A Different Similar Center (030)

A Different Similar Center asks the question: how do we identify similarity and difference? Using data from a machine learning model, the center tile of an aerial image is replaced with a cycle of similar but geographically disparate tiles.

Although visually similar, the edge of the center tile hints at something strange. Roads form dead-ends, buildings are sliced in half, paths are disconnected. Yet, at times these same edges are almost indistinguishable. A tree canopy from one area blends into another or an unmarked field slyly fits into a patch elsewhere.

By re-contextualizing the center, the exercise asks:

• What is similar — the color of the trees, the total area of pavement, the membrane on the roofs, the edge of the extent?
• What role does context have in the identification of similarity, for an algorithm and for direct observation?
• Do we identify similarity in the same way as an algorithm, especially when that algorithm was designed by humans?

Technicals

This project uses data from Terrapattern, which identified visual similarity in the aerial images of large geographic areas. When you click on a location, visually similar places are downloadable as a GEOJSON file. The similarity data was collected for ten locations in New York City, focusing on cemeteries and public housing. “A Different Similar Center” displays a different starting point with each page load and internally iterates through the matches.

Terrapattern was developed by Golan Levin, David Newbury, Kyle McDonald, Irene Alvarado, Aman Tiwari, and Manzil Zaheer, at the Frank-Ratchye STUDIO for Create Inquiry at Carnegie Mellon University. The Github repository for the project extensively describes the backend processes.

Next Steps

• Ask Terrapattern if a complete dataset matching all points with all similar points is available.
• Collect the similarity GEOJSON files for more locations manually.
• Render as images, not geolocated, to improve performance.