Spatial Rule Learning
Spatial Rule Learning is a data mining methodology that identifies implicit, significant spatial relationships and patterns from geospatial data by deriving rules that characterize the spatial dependencies, proximities, or interactions among geographic entities.
15
Publications
526
Citations
48
Authors
27
Institutions
Publications per year
2017–2026
3
Parents
15
Compartmental and Spatial Rule-Based Modeling with Virtual Cell
Michael L. Blinov, James C. Schaff, Dan Vasilescu et al. · Biophysical Journal · 2017 · 54 citations · Full text
A method for extracting rules from spatial data based on rough fuzzy sets
Hexiang Bai, Yong Ge, Jinfeng Wang et al. · Knowledge-Based Systems · 2013 · 46 citations
Foraging Ecology Predicts Learning Performance in Insectivorous Bats
Theresa M. A. Clarin, Ireneusz Ruczyński, Rachel A. Page et al. · PLoS ONE · 2013 · 43 citations · Full text
Life Sciences, Biology, Zoology +12
1–5 of 15
48
2
h-index
2
h-index
Institute of Geographic Sciences and Natural Resources Research, China
2 publications · 82 citations
2
h-index
2
h-index
1
h-index
1–5 of 48
27
Kent State University
Kent, United States
2 h-index
2 publications
31 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
7 publications
282 citations
Institute of Geographic Sciences and Natural Resources Research
Beijing, China
2 h-index
7 publications
282 citations

University of Alberta
Edmonton, Canada
1 h-index
1 publications
42 citations
Max Planck Institute for Ornithology
Pöcking, Germany
1 h-index
3 publications
129 citations
University of Haifa
Haifa, Israel
1 h-index
2 publications
158 citations
National Institute of Mental Health
Bethesda, United States
1 h-index
1 publications
36 citations
Smithsonian Tropical Research Institute
Panama City, Panama
1 h-index
1 publications
43 citations
1–8 of 27
1 publications · 16 citations
1
h-index
1 publications · 24 citations
1
h-index
1 publications · 36 citations
1
h-index
1 publications · 46 citations
1
h-index
1 publications · 43 citations
1
h-index
1–5