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Editorial

Introduction to Big Data Computing for Geospatial Applications

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://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080487
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)
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. View Full-Text
Keywords: geospatial big data; geospatial computing; cyberGIS; GeoAI; spatial thinking geospatial big data; geospatial computing; cyberGIS; GeoAI; spatial thinking
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://0-doi-org.brum.beds.ac.uk/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://0-doi-org.brum.beds.ac.uk/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://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080487

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