December 16, 2021
Exploring Data-Driven Urbanism at the Chicago Architecture Biennial
“How might increasing ubiquitous technologies like neural networks recognize the impacts of gentrification, economic imbalances and historic justices like redlining?” Zeigler asked in her introduction. “Machine learning is indeed a construct: It’s not neutral, it’s not magical and it ultimately reflects the subjectivities that are built into datasets.”
Each of the panelists and their affiliates have in recent years developed projects which take advantage of improvements in advanced computing to understand our cities. Such advances, built on existing infrastructures and used to analyze already-prevalent trends like gentrification, nevertheless open up possibilities for as-yet-unrealized changes in urban space. For example, Rehm discussed student projects at SCI-Arc that used satellite imagery and machine learning to map potential sites for accessory dwelling unit and solar panel construction; Hupalo described research using platforms like Zillow and Redfin and their use in tracking speculative property sales in fast-gentrifying Los Angeles neighborhoods; and Tedbury highlighted semblr, a project he created to imagine robotics-assisted, creative construction projects using sustainable timber, creating flexible building structures that have been explored by U.K. council estates as a way of better using public spaces.
Smith, the panelist whose work is focused on the city of Chicago, examines the ways in which shifts in real estate markets help explain broader economic trends for the windy city’s neighborhoods. One recent report by the Institute for Housing Studies looked at the installation of the 606, an off-street elevated trail on a disused railroad track, and the ways in which it has fueled property speculation at its western end. Smith addressed the challenges in making significant assumptions about the future through simulation, noting how unpredictability has only grown greater in recent years, a crucial theme that resonated across many of the panelist’s research projects.
“Simulations and forecasting are all based on assumptions, and assumptions change,” Smith said. “All sorts of unpredictable things happen in the world, so I think if you’re transparent about the assumptions built into your model, it can be an interesting exercise.”
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