Lecture 9


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Weather Recording Instruments [Image] 2011

Data collection doesn’t always mean graphs – sound recording, satellite imagery and offshore buoys are also considered as data collection tools just as much as spreadsheets and computers.

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Weather Beads [Image] 2011

Every single bead is a weather band that plays out a musical instrument reflecting behavioural elements not shown in a 2D graph. Translating a new medium of weather data to musical scores bridges a gap between art vs science. It is not always about the differences between them but what can also make them similar.

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Weather as Sheet Music [Image] 2011

The weather band instrument reads differently wherever it is placed, in an art museum or as a musical piece. It’s an alternative entry point into understanding science that may not be a traditional perspective.

The science of data collection does not always have to be reflective of graphs and statistical numerics. This example reflected by Dr Miebach exemplifies we can understand data visualisation through music and art sculptures. We understand language and communication through visual forms and sounds just as much as images. It is up to us as humans how we perceive and breakdown the information. If the consume data in an art museum we may not be aware that it could also be consumed in a science museum just as effectively.


Lecture 8


(The Billion Dollar-o-Gram 2014, Jan)

Meaningless without context – needs relevance.
Purple is fighting, red money giving away, green profiteering
Allows connection towards money
Visualising the information turns it into a landscape, an information map.

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(Mountains Out of Molehills [Image] 2007).

Heights = Intensity of certain fears in the media

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(Mountains Out of Molehills [Image] 2007).

Patterns found – twin peaks everywhere in same month for video games
Christmas = resurgence
April = Columbine shooting, fear remains with media, retrospective and anniversary

Data is the new oil – ubiquitous resource to provide new resources

Data is the new soil – fertile creative medium, visualisations, graphics are flowers that bloom from this medium. Rather than just facts they are patterns.

“Peak break up times according to Facebook”

Spring break, post weekends, prior summer and Christmas.

Eye is sensitive to patterns and shape

Data as a language alters our perceptions and change our views.

Military budget with respect to GDP?

USA has biggest budget upwards of 600 billion yet Myanmar has biggest comparative to GDP.

Military size per 100,000?

China has the largest military due to it’s overwhelming population however South Korea has largest per 100,000.

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Snake Oil Supplements [Image] (2015).

Balloon Race Diagram
Bubbles correspond to popularity due to Google hits
Evidence are higher above line.
Visualisation is a form of knowledge compression, curating the data accordingly to space.
Filter out effects according to human health; natural ingredients, heart health.

This TED talk/lecture pod reimagined data visualisations by reshaping perceptions reflecting on patterns and adding context. Data is ultimately meaningless without context, a mere reflection on graphics and statistics unless it’s compared to create a narrative. We can parallel data with preexisting knowledge and change our views. With the Balloon Race Diagram we saw bubbles corresponding to popularity, equating data as a form of knowledge backed by scientific information. By altering the information we can then produce a real time change in data, allowing a graphic that becomes relevant with current

Reference List
McCandless, D. (2010, July). The beauty of data [Video file]. Retrieved from  http://www.youtube.com/watch?v=

The Billion Dollar-o-Gram [Image] (2014, Jan). Retrieved  Oct 10 2018, from http://www.informationisbeautiful.net/visualizations/billion-dollar-o-gram-2013/ 

Mountains Out of Molehills [Image] (2007). Retrieved  Oct 10 2018, from https://informationisbeautiful.net/visualizations/mountains-out-of-molehills/

Lecture 6


Journalism is about telling a story with the use of data. Using key information sets, key data, key reference elements to inform a story. Processing the data requires asking the right questions. It is not restricting you to text, rather telling a story visually. Trusting journalists can be recognised through data and statistics, being open and transparent.

Data journalism is the recognition of power and measurement in helping public discourse. Scrapping, mining and statistical data reveals patterns and trends. Not for mere entertainment however affecting public and political discourse.

Data journalism history

Graphics made of type – easily reproduced (The Manchester Guardian, 1901).

Diagram and representation of data made of type – alphabet, lines which represented where the front half of battalion was, firing lines, infantry.

