Making data meaningful and accessible using various forms of visualization.
A physical data installation showing the social mobility comparison between parents and their children. This installation would live within a public building or policy office to encourage conversations and discussions around the topic. This exploration is only a prototype of what a full installation could look like.
Encourage conversation around factors influencing social mobility.
2018, Communication design + Data visualization
Carnegie Mellon University, Communication Design
After Effects, Illustrator, Photoshop, Physical prototyping, Laser cutter, Wood, Acrylic, U.S. Census data, Allegheny County data, and Excel
Design strategist, Data analyst, Creator,
Puzzle in geographic/cartesian form.
Physical model that reflects the idea of Pittsburgh being hilly.
Parental social mobility hangs below the board as the “foundation.”
Student social mobility rises from eh board.
Comparison between neighborhoods
Each colored layer has meta data engraved to allow taking apart of the model and delving into the data.
Understanding the problem
Social mobility is a complex issues that affects everyone but is difficult to explain and even more difficult to see in the world around us. So, how does making the invisible social mobility scores of 20.3, 80.5, etc influence decision making. This problem is so huge and usually boiled down to a single number, I wanted to make the data speak for itself and be intractable. The more layer for each neighborhood means a positive factor contributing to the overall social mobility score measured by the height of each stack.
The exploratory research consisted mostly of digging through data bases and compiling them into one excel document to determine social mobility factors.
In general, the social mobility scores between parents and students is within 1-2% difference. meaning that after adjusting for inflation (educational, economic, etc) student’s social mobility scores remain similar to their parents on the aggregate. However, the factors playing into each score do vary.
Here I began exploring the facets of data physicalization that would need to be considered and implemented. While also testing the ideas of physical data visualization with peers. Their brought me to realize that in order to layer the depth of information I wanted to show, that 2D aspects would not be enough nor leverage the completeness that a 3D model could provide.
I created small prototypes to test comprehension, feasibility, and form factor
Early Cut prototypes
I created several early physical prototypes, including this one to inform the final form factor.
The final form inspired a lot of buzz around social mobility and peeling back the layers to better understand the differences and possible interventions. Overall, this was considered successful making the data clear and tangible.
Reflection + Next Steps + My role
I was initially focused on creating various heights based on social mobility. As I worked with the data, I realized I had to run some predictive models and that the small differences were too little meaningfully show physically without misrepresentation. So, I went back to making the invisible factors that make up social mobility visible.
I found the colors useful to discuss the weights or importance of different type of data that could be categorized and easily seen with a quick look.
Present model prototype to Allegheny county
Re cut the data for clarity
Add physical data key
Complete the prototype after further feedback from the county and local citizens.
Design implementation strategy