Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters

Yongwan Chun (), Daniel A. Griffith (), Monghyeon Lee () and Parmanand Sinha ()
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Yongwan Chun: The University of Texas at Dallas
Daniel A. Griffith: The University of Texas at Dallas
Monghyeon Lee: The University of Texas at Dallas
Parmanand Sinha: The University of Tennessee

Journal of Geographical Systems, 2016, vol. 18, issue 1, No 4, 67-85

Abstract: Abstract Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment.

Keywords: Spatial filtering; Spatial autocorrelation; Eigenvectors; Spatial autoregressive regression (search for similar items in EconPapers)
JEL-codes: C0 C13 C14 C21 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s10109-015-0225-3

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