Co-Chairs : Jong Lee(NCSA), and Jana Diesner(GSLIS)
Initial participants : Jon Gant(GSLIS), Nora El-Gohary and Joshua Peschel(CEE), Mei-Po Kwan and Shaowen Wang(Geography), and Scott Poole(Communications), Momcilo Markus(PRI), Jack Snoeyink(CS at UNC), and Charlie Catlett(Argonne)
Gaining insights on urban grand challenges using novel Big Data algorithms and cyberinfrastructure (CI) that provide insights on urban grand challenges from heterogeneous and heterogeneous, multi-scale data and models.
(1) How can urban data be made useful for informing research, policy, and commercial applications (e.g., flood warning systems and transit optimization around flooded areas), while avoiding privacy breaches (e.g., social media posters that report flooding at socially stigmatized locations)?
(2) How can missing data be inferred from publicly available sources? For example, aggregated data are currently used for hazard damage modeling, but missing data on individual buildings or infrastructure could be inferred from a variety of sources such as satellite data, aerial photos, and street view data.
(3) When working with datasets from different providers or points in time or space, how can ontology mismatches be detected and computational solutions be designed for solving them?
(4) How can creative and innovative visualization of scientific information be used to inform and inspire decision making by diverse stakeholdersWhat methods and computational solutions can best address Big Data challenges of variety, volume, and velocity, as well as spatiotemporal integration?
How can technology be used to better define, measure, and advance urban sustainability and resilience?
What types of visualizations of scientific findings would best enable informed decision making by diverse stakeholders?
What types and scales of computational models are most accurate in predicting behavior of “systems of systems” in urban settings?