The Algorithmic Zoning project explores dynamically reconfigurable incentives that encourage the pro-social development of urban areas so as to respond to citizens’ aspirations. As cities become increasingly populated, urban planning plays a key role in ensuring equitable and inclusive evolution, this project surging from the necessity of adding people’s desires of having greener, more walkable, and more diverse metropolitan districts to the equation.
The MIT City Science group has long been working on the creation of tools that will make participatory decision-making processes a reality, creating data-driven platforms such as CityScope Volpe. The easily-interpretable real-time feedback given by the platform makes different stakeholders, such as government representatives, urban planners, developers, and citizens, collaboratively decide which intervention shapes the most favorable urban scenario.
In this first algorithmic CityScope module, the lack of affordable housing in Downtown areas is addressed. Thanks to Agent-Based Modeling (ABM) methods, citizens' housing and mobility mode criteria are identified and the monitorization of people's reactions to dynamically adaptable zoning policies held. This opens the door to the study of consequences that certain housing incentives and urban disruptions might entail.
The aforementioned behavioral patterns have been determined through a calibration and validation process for the particular use case of Kendall Square. However, the generic nature of the suggested methodology enables its potential deployment in the study of such policies in cities all around the world.
This case study is of great interest, though, since the explosion of housing prices following the technological development of the surroundings makes it challenging for everyone working in the Square to be able to live within a 20-minute walk of their workplace.