CyberGIS-Jupyter for reproducible and scalable geospatial analytics

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

doi:10.1002/cpe.5040

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 journal › Article › peer-review

30 Scopus citations

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 language	English
Article number	e5040
Journal	Concurrency and Computation: Practice and Experience
Volume	31
Issue number	11
DOIs	https://doi.org/10.1002/cpe.5040
State	Published - Jun 10 2019
Externally published	Yes

Funding

National Science Foundation (NSF), Grant/Award Number: 1047916, 1443080, 1551492, and 1664119; NSF-supported ROGER supercomputer, Grant/Award Number: 1429699; ECSS program of NSF XSEDE, Grant/Award Number: 1053575 This work is supported in part by the National Science Foundation (NSF) under grant numbers: 1047916, 1443080, 1551492, and 1664119. The computational work used the NSF-supported ROGER supercomputer (1429699). This work is also supported in part by the ECSS program of XSEDE, which is supported by NSF under grant number 1053575.

Funders	Funder number
NSF-supported	1429699
National Science Foundation	1443080, 1551492, 1053575, 1047916, 1664119

Keywords

cloud computing
computational reproducibility
cyberGIS
geospatial big data

Access to Document

10.1002/cpe.5040

Cite this

@article{681e4a6117de40dbaebf4cc3ecbcbf0c,

title = "CyberGIS-Jupyter for reproducible and scalable geospatial analytics",

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.",

keywords = "cloud computing, computational reproducibility, cyberGIS, geospatial big data",

author = "Dandong Yin and Yan Liu and Hao Hu and Jeff Terstriep and Xingchen Hong and Anand Padmanabhan and Shaowen Wang",

note = "Publisher Copyright: {\textcopyright} 2018 John Wiley & Sons, Ltd.",

year = "2019",

month = jun,

day = "10",

doi = "10.1002/cpe.5040",

language = "English",

volume = "31",

journal = "Concurrency and Computation: Practice and Experience",

issn = "1532-0626",

publisher = "wiley",

number = "11",

}

TY - JOUR

T1 - CyberGIS-Jupyter for reproducible and scalable geospatial analytics

AU - Yin, Dandong

AU - Liu, Yan

AU - Hu, Hao

AU - Terstriep, Jeff

AU - Hong, Xingchen

AU - Padmanabhan, Anand

AU - Wang, Shaowen

PY - 2019/6/10

Y1 - 2019/6/10

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 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 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 - cloud computing

KW - computational reproducibility

KW - cyberGIS

KW - geospatial big data

UR - http://www.scopus.com/inward/record.url?scp=85056126901&partnerID=8YFLogxK

U2 - 10.1002/cpe.5040

DO - 10.1002/cpe.5040

M3 - Article

AN - SCOPUS:85056126901

SN - 1532-0626

VL - 31

JO - Concurrency and Computation: Practice and Experience

JF - Concurrency and Computation: Practice and Experience

IS - 11

M1 - e5040

ER -

CyberGIS-Jupyter for reproducible and scalable geospatial analytics

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this