The Somme Battle Achievement

Sections of the lands after the war

The Manchester Guardian Commercial 1938

Importance and freedom of colour gives us as opposed to the cross hatching used at the time – complicated data

Meteorite – 54,000 events. Latitudes and longitudes in the data

Interactive/able to search

The guardian came up with a code/algorithm which looked at the achievement of gold medals, and how much more value was derived from country’s who had less population and less expectation of winning compared to more favoured country’s.

A visual interpretation was utilised from a google spreadsheet and changed the graphics live with up to date information. Population adjusted figures – it allowed for greater discourse between the public, discussing why certain countries were able to do well or not so well (economic/population reasons), why one country is particularly well in one sport. Allowed for individual research, explore with data journalism through personal interest rather than focusing on the popular/main winners.

This lecture pod was an important reflection of journalism and reinforcing the ideas of visualisation through narrative. Telling a story through numbers allows the audience a clearer idea of comparisons rather than appealing to emotion or persuasive language. A narrative can be created which dissects the equality of statistics, for example in the olympics the use of data allowed the public to view population adjusted figures of gold, silver and bronze medals, adding greater weight to the achievements of nations who are less likely to succeed due to socioeconomic factors. Further discussion among the public can ultimately create change as issues are presented on a surface level with statistical analysis and supporting visualisations.

Lecture 5

Why do we use graphs? To make comparisons easier

Designers choose what is aesthetic or fashionable – overuse of bubble charts. Our vision and brains struggle to measure surface area, better equipped at analysing length. Due to this we struggle to differentiate statistics that involve circles and tend to underestimate size, figures and values.

The more accurate and easier it is to make a judgment the more likely the reader will take away a perception of the presented patterns. This reflects the notion of the human brain finding it easier to compare simple measures, dollar values or figures. Lines or bar based charts on a single axis are the best visual representations for data.

The three most common types of charts used are time series charts (plotting changes over time, renowned in stock markets), bar charts are one dimensional which compares things and a scatter plot that has a variable on each axis.

Reimagined data can have serious implications on the wellbeing and health of professionals and public. In the case of the NASA launch, the rocket manufacturer’s graphics were inconclusive and poorly displayed. Despite this information the launch still went forward and ended in disaster. The mode of data presentation affects our mentality and understanding towards the issue.


Bar Chart
Incredibly useful, easy to use and pre-exisiting understanding. Quick to compare information, highs and lows revealed at a glance. Trends are visible within the graph, good for comparing categories.


Line Chart
As popular as bar charts and frequently used. Line charts connect individual data points allowing us to understand a sequence. Their primary value is to understand trends over time, eg. Stock price change over a 5 year period.


Pie Chart
Commonly used, and sometimes poorly executed. They should be used to show the relative proportions and percentages of information and only those things. Limit to 6 proportions. If using more than 6, consider a bar chart.


We use graphs as a tool of presenting information as a pattern or simplifying data so the human brain can easily measure size, figures and values. The lecture was important as it reflected the benefits of showcasing data for easily consumed purposes. In a professional setting graphs can even save lives as seen in the NASA example. The mode of data presentation affects our mentality and understanding towards the issue.

Lecture 4

Lecture Pod 4  Historical and Contemporary Visualisation methods

The Functional Art: An introduction to information graphics and visualisation


Fertility rate – average number of children born to women

Earth is forecasted at 9 billion population in two decades from now. If the replacement rate in a country is significantly lower than 2.1 the population will shrink over time. If its much higher than 2.1 you’ll have a much younger population down the road – potentially greater rates of violence and crime.

On average fertility in rich countries is relatively low, but has up-trended in recent years. Poor countries are beginning to show a decrease in average fertility.

The first and main goal of any graphic or visualisation is to be a tool for your eyes and brain to perceive what lies beyond natural reach. The data in a table form becomes difficult to analyse, memorising numbers and then comparing is tedious and tiresome. Placing these numbers as a line graph allows our eyes to follow the data with ease.


