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A New Perspective on Moran’s Coefficient: Revisited
by
Yamada, Hiroshi
in
Decomposition
/ eigenvector spatial filtering
/ Eigenvectors
/ Fourier series
/ Geary’s c
/ Geospatial data
/ linear algebraic graph theory
/ Moran’s I
/ Representations
/ Spatial analysis
/ spatial autocorrelation
/ Spatial data
/ Variables
2024
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A New Perspective on Moran’s Coefficient: Revisited
by
Yamada, Hiroshi
in
Decomposition
/ eigenvector spatial filtering
/ Eigenvectors
/ Fourier series
/ Geary’s c
/ Geospatial data
/ linear algebraic graph theory
/ Moran’s I
/ Representations
/ Spatial analysis
/ spatial autocorrelation
/ Spatial data
/ Variables
2024
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Do you wish to request the book?
A New Perspective on Moran’s Coefficient: Revisited
by
Yamada, Hiroshi
in
Decomposition
/ eigenvector spatial filtering
/ Eigenvectors
/ Fourier series
/ Geary’s c
/ Geospatial data
/ linear algebraic graph theory
/ Moran’s I
/ Representations
/ Spatial analysis
/ spatial autocorrelation
/ Spatial data
/ Variables
2024
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Journal Article
A New Perspective on Moran’s Coefficient: Revisited
2024
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Overview
Moran’s I (Moran’s coefficient) is one of the most prominent measures of spatial autocorrelation. It is well known that Moran’s I has a representation that is similar to a Fourier series and is therefore useful for characterizing spatial data. However, the representation needs to be modified. This paper contributes to the literature by showing the necessary modification and presenting some further results. In addition, we provide the required MATLAB/GNU Octave and R user-defined functions.
Publisher
MDPI AG
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