#datatoy

[petlabdatatoys]

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 

[petlabdatatoys]

Gear Up

Silent brainstorming notes from Gear Up.

  “Gear Up” is a toy developed by team Aero, Alex, Robert, and Patricio.

The concept for Gear Up emerged from a silent brainstorming session. After sorting through many different ideas and the datasets provided by the Migration Policy Institute (MPI), the team agreed on using gears to depict how education levels of immigrants and natives impact employment opportunities. In the next meeting, Alex found an iPad application that demonstrated how gears could be used for this specific angle-players would use ‘factor’ gears to connect a spinning ‘job’ gear with an unmoving ‘immigrant’ or ‘native’ gear, so that the whole machine would function.

   Gears in motion - prototype iteration #1 by Patricio

  Patricio crafted the team’s first prototype in openFrameworks, showcasing the basic mechanics of the gears in motion. After presenting this iteration to the class, the next step became clear-the group had to start incorporating a data-based story into the toy. After doing some research, Alex noticed that, in most cities, low-educated immigrants had much lower unemployment rates than natives, whereas the opposite was true of highly educated immigrants.  Alex spoke with Madeline Zavodny, an economics professor at Agnes Scott College who has published many articles on the topic of immigration, including some for MPI, to investigate this topic. The conversation sparked an interest on the subject of ‘brain waste,’ which occurs when highly educated immigrants are unable to obtain highly skilled jobs in America. I’m sure some of us have heard similar horror stories in which a foreign doctor ends up working as a taxi driver because they are unable to transfer credentials, among other things.

After reading over an article by MPI, “Uneven Progress: The Employment Pathways of Skilled Immigrants in the United States” (2008), the team decided to make their toy more specific, using the data found from the study by Jeanne Batalova and Michael Fix as a framework. For example, one key finding of the study is that highly skilled immigrants who had a limited knowledge of English were twice as likely to work in lower skilled jobs than their qualifications demand. Therefore, users of the toy can alter whether or not their immigrant has fluent, limited, or no proficiency in English. This decision impacts their likelihood of finding a highly skilled job. 

Size and resistance are elements that influence and differentiate the factor gears. These elements depict the ease or difficulty of finding specific jobs. Immigrant gears that had favorable factors would be more likely to connect with the spinning gear (larger size), plus they spin faster.  The toy was made to function with the following principles:

  1. Two gears will be placed on the interface-they represent an immigrant (not spinning) and highly skilled job (spinning)

2. Users will place their own gears on the screen, one at a time. While doing so, a prompt will appear, describing their selection.

3. The size of the gear will be determined by the distance of the cursor to the previous gear. The size is flexible and chosen by the user, but each size represents particular factors that either help or hurt the chances of being employed

4. The larger each gear is, the more likely you are to get the job and the faster the machine will spin. For example, the gear size for an undocumented immigrant is small and will not spin, whereas an employment-based visa gear is large and spins fast.

  The toy is meant to operate much like the Game of Life, as a choose your own pathway scenario. The factors that players can alter are as follows:

1. Visa Situation (no visa, here for a job, with family/spouse, greencard)

2. English Fluency (no English, some English, fluent English)

3. Education (high school diploma/GED, associate’s degree, degree from foreign university, degree from American university)

4. Origin (Europe or Asia, Latin America, Africa)

5. Time Spent in US (working here already, just arrived)

  Looking ahead: If the group were to move forward with this project, they would investigate the employment opportunities of immigrants and natives seeking different level jobs in order to depict, on a larger scale, how education level impacts employment opportunity in America for both groups. 

Find it on GitHub here.