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L. Cmielewski Historical and Contemporary Visualisation Methods (2016)
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UNDATA (2018, August) Total Fertility Rate, Sweden and Spain. Retrieved from http://data.un.org/Data.aspx?q=fertility+Spain+sweden&d=PopDiv&f=variableID%3a54%3bcrID%3a724%2c752


Throughout this lecture pod it was important to learn how data comparisons can be simplified especially when comparing two variables. The data is easiest to consume when following a linear path rather than using memorising numbers and tracking the discrepancies of dates or statistics. Data is constantly changing, how we choose to consume it makes it easier for us to interpret and evaluate.

Lecture 3


Visualisation: War & Death

In the middle of 1812 Napoleon’s army of 400,000+ the army had to strategise their journey due to the grand scale and constant demands. Starvation and disease took their tolls as they were attacked by Russians and led to retreat. Only about 10,000 of Napoleon’s army survived the campaign.

To visualise the army’s journey, it’s possible to recognise and map their quest over time. Two charts/lines can map the army, as thickness of the line correlate to the location and size of the campaign during the current time. The mapping bodies information visualisation, translating the campaign in severity, reflecting the power of the image. It augments the viewer’s capacity to grasp and interrelate the complex data. It’s important to give the audience tools to analyse data and attempt to understand what is happening relative to its magnitude.

1812 by Prianishnikov http://www.tretyakovgallery.ru

During the Crimean War in 1858, Florence Nightingale recognised soldiers were dying needlessly due to malnutrition and poor nutrition. She strove to improve the living conditions of the troops by maintaining meticulous records and turning them into graphs.

L. Cmielewski Historical and Contemporary Visualisation Methods (2016)

By using Nightingale’s graphs it can be viewed effectively as the superimposed triangles demonstrate that troops were dying at 32x the rate of disease rather than battle wounds (red). The information would’ve been emphasised had it been presented as a bar chart. However a bar chart is not as effective in showcasing statistics over time.

L. Cmielewski Historical and Contemporary Visualisation Methods (2016)

Otto Neurath’s ISOTYPE – International System of Typographic Picture Education

Neurath used visual education to transform the masses with his implementation of visual type. It was mainly used to demonstrate comparisons of statistics over time in an educational way.

L. Cmielewski Historical and Contemporary Visualisation Methods (2016)


Throughout this lecture it was interesting to learn the different uses of graphs and data visualisation within historical context. The implementation of graphs in the 1800’s is revolutionary in displaying information that helps us understand the difficulties faced at that time. It’s also important to understand, for example Florence Nightingale reflected her collated data regarding illness post battles which could also be  displayed in bar graph emphasising her findings.

Data Types – Lecture Two


Data Visualisations Lecture 2 – Data Types

Nominal – pertaining to names. It remains unordered and refers to categories. An average cannot be calculated from nominal data however percentages can be determined. When there are only two categories available the data is referred to as dichotomous such as yes or no.

Ordinal – refers to order.

Survey questions which have answers such as Disagree – Neutral – Agree pertaining to the numbers 1, 2 and 3 representing each category is ordinal as long as they remain in order.

Interval – relates to measurement of time or period of time. does not have a meaningful zero point, can refer to zero as the start of a new day when discussing time rather than 0am.

Ratio – Data is numeric and has a meaningful zero point, referring to an absence. Zero means you don’t have anything. Examples could be height, weight or age.

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Qualitative vs Quantitative

Qualitative Data – Descriptive information

“I drink coffee everyday”

Quantitative Data – Numerical information

Discrete (counted) –  I drink 4 coffees everyday)

Continuous (measured)  – I drink 80 grams of coffee everydaylecture2 dichotomy.jpeg



Data types allows various forms of information to be placed as Numerical or Categorical. If one was to use their shopping list as data they could refer to their items as being Ratio, Interval, Ordinal and Nominal. The section of the shop is Nominal, the aisle is Ordinal, quantity of items are interval and ratio is the calculated cost of each item. We are using different forms of data in our everyday life whether we may be aware or not. Placing data within context allows greater understanding of information presented.