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

In: Perspectives on Spatial Data Analysis