Visualize NYC 2021: Behind the Data – Building Emissions and Accessibility

*This event is occurring as a live webinar. Registrants will be emailed a link to access the program.*

Some of the content in this program was originally scheduled for 12/11

How can data communicate the complexities of New York City? This program series shows how the data visualizations for the digital project Visualize NYC 2021 were produced, providing a behind behind-the-scenes perspective from both the people who created the data visualizations and the data itself. Learn how data can create civic action through a conversation on the process by which city agencies and other groups collect data, use it to inform policy, and collaborate with designers to communicate to the public, thereby generating the support needed to make policy changes.

This program will explore the data visualizations “Project Accessible NYC” and “Devil’s in the Data: The Real Pathway to Reducing NYC’s Building Emissions” which are included in the Expanding Public Realm and Climate Change sections of Visualize NYC 2021. “Project Accessible NYC” evaluates which metro stations in New York City are inaccessible compared to demographic data on populations of elderly residents, residents with disabilities, and families with young children in the stations’ walksheds. It then questions if there is a correlation between which stations are accessible and other factors, such as the race and socioeconomic status of residents in the stations’ walksheds or station use rates. 
“Devil’s in the Data” looks at how to tackle climate change, NYC has charted a path to reducing its greenhouse gas (GHG) emissions by 80% by 2050. The recently implemented Local Law 97 (LL97) aims to reduce the city’s emissions by 40% by 2030 by requiring large buildings to reduce their emissions. But the pathway to reducing emissions can’t start without accurate data. Is NYC on track to meet this goal?

Visualize NYC 2021 is organized by AIA New York and Center for Architecture with MIT’s Civic Data Design Lab (CDDL).