Article by Samuel Greengard: “Urban planning has always focused on improving the way people, spaces, and objects interact. Yet, translating these complex dynamics into a livable environment is remarkably difficult. Seemingly small differences in design can unleash profound impacts on the people who live in a city.
To better navigate this complexity, planners increasingly are turning to digital technology, including artificial intelligence (AI). While data-driven planning isn’t new, these tools deliver a more sophisticated framework. This evolution, referred to as algorithmic urbanism, blends traditional planning techniques with advanced analytics to address challenges like congestion, health, safety, and quality of life.
“Buildings, streets, trees, and numerous other factors influence how people move about, how economic activity takes place, and how various events unfold,” said Luis Bettencourt, professor of Ecology and Evolution and director of the Mansueto Institute for Urban Innovation at the University of Chicago. “Tools like AI and digital twins spot opportunities to rethink and reinvent things.”
This might include anything from optimizing a network of bicycle lanes to simulating zoning changes and land-use scenarios. It could also incorporate ways to improve recreation, congestion, and energy use. Yet, like other forms of AI, algorithmic urbanism introduces risks, including the potential for perpetuating historical data biases, misuse or abuse of data, and concealing how decisions take place.
The idea of using data and algorithms to design better cities extends back to the 1970s. That’s when computing tools like geographic information systems and business intelligence began to extract insights from data—and to provide more precise methods for managing urban growth.
Satellite imagery, vast databases, and environmental sensors followed. “The technology emerged as a valuable tool for strategic planning,” said Rob Kitchin, Professor of Human Geography at Maynooth University in Ireland. “It allowed planners to run detailed simulations and better understand scenarios, such as if you add a shopping mall, how will it impact traffic flow, congestion, and surrounding infrastructure.”…(More)”
