For this weeks tutorial class we were asked to develop a dashboard based around student screen use. We used dashboards to help convey the story being told. The dashboards below depict how screen time is being used throughout the week.
The dashboard below is compiled of 3 sheets. The first graph shows the amount of records per hour based over the period of a week. It highlights the fact that screen usage increases towards the afternoon and later at night. The highest amount of screen time is 10pm closer to when people start going to bed. I have also included screen usage per category and activity which shows that almost every category and activity involves some type of screen usage.
UNIVERSITY STUDENTS AVERAGE SCREEN USE
Below is another example of a dashboard created to depict university students time spent using a screen. I have used a bar graph to sort the data into 2 categories (Male and Female) to see any similarities or differences in how they used screens. Interestingly, Female spend more time using a laptop, whilst males prefer to use a desktop device.
In our week 10 tutorial week look at the basics of how to create a dashboard in Tableau. The above dashboard is a group of graphs made to show how university students use their time throughout the period of a week. Included in the dashboard is a bar graph showing the average hours spent in each activity, a tree map of average hours per category and then a simple pie chart which shows students screen use and the average hours spent on each device.
ON AVERAGE HOW MUCH SLEEP ARE UNIVERSITY STUDENTS GETTING?
The story here is to show just how much sleep University students are getting compared to the rest of their daily activities. As we can see from the bar graph above, Uni students over the period of a week, are getting on average 8hrs 30 mins sleep each day. From the visualisation above it tells us that sleep is a vital part in our everyday routine and therefor is much larger than the other activities.
Although we already know that sleep is vital to our life, we wanted to look into how our sleep measured up in comparison to national averages. The requirements of sleep hours tend to stabilise out in early adult life around the age of 20 and although individuals sleep varies, the required amount of sleep is between 7 and 9 hours a night. From this information is clearly shown that from the university students we collected data from, almost everyone was getting close 9hrs per night.
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.
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.
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.
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.
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.
DATA VISUALISATION 