Prediction Under Spatial Misalignment
Prediction Under Spatial Misalignment is a spatial prediction framework that addresses the challenge of making accurate inferences when observational data and target spatial units are misaligned in their geographic support, requiring explicit modeling of spatial disaggregation or integration processes to reconcile scale and extent differences.
17
Publications
1.1K
Citations
57
Authors
41
Institutions
Publications per year
2017–2026
5
Parents
17
Resolving misaligned spatial data with integrated species distribution models
Krishna Pacifici, Brian J. Reich, David A. Miller et al. · Ecology · 2019 · 75 citations · Full text
Computing, Ecology, Spatial Ecology +12
1–5 of 17
57
3
h-index
3
h-index
1
h-index
1
h-index
1
h-index
1–5 of 57
41

University of Minnesota
Minneapolis, United States
3 h-index
5 publications
155 citations
University of Michigan
Ann Arbor, United States
1 h-index
1 publications
195 citations
University of Connecticut
Storrs, United States
1 h-index
2 publications
42 citations
Shannon Applied Biotechnology Centre
Tralee, Ireland
1 h-index
1 publications
21 citations

University of Strathclyde
Glasgow, United Kingdom
1 h-index
2 publications
72 citations
Institute of Art
Warsaw, Poland
1 h-index
2 publications
124 citations
Michigan State University
East Lansing, United States
1 h-index
1 publications
25 citations

Statistical and Applied Mathematical Sciences Institute
Durham, United States
1 h-index
1 publications
195 citations
1–8 of 41
3 publications · 414 citations
3
h-index
Conference · 2 publications · 105 citations
2
h-index
2 publications · 242 citations
2
h-index
1 publications · 33 citations
1
h-index
1 publications · 75 citations
1
h-index
1–5