Bias in Citizen Complaint Data
This project analyzes the factors the influence citizen reporting, and develops models to account for individual and neighborhood differences in reporting behavior.
Data for Climate Action
This project, in collaboration with the City of New York, the United Nations, Lawrence Berkeley National Lab, and others integrates numerous big data sources and develops methods and tools that combine data-driven statistical and physical models to generate a first-of-its-kind dynamic, hyperlocal model of urban carbon emissions.
A Real-time Census of the City
This project uses large-scale locational data (over 1,000,000,000 records) to understand patterns of mobility across the U.S. We are working to understand evacuation and recovery during natural disasters, public space and park activity and utilization, neighborhood change and socioeconomic integration, and real-time population census to supplement existing survey-based methods.
The Neighborhood Exposome
This initiative consists of hyperlocal sensor networks and participatory sensing across five large-scale real-world community test-beds in New York City. This intensive study of neighborhoods is creating the observational and participatory data to build data-driven models of neighborhood dynamics and support direct engagement with residents for community-led, and data-informed, planning processes.