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.
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.
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.
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.
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.
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.
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 everyday
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.
Data has no meaning, for it to become information it must be interpreted.
Data visualisation involves a creation and study of information that has been abstracted in some schematic form, including attributes or variables for the units of information.
Not all information visualisations are based on data, but all data visualisations are information visualisations.
Effective visualisation helps users analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic follows the task.
When explaining two variables a bar graph is effective in interpreting and publishing data as information to be consumed. A line chart is effective in defining data placed within a time period.
Throughout this lecture it was important to understand that data doesn’t necessarily have any meaning, however once it becomes interpreted it can be visualised as information and graphics. By analysing data we are able to display complex numbers or information with greater accessibility. Using data and visual design principles we are breaking down tasks or information to suit a certain audience, whether it is a STOP sign or line chart calculating periods of time.