Problem Statement: The NYC 311 service request system represents one of the most significant links between citizens and city government, account for more than 8,000,000 requests annually. Increasingly, these data are being used to develop predictive models of citizen concerns and problem conditions within the city. However, predictive models trained on these data can suffer from biases in the propensity to complain (or make a request) that can vary based on socio-economic and demographic characteristics of an area, cultural differences that can affect citizens’ willingness to interact with their government, and differential access to internet connectivity.

Research Objectives: The goal of this project is to estimate the likelihood of citizens to utilize the 311 system across NYC’s neighborhoods.

Background/Context: Cities across the United States are implementing information communication technologies in an effort to improve government services.  One of such innovations in e-government is the creation of 311 systems, offering a centralized platform where citizens can request services, report non-emergency concerns, and obtain information about the city via hotline, mobile or web-based applications. These systems are generating massive amounts of data that, when properly managed, cleaned, and mined, can yield significant insights into the real-time condition of the city. Similarly, the use of machine learning algorithms to predict potential problems in the city is expanding, and 311 data have become a popular source of training data.

Methods: We introduce a three-step process to evaluate the propensity to complain: (1) we identify the ratio between complaints and violations, as an indicate of actual conditions in a neighborhood, (2) we predict the expected volume of a particular violations in a given area, and (3) we compare the actual number of complaints to the predicted violation volume to quantify discrepancies across the City.

Expected Results and Outputs: The novel opportunity to predict complaint volumes over time will contribute to the efficiency of the 311 system by informing short- and long-term resource allocation strategy and improving the agency’s performance in responding to requests. For instance, the outcome of our longitudinal pattern analysis allows the city to predict building safety hazards early and take action, leading to safer residential accommodations. Furthermore, findings will provide novel insight into equity and community engagement through 311, and provide the basis for acknowledging and accounting for bias in machine learning applications trained on 311 data.

Partners and Collaborators

NYC311, NYC Mayor's Office of Operations

Team Members

Boyeong Hong, Kristin Korsberg, Xinshi Zheng, Constantine Kontokosta


MacArthur Foundation

Research Team

Constantine E. Kontokosta, PhD, PE

DIRECTOR, URBAN INTELLIGENCE LAB; Associate Professor of Urban Science and Planning; Director, Civic Analytics Program

Boyeong Hong

PhD Candidate

Constantine E. Kontokosta, PhD, PE

DIRECTOR, URBAN INTELLIGENCE LAB; Associate Professor of Urban Science and Planning; Director, Civic Analytics Program

Prof. Kontokosta brings training urban planning, data science, economics, and systems engineering to the data-driven study of cities.

Constantine E. Kontokosta, PhD, PE, 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 (CUE). He is affiliated faculty at the Wagner School of Public Service, Visiting Professor of Computer Science at the University of Warwick (UK), and a Senior Scholar at the New York Academy of Medicine. Previously, he served as the inaugural Deputy Director of CUSP and Assistant Professor of Urban Informatics at CUSP and CUE, where he was part of the Center’s founding leadership team and designed and launched the first graduate program in urban informatics. He is the founding Principal Investigator of the Quantified Community research initiative that integrates hyperlocal urban sensors with city-scale data analytics to understand neighborhood dynamics and well-being, and is one of the largest community-driven IoT projects in New York City. He is a 2017 recipient of the National Science Foundation CAREER Award for his research in urban informatics for sustainable cities.

Trained in urban planning and computational methods (Columbia), finance and economics (NYU), and systems science and engineering (UPenn), Constantine brings an inter-disciplinary perspective to urban science that integrates fundamental research with impact-driven, use-inspired needs. His work leverages large-scale data with computational methods to understand and drive change in energy efficiency and climate policy, neighborhood change and the impacts of urban development, and community-driven air quality monitoring and environmental justice. Recent projects include research with NYC311 and Kansas City to measure bias in citizen complaint reporting for predictive analytics; with a homeless shelter provider to apply machine learning algorithms to identify at-risk homeless families; and with the City of New York, Washington, DC, and the UN to leverage large-scale data analytics for building energy and climate policy. Constantine’s research groups – the Civic Analytics Program and the Urban Intelligence Lab – are motivated by a desire to bring evidence to policy-making, to democratize knowledge through information transparency, and to uncover discrimination and bias in data-driven decision-making.

Constantine’s research is funded by the National Science Foundation (NSF), MacArthur Foundation, Sloan Foundation, the U.S. Department of Transportation, the NYC Mayor’s Office of Sustainability, the Lincoln Institute of Land Policy, and the U.S. Department of Housing and Urban Development, among others, and he has received several honors for his work, including the IBM Faculty Award, the Google IoT Research Award, the UN Data for Climate Action Challenge Award,  the Goddard Junior Faculty Fellowship, the Charles Abrams Award for Social Justice Research, and a NYU Award for Teaching Excellence. Constantine has published more than 70 peer-reviewed publications in leading academic journals – in fields ranging from urban planning to signal processing – and has two forthcoming books on urban analytics and data-driven climate action. His research has been featured in the New York Times, the Wall Street Journal, the Economist, FastCompany, CityLab, Wired, CNN, NPR, and other media outlets. He holds a PhD, M.Phil, and M.S. from Columbia University, a M.S. from New York University, and a B.S.E. from the University of Pennsylvania.

He serves on committees and advisory boards at the National Academies, DARPA, and the NSF Northeast Big Data Hub, and previously served on the boards of the UNEP Sustainable Buildings and Climate Council, the Royal Institution of Chartered Surveyors, and as Vice Chair and Commissioner of the Suffolk County (NY) Planning Commission. In addition to his academic work, Constantine is an accomplished entrepreneur and, together with his brother, Michael Kontokosta, designed, built, and owns Kontokosta Winery, the Harborfront Inn, and Cove Place Inn, all on the North Fork of Long Island, as well as numerous properties in New York City and the East End.

Boyeong Hong

PhD Candidate

My research interests focus on how to apply urban informatics to real world problems in urban planning and operations.

I hold a master degree in Applied Urban Science and Informatics from NYU Center for Urban Science and Progress (CUSP). While at CUSP, I was a Graduated Research Assistant in Identifying E-Waste (Electronic waste) generation in New York City project in addition to working on data analytics for capital planning with NYC Department of City Planning as part of my capstone project. Most recently, I have been working at the Pratt Center for Community Development translating geospatial data into problem solving insight through GIS mapping and analysis. Prior to CUSP, I have participated in various research projects related to urban planning and data analytics in Seoul, South Korea. I have a Bachelor degree in Architecture from Yonsei University and a Master of City Planning degree from Seoul National University.