Spatial Filtering and Missing Georeferenced Data Imputation: A Comparison of the Getis and Griffith Methods

Daniel Griffith ()
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Daniel Griffith: The University of Texas at Dallas

Chapter Chapter 16 in Perspectives on Spatial Data Analysis, 2010, pp 227-233 from Springer

Abstract: Abstract Spatial filtering first introduced independently by Getis and by Griffith is beginning to mature, with a third version now being developed by Legendre and his colleagues. Like the Getis formulation, this newest version is distance-based; like the Griffith formulation, it uses eigenfunctions, but extracted from a modified distance matrix – it is a mixture of the other two. Bivand (2002) comments that “the Getis filtering approach … seems to admit prediction to new data locations …. The Griffith eigenfunction decomposition approach …does not ….” Missing data prediction equations are presented for each of these two original formulations, and then compared with several popular datasets.

Keywords: Spatial Filter; Semivariogram Model; Imputation Result; Conventional Linear Regression; Multivariate Normal Probability (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-3-642-01976-0_16

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