Tag: Data Visualisation

Analysis of Data visualisation

Analysis of Data visualisation

by jordanevans334543798

By Jordan Evans and Jose Fagel

https://flowingdata.com/2019/03/06/women-men-timeuse/?fbclid=IwAR1xxxRT6oWuityIb3hrSSjbYXCL2k78g0889wfMvBW9dUSVTUx_x5Wvl4M

What story does it tell? 

It tells us what a working day for American men and women looks like, to see how their schedules differ from each other.

It highlights the relationships between men and women throughout daily activities and explores the journey people take throughout the day.

How does it tell it? 

By dots cyan dots representing women and orange dots representing men. Each dot represents a person, and as time moves forward on the clock it shows what the dot is currently doing.

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? 

The dots are sorted into categories of activities but there are no other levels of information that detail what the person is specifically doing.

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

It is difficult to track one single dot for the duration of the day however you can create stories as a whole, you can see what a large number of people are doing at a specific time. For example, you can see at 5:00 am the majority of the people are sleeping but at 6:30 am there’s a lot of movement and the group splits into smaller categories by 8:00 am such as traveling, household, and work.

Also, you could read a commentary on men and women’s roles/activities. Like this snapshot at 10:57am. Which shows some disparity in activities (Household care).

https://flowingdata.com/2019/03/06/women-men-timeuse/?fbclid=IwAR1xxxRT6oWuityIb3hrSSjbYXCL2k78g0889wfMvBW9dUSVTUx_x5Wvl4M

The movements the dots take also create patterns and tell of people’s movements throughout the day. Such as dots ping-ponging back and forth from eating and drinking at certain times. Or when large clusters quickly dissolve like at the end of the workday. 

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

The visual layout and design are effective to be able to see the data flow, the typography is easy to read and laid out good. The user is given several control points to speed up and pause the motion graphic. 

What improvements would you suggest? 

The color choice for the dots could be more effective and more recognizable. If females were pink or red and men blue or green it could be easier to track the dots.

The option to highlight and track a dot could be useful so you can get to see single stories of peoples working day and routine.

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

The data was collected over the last few years from the American Time Use Survey which is located on the Bureau of Labour Statistics site.

The source of the data is very reliable as it’s a federal agency that is responsible for measuring labor market activity, working conditions, and price changes in the economy. Its mission is to collect, analyze, and disseminate essential economic information to support public and private decision making.

Lecture Pod Five

Lecture Pod Five

by jordanevans334543798
https://vimeo.com/177306425

Data Presentation Styles: Why use Graphs

Why do we use graphs? To make comparisons easier

There is a great range of ways to present data to achieve this goal, often graphic designers use the wrong way because of aesthetic or what’s fashionable. 

An example is the overuse of bubble charts

A chart recreated by Alberto Cairo from a Bloomberg news story, in his book “The Functional Art”

Alberto Cairo, The Functional Art, 2013

The chart shows the change in market capitalisation of various banks between 2007-2009.

The light grey bubbles represent 2007 and the dark grey bubbles represent 2009.

From this snapshot we can say over the two years their capitalisation cap decreased, all of these banks experienced a fall in their stock price.

The bubble chart is difficult to interpret figures from but looking at the same data in a bar chart it’s much easier to make a comparison and get the estimated figure.

You can make a graph wanting your audience to compare areas but they will automatically compare heights and widths. Using circles always makes us underestimate the size difference.

A ranking of different graphic approaches to compare data

Alberto Cairo, The Functional Art, 2013

Based on human visual perception, as we need to tailor the way that we show stuff. We need to understand the way human perceptions work to decide which way we’ll present data. The more accurate and easier the judgment is for your audience to make, the more likely they’ll take away the correct perception of the patterns you’re presenting.

Alberto Cairo, The Functional Art, 2013

A case of disastrous use of poorly designed charts is the Space Shuttle Challenger launch accident. 

  • Cold weather leading up to launch day at the Kennedy Space Station
  • There were discussions between NASA and the manufacturers of the booster rocket about should the shuttle be launched on a cold day.
  • The discussion was about the O-rings which sealed the sections of the booster rocket and the possibility of if they would become damaged and unsafe.
  • The booster rocket engineers made a no launch recommendation, which was their first no launch recommendation in 12 years.
  • They faxed 13 graphics to support their recommendation.
History of O-Ring Damage in Field Joints

It shows a catalogue of all the earlier launch damage to the booster seals. It’s shown in the historical launch order from 1 to 24. 

The problem is that obscures the two most important variables of interest, the relationship between temperature and the degree of damage. 

