Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters

Yongwan Chun (), Daniel Griffith (), Monghyeon Lee () and Parmanand Sinha ()

Journal of Geographical Systems, 2016, vol. 18, issue 1, 67-85

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. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Spatial filtering; Spatial autocorrelation; Eigenvectors; Spatial autoregressive regression; 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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