Justin Blinder had just been laid off when he first came up with the idea for Ground Truth. It was 2020, two weeks before covid lockdown went into effect, and the Brooklyn-based artist and technologist had no idea what the future would hold.

“I really started to kind of become a bit more aware of how different neighborhoods, often right next to each other, could feel very economically and psychologically very far apart, even though it’s just really within close proximity,” he said. “And also just how those inequalities can sometimes feel quite unusually physical.

A lot of times higher elevation can contribute to the desirability and cost of a neighborhood through better views and lower flood exposure.” 1/4Ground Truth, Blinder’s new art project, is a small gray box that attaches to the front fork of a bike and changes the physical difficulty of riding according to the housing affordability of the surrounding neighborhood.

When housing is more affordable, pedaling becomes easier. “And so it’s really thinking about how to take economic abstractions in this case, really the affordability of cost-of-living prices, and turn them from something that really reduces people to numbers and brings it back into more of a direct physical experience that’s rooted in reality and in the streets,” he said.

After abandoning some early ideas for fear of unexpected legal consequences, Blinder settled on building an external mechanism that would allow him to affect the bike without penetrating its closed electronic systems. Using a Citi Bike as his “canvas,” he noticed that the front drum-brake mechanism was physically exposed.

When the rider pulls the front brake handle, a lever on the front fork moves to apply pressure. He realized he could build a wireless mechanism that attaches to this lever and effectively acts as an additional brake actuator, allowing the device to increase or decrease the bike’s resistance.

The hardware itself is relatively simple, consisting of an ESP32-S3 microcontroller, a GPS module, a high-torque servo motor capable of precisely controlling its rotation, and an SD card reader for storing the data set locally. 1/4The issue of finding the right housing data to use turned out to be the real challenge.

He eventually settled on using the US government’s American Community Survey’s Rent Burden dataset, so as to accurately capture the proportion of a household’s income that was being put toward rent and utilities. Blinder said he uses two measures: the median share of income spent on rent and the percentage of renting households that spend at least half of their income on housing.