methods-Part 1

Part 1:Screen Shot 2016-08-03 at 11.03.27 AM.png
Napoleon’s Invasion of Russia 1812. (painting)

Screen Shot 2016-08-03 at 11.05.30 AM.png Created by french engineer in the 60’s. (map)

  • polish border (left) and moscow border (right).
  • shows the loss of men from left to right and right to left through the thickness of the lines.
  • Temperature is also shown by vertical lines.
  • Reduces the time to understand the data.


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South of Ukraine (Crimean) 

  • War between Russia and England.
  • 6 Months to get access to medicine.
  • Soldiers were dying and wanted to improve the lives of soldiers.

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  • Graphing the causes of death during the war.
  • The real threat was disease to the soldiers.
  • Shows comparison of two years for each graph.
  • Made through area, the smaller numbers in the middle to at least be visible.
  • Visually show the impact over a period of time.


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Poster for an exhibition.

  • Make issues understandable for the uneducated through visual education.
  • Popularised multiples of something to equal something greater.
  • Bringing the museum to the people, to distribute ideas.

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Using visual education to transform the masses.

Part 2:

Why visualise?

Help us understand complex data and see the trends that are not able to be seen with lots of numbers. Through the use visuals the understanding is made clearer and faster.

Screen Shot 2016-08-09 at 1.51.58 PM.png(Visualised: data has been plotted into a graph making it clear to the viewer about what the data is saying .)

Screen Shot 2016-08-09 at 1.52.09 PM.png(Raw data: too much information for the viewer to decode)

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(Useless visualisation when the software export the data into a graph.)

  • Use visualisations to convince the audience of the message or claim the data is making.
  • Let the audience extract their own meaning.





Analysis of Data Visualisation



Flightradar24com(2016)Flightradar24comRetrieved 20 July, 2016, from

It tells the story of human travel across time by tracking aircrafts and their destinations on a geographic scale. Each air craft is represented by a yellow icon (Red icons symbolise aircrafts flying at a higher altitude) in real time. We can draw all sorts of stories from this data visualisation such as how economically prosperous a country is based on the traffic of flight influenced by popular tourist destinations, we can tell the story of human pollution emissions due to environmental footprints caused by C02 in plane travel over time, and we could even determine the causality of flight disturbances such as weather turbulence if predetermined flight courses are not reached in expected arrival time.

This model tells the story of how far human radar and tracking technology has come in terms of providing people with the tool to track and self organise. A family member who may be expecting the arrival of loved ones may use this as a website or an APP to keep track of unforeseeable delays before picking family up from the Airport and to better manage time. It also informs us of the story in security and safety surveillance as it is constantly recording and back logging the history of human flight activity.

It is in real-time GPS (Global Positioning System). Quantifiable and tangle units measuring distances (km/miles), time (hour), altitude (ft), speed (knots/hr), pressure (IOM/Inch of Mercury), temperature (Degrees/infrared red), History (timeline), and direction (Represented in a line). It also reports in live blog/tweet posts informing events, delays, technical issues, facts, and line of flight. The panel is located on the top left and drop down bars appear on the top.

Yes, if the viewer has access to a specific flight number, they may be able to use them to draw coordinates by entering this into the search bar (located on the top right). Like a calculator, users may investigate and discover live fluctuations of movement in flight.

Yes, you can draw stories as a user, traveler, flight worker, researcher, investigator, or environmentalist. A traveler may use this data visualisation like a watch on flight to check how much longer they would stay on board until they land at their destination. Today most flight GPS assimilations may be accessed behind all passenger seats. A flight worker may view the data visualisation from arrival and departure towers to instruct pilots and confirm when the line of passage to land is clear. Researchers may study the tool to further improve and make flying safer. Investigators may back track flight history to find out what had happened to crash scenarios.

Pragmatic, doesn’t mess around and gets straight to the point, logical, scientific, mathematical, geographical, realistic arial representation, to scale, natural colours, map orientated, orderly, (The Guardian describes it as “Authoritative”).

It’s already a visually comprehensive design, however it doesn’t include fuel and c02 emission measurements to inform travellers of their greenhouse impact when moving around on the continent.

Data Types

There are different levels of measurements when it comes to types of data, these include nominal, ordinal, interval and ration. It is important to know the difference between these data types as to prevent communication mistakes when presenting data.

Nominal data is a type of data that has a list of name and is unordered.nominal.png

Ordinal data refers to data that is ordered in some way.ordinal.png

Interval data is numeric in value and the exact differences as well as the order are known.Screen Shot 2016-08-03 at 10.27.21 AM.png

Ratio is also numeric but the difference between interval and ration is that the 0 has value.



In order for the audience to understand the data more clearly the right type of data type must be used to relay the information in order to not make any communication errors.

Introduction to Data Visualisation: Infographics and Data Visualisation

What is data?

Data is an essential part of communication and is the result of of measurements and can be quantified through either qualitative and quantitative data and can be visualised through graphs and/or images. In order for the user to understand the information within the data the data must be interpreted and take on a meaning.

What is data visualisation?

Data visualisation is the study and creation of the visual representation of data which is the modern reflection of visual communication. In order for a data visualisation to be successful it needs to clear and efficient using statistical graphs, plots and info-graphics.

Difference between information graphics and data visualisations.

  • Not all information visualisations are based on data but all data visualisations are information visualisations. Efficient visualisations help the user analyse the data and makes the complex data more easy to understand.


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  • Showcases how info graphics aren’t data visualisations because of the lack of quantifiable data.


Data visualisation is an important aspect of communication as it can help us understand complex data more easily. The world is becoming more and more rich with data so the creation of efficient visualisation is important in the consumption of data.


4X4 Model


The model has 4 key models and 4 key components these components include visualisation, story telling. interactivity and shareability. The four models are The watercooler, the cafe, the research library and the lab.


Private video on Vimeo. (2016). Retrieved 27 July 2016, from

(2016). Retrieved 27 July 2016, from