LECTURE POD 4- VISUALISATION: HISTORICAL AND CONTEMPORARY VISUALISATION METHODS (PART 2)

Summary:

This lecture extends upon the previous lecture and discusses historical examples. It also answers the question of the functionality of visualisation. Leon advocates that it is not just for aesthetic purposes but also because it allows us to gain an understanding and an insight into complex issues.

Leon utilises a book, ‘An introduction to information graphics and visualisation’ by Alberto Cairo as an example of the topic. Leon discusses the World’s population and discusses the fertility rate of women in each country whilst outlining statistics and the debatable explanations behind them. For example:

  • Rising fertility in poor regions is the reason why the earth has to support 7 billion people now with a forecast of 9 billion in two decades from now.
  • If the replacement rate in each country is below 2.1, the population will shrink overtime. If it is much higher than 2.1, there will be a much younger population further than the road, potentially causing issues, such as being more vulnerable to crime.

The author contradicts both reasons by analysing two trends. In wealthier countries, fertility averages are low however, it is beginning to rise. Poorer countries are steadily declining in fertility. In 1950, the average fertility per woman was six. As of 2010, it is below 2. The author suggests, fertility trends will potentially drop to 2.1 in the upcoming decades and the world population will stabilise and approximately 9 billion.

Visualising data and numbers allows the observer to save time and energy. The graph makes it easier to see the trends.

Leon also compares the fertility rates between Spain and Sweden and also displays a graph that compares international trends.

In this case, the graph highlighted some wealthier countries and some poorer countries and lists the reasons for the steady decline in fertility rates. For example:

  • An increase in per average capita income
  • Better access to education
  • Shrinking of infant mortality figures
  • Better family planning

In conclusion, readers should be given enough information to be able to follow an argument or use their own intelligence to come to their own interpretation and extract their own meaning.

As designers, Leon advocates that it is essential we honour intelligence and the curiosity of the data while developing it to be engaging and visually appealing.

Reflection:

The graphs that were displayed in the lecture were easy to follow as they were colour coded and allowed for the observer to establish an informative conclusion. I have been told in the past that the population of earth has exponentially increased and will keep rising. I like that Cairo shows evidence that this may not be the case and that earth’s population will stabilise at 9 billion in the next two decades. He shows graphs and evidence that clearly supports his opinion, as they are labelled and are created from reliable sources.

After watching four lectures, I feel I have gained a further understanding of data visualisation and its significance. I will try to apply Leon’s advice and honour the intelligence of data in my visual projects, while using my design abilities to engage the audience and illustrate valuable information in an interpretative way.

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The functional art 

 

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Brazil’s demographic opportunity 

References in APA

Brazil’s demographic opportunity [Image] (2017). Retrieved September 19, 2017,  i.pinimg.com/736x/63/7b/22/637b22f882f9ec5f39d013e1101948c1–information-design-adobe-illustrator.jpg

The functional art [Image] (2017). Retrieved September 19, 2017, from image.slidesharecdn.com/civilhackingshort-130601124311-phpapp01/95/alberto-cairo-visualizing-data-5-principles-to-live-by-2-638.jpg?cb=1370090973

 

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LECTURE POD 3- Historical and contemporary visualization methods (part 1)

Summary

Data visualization example (1): Data visualization strategies have been utilized over two centuries. Lecture pod 3 first discusses a data visualization that depicts the failure of Napoleon’s invasion into Moscow which occurred in 1812.

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Napolean invasion chart 

A French engineer created this map, 50 years after the failure of Napoleon’s invasion into Moscow which occurred in 1812. This diagram displays several different variables.

The thickness of the line indicates the strength of the army at critical points. From left to right is the army crossing the river, with 422,000 and arriving in Moscow with only 100,000 men. From right to left (the darker line) shows the army returning to the west. Only 10,000 men survived. The vertical lines connect the temperature to the location.

Data visualization example (2): The second data visualization that lecture pod 3 discusses is one that was created by Florence Nightingale who played a significant role in the Crimean War (1858). The Crimean war was between the Russians and alliance with the ottoman empire and the British. Florence nightingale also helped to care for wounded soldiers.

The graph demonstrates that soldiers died from diseases more than wounds in battle. It goes around in a circus for a full year then crosses to the second year (left to right).

Nightingale wasn’t just famous for being a nurse, she was also the first female statistician. She was a significant part in developing proper sanitation for wounded soldiers and helped solve malnutrition among them. The graph below demonstrates how the death toll of  soldiers decreased over a period of time due to Nightingale’s effective attempt to prevent disease and malnutrition by providing proper sanitation and adequate nutrition.

