Neighborhoods are vital components of political power, social connectedness, and long-term well-being. The neighborhood in which one is born and raised has been found to have significant implications for various measures of health and opportunity. However, empirical studies of the relationship between neighborhood environmental factors and health impacts have been constrained by data limitations and methodological challenges, particularly in assessing the inter-relationships between nonlinear causal factors and outcomes. The challenge is unpacking the “black box” of neighborhood effects: we know that neighborhoods matter, but it is still unclear exactly why, and how people behave within them. Sensors can help provide more nuance about actual conditions in neighborhoods that are generally unobserved (like air quality and noise), and can also help us understand how people navigate their neighborhoods, what local services they use, and what public spaces they use or avoid. We address this challenge through our Quantified Communities (QC) project.
The QC research initiative, funded and supported by community partnerships and the U.S. Department of Transportation, Lockheed Martin, the New York Academy of Medicine, Google, Inc., and the Trust for Governor’s Island, is focused on sustained, high-resolution, and community-guided data collection and analysis to conduct comparative and longitudinal studies of the neighborhood exposome – how the environmental, physical, and social dynamics of a neighborhood impact its residents. The 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.