Geostatistical Process Modeling is a spatial process modeling approach that integrates stochastic models of spatially correlated random fields with statistical inference to characterize and predict continuous spatial phenomena based on observed data.
93
Publications
6.3K
Citations
201
Authors
129
Institutions
Publications per year
2017–2026
18
Parents
Publications
93
1–5 of 93
Authors
201
- SB
8
h-index
- AE
7
h-index
- ZW
3
h-index
- RM
3
h-index
- DS
3
h-index
1–5 of 201
Institutions
129
Duke University
Durham, United States
7 h-index
8 publications
625 citations
University of California, Los Angeles
Los Angeles, United States
5 h-index
8 publications
2.7K citations
North Carolina State University
Raleigh, United States
4 h-index
5 publications
247 citations
University of Michigan
Ann Arbor, United States
3 h-index
6 publications
332 citations
University of Minho
Braga, Portugal
3 h-index
5 publications
425 citations
University of Tulsa
Tulsa, United States
3 h-index
9 publications
564 citations
Politecnico di Milano
Milan, Italy
3 h-index
5 publications
277 citations
Florida State University
Tallahassee, United States
2 h-index
4 publications
76 citations
1–8 of 129
Venues
- Journal of the American Statistical Association
7 publications · 1.2K citations
7
h-index
- Mathematical Geosciences
4 publications · 202 citations
4
h-index
- Water Resources Research
3 publications · 83 citations
4
h-index
- Computers & Geosciences
CAGEO · 4 publications · 124 citations
4
h-index
- Environmetrics
3 publications · 117 citations
3
h-index
1–5