Resident Datatoys researcher / Unity developer Robert Yang recently wrote about hooking Google Docs spreadsheet data into Unity, to better facilitate collaboration and make the data flow into our toys easier. Huzzah!

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Resident Datatoys researcher / Unity developer Robert Yang recently wrote about hooking Google Docs spreadsheet data into Unity, to better facilitate collaboration and make the data flow into our toys easier. Huzzah!
Immigration Auditization
Objective:
Utilizing the 2000-2010 dataset on immigration, our goal was to create a toy that abstracted the data through auditization.
Process:
Our original thoughts were to abstract and assign different data point to various elements of music (such as pitch, reverb, volume, key, etc.) This would give us a system map of how the data would turn into audio and also visualization.
Using a midi controller for inspiration, we experimented with the user experience of playing with buttons, knobs and sliders to manipulate the audio for each city, and each year represented in the data.
We were interested in taking a sampling of cities, and assigning a loop/sample to one aspect. For example: Low-Educated Immigrants or Native Unemployment. These loops/samples would be the same for each city, but manipulated slightly by the data. When a city is chosen, a composition is played. By choosing a city, a year, and playing/isolating the elements the player would start to hear the unique differences in the audio of each city.
In order to faithfully abstract the data, we embedded the lowest and highest values for each category in the data, turning them into values between 0 and 100. The different variables are assigned a volume level based on this. Certain data points, for instance a higher unemployment rate, would cause that track of the composition to be more prominent than others.
We created audio samples for Birmingham and Los Angeles (cities with polar immigrant populations) based on the data, manipulating BPM and volume levels for the points of low, mid and high educated immigrants.
For our final prototype, we decided to create a digital interface that combined the auditory and visual elements. The player is able to select one city at a time, toggle between the years, and isolate the different variables (.wav samples) that have been manipulated by volume based on the data.
The screen is divided into 6 sections and a ball (manipulated by the mouse) travels through these sections. The top sections are related to high education, the bottom sections are lower education. The left side is specific to education, the right side is specific to unemployment. As the ball passes through the sections an audio clip is played whose volume level corresponds to that data point percentage.
Players can control the ball and manipulate the velocity, and toggle through the data by year (2000/2005/2010).
posted by matthew willse, simone egipciaco, and paul cheng
Data Toys Project
I recently had the pleasure of working with Parsons MFA Design and Technology professors, Colleen Macklin and John Sharp, on their Data Toys research initiative. I was tasked with creating a modular toy which would load different sets of data on an iPad depending on which head was attached to the main body
I documented my build process on the following Flickr set... http://www.flickr.com/photos/52082118@N05/sets/72157632976117691/detail/
From PETLab Website Employable? is an early prototype for the "Making it in America" project based on the project API. The toy models the relationships between 25 cities in the US and the potential an immigrant has for finding employment in order to reflect on how likely immigrants of different education levels are to find employment.
A set of toy characters represent immigrants with three different education levels—no high school diploma, up to an associate’s degree, and a bachelor’s degree or better. By placing a toy on the iPad, the player selects a city, then one of the three years for which we have data. The player can then see the employment pool within which the immigrant would compete. The competition conveys the percentage of potential applicant pool broken down by level of employment and employment status.
More information on this project... petlab.parsons.edu/datatoys/projects/employable/