Geographic Information Systems (GIS) have gone through rapid growth since originally developed in the 1960s. In the foreseeable future, GIS software will continue to play essential roles for breaking through scientific challenges in numerous fields and improving decision-making practices with broad societal impacts. However, fulfilling such roles is increasingly dependent on the ability to handle very large spatiotemporal data sets and complex analysis software based on synthesizing computational and spatial thinking enabled by cyberinfrastructure, which conventional GIS-based software approaches do not provide. This project will establish CyberGIS as a fundamentally new software framework comprising a seamless integration of cyberinfrastructure, GIS, and spatial analysis/modeling capabilities.
Project objectives:
- Engage multidisciplinary communities through a participatory approach to evolving CyberGIS software requirements
- Integrate and sustain a core set of composable, interoperable, manageable, and reusable CyberGIS software elements based on community-driven and open source strategies
- Empower high-performance and scalable CyberGIS by exploiting spatial characteristics of data and analytical operations for achieving unprecedented capabilities for geospatial scientific discoveries
- Enhance an online geospatial problem-solving environment to allow for the contribution, sharing and learning of CyberGIS software by numerous users, which will foster the development of crosscutting education, outreach and training programs with significant broad impacts
- Deploy and test CyberGIS software by linking with national and international CI to achieve scalability to significant sizes of geospatial problems, amounts of CI resources, and number of users
- Evaluate and improve the CyberGIS framework through domain science applications and vibrant partnerships to gain the better understanding of the complexity of coupled human-natural systems (e.g. for assessing impacts of climate change and rapid emergency response).
Grant Number: NSF award 1047916
