CyberGIS-Jupyter for reproducible and scalable geospatial analytics

Dandong Yin, Yan Liu, Hao Hu, Jeff Terstriep, Xingchen Hong, Anand Padmanabhan, Shaowen Wang

Research output: Contribution to journalArticle

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 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)
Article numbere5040
JournalConcurrency Computation
Volume31
Issue number11
DOIs
StatePublished - Jun 10 2019

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Information science
Information systems
Gateway
Supercomputers
Cloud computing
Supercomputer
Cloud Computing
Work Flow
Accelerate
Sharing
Framework
Big data

Keywords

  • cloud computing
  • computational reproducibility
  • cyberGIS
  • geospatial big data

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

Cite this

CyberGIS-Jupyter for reproducible and scalable geospatial analytics. / Yin, Dandong; Liu, Yan; Hu, Hao; Terstriep, Jeff; Hong, Xingchen; Padmanabhan, Anand; Wang, Shaowen.

In: Concurrency Computation, Vol. 31, No. 11, e5040, 10.06.2019.

Research output: Contribution to journalArticle

Yin, Dandong ; Liu, Yan ; Hu, Hao ; Terstriep, Jeff ; Hong, Xingchen ; Padmanabhan, Anand ; Wang, Shaowen. / CyberGIS-Jupyter for reproducible and scalable geospatial analytics. In: Concurrency Computation. 2019 ; Vol. 31, No. 11.
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