Ai+ Training

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.