Using Machine Learning and Small Area Estimation to Predicting Building-level Waste Generation and Recycling

Research Focus

Data-driven city operations

Primary Subject Area

Sustainability and waste

In New York City, it is estimated that the typical resident disposes of 15 pounds of waste at home and an additional nine pounds at the office or from commercial establishments each week (City of New York 2015). The more than six million tons of waste, including recyclables and organics, annually generated in NYC – and considerable amounts produced in cities throughout the world – create significant operational and disposal costs, negatively impact quality-of-life, and contribute to increasing carbon emissions. Waste management is a primary component of urban sustainability, and NYC has established an ambitious target of zero waste to landfills by 2030. Achieving this goal will require novel, data-driven strategies to measure and benchmark waste generation that can enable more efficient routing and collection systems, targeted outreach programs, and behavior change mechanisms. Similar approaches are being used for energy efficiency in buildings and urban water use (Kontokosta 2013, 2015; Kontokosta and Jain 2015).

Goals and objectives. Our goal is to model and predict building-level waste generation and recycling activity for each of the more than 900,000 residential properties in NYC.  Due to cost and feasibility constraints, the City of New York Department of Sanitation (DSNY) does not maintain data on individual building collection; rather, it tracks the waste collected over an entire truck route. Using robust regression modeling and machine learning prediction algorithms, we will pursue the following objectives:

  • Objective 1: Integrate and analyze data from multiple sources to estimate weekly waste generation rates at the building-level;
  • Objective 2: Validate model performance with DSNY through targeted, hand-sampling of building-level waste collection;
  • Objective 3: Establish citywide building-level generation estimates to create waste benchmarking and performance management metrics;
  • Objective 4: Apply model estimates to support operational efficiencies through optimized route planning and collection schedules and frequencies to maximize service delivery and truck tonnage collection targets.

Scalable impact. This research is expected have a substantial, scalable impact on solid waste management in cities through improved operational efficiencies and data-driven strategies to achieve longer-term landfill waste reduction and diversion goals.

Agency Partner

NYC Department of Sanitation

Team Members

Boyeong Hong, Xinshi Zheng, Geoff Perrin, Nick Johnson, Constantine Kontokosta

Funding

MacArthur Foundation

Research Team

Constantine E. Kontokosta, PhD, PE

DIRECTOR, URBAN INTELLIGENCE LAB; Assistant Professor of Urban Informatics; CUSP Deputy Director for Academics; Head, Quantified Community

Nicholas Johnson

PhD Candidate and Assistant Research Scientist

Boyeong Hong

PhD Candidate

Xinshi Zheng

Civic Analytics Graduate Student Fellow

Geoff Perrin

Civic Analytics Graduate Student Fellow

Constantine E. Kontokosta, PhD, PE

DIRECTOR, URBAN INTELLIGENCE LAB; Assistant Professor of Urban Informatics; CUSP Deputy Director for Academics; Head, Quantified Community

ckontokosta@nyu.edu

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

Professor Constantine E. Kontokosta, PhD, PE, is an Assistant Professor of Urban Informatics in the Department of Civil and Urban Engineering and the Center for Urban Science and Progress (CUSP) at New York University, is the Director of the Urban Intelligence Lab, and is the Deputy Director for Academics at CUSP. He holds a faculty appointment as Visiting Professor of Computer Science at the University of Warwick, and is affiliated faculty at the NYU Wagner School of Public Service and Marron Institute for Urban Management. He is also the Principal Investigator and Head of the CUSP Quantified Community research facility, a groundbreaking project underway at five districts in New York City – at the Hudson Yards development in New York City; in Lower Manhattan; at Governor’s Island, and in Red Hook and Brownsville, Brooklyn – that is building sensor-enabled urban neighborhoods to study the impact of the built environment on well-being and human behavior. As one of the first faculty to join CUSP, Constantine is part of the CUSP founding leadership team, setting the Center’s strategic priorities and leading the design and implementation of its academic programs in urban data science, growing from just two to over 50 faculty and staff and 100 graduate students in three years. He serves as Faculty Engineer-in-Residence at the NYU Tech Incubators, where he mentors cleantech and smart city start-up companies from early stage idea refinement to technology demonstration and deployment. He is a 2017 recipient of the NSF CAREER award for his research in urban informatics for smart, sustainable cities.