Although her charts were not be perfect but they were a huge innovation in her time.

 

crimeanchartmedium

 

Nightingale visualisation 

Data visualization example (3): The third example that lecture pod 3 discusses is the work of Otto Neurath (1882 – 1945). He was a pioneer for socialism. He started a museum where he aimed to make social and economic relationships understandable, especially for the uneducated.

He developed a system known as the ‘international system for infographic picture information.’

Neurath also introduced exhibition packs that were made for the general people, as Otto believed that museums should be brought to the people, not the other way around. These were shipped all over the country and put on display at all sorts of venues to widen ideas. The precedent below displays a photograph of Neurath cutting out pieces to create an exhibition pack to distribute to the masses.

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Neurath photograph 

Reflection

Viewing lecture pod 3 has allowed me to gain an appreciation of how one of the most significant strengths of data visualization is that it can reduce the time to understand a certain event. Attempting to analyse paragraphs of texts, facts, theory and trying to make sense of it can be quite tedious and I have found that data visualization has augmented my capacity to absorb the data efficiently.

Another aspect of this lecture that I found intriguing was how Neurath created educational visualizations that could be communicated effectively towards the masses, including the uneducated. As education was a luxury during early 1900’s, I admire that Neurath acknowledged this and put in considerable effort to ensure his message reached the masses through an easy-to-understand method.

Lecture pod 3 has allowed to me to gain an understanding of the ways that data visualization was historically used and how effective it has helped us interpret data in a unique way that allows us to identify trends, patterns and correlations to advance our understanding of the world.

References in APA

Napoleon invasion chart [Image] (2016). Retrieved July 26, 2017, from http://www.google.com.au/search?q=napoleon+data+visualization&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiyyPr7oKfVAhUKwbwKHWAqBmMQ_AUICigB&biw=1920&bih=950#imgrc=FCt4y-Pl4KRuoM:

Neurath photograph [Image] (2010). Retrieved July 26, 2017, from www.google.com.au/search?biw=1920&bih=950&tbm=isch&sa=1&q=otto+neurath+exhibition+pack&oq=otto+neurath+exhibition+pack&gs_l=psy-ab.3…33209.36271.0.36448.21.15.1.0.0.0.317.2604.0j3j6j2.11.0….0…1.1.64.psy-ab..11.1.204…0i8i30k1.MTRBnzSYurw#imgrc=ubUz6q-qNxAT5M:

Nightingale visualisation  [Image] (2017). Retrieved July 26, 2017, from https://www.google.com.au/search?biw=1920&bih=950&tbm=isch&sa=1&q=florence+nightingale+data+visualization&oq=florence+night&gs_l=psy-ab.3.1.0j0i67k1l2j0.704123.705668.0.707212.14.8.0.0.0.0.342.678.3-2.2.0….0…1.1.64.psy-ab..12.2.677.e8dvL4r_S6U#imgrc=9s6FSEpRZ7JfZM:

 

 

LECTURE POD 2- DATA TYPES

Summary:

There are 4 different types of data that are discussed in this lecture pod. They include:

  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio

Nominal data derives from the Latin word, ‘nomen’ meaning ‘pertaining to names.’

An example of nominal data is if an individual went grocery shopping and observed that various items would fall into different categories. ‘Nominal data’ is inherently unordered. You cannot take the ‘average’ of nominal data. When there are 2 categories, the data is then referred to as ‘dichotomous.’

Ordinal data can be defined as when numbers are assigned to determine something that is quantitatively immeasurable. For example, a survey may ask a shopper to rate their experience from one to five- one meaning ‘very unsatisfied’ and five representing ‘very satisfied.’ As emotions are subjective to each individual, this would be the most appropriate method to gain an insight into a consumers thoughts and feelings.

MD-1348_MultipleChoice_graphic3b

Scale 

Interval data can be defined when numbers are assigned to determine something that is quantitatively measurable, unlike ordinal data. For example, the time of a clock. When you say ‘0:00 am’ this does not mean that there is an absence of time. It just means that it is the beginning of a new day. Other examples of interval data in every day life include temperature.

Unlike ratio data, ‘zero’ does not mean the absence of a variable but simply a measurement.

clock_in_flash8.jpg

Ratio data 

Ratio data can be defined when numbers are assigned to determine something that is quantitatively measurable. However, unlike ‘interval data’, the value of 0 indicates an absence of whatever you are measurable.

For example, 0 minutes or 0 dairy products in the basket. Some other frequent examples of ratio data include:

  • Height
  • Weight
  • Age
  • Income

Furthermore, an example of qualitative would be an individual stating  “I drink coffee every day.”

