CIF21 DIBBs: Scalable Capabilities for Spatial Data Synthesis

Spatial data often embedded with geographic references are important to numerous scientific domains (e.g., ecology, geography and spatial sciences, geosciences, and social sciences, to name just a few), and also beneficial to solving many critical societal problems (e.g., environmental and urban sustainability). In recent years, this type of data has exploded to massive size and significant complexity as increasingly sophisticated location-based sensors and devices (e.g., social networks, smartphones, and environmental sensors) are widely deployed and used. However, the tools and computational platforms available for processing synthesizing such data remain limited. Over the past couple of years, this project has helped establish CyberGIS-Jupyter as a platform for making geospatial data processing and analytics capabilities accessible. CyberGIS-Jupyter is an online geospatial computation platform for a large number of users to conduct and share scalable cyberGIS analytics via Jupyter Notebooks supported by advanced cyberinfrastructure resources such as those provisioned by the Extreme Science and Engineering Discovery Environment (XSEDE). This poster presents details of both CyberGIS-Jupyter platform in terms of both technical progress made and the enabling role it plays in enhancing cyberGIS research and education

This poster was developed for 2020 NSF CSSI PI meeting