Integrating Urban Open Data for Public Good
Integrating Urban Open Data for Public Good


Two concurrent phenomena, digitization of cities and urbanization of the Internet, create new opportunities and complexities for cities. Although previous theory conceptualized cities as complex social-ecological-technical systems-of-systems with data as intermediate layers, ongoing ad-hoc smart city development has shaped a fragmented data landscape with organizational barriers and socio-technical conflicts. Large volume, variety, and velocity of data bring methodological and practical challenges for analyzing holistic urban systems with real-world data. Focusing on New York City, this talk discusses cross-domain data mining, integration, and modeling. In particular, it targets on three sub-problems in (1) data integration for quantifying hyper-local urban condition; (2) multivariate models for analyzing urban phenomena driven by cross-domain factors; and (3) unstructured data mining and knowledge discovery for information integration across multiple cities. After an overview of the research context and current methods, three research projects investigated methods for integrated analytics at hyper-local, micro, and city scale, with implementations related to urban built-ecological-socioeconomic systems. As the cities becoming increasingly smart and connected, an integrated and systematic data intelligence framework becomes critical for supporting better-informed, data-supported and collaborative urban systems.


Yuan Lai is a Lecturer in Urban Science and Planning with a focus on data analytics, data visualization, and machine learning. His expertise lies at the intersection of urban information, applied data science, and urban systems. His work has been featured at the United Nations Global Pulse, Bloomberg Technology, Data for Good Exchange, NYC Media Lab, American Planning Association, American Society of Civil Engineers, and Urban Design Forum. Prior to coming to MIT, Yuan was a research affiliate at NYU Marron Institute of Urban Management and NYU Center for Urban Science and Progress (CUSP). His work involves applied analytics and machine learning using large volume and variety of data related to urban environment, population health, social media, sensing network, and economic transactions. Yuan holds a Ph.D. in urban systems and informatics from NYU, a M.S. in applied urban science and informatics from NYU CUSP, as well as a Master of urban planning and a Bachelor of landscape architecture.

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