Auroral Spectrum
Introduction
Brief
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Artist Statement
Not all light is equal. White light can be broken up into millions of colors. When visible light is split up the entire color spectrum can be observed. Understanding the complex variations of sunlight as both a solar phenomenon and a power source is key to global sustainability issues. Understanding the harmful effects of artificial light pollution is equally key.
This piece was inspired by the Aurora Australis and Borealis phenomenon that occurs in the North and South Pole. The Lights are a result of a scientific process where electrically charged particles from the sun collide with each other as they enter the earths atmosphere. The Aurora is sort of a scientific visualisation in real life. The phenomenon is a direct result of natural conditions. Visualisation in science and art is to try and find a balance between visualising something in an aesthetic sense while still maintaining a connection with the science behind it. The auroral phenomenon is sort of natures way of doing this.
Auroral Spectrum is an immersive 3D visualisation that compares the most light polluted place on earth with the least. The sharp contrast of Hong Kong in comparison with Antarctica provides with an interesting difference to draw. The software compares the data and the values are used to effect the particles in the visualisation. The particles themselves are 3D objects in 3D space prerendered for the animation. They can behave according to external effects such as turbulence and boid algorithms and these algorithms are completely controlled through scripting and drivers.
A Spectrometer was used to collect wavelength and light intensity values from both Hong Kong and Antarctica. The numbers were processed using a custom software I wrote with python and then fed into a controller mechanism in the Open Source 3D software Blender that then drives a mesh which acts as an emitter for particles. The materials for the particles are also driven by the program. Images from Hong Kong and Antarctica are projected onto the particles in 3D and their visibility is driven by the data. This gives some visual cues to the audience about what is going on as they can occasionally see familiar images come together in the visualisation and then quickly disappear. This serves to give a bridge between the data and prevents it from being just a glorified graph without becoming too literal.
Auroral Spectrum tries to evoke a feeling of a sense of wonder in the audience causing them to wonder how these forms are being created. The immersive experience submerges the viewer in a wonderful spectacle. It brings to light how far from perfection we are in terms of the light situation in Hong Kong.
Context
Using spectrometers, we measured the colour spectrum embedded in the natural light of Antarctica and the artificial light pollution found in Hong Kong over periods as long as five hours. Various locations in Antarctica where no artificial light exists were measured as well as the Tsim Sha Tsui district of Hong Kong on a weekday evening—literally the most light-polluted place and time on the planet. The wavelength data of light entering the earth’s atmosphere and the light generated by man found in each of the two datasets was visualised in 3D using procedural animation, mapped over time and converted into a thinking particle simulation.
Using a 360-degree display and 3D optical technologies, the artwork compares the spectral data of Hong Kong, heavily polluted by artificial light, and the purely natural spectral data from Antarctica. Using the metaphor of the Aurora Australis, the southern lights, the data is interpreted, mapped over time, and presented to viewers by enveloping them in the colours hidden in different types of light.
Technology
For the data collection we used an Arduino based specrometer called the Spectuino to gather ambient light data in both Hong Kong and Antarctica. We then used python to sort through this data and organise it so it was readable to the Open source 3D software called Blender3D. Blender read through this data and used it to drive the animation. We then rendered it out and used a 360 degree projection system to display the visualisation.
Reflection and Learnings
Challenges:
- This was my second real data visualisation project. Unlike the first one which was a robotic installation based on Arduino, this one was particle based and involved a lot more programming. I had massive amounts of data in CSV format and I had to process and parse it to be able to use it to drive my animation. Doing it in real time proved out of my skillset at the time so I decided to use python to drive an animation in Blender 3D. Here's the kicker, I didn't know python. I learned Python from scratch in order to overcome this obstacle.
- I presented the work in the Gallery 360 in the Run Run Shaw Creative Media Center in Hong Kong. This was essentially a 360 Degree 3D cinema set up, with 5 High Definition 3D projectors mounted pointing outwards towards a 360 degree screen. The result was an immersive screen experience. This presented its own set of problems. The sheer size of the render. The minimum acceptable resolution to get a decent image was 8192 × 1024 and even then it wasn't perfect. I rendered at this resolution to balance render times and quality. I did not have access to a render farm at the time so I essentially built my own. I got to the lab early in the morning (I often would not leave leave for the night) and set up 20+ computers to render to a cloud drive. This is how I rendered such a high resolution.
Final Notes:
I learned so much doing this project. Rendering, particle effects, Python, Data visualisation, Using the Arduino Spectrometer to collect data. Even collaborating with a partner who was in Antarctica was a challenge. This was a truly multidisciplinary effort and I grew and learn so much from it.