DataPhysicalization

Years lost to gun violence

An image from the project

Three Days – 4,000 Years

The statistics on gun violence in this country are heartbreaking. Guns killed 11,419 people in 2013. A loss of over 500,000 years of life. Unfortunately, the rate of gun violence continues unchecked. Three Days – 4,000 Years is an adaptation of the powerful U.S. Gun Deaths visualization the data design firm Periscopic created a few years ago in response to the school shooting in Newtown CT.

The Periscopic visualization has a powerful emotional impact. Translating that impact into a physical representation of the years stolen by guns was the motivation behind this adaptation.

The original visualization show all 11,419 deaths while still allowing each and every individual to be seen.

The physicalization based on this data focuses on just a few days instead of a full year. A physical representation of an entire year at this scale would be almost 15 meters (48 ft) long.

physicalization of three days of gun deaths

Interacting with the model is an emotional experience. As you move your fingers along each arc, you are following the actual, then the potential trajectory of a life ended by gun violence.

There are seasonal patterns, and the absolute number varies from day to day, but roughly thirty people are killed by guns EVERY DAY. Therefore about 100 deaths occur every three days adding up to 4,000 years of life lost. Four thousand years every three days.

An online visualization based in these three days of data looks like this:

The physicalization is intended to be viewed from above with the color change marking the point where lives end and stolen years begin. It can also be viewed from below. From below, the surface of the stolen year arcs forms an area chart of stolen years.

physicalization as an area chart of years lost

About the methods
The gun death data is a subset of the data used in the 2013 version of the Periscopic project. The raw data did not contain the alternate ages Periscopic used to calculate stolen years. So, I generated alternate ages using actuarial tables from the Social Security Administration. This approach did not link the alternative age to a cause of death as in the original visualization, but it did give me stolen years estimates that are in rough agreement with those in the original piece.

About the workflow
I used javascript and node.js to process the data and generate a file for OpenSCAD to render into a 3D object.

Thank you to Periscopic for making this data available.