CyberGIS-Jupyter is an innovative cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook. The framework adapts the Notebook with built-in cyberGIS capabilities to accelerate gateway application development and sharing while associated data, analytics, and workflow runtime environments are encapsulated into application packages that can be elastically reproduced through cloud computing approaches. As a desirable outcome, data-intensive and scalable geospatial analytics can be efficiently developed and improved, and seamlessly reproduced among multidisciplinary users in a novel cyberGIS science gateway environment.
Sponsored by: National Science Foundation (NSF)
People: Anand Padmanabhan, Fangzheng Lu, Zhiyu Li, Rebecca Vandewalle, Nattapon Jroenchai, Dandong Yin, Shaowen Wang
Go to CyberGIS-Jupyter
- Lyu, F., Yin, D., Padmanabhan, A., Choi, Y., Goodall, J. L., Castronova, A., Tarboton, D., Wang, S. (2019) “Bridging Reproducible Hydrological Modeling with CyberGIS-Jupyter: A Case Study on SUMMA”. In: Proceedings of Practice and Experience in Advanced Research Computing (PEARC19), July 28-August 1, 2019, Chicago, IL, USA. (in press)
- Padmanabhan, A., Yin, D., Lyu, F., Wang, S. (2019) “Bridging Local Cyberinfrastructure and XSEDE with CyberGIS-Jupyter”. In: Proceedings of Practice and Experience in Advanced Research Computing (PEARC19), July 28-August 1, 2019, Chicago, IL, USA. (in press)
- Yin, D., Liu, Y., Hu, H., Terstriep, J., Hong, X., Padmanabhan, A., and Wang, S. (2018) “CyberGIS‐Jupyter for Reproducible and Scalable Geospatial Analytics”. Concurrency and Computation: Practice and Experience. https://doi.org/10.1002/cpe.5040
- Yin, D., Liu, Y., Padmanabhan, A., Terstriep, J., Rush, J., and Wang, S. (2017). A CyberGIS-Jupyter Framework for Geospatial Analytics at Scale. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (p. 18). ACM.