Introduction to Big Data Computing for Geospatial Applications

Li, Zhenlong; Tang, Wenwu; Huang, Qunying; Shook, Eric; Guan, Qingfeng

doi:10.3390/ijgi9080487

Open AccessEditorial

Introduction to Big Data Computing for Geospatial Applications

by

Zhenlong Li

^1,*

,

Wenwu Tang

²,

Qunying Huang

³

,

Eric Shook

⁴ and

Qingfeng Guan

⁵

¹

Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC 29208, USA

²

Center for Applied Geographic Information Science, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA

³

Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA

⁴

Department of Geography, Environment, and Society, University of Minnesota, Minneapolis, MN 55455, USA

⁵

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

^*

Author to whom correspondence should be addressed.

ISPRS Int. J. Geo-Inf. 2020, 9(8), 487; https://doi.org/10.3390/ijgi9080487

Submission received: 3 August 2020 / Accepted: 10 August 2020 / Published: 12 August 2020

(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)

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Abstract

The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section.

Keywords: geospatial big data; geospatial computing; cyberGIS; GeoAI; spatial thinking

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MDPI and ACS Style

Li, Z.; Tang, W.; Huang, Q.; Shook, E.; Guan, Q. Introduction to Big Data Computing for Geospatial Applications. ISPRS Int. J. Geo-Inf. 2020, 9, 487. https://doi.org/10.3390/ijgi9080487

AMA Style

Li Z, Tang W, Huang Q, Shook E, Guan Q. Introduction to Big Data Computing for Geospatial Applications. ISPRS International Journal of Geo-Information. 2020; 9(8):487. https://doi.org/10.3390/ijgi9080487

Chicago/Turabian Style

Li, Zhenlong, Wenwu Tang, Qunying Huang, Eric Shook, and Qingfeng Guan. 2020. "Introduction to Big Data Computing for Geospatial Applications" ISPRS International Journal of Geo-Information 9, no. 8: 487. https://doi.org/10.3390/ijgi9080487

APA Style

Li, Z., Tang, W., Huang, Q., Shook, E., & Guan, Q. (2020). Introduction to Big Data Computing for Geospatial Applications. ISPRS International Journal of Geo-Information, 9(8), 487. https://doi.org/10.3390/ijgi9080487

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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