TY - GEN
T1 - A cybergis-jupyter framework for geospatial analytics at scale
AU - Yin, Dandong
AU - Liu, Yan
AU - Padmanabhan, Anand
AU - Terstriep, Jeff
AU - Rush, Johnathan
AU - Wang, Shaowen
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/7/9
Y1 - 2017/7/9
N2 - 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.
AB - 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.
KW - Computational reproducibility
KW - CyberGIS
KW - Flood mapping
KW - Geospatial big data
KW - Science gateway
UR - http://www.scopus.com/inward/record.url?scp=85025820717&partnerID=8YFLogxK
U2 - 10.1145/3093338.3093378
DO - 10.1145/3093338.3093378
M3 - Conference contribution
AN - SCOPUS:85025820717
T3 - ACM International Conference Proceeding Series
BT - PEARC 2017 - Practice and Experience in Advanced Research Computing 2017
PB - Association for Computing Machinery
T2 - 2017 Practice and Experience in Advanced Research Computing, PEARC 2017
Y2 - 9 July 2017 through 13 July 2017
ER -
