CIF21 DIBBs: Scalable Capabilities for Spatial Data Synthesis

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

This project will develop a set of tools for spatial data synthesis through scalable data aggregation and integration based on cloud computing, CyberGIS, and other existing tools. Many scientific problems require the aggregation and integration of large and varied spatial data from a multitude of sources, yet existing approaches and software cannot effectively synthesize the enormous amounts of spatial data that often are available. This project will resolve problems associated with the use of massive spatial data, thus facilitating work dependent on this type of data for scientific problem solving, such as research on population dynamics and urban sustainability. Learning materials derived from the research activities will be openly accessible through the CyberGIS Science Gateway. Targeted massive open online course development will provide inexpensive and efficient ways to teaching students about the capabilities and underlying scientific principles of spatial data synthesis. A summer school will be offered during the second half of the project to provide a more focused and in-depth training event. This research project will create scalable capabilities for spatial data synthesis enabled by cloud computing and CyberGIS. The project will begin by developing the capabilities for solving specific scientific problems and then move on to engage a broader community for validating and improving the core capabilities. The research will incorporate two interrelated themes: (1) measuring urban sustainability based on a number of social, environmental, and physical factors and processes; and (2) examining population dynamics by synthesizing multiple population data sources with social media data. Spatial data synthesis capabilities that the project will provide include extracting metadata and dealing with problems of spatial references and units. The project also will develop a fundamental capability to characterize uncertainty in data and its propagation.