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Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis

Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis METHODS Spatial autocorrelation is defined in terms of univariate data observations. Moran’s coefficient Z (Moran 1948, 1950; Cliff and Ord 1981), for example, is the weighted sum of the product of separate data observations, centered to the expected value of the observations, standardized to adjust for the variance of the observations, and normalized for the total sum of the weights. The following Contribution No. 539 in Ecology and Evolution from the State University of New York, Stony Brook. This work is part of a doctoral dissertation submitted to the Department of Ecology and Evolution, State University of New York, Stony Brook. The author thanks Drs. R. R. Sokal, F. Rohlf, J. D. Thomson, & . and R. C. Grimson for comments on this manuscript and Drs. N. Oden and R. I Setzer for many hours of helpful discussions. A reviewer pointed out the problem of negative eigenvalues and provided helpful guidance. B. Thomson and D. DiGiovanni assisted with technical aspects of this study. This research was supported by grant GM 2826202 from the National Institute of General Medical Sciences to R. R. Sokal. Daniel Wartenberg is f e h , Interdisciplinary Programs in Health, Harvard School of Public http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geographical Analysis Wiley

Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis

Wartenberg, Daniel
Geographical Analysis , Volume 17 (4) – Oct 1, 1985
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References (20)

Publisher
Wiley
Copyright
1985 The Ohio State University
ISSN
0016-7363
eISSN
1538-4632
DOI
10.1111/j.1538-4632.1985.tb00849.x
Publisher site
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Abstract

METHODS Spatial autocorrelation is defined in terms of univariate data observations. Moran’s coefficient Z (Moran 1948, 1950; Cliff and Ord 1981), for example, is the weighted sum of the product of separate data observations, centered to the expected value of the observations, standardized to adjust for the variance of the observations, and normalized for the total sum of the weights. The following Contribution No. 539 in Ecology and Evolution from the State University of New York, Stony Brook. This work is part of a doctoral dissertation submitted to the Department of Ecology and Evolution, State University of New York, Stony Brook. The author thanks Drs. R. R. Sokal, F. Rohlf, J. D. Thomson, & . and R. C. Grimson for comments on this manuscript and Drs. N. Oden and R. I Setzer for many hours of helpful discussions. A reviewer pointed out the problem of negative eigenvalues and provided helpful guidance. B. Thomson and D. DiGiovanni assisted with technical aspects of this study. This research was supported by grant GM 2826202 from the National Institute of General Medical Sciences to R. R. Sokal. Daniel Wartenberg is f e h , Interdisciplinary Programs in Health, Harvard School of Public

Journal

Geographical Analysis – Wiley

Published: Oct 1, 1985

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