Quantitative, on the other hand, is numerical information. There are two types:

Discrete (counted)-  “I drink 4 coffees everyday”
Continuous (measured)- “I drink 80 grams of coffee everyday”

Reflection:

I understand that there are several different types of data but I like how the lecture pod describes each of the four types of data thoroughly and with effective examples. I now acknowledge the difference between quality and quantifiable data. At first, I was confused between ‘interval data’ and ‘ratio data’ but the lecture pod explains the the differences well. I feel confident in my new gain knowledge of the four types of data and believe they will act as effective tools to help me enhance my data visualization skills in this unit.

APA Referencing

Ratio data [Image] (2017). Retrieved July 26, 2017 from https://www.google.com.au/search?biw=1920&bih=901&tbm=isch&sa=1&q=digital+clock&oq=digital+clock&gs_l=psy-ab.3..0l4.43962.46168.0.46343.17.13.0.0.0.0.291.2015.0j2j7.9.0….0…1.1.64.psy-ab..8.8.1831.0..0i67k1.BiiryTcuQL4#imgrc=Z9vAr5sAgrS_wM:

Scale [Image] (2017). Retrieved July 26, 2017, from https://www.google.com.au/search?q=survey+1+to+10&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjUu_rzsafVAhUGPrwKHfeGCuUQ_AUICigB&biw=1920&bih=901#imgrc=Hd-9iRZZ7qpZpM:

 

Lecture 1- INTRODUCTION TO DATA VISUALISATION

Summary:

Data visualization is both an art and a science. It can be simply defined as data that has been visualized. Data values are qualitative or quantitative variables belonging to a set of ideas. Data itself has no meaning. For data to carry on information, it must be interpreted.

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Life Visualisation

Although infographics and data visualization are similar, they are not the same thing. The difference is that data visualization visually represents data, that allows for the audience to interpret the meaning accurately. Not all information visualizations are based on data, but all data visualizations are information visualizations. Data visualization aims to help people understand the significance of data. The beauty of data visualization is that patterns, trends and correlations can be exposed and recognized easier as opposed to just facts and theory.

Data visualisation is an essential part of the communication process. A data driven story without a chart is like a fashion story without a photo.

The growing scope of digital data has had a significant effect on our world, especially in the 21st century. Today, we are trying to understand the complex layers of the social, environmental and political systems. However, with new visualisation strategies, it makes it easier for us to make sense of it all. We are now a part of a data economy that is more complex and generative than what the world 50 years ago, could have possibly imagined.

“There is a tsunami of data that is crashing onto the beaches of the civilized world. This is a tidal wave of unrelated, growing data formed in bits and bytes, coming in an unorganized, uncontrolled, incoherent cacophony of foam. None of it is easily related, none of it comes with any organisation methodology.” –  Wurman, R. (1996). Information Architects. Graphis, 1997, p.15.

There are various ways for data to be visualized. Two examples include:

Line chart: Perfect for viewing data overtime
Bar chart: Perfect for comparing 2 variables

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Line and bar chart

Thus, effective visualization helps users analyse and reason about data and evidence.

Reflection: 

Before viewing this lecture, I didn’t know what the difference was between infographics and data visualization. Watching and studying the lecture gave me a clear understanding of the difference- that while not all information visualizations are based on data, but all data visualizations are information visualizations. Furthermore, the lecture also allowed me to gain an understanding of the importance of how data visualization can highlight patterns, trends and correlations that can possibly be missed with simply observing and analyzing theory.

I admire how Wurman uses the metaphor of a ‘tsunami’ that ‘crashes onto the beaches of the civilized world to describe the exponential rise of data in our technological world. I believe that data visualization can assist us to grasp some concept of the chaos of the world, so that we can gain a little understanding, to advance humanity and make the world a better place.

APA Referencing 

Life Visualisation [Image] (2013). Retrieved July 23, 2017, from https://www.google.com.au/search?q=data+visualisation&source=lnms&tbm=isch&sa=X&ved=0ahUKEwim2eTLs6fVAhVEUrwKHe2ICOYQ_AUICigB&biw=1920&bih=901#imgrc=EBbbkYry4RuhbM:

Line and bar chart [Image] (2015). Retrieved July 23, 2017, from https://www.google.com.au/search?biw=1920&bih=901&tbm=isch&sa=1&q=line+vs+bar+chart&oq=line+vs+bar+chart&gs_l=psy-ab.3..0i8i30k1l2.38513.40504.0.40615.17.11.0.0.0.0.272.1285.0j2j4.6.0….0…1.1.64.psy-ab..11.6.1278…0j0i5i30k1.6h8–v2lSho#imgrc=8IVYhkmpcniS0M: