Spatial Autocorrelation Modeling
Spatial Autocorrelation Modeling is a geostatistical methodology that quantifies and accounts for the non-random spatial dependence among observations in a geographic domain, distinguishing itself by explicitly formalizing the degree to which proximal locations exhibit similar values as a core structural component of spatial process inference.
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Citations
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1.3K
Institutions
Publications per year
2017–2026
477
1.3K
Spatial Autocorrelation: Trouble or New Paradigm?
Pierre Legendre · Ecology · 1993 · 3.7K citations
Spatial Processes, Models and Applications
Luc Anselin, Andrew Cliff, J. Keith Ord · Economic Geography · 1983 · 2.7K citations
Computing, Geography, Urban Planning +12
<i><b>GeoDa</b></i>: An Introduction to Spatial Data Analysis
Luc Anselin, Ibnu Syabri, Youngihn Kho · Geographical Analysis · 2005 · 2.5K citations
Computing, Geography, Visualization +17
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The University of Texas at Dallas
Richardson, United States
25 h-index
56 publications
3.7K citations

Chinese Academy of Sciences
Beijing, China
21 h-index
117 publications
3.8K citations

Peking University
Beijing, China
18 h-index
34 publications
2.3K citations
Stony Brook University
Stony Brook, United States
17 h-index
30 publications
5.4K citations
Institute of Geographic Sciences and Natural Resources Research
Beijing, China
17 h-index
50 publications
1.8K citations
University of Chinese Academy of Sciences
Beijing, China
17 h-index
63 publications
2K citations
Wuhan University
Wuhan, China
15 h-index
71 publications
2K citations
State University of New York
Albany, United States
15 h-index
23 publications
4.8K citations
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47 publications · 7.1K citations
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36 publications · 1.9K citations
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35 publications · 1.2K citations
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39 publications · 1.1K citations
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22 publications · 4.7K citations
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