Planet 11

This project required a creative means of data visualisation, using an API to draw "Big Data" collected from various types of sensors in UTS building 11. Our group decided on using measurements of: the total number of people, represented by buildings; CO2 levels, represented by clouds; oxygen, and temperature, represented by colour. This was done over a planet, that changed in real-time to reflect the measurements gathered by these sensors. These all had numerical values as well.

Due to the nature of the statistics being measured, a planet was deemed to be the most appropriate creative solution for presenting the visualisation of this data, due to being thematically appropriate and easily understandable.

Due to there being a significant number of sensors available to choose from, which in turn meant there was an even more significant number of ways to cohesively and visually present the data gathered by more than one of these sensors, it was necessary to choose which sensors would be used. Based on the criteria of having sensors that gathered data that could be considered to overlap or otherwise relate to one another and the data gathered needing to be at a certain minimum level during both peak and non-peak activity periods, the sensors for people, CO2 levels, oxygen and temperature were chosen. It was decided to have each sensor's measured numerical value included in the top-left-hand corner, in addition to the data's visualised form.

The respective visual choices for these measured data sources were chosen after determining what sensors to use, based on the theme of a planet image having been deemed to be most relevant.

This project's prototype was entirely of a digital nature, which meant the functionality required being completed and implemented through coding. This required ensuring that the API that contained the data connected to the sensors could successfully be collected and connected to the code; and then represented through the previously planned method for visualisation.

This stage encountered the most difficulties, and required the most time to ensure the visualised data was all presented to the user; updated in real-time for the duration of time the sensors gathered data was being used; and was easily understood by anyone viewing the end result prototype.

The prototype needed to be repeatedly tested and adjusted, to fine-tune the functional and visual aspects of the visualisation, most notably the ratio of measured data to visualised data, and colouring and layout choices. Through testing and adjustments made, it is clear to see the change in the sensor gathered data overtime, whilst ensuring that an overload of data in the visualisation is prevented.

It was during the testing stage, that a decision to introduce a slider for adjusting the speed at time for the set, measured time period flows could be adjusted, as it would add a personalisation and level of human-interaction for each user.