A cybergis-jupyter framework for geospatial analytics at scale

Dandong Yin, Yan Liu, Anand Padmanabhan, Jeff Terstriep, Johnathan Rush, Shaowen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The interdisciplinary field of cyberGIS (geographic information science and systems (GIS) based on advanced cyberinfrastructure) has a major focus on data-and computation-intensive geospatial analytics. The rapidly growing needs across many application and science domains for such analytics based on disparate geospatial big data poses significant challenges to conventional GIS approaches. This paper describes CyberGIS-Jupyter, an innovative cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using the Jupyter Notebook based on ROGER-the first cyberGIS supercomputer. 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.

Original languageEnglish (US)
Title of host publicationPEARC 2017 - Practice and Experience in Advanced Research Computing 2017
Subtitle of host publicationSustainability, Success and Impact
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450352727
DOIs
StatePublished - Jul 9 2017
Event2017 Practice and Experience in Advanced Research Computing, PEARC 2017 - New Orleans, United States
Duration: Jul 9 2017Jul 13 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F128771

Other

Other2017 Practice and Experience in Advanced Research Computing, PEARC 2017
CountryUnited States
CityNew Orleans
Period7/9/177/13/17

Keywords

  • Computational reproducibility
  • CyberGIS
  • Flood mapping
  • Geospatial big data
  • Science gateway

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Yin, D., Liu, Y., Padmanabhan, A., Terstriep, J., Rush, J., & Wang, S. (2017). A cybergis-jupyter framework for geospatial analytics at scale. In PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact [a18] (ACM International Conference Proceeding Series; Vol. Part F128771). Association for Computing Machinery. https://doi.org/10.1145/3093338.3093378