Lecture Pod 8: Data Visualisation Case

Miebach, N. (2019). Art made of storms. Retrieved 6 October 2019, from https://www.ted.com/talks/nathalie_miebach

In the week 8 lecture pod we looked at Nathalie Miebach’s TED talk “Art From Data” in which she talks about how data can be used in combination with music and art. Miebach highlights the fact that she uses a combination of sculptures and music to make data not only visible but tactile and audible.

Nathalie Miebach talked about how she used the interactions of barometric pressure, wind and temperature readings that were recorded from Hurricane Noel in 2007, to create a 3 Dimensional model and musical scores. The 3D model was made up of individual beads, bands which all represent different weather elements that can be read as musical notes.

Colorful Basket Weaving Sculptures by Nathalie Miebach Transform Weather Data into Visual Art. (2019). Retrieved 6 October 2019, from https://www.thisiscolossal.com/2016/03/nathalie-miebach-weather-sculptures/

The process starts by extracting information from a specific environment and then comparing it to information found online from satellite images and weather data. The information is then compiled onto clipboards with 2-3 variables, it is then translated onto a basket made up of vertical and horizontal elements with assigned values. Over time, elements of form reveal relationships that may not be visible on 2D graphs.

This TED talk shows that you can be creative with data and thinks outside the barriers of 2D graphs and use other mediums to visualise data.

6th Oct 2019 0

Lecture Pod 7: The Beauty of Data Visualisation

For this weeks lecture pod we looked at a TED talk by David McCandless, were he talks about the Beauty of Data Visualisation. His opening point is, “it feels like we’re all suffering from information overload, but the good news is that the solution is to just use our eyes more”. By visualising information we can see the patterns and connection that matter and then design information to make sense, tell a story and only focus on the important information.

The Billion Dollar O Gram arose out of frustration that Daniel McCandless had after dealing with reporting on the billion dollar amounts in the press. He decided to collect a bunch of reported figures from various news outlets and scaled boxes according to those amounts.

Beautiful, I. (2019). The Billion Dollar-o-Gram 2009 — Information is Beautiful. Retrieved 7 October 2019, from https://informationisbeautiful.net/visualizations/the-billion-dollar-o-gram-2009/

Each colour represents the motivation behind the money. By doing so you now start to see patterns and connections between numbers that otherwise would have been scattered across multiple reports. Daniel McCandless has now turned what was unorganised, scattered data into a visualisation that the audience can now explore.

McCandless talks about how he keeps hearing the phrase “ Data is the new oil” but he adapts this metaphor slightly to say that “data is the new soil”. For him, it feels more like a fertile, creative medium. He explains this as over the years, we’ve produced a huge amount of information and data that is irrigated with networks and connectivity, and it’s been worked and tilled by unpaid workers and governments.

David McCandless looked at the work of danish physicist Tor Norretranders in which he converted the senses into computer terms. The above graph shows that your sense of sight is the fastest about the same bandwidth as a computer network. Then you have touch, which is about the speed of a USB key. Followed by hearing and smell, which are similar to that of a hard disk. And then you have taste, which is the smallest and he describes as barely the throughput of a pocket calculator. He talks about how the bulk of our vision is visual and unconscious.

In summary, he states the fact that he feels design is about solving problems and providing elegant solutions, and information design is about solving information problems.

6th Oct 2019 0

Lecture Pod 6: Data Journalism

PART 1: What is Data Journalism?

In week 6 we looked at what Data Journalism is and how it is used in the Guardian . Professor Nigel Shadbolt describes Data Journalism as the use of key information sense, key data and key reference elements to inform a story. It’s not just about the existence of data or obtaining it and putting it out there, but the processing that goes into it to work out what it tells you. Paul Lewis states that you have to ask the right questions to get the right answers. The first video talks about how data visualisation lets you tell a story in a way that people watching it will understand and enjoy.

PART 2: History of Data Journalism

In part 2, we looked at the history of data visualisation used by the guardian from the very start issue in 1821. Since the start the guardian has been wrestling with data, trying to present data in an interesting way that brings the story alive.

