Urban Science for Data-Driven Policy and Planning

Spring 2019 Course from Professor Constantine Kontokosta

This course introduces students to the emerging field of urban science. Students are exposed to a range of data science and machine learning methods, urban data sources (including social media, geolocation data, 311 complaints, energy use, and many others), and urban policy and planning from the perspective of data-driven decision-making. Cities are increasingly data-rich environments, and data-driven approaches to operations, policy, and planning are beginning to emerge as a way to address global social challenges of sustainability, resilience, social equity, and quality of life. Understanding the various types of urban data and data sources – structured and unstructured, from land use records to social media and video – and how to manage, integrate, and analyze these data are critical skills to improve the functioning of urban systems, more effectively design and evaluate policy intervention, and support evidenced-based urban planning and design. While the marketing rhetoric around Smart Cities is replete with unfulfilled promises, and the persistent use (and mis-use) of the term Big Data has generated confusion and distrust around potential applications, the reality remains that disruptive shifts in ubiquitous data collection (including mobile devices, GPS, social media, and synoptic video) and the ability to store, manage, and analyze massive datasets require students to have new capabilities that respond to these innovations.

Instructor: Dr. Constantine Kontokosta is an Associate Professor at the Marron Institute of Urban Management. He holds a faculty appointment as Visiting Professor of Computer Science at the University of Warwick and he is a 2017 recipient of the NSF CAREER award for his research in urban informatics for smart, sustainable cities. Professor Kontokosta's 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. 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.

Registration for the course is now open on Albert > Graduate School of Arts and Sciences > Politics (POL-GA) > POL-GA 2334 > Section 2 > Topics in Urban Management: Urban Science for Data-Driven Policy and Planning.

Prerequisite: POL-GA 1120 or equivalent. Students should be familiar with probability and statistics and have a basic understanding of regression analysis and statistical modeling.

This course is open to students from across the NYU campus. NYU students with questions about this course should feel free to reach out to the Marron Institute at marron.institute@nyu.edu.

Draft Syllabus: Urban Science

Section 2 of POL-GA 2334, Topics in Urban Management: Urban Science for Data-Driven Policy and Planning

Albert Class Number

Day and Time
Thursdays from 10:00 - 11:50AM

Politics Department Seminar Room
19 West 4th Street, Room 217
(West 4th St between Greene & Mercer)

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