Enabling computationally intensive geospatial research on CyberGIS-Jupyter with CyberGIS-Compute

Abstract: Geospatial research and education have become increasingly dependent on cyberGIS, defined as geographic information science and systems based on advanced cyberinfrastructure (CI), [1] to tackle computation and data challenges. However, the use of advanced cyberGIS capabilities has typically been constrained to a small set of research groups who have the technical expertise of using CI resources. Over the past few years CyberGIS-Jupyter [2,3] has been developed to provide access to cyberGIS capabilities through an easy-to-use Jupyter Notebook interface which has made cyberGIS more accessible. For many cyberGIS and geospatial applications accessing CI resources needed for solving complex problems at scale. However, leveraging CI resources for geospatial application is challenging both due to the steep learning curve and lack of appropriate tools. CyberGIS-Compute fills this gap by providing an easy-to-use middleware tool for using and contributing geospatial application codes that leverage CI resources. This substantially lowers the learning curve for both geospatial users and developers to access cyberGIS capabilities at scale. CyberGIS-Compute is backed by Virtual ROGER (Resourcing Open Geospatial Education and Research); a geospatial supercomputer with access to a number of readily available popular geospatial libraries.

With CyberGIS-Compute we have designed an easy-to-use middleware and associated Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. This presentation will first describe the basics of CyberGIS-Jupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. Lastly, we will also descrive mechanism to contribute applications to the CyberGIS-Compute framework.

Download paper here

Recommended citation: Padmanabhan, Anand, Zimo Xiao, Rebecca Vandewalle, Alexander Michels, and Shaowen Wang (2021). "Enabling computationally intensive geospatial research on CyberGIS-Jupyter with CyberGIS-Compute" Gateways 2021, DOI: 10.5281/zenodo.5570056