Scalable Spatial Computing
Scalable Spatial Computing is a distributed computing paradigm that enables efficient processing and analysis of large-scale geospatial data by dynamically allocating spatial computation resources across heterogeneous networked systems to maintain performance and responsiveness as data volume and user demand grow.
30
Publications
1.3K
Citations
95
Authors
56
Institutions
Publications per year
2017–2026
9
30
Yan Liu, David R. Maidment, David G. Tarboton et al. · JAWRA Journal of the American Water Resources Association · 2018 · 113 citations
Li, Fotheringham, Li et al. · International Journal of Geographical Information Science · 2018 · 94 citations · Full text
Shashi Shekhar, Steven Feiner, Walid G. Aref · Communications of the ACM · 2015 · 84 citations
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56
Arizona State University
Tempe, United States
3 h-index
7 publications
792 citations
University of California, Santa Barbara
Santa Barbara, United States
2 h-index
3 publications
451 citations
Hewlett-Packard (United States)
Palo Alto, United States
2 h-index
2 publications
33 citations
Georgia State University
Atlanta, United States
2 h-index
9 publications
282 citations
University of Minnesota System
Minneapolis, United States
2 h-index
2 publications
110 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
8 publications
298 citations
University of Wisconsin–Madison
Madison, United States
1 h-index
1 publications
27 citations
Microsoft (United States)
Redmond, United States
1 h-index
2 publications
690 citations
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2 publications · 26 citations
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2 publications · 69 citations
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1 publications · 32 citations
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1 publications · 11 citations
1
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1 publications · 161 citations
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