Dr. Kontokosta‘s research lies at the intersection or urban planning, data science, and systems engineering, focusing on using big data and new sensing technologies to better understand the dynamics of physical, environmental, and social systems in the urban environment. His work has been published in leading academic journals in fields including science, economics, urban policy and planning, and engineering, and has two forthcoming books, one on data-driven city operations and planning and the other on the subject of big data and urban sustainability. He collaborates with numerous city agencies in the U.S. and internationally on issues of urban sustainability and resilience policy and planning and city operations, including a multi-year effort to lead data analysis on building energy efficiency with the NYC Mayor’s Office of Sustainability. Dr. Kontokosta’s work has been featured in the Wall Street JournalNew York TimesCNN, NPR, Fast CompanyCityLab, Bloomberg NewsFinancial Times, APS Physics, and ASCE’s Civil Engineering Magazine, among other national and international media outlets.

Dr. Kontokosta holds a Ph.D., M.Phil, and M.S. in Urban Planning, specializing in urban economics and econometrics, from Columbia University, a M.S. in Real Estate Finance from New York University, and a B.S.E. in Civil Engineering Systems from the University of Pennsylvania. He has received research funding from the MacArthur Foundation, the Sloan Foundation, the National Science Foundation, Siemens Corp., CBRE Group, Inc., and the U.S. Department of Housing and Urban Development, among others. Kontokosta has won the IBM Faculty Award, Goddard Junior Faculty Fellowship, the Google IoT Research Award, and NYU’s Award for Teaching Excellence. He is also a recipient of the C. Lowell Harriss Dissertation Award, the Columbia GSAS Dissertation Award, the Charles Abrams Award for Research in Social Justice, the Best Paper Award at the Bloomberg Data for Good Exchange in 2015, and has been named a Fulbright Senior Scholar in Urban Planning.

Dr. Kontokosta is a licensed Professional Engineer, a member of the American Institute of Certified Planners, a USGBC LEED Accredited Professional, and has been elected a Fellow of the Royal Institution of Chartered Surveyors (RICS).  In addition, he has served on the NYC Mayor’s 80×50 Task Force, as Vice Chair of the Suffolk County Planning Commission, and on the Boards of the UNEP SBCI and the Royal Institution of Chartered Surveyors. He is an accomplished real estate 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.

Nicholas Johnson

PhD Candidate and Assistant Research Scientist

Nicholas.johnson@nyu.edu
646.997.0590

Nicholas E. Johnson is a Ph.D candidate in Urban Science at the University of Warwick's Institute for the Science of Cities and an Assistant Research Scientist at NYU's Center for Urban Science and Progress.

He obtained a Masters degree from NYU’s Interactive Telecommunications Program in 2013 centering his work on exploring the impact and pervasiveness of waste streams in urban environments through physical computing and interaction design.  He has launched several citizen science initiatives and continues citizen-driven research as an organizer for the Public Laboratory for Open Technology and Science.  Nicholas’s current research focuses on the design and development of cyber-physical systems for monitoring urban environments and data-driven analyses to understand urban phenomena including waste generation and urban mobility,

Boyeong Hong

PhD Candidate

boyeong.hong@nyu.edu

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.

Xinshi Zheng

Civic Analytics Graduate Student Fellow

xz1845@nyu.edu

Xinshi is a Master student and a Civic Analytics Graduate Student Fellow in the Urban Intelligence Lab.

He holds a M.S. in Civil Engineering from University of Illinois at Urbana-Champaign (2015), and a B.Eng in Architectural Environment Engineering from University of Nottingham, UK (2013). His research interests include developing data science applications for urban infrastructure planning and optimization, as well as geospatial analysis.

Geoff Perrin

Civic Analytics Graduate Student Fellow

gtp232@nyu.edu

Geoff Perrin graduated from the University of Michigan with a BS in Economics and Mathematics, and is currently pursuing an MS in Applied Urban Science and Informatics at NYU’s Center for Urban Science and Progress. He’s built machine learning algorithms relating to various topics, from predicting Medicare fraud to predicting Levi’s jeans sales.