History of Data Journalism at The Guardian. (2019). Retrieved 6 October 2019, from https://www.youtube.com/watch?v=iIa5EoxyvZI

The Guardians first inclusion of data visualisation was a long table of data that shows a list of every school in Manchester and the statistics of people attending as education was not compulsory for another 60 years. The next visualisation to be recorded was in October 1916, showing the groundwork of what was still to come by showing sections of the land ahead. In 1938, the Manchester Guardian commercial visualised London clearing bank assets by using proportional, stacked line charts. More recently, the Guardian produced a data visualisation were they collected data collected by The Meteorological Society, and showed every meteorite that they know of and their position.

PART 3: Data Journalism in Action – London Olympics

Data journalism in action: the London Olympics. (2019). Retrieved 6 October 2019, from https://www.youtube.com/watch?v=WyjBJzigm0w

The medal tables used during the Olympics are a good examples of data visualisation as everyone wants to know how their country ranks and who’s in the lead. The visualisation made by the Guardian for the 2012 London Olympics allowed viewers to interpret the data themselves and interact based on their own interests.

The most important aspect to remember here is that whilst Data Visualisations have been used for many years now, they are now being produced at increasing rates and we are able to make more complex data easier to understand for viewers to then interact with.

6th Oct 2019 0

Lecture Pod 5: Data presentation Styles 1: Graphs

Why do we use graphs? Simply, Graphs make it easy to make comparisons. As designers we need to tailor the way we show stuff to be read and understood easily. The more accurate and easy the judgement is to make, the more likely that readers will take away the correct perception of patterns you are presenting.

Bar charts are a great example of charts that make it simple to make comparisons, bubble charts on the other hand are not so good at this. By using circles, the audience is likely to misinterpret the data being displayed as they will likely only look at the hight and width of the bubble to make comparisons, rather than the whole area. this is good if you only want a general idea of the data but not good for detail.

We also looked at the three most common types of graphs used.

Bar Chart: these are simple and easy to use as we have a familiarity with them and therefore don’t have to spend time learning how to read a new type of graph. Bar charts are used when comparing data across categories and effective with numerical data that splits into different categories. They help to real the highs and lows of data.

Line Chart: Line charts cones individual numerical data points and are a simple way to visualise a sequence of values. Their primary use is to display trends over a period of time.

Line Chart in Excel. (2019). Retrieved 7 October 2019, from https://www.excel-easy.com/examples/line-chart.html

Pie Chart: Pie charts are used to show the relative proportions and percentages of information. These are often the most incorrectly presented type of graph with many people not knowing how to correctly create a pie chart.

Everyday maths 1. (2019). Retrieved 7 October 2019, from https://www.open.edu/openlearn/ocw/mod/oucontent/view.php?id=81977&section=4

From this lecture pod, the main point was to show us how different types of graphs can be used depending on what data and story you wish to present. We learned that sometimes if the incorrect type of data is presented, it can be misleading and lead to accidents happening.

1st Oct 2019 0

Lecture Pod 3&4: Historical and contemporary visualisation methods

We use visualisation as a way to present large pr complex data sets in a way that enables our audience to grasp the complexities with the least amount of work possible. In this week lecture pod, week looked at historical uses of data visualisations and more specifically at Napoleons Invasion on Russia, Florence Nightingale Crimean War data and Otto Neurath’s introduction of ISOTYPE.

We first viewed Napoleons invasion of Russia in 1812 and the graph below shows the strength of troops at the start of the invasion and the decrease of strength towards the end. It does this by the line thickness becoming thinner to represent a decrease in strength. This visualisation enables viewers to grasp a difficult concept and allow them understand the concept faster. By constructing a graph, it makes interpreting the data easier than viewing the raw data collected. Graphs allow the audience to view information much faster and get the full picture without the complications of sorting through data.

From Napoleon to Tableau: A Brief History of Data Visualization as it evolved – Adaptive Systems. (2019). Retrieved 6 October 2019, from https://adaptivesystemsinc.com/from-napoleon-to-tableau-a-brief-history-of-data-visualization-as-it-evolved/

During the Crimean War in 1858, Florence Nightingale realised that soldiers were dying from causes that were not related to battle wounds. Nightingale strived to improve the living conditions of injured troops and kept a record of the death tolls as evidence to put forward an argument to the British Military. She turned her records into graphs that show the causes of mortality in the easts army soldiers. The graph uses area to show mortality rates for each month of the year. Interestingly, she discovered that troops were dying of diseases 32x the rate of those with battle wounds.

Otto Neurath was a pioneer in socialist politics and economics in Vienna. His mission was to make social and economic relationships understandable though visualisation. The point being made is that rather than overwhelming the audience with information, Neurath has simplified the data into a visualisation using graphics that are simple and easy to understand.

ISOTYPE Visualization. (2019). Retrieved 6 October 2019, from http://steveharoz.com/research/isotype/

Why Visualise?

Visualising helps us to gain an insight and understanding into complex issues.

Extracting meaning from a table is difficult as our brains have not developed to deal with the large amount of data in this form. By using graphs with information as a visual tools, helps us to understand the data and save time/energy that would have been used to try and understand the information.

The key point from this lecture is that methods of data visualisations have evolved over time to make reading data easier and that simplifying data is effective in communicating a story effectively and quickly. The idea is that if you don’t present your data to readers so they can see, read, explore and analyse it, then they may not even take your word for it. You need to try and convince the audience or give them the information to convince themselves.

1st Oct 2019 0

Lecture Pod 2: Data Types

This weeks lecture pod was about Data Types. There were four data types discussed in this lecture, Nominal, Ordinal, Interval and Ration.

Data Levels of Measurement. (2019). Retrieved 6 October 2019, from https://medium.com/@rndayala/data-levels-of-measurement-4af33d9ab51a

Nominal data (pertaining to names) consists of named categories into which data falls. Nominal data can be counted to calculate percentages but it is not able to take the average of something. An example of Nominal data is at a supermarket were food items are sorted into specific categories ( Dairy, Fresh produce, Packaged and Frozen)

Ordinal data (the order) can also be used to find percentages, however there is still some debate over whether you are able to calculate averages with this type of data. The easiest example of this data is supermarket lines (Short lines, Medium lines and Long lines).

Interval Data (time) is numeric data which is fixed between specific points. It refers to an intervals between each consecutive point of measurement being equal to each other. In this type of data the value of 0 is still a measurement as 0°C is still a temperature as well as 0 seconds is still a measurement of time. The most common example of this is something we use everyday, Time is broken down in 60 seconds a minute.

Ratio data is numeric data that is similar to interval dat except it does have a meaningful zero point. The value of 0 indicates an absence of what is being measured. This can be found in a persons age, weight and height.

Lastly, we looked at Qualitative and Quantitative data. Qualitative data is non numerical descriptive information and quantitative data is numerical information.

The key points to remember from this lecture is that we need to be able to sort data in data types to allows for the correct information to be portrayed. Data types are used to prevent mistakes from happening such as interpreting the data incorrectly. Data types are used to breakdown data into simplifies categories that are easy to understand and relate to for everyday use.

1st Oct 2019 0

Lecture Pod 1: Infographics and Data Visualisation

Carless, H., & Carless, H. (2019). Data Visualisation: The Three Minute Guide for Marketers. Retrieved 7 October 2019, from https://www.vertical-leap.uk/blog/three-minute-guide-to-data-visualisation/

In the first Lecture Pod we were introduced to the idea of Data Visualisation and what it is. Data are values of qualitative or quantitive variables belonging to a set of items. Typically, Data is the result of measurements and can be visualised using graphs. By itself data carries no meaning, for data to become information it must be interpreted for it to take on a meaning.

The first important point made was that data is growing at an exponential rate and their is now more data than any previous records throughout history. Records shows that 23 exabytes (1 exabytes = 1 billion gigabytes) of information was recorded and replicated in 2002, now we do this in seven days. Even as individuals we are recording large amounts of data each day, whether its data collected from a fitful or Apple Watch, internet history and credit card purchases. Due to increasing levels of data being accumulated, we have developed strategies to deal with the growing amounts of data. From dynamic weather maps, heritage and epidemiology, tracking polluted water and crimes, new visualisation strategies along with old ones, help us to make sense of it all.

17 Beautiful Examples Of Clean And Minimal Infographics | Graphic Design | Data visualization, Information visualization, Information design. (2019). Retrieved 7 October 2019, from https://www.pinterest.com.au/pin/178947785171841309/?lp=true

Data Visualisation is the visualisation of data that is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data, meaning that information has been abstracted in a schematic form and includes its variables and attributes for the units of information.

The most important point to remember from this lecture is that Data Visualisation is now a mass medium and an essential part in the communication process. It is important for us as designers to engage with the overall aesthetic, forms and politics of data presentations. By using effective visualisation methods it helps users to analyse and think about data and makes complex data more accessible, understandable and usable.

2nd Sep 2019 0