Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis
By D. Wartenberg. 2010
249 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/ddb2f6d7167d4515fd2380fb8ce8f78bca856d8a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Stéphane Dray
- Marie-Josée Fortin
- P. Peres‐Neto
- Pierre Legendre
- H. Wagner
- F. Guillaume Blanchet
- S. Saïd
- François Débias
In Global and Planetary Change
- Marie-Josée Fortin
- Pierre Legendre
- Stéphane Dray
- P. Peres‐Neto
- H. Wagner
- F. Guillaume Blanchet
- S. Saïd
- François Débias
Citations in the corpus
- Geographically Weighted Colocation Quotients: Specification and Application (2014, methodology): “Focusing directly on spatial association, Wartenberg (1985) described a method of multivariate spatial correlation that is a latent root analysis of Moran’s I coefficients on the principal diagonal and Mantel coefficients as the off-diagonal elements of an n × n matrix of locations.”
- The Colocation Quotient: A New Measure of Spatial Association Between Categorical Subsets of Points. 协同区位商：点集分类子集间空间关联性的新度量标准 (2011, unknown): “Aspects of spatial association are captured in the concepts of ‘‘spatial overlay,’’ ‘‘cross-correlation,’’ and ‘‘colocation’’ (Wartenberg 1985; de Smith, Goodchild, and Longley 2009).”
- Community ecology in the age of multivariate multiscale spatial analysis (2012, methodology): “This leads to the variogram matrix (sensu Wagner 2003) or to the spatial correlation matrix (Wartenberg 1985) depending on whether the Geary (1954) or Moran (1950) indices were used to estimate spatial autocorrelation.”
- Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation (2008, background): “The first interesting attempt that aimed at depicting basic multivariate spatial patterns was due to Wartenberg (1985).”
- Principal Component Analysis on Spatial Data: An Overview (2013, unknown): “The sPCA technique has strong conceptual links to the multivariate spatial correlation technique of Wartenberg ( 1985a , 1985b ) where both techniques require the computation of a spatial weighting matrix W to account for spatial autocorrelation between spatial units.”