The 4×4 Model for Knowledge Content is a guide to get users to engage with your website. The content you produce on your website needs to stand out as on average users will on spend 10 seconds viewing your content.
The Four Models are…
The Water Cooler: The purpose is to grab the users attention and to be direct and to the point. Typically these are headings. Depending on whether you are interested in the topic determines if your choose to engage or not
The Coffee Shop: This is were the content is explore in greater detail but not a fully in depth. It follows on from the water cooler and explains the ideas rather than introducing them.
The Research library: The library is where you go into more dept and detail about a specific topic. It’s the committed time to investigate a topic and contains research and data.
The Lab: This is where users can interact with the data from the research library. It gives the audience the ability to filter and play with data directly.
This data visualisation tells a story about the geological finds of dinosaurs and mammals throughout time, via the use of a map. It tells you a story about what was living on earth during certain periods of time and the location of were scientist have found prehistoric fossils.
How does it tell it?
The way it tells the story is through all the dots on the map and the timeline at the bottom. The dots are colour coded to match what kind of species the creature was and where it was found. When a dot is clicked, the user will get an in-depth description of the fossil. The time at the bottom is also colour coded to match the time period. Each row going down representing a more specific time period.
Does it allow for different levels of interrogation that can be seen or used on the part of the reader? e.g. can they drill down to discover more detail?
The PBDB Navigator does allow for different levels of interrogation on the part of the viewer, as viewers can narrow their search down to a specific fossil type, period of time or even continent. The viewer can also access more info on a particular circle by clicking on it, this will reveal more information about the fossil discovery.
Are you able to create multiple stories from it? If so, what are they?
The website has a collections count in the bottom right-hand corner, which counts the number of collections on the screen at that time. From the website, users are able to create multiple stories as they can gather information on multiple time-periods, types of fossils, and where they were collected. Users can discover what fossils where found when and in which locations they were found the most.
What can you say about the visual design- layout, colour, typography, visualisation style?
I think the site could be more aesthetically pleasing. The map is quite basic, and I found the navigation quite confusing before watching the walkthrough video. The colour choice is basic, but it does allow the dots to stand out, and suits the use of the site. I like the visualisation style, but I think it could be better designed to create easier navigation and allow first time users to understand it quicker.
What improvements would you suggest
When first opening the website I would suggest having a walkthrough tutorial to show how to use the data navigator and what each device does, rather than an overload of information at once. I would suggest having different shapes for the varying periods of time as its uses dots for the whole thing and it’s hard to differentiate the periods of time as the colour shades are similar and don’t stand out from one another
Where does the data come from, and comment on its source
The source of this data comes from fossil occurrences that have been found throughout time and placed in scientific publications which are added to the database by the websites contributing members. The data is collected by nearly 400 scientists and 130 institutions in over 24 countries to provide scientists and the public with information about the fossil record
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.
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.
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.
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.
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.
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
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.
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.
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.
*This image was taken from the Lecture
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.
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.
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.
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.
DATA VISUALISATION 