  • The temperature is shown in the nose of the rockets
  • The degree of damage is in the legend in shaded areas

The rocket engineers chose to order their information by time, that is the order of launch not be temperature or degree of damage. This makes the diagrams cluttered and any pattern difficult to see. 

Edward Tufty took that same data and redrew the rocket diagram as a starter plot graph.

Showing the relationship between temperature and O-ring damage. It reveals a clear pattern of damage and severity.

Scatter Plot of O-Ring Damage and Temperature of Field Joints

Bar Chart

Easy to use and the audience have a familiarity with them. It makes it quick to compare information and reveal highs and lows at a glance. Effective when you have numerical data across categories.

Line Chart

Connect individual numeric data points. Primary use is to display trends over a period of time.

Pie Chart

Commonly used but also very commonly misused. They are used to show the relative proportions or percentages of information. Limit the number of wedges to 6, if you need more than use a bar chart.

Reflection

In Lecture Pod Five, I learned why we use graphs as well as graphs being overused and not used properly and ultimately leading to disaster. Then the use of particular charts such as bar, line and pie charts are defined.

Lecture Pod Four

Lecture Pod Four

by jordanevans334543798
https://vimeo.com/176255825

Historical and Contemporary Visualisation Methods – Part 2

Why visualise?

To help us gain an insight and an understanding into complex issues

The Functional Art:

An introduction to information graphics and visualisation by Alberto Cairo.

Visualisations are Useful and Functional 

Cairo reads an article on the population of the world, he then reads of the conflicting ideas on the fertility rates; the average number of children born in each country.

Rising fertility in poor regions is the reason the earth has to support 7 billion people now and a forecast of 9 billion in the next 2 decades. Other doomsayers focus on the aging populations in developed countries where fertility rates are below 2.1 children per woman. This number is known as the replacement rate. If the replacement rate in a country is significantly below 2.1 that population will shrink over time, if it’s much higher than 2.1 you’ll have a much younger population in the future which can cause problems. Predominantly younger populations show greater rates of violence and crime. 

The author of this article contradicted both of these apocalyptic thinking by discussing two interesting trends. On average fertilely in rich countries is very low but in the past few years, there has been a slight increase in the trend. On the other hand, poor countries are showing a decrease in average fertility. The author suggests that due to these trends, fertility rates everywhere will converge around 2.1 in a few decades and the world population will stabilise at 9 billion people.

The Rational Optimist: How Prosperity Evolves by Matt Ridley

The graph, however, does have some insufficient information, the graph is an aggregate of the data of all countries in the world it doesn’t show the multiple patterns the author discussed, it doesn’t show the rich countries with recovering fertilities and poor countries stabilizing their populations.

It’s hard to extract meaning from a table. Data from a table is difficult to look at and find a number and then memorise additional numbers and then make a comparison. It’s much clearer when looking at a graph and then easier to make compassions.

All the examples of graphs require an active engaged reader. 

Reflection

Part Two to the history lecture pod continued to expand my knowledge and understanding of why we visualise data, how useful they are their primary functions.

Visual Vocabulary

Visual Vocabulary

by jordanevans334543798

Designing with data

There are so many ways to visualise data – so how do we
know which one to pick? In the link use the categories across the
top of the page to decide which data relationship is most important
in your story, then look at the different types of chart
within the category to form some initial ideas about what
might work best. This list is a useful starting point for making
informative and meaningful data visualisations.

Download Here

Lecture Pod Three

Lecture Pod Three

by jordanevans334543798
https://vimeo.com/176255824

Historical and Contemporary Visualisation Methods – Part 1

We use visualisation as a way to present large or complex data sets in a way that enables our audience to grasp those complexities with the least amount of work possible on their part. 

Visualisation: War and Death 

Napoleon’s invasion of Russia 1812

Napoleon’s Grande Armée of over 400,000 men, the largest army that’d been ever assembled up to that point in European history, headed towards Moscow.

Once they arrived in the Russian capital they found an empty city, it had been completely evacuated and stripped of its supplies. The army had to retreat and supplying the army on the way back was near impossible due to the harsh weather. The lack of grass weakened the armies horses, all of which died or were eaten by starving soldiers. Without horses the French cavalry became foot soldiers, cannons and wagons had to be abandoned depriving the army of artillery and support convoys.

As starvation and disease took their toll and desertion rates soared. The grand army was attacked several times by the Russians in their retreat. The crossing of the river Berezina was the final french catastrophe as two seperate Russian armies inflicted horrendous casualties on the reminisce of The Grande Armée. On December 14th 1812, The Grande Armée was expelled from Russian territory, only 10,000 out of 400,000 survived the Russian campaign.

Charles Joseph Minard 

Napoleon’s invasion of Russia 1812

Made 50 years after the map shows the Polish border. The distance from the Polish border on the left to Moscow on the right is 900km 

The orange line left to right shows the army crossing the Neman River and advancing to Russian territory. From right to left the dark line shows the army returning to the west crossing the Neman River with only 10,000 men.

The lower portion of the diagram is a graph, it reads from right to left and shows the temperature the army endured through Russia, the vertical lines connect the temperature with the army at those certain times. Starts at 0 degrees at Moscow and minus 30 degrees towards the retreat they made.

The diagram shows the magnitude of the events and how the campaign went from bad to worse over the course of a few months. A strength of data visualisation is that it can reduce the time necessary for understanding a given event.

Florence Nightingale Crimean War 1858

Crimea, South of Ukraine

War between Russia and the alliance including the British and the old ottoman empire

Florence Nightingale pioneered modern nursing practices while caring for wounded soldiers 

Nightingale and volunteer women came to work in the hospital to take care of the soldiers. In 1855 things got critical and care was needed, Nightingale saw that soldiers were dying from malnutrition, poor sanitation and lack of activity. She strove to take care and improve the living conditions of the wounded troops.

She kept meticulous records of the death tolls in the hospital as evidence of the importance of patient welfare, she turned those records into graphs to show British commanders.

Otto Neurath 1882-1945

Otto Neurath ISOTYPE

A pioneer in the realms of socialists politics and economics in Vienna.

He invented a system called ISOTYPE 

International System of TYpographic Picture Education

Serialisation of images, multiples of the same size

The reason of the industrial approach was a key idea was to bring the museum to the people. A exhibition pack could be shipped and then used to be put up display everywhere.

Reflection

I enjoyed this lecture pod as I like the historical events mentioned and how data was used to show statistics and hardship through stories. Using data historically taught me just how useful it is; as it changes perspective and helps tell history more accurate.

Lecture Pod Two

Lecture Pod Two

by jordanevans334543798
https://vimeo.com/176274669

Data Types

What they are and what you can do with them

Levels of measurement 

Nominal, Ordinal, Interval and Ratio

Nominal, Ordinal, Interval and Ratio

Nominal Pertaining to names; Named categories

Ordinal Numbers assigned to place groups and items in a certain order; Order

Interval Numeric, has no meaningful 0 point 

Ratio Like interval data except it does have a meaningful 0 point

Nominal, Ordinal, Interval and Ratio

Qualitative and Quantitative Data

Qualitative and Quantitative Data

Qualitative Non-numeric data 

Quantitative Numeric data and quantifiable 

Discrete Counted 

Continuous Measured 

Reflection

I found this lecture pod useful as I learnt about the types of data there are and what you can do with them. I also learnt how to distinguish these types of data between them all.

Lecture Pod One

Lecture Pod One

by jordanevans334543798
https://vimeo.com/175177926

Introduction to Data Visualisation

We live in our most data rich time…

As individuals, we create a lot of data each day such as social media traces, smartphone trails, credit card purchases, travel locations.

Everything we do is quantified, this leaves an everlasting data trail. 

New visualisation strategies and older ones are used to make sense of it all. We are enmeshed into a data economy that is more complex and generative than ever.

To Put Data In Perspective…

23 Exabytes (EB) of information was recorded and replicated in 2002. We now recored and transfer that much information every 7 days.

– School of information management and systems, University of California, USA.

* 1 EB = 1 Billion gigabytes

What is Data Visualisation ?

Data vis is the vis of data, it involves the creation and study of data

Data vis is an essential part of the communication process 

Data rich time. contemporary digital world 

Why We Need It

Users may have particular analytical tasks, such as making compassions or understanding causality, and the design principle of the graphic follows the task.

What Data Is

Data are values of qualitative or quantitative variables belonging to a set of items, can be visualised using graphs or images.

Effective visualisations helps users analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable. 

A bar chart may be simple but it is the best form of data visualisation if you have two variables.

A line chart is a good choice to show data over time.

Difference between a data visualisation and an infographic

Not all information visualisations are based on data, but all data visualisations are information visualisations.

In essence, some examples of infographics are just lists with pictures and not a data visualisation.

Effective visualisations help users analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable.

Reflection

This lecture pod was useful as it introduced me to what data visualisation is and when it is needed in certain situations. My understanding of data visualisation has changed and I now see data visualisation from a while new perspective.