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The Quantified Community and Neighborhood Labs

A Framework for Urban Science and Civic Technology Innovation

+ Constantine Kontokosta


The Quantified Community and Neighborhood Labs: A Framework for Computational Urban Science and Civic Technology Innovation

Journal of Urban Technology Volume 23, 2016 - Issue 4

Instrumentation of the urban environment is not by itself sufficient to have a meaningful impact on the quality, sustainability, and resilience of cities – or more broadly on urban policy and planning. Understanding the social, economic, and cultural dynamics of urban life requires both an appreciation of the social sciences and a substantive engagement with communities across neighborhoods. The “Smart City” messaging is replete with claims of the potential for sensors and information and communication technologies (ICT) to re-shape urban life, although such rhetoric ignores the practical realities and constraints of urban decision-making and the social and distributional concerns of policy outcomes. Rather, significant progress could be achieved at the neighborhood scale by focusing diverse, intensive, and persistent real-time data collection and analysis on a “Quantified Community” (QC). The QC—a long-term neighborhood informatics research initiative—is a network of instrumented urban neighborhoods that collect, measure, and analyze data on physical and environmental conditions and human behavior to provide a rich resource to better understand how neighborhoods and the built environment affect individual and social well-being. The resulting unique experimental environment provides a testing ground for new physical and informatics technologies, policies, and behavioral interventions, allowing for unprecedented studies in urban planning and design, urban systems engineering and management, and the social sciences. Focusing on the neighborhood scale also allows for meaningful interaction with, and participation by, the people who live, work, and play in that space and shifts the emphasis of data-driven design away from top-down routinization to a human-centric problem-solving. This paper presents the conceptual framework and justification for the QC, built on the lessons learned from three initial deployments in New York City, and a networked experimental environment of neighborhood labs.


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Constantine E. Kontokosta, Ph.D., is an Associate Professor of Urban Science and Planning and Director of the Civic Analytics program at the NYU Marron Institute of Urban Management. He also directs the Urban Intelligence Lab and holds cross-appointments at the Center for Urban Science and Progress (CUSP) and the Department of Civil and Urban Engineering at the NYU Tandon School of Engineering, and is affiliated faculty at the NYU Wagner School of Public Service.

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