THIS IS WHAT WE BREATHE
Data-driven Generative Audio-Visual Project
This is what we breathe is an audiovisual project that aims to shed light on the issue of air pollution through a Generative Art work that varies in movements, shapes and colors based on air quality.
The data, used as raw material, are collected in real time from survey stations around the world through the Air Quality Index (AQI).
The AQI summarizes data on the concentration of air pollutants at a given location, using a numerical value ranging from 0 to 500. As the level of air pollution increases, the AQI value increases and with it the risks to human health.
Different levels of pollution are assigned different color palettes that go into changing the work according to Plutchik's Wheel of Emotions, a theory that associates emotions and colors.
Sound, produced specifically for the project, also accompanies the graphic pattern by varying according to data collected in real time.
The goal of the project is to raise awareness about the quality of the air we breathe and stimulate proactivity, remembering that the main causes of air pollution are industrial emissions, motor vehicle traffic and the use of fossil fuels for energy and heating.
The data, used as raw material, are collected in real time from survey stations around the world through the Air Quality Index (AQI).
The AQI summarizes data on the concentration of air pollutants at a given location, using a numerical value ranging from 0 to 500. As the level of air pollution increases, the AQI value increases and with it the risks to human health.
Different levels of pollution are assigned different color palettes that go into changing the work according to Plutchik's Wheel of Emotions, a theory that associates emotions and colors.
Sound, produced specifically for the project, also accompanies the graphic pattern by varying according to data collected in real time.
The goal of the project is to raise awareness about the quality of the air we breathe and stimulate proactivity, remembering that the main causes of air pollution are industrial emissions, motor vehicle traffic and the use of fossil fuels for energy and heating.
A variety of tools and techniques were used to collect, process, and visualize the data, starting with the Air Quality Index, moving through Python programming, and ending with TouchDesigner visual programming software.
The end result is a video product that collects real-time air pollution data and modifies an abstract generative graphic based on the emotion-related colors of Plutchik's Wheel of Emotions.
The data processing begins with the user entering a city, the text string is collected by the program that queries the Google Maps APIs to receive as output the latitude and longitude coordinates of the input location. Around these coordinates, a Python script calculates a geographic area in which pollution data will then be collected. This collection is done by querying other public libraries, the Air Quality Programmatic APIs. These APIs, by sending as input the edges of the area of interest, return as output information regarding all the air quality sensing stations within the area, via a JSON file.
At this point, the JSON file is parsed and the data of interest to the project are extracted.
The end result is a video product that collects real-time air pollution data and modifies an abstract generative graphic based on the emotion-related colors of Plutchik's Wheel of Emotions.
The data processing begins with the user entering a city, the text string is collected by the program that queries the Google Maps APIs to receive as output the latitude and longitude coordinates of the input location. Around these coordinates, a Python script calculates a geographic area in which pollution data will then be collected. This collection is done by querying other public libraries, the Air Quality Programmatic APIs. These APIs, by sending as input the edges of the area of interest, return as output information regarding all the air quality sensing stations within the area, via a JSON file.
At this point, the JSON file is parsed and the data of interest to the project are extracted.
The AQI values from the survey stations within the area are then collected and the average between them is calculated. This value is then correlated to one of six categories, defined by "The World Air Quality Index Project," that determine how harmful air pollution is to humans (0-50: Good, 51-100: Moderate, 101-150: Dangerous for sensitive groups, 151:200: Harmful, 201-300: Very Harmful, 301-500: Dangerous). Based on the correlation between AQI value and category, the graphics and sound displayed to the public vary. The display of abstract graphics varies in movements, shapes, and colors, going from fluid motions, restrained shapes, and colors related to positive emotions when the atfospheric pollution index is low, to jerky motions, irregular shapes, and colors related to negative emotions for amtospheric pollution indices with higher values. Such shapes, are base produced through a Perlin Noise algorithm, which randomly generates visual textures based on parameters entered as input by the developer, just based on such parameters vary shapes, movements and colors, while the texture produced is randomly created by the machine, in a way that is never the same, ensuring originality and randomness.
As for the sound accompaniment, on the other hand, produced entirely in Ableton Live, the audio track superimposes different sounds as air pollution increases. It thus starts with dulcet and pleasant sounds for low pollution values, and then gradually adds electronic and unpleasant sounds as the AQI increases.
