Week 4: Analysis of a Data Visualisation
In week one we were assigned into a group for a data visualisation anaylsis and critique. Our group decided to choose..
What story does it tell?
It tells the story of human travel across time by tracking aircrafts and their destinations on a geographic scale. Each air craft is represented by a yellow icon (red icons symbolise aircrafts flying at a higher altitude) in real time. We can draw all sorts of stories from this data visualisation such as how economically prosperous a country is based on the traffic of flight influenced by popular tourist destinations. We can tell the story of human pollution emissions due to environmental footprints caused by C02 in plane travel over time, and we can even determine the causality of flight disturbances, such as, weather turbulence if predetermined flight courses are not reached in expected arrival time.
This model tells the story of how far human radar and tracking technology has come in terms of providing people with the tool to track and self organise. A family member who may be expecting the arrival of loved ones may use this as a website or an APP to keep track of unforeseeable delays before picking family up from the airport and to better manage time. It also informs us of the story in security and safety surveillance as it is constantly recording and back logging the history of human flight activity.
How does it tell it?
It is in real-time GPS (Global Positioning System). Quantifiable and tangle units measuring distances (km/miles), time (hour), altitude (ft), speed (knots/hr), pressure (IOM/Inch of Mercury), temperature (Degrees/infrared red), History (timeline), and direction (Represented in a line). It also reports in live blog/tweet posts informing events, delays, technical issues, facts, and line of flight. The panel is located on the top left and drop down bars appear on the top.
Does it allow for different levels of interrogation that can be seen or used on the part of the reader?
Yes, if the viewer has access to a specific flight number, they may be able to use them to draw coordinates by entering this into the search bar (located on the top right). Like a calculator, users may investigate and discover live fluctuations of movement in flight.
Are you able to create multiple stories from it?
Yes, you can draw stories as a user, traveler, flight worker, researcher, investigator, or environmentalist. A traveler may use this data visualisation like a watch on flight to check how much longer they would stay on board until they land at their destination. Today most flight GPS assimilations may be accessed behind all passenger seats. A flight worker may view the data visualisation from arrival and departure towers to instruct pilots and confirm when the line of passage to land is clear. Researchers may study the tool to further improve and make flying safer. Investigators may back track flight history to find out what had happened to crash scenarios.
What can you say about the visual design?
It is pragmatic, logical, scientific, mathematical, geographical, to scale, natural colours, map orientated, orderly and conveys a realistic arial representation (The Guardian describes it as “Authoritative”).
It’s already a visually comprehensive design, however it doesn’t include fuel and c02 emission measurements to inform travellers of their greenhouse impact when moving around on the continent.
Where does it get its source’s from?
* Automatic dependent surveillance-broadcast (ADS-B)
* Multilateration (MLAT)
* Federal Aviation Administration (FAA)
Week 10: Study Time vs Travel Time
Today in class we compiled all of the data from the 48 students across data visualisation. We discovered by comparing university study time and travel time to see how much time is wasted by the average university student.
As you can see in the bar graph above, travel time is roughly more than half of the time at 6.6% (public and private transport) and time spent studying equaled to 11.1%. In addition we found out that public transport equaled to 1.3% and private transport equaled to 5.3% showing that private transport is used 5 times more than public transport, therefore better time use while on private transport could equate to more hours studying.
We worked this out by totalling the number of entries of study time and travel time:
Step 1 – Public transport: 158 / 11761 (total number of entries by all 48 students) x 100 (to work out percentage) = 1.3%
Step 2 – Private transport: 624 / 11761 (total number of entries by all 48 students) x 100 (to work out percentage) = 5.3%
Step 3 – We added the public transport and private transport percentage, which = 6.6%
Step 4 – Study Time: 1308 / 11761 (total number of entries by all 48 students) x 100 (to work out percentage) = 11.1%
As a result of this, it is necessary that universities could think of ways in using this transport time wisely. Such as, a podcast of a lecture, which could be tuned via the radio.