Title: ‘On Reclaiming the Streets for the People’
Description: Streets are a vital part of the public realm. They no longer simply exist for mobility but as a way to act out democracy. As New York City garnered global attention in becoming an epicenter of the COVID-19 pandemic, city officials looked to streets to provide outdoor respite from months of indoor isolation for New Yorkers. This came as a response to the ‘new normal’ that unfolded as a result of the pandemic; New Yorkers were spending time outdoors and taking to the streets for everyday activities while being able to safely follow social distancing protocols. Open Streets were a lifeline for the city’s recovery. The pandemic gave urban planners and policymakers an opportunity to rethink our streets for a more climate-resilient, less car-dependent future. If these Open Streets bring with it social, economic and environmental benefits, it is only fair that all New Yorkers have the same level of access to it. The approach will have to begin with justice. It would be the most reasonable to have Open Streets for those that need it the most -- the neighborhoods hit the hardest by COVID-19, the ones with the least access to quality public open spaces, and the communities with the least mobility options.

This thesis explores the meaning behind equity with regard to this program. However, the rise of community organizing surrounding the program has given rise to some of the city’s best Open Streets. Equity, with regard to this program, then calls for a more nuanced definition -- it is about understanding and responding to the needs of each community. The thesis looks into the genesis of the Open Streets program in New York City and analyzes its implementation. Through a case study analysis, it evaluates Open Streets in Williamsburg and Bedford-Stuyvesant in Brooklyn and Jackson Heights in Queens. In comparing how different communities across the city experience this program, this research aims to explore what has and hasn’t been successful, in considering a more robust, permanent model for the future.