Introducing Algorithmic Zoning

The latest in a series of innovations in planning and zoning is here: algorithmic zoning.

Danny Crichton introduces the algorithmic zoning concept as "dynamic systems based on blockchains, machine learning algorithms, and spatial data, potentially revolutionizing urban planning and development for the next one hundred years."

Crichton credits the ideas described in the article to Kent Larson, principal research scientist at the MIT Media Lab, and John Clippinger, co-founder of Swythch.io. Here's the explanation of the concept:

The idea is to first take datasets like mobility times, unit economics, amenities scores, and health outcomes, among many others and feed that into a machine learning model that is trying to maximize local resident happiness. Tokens would then be a currency to provide signals to the market of what things should be added to the community or removed to improve happiness.

A luxury apartment developer might have to pay tokens, particularly if the building didn’t offer any critical amenities, while another developer who converts their property to open space might be completely subsidized by tokens that had been previously paid into the system. “You don’t have to collapse the signals into a single price mechanism,” Clippinger said. Instead, with “feedback loops, you know that there are dynamic ranges you are trying to keep.”

Larson and Clippinger speak of the highest possible ideals when making the case for algorithmic zoning in their own words, and also drop a bomb at the end of the article, calling the possibility of algorithmic zoning a step toward "post-smart cities." According to Crichton, Barcelona and several Korean cities have already implemented limited trails of algorithm-based models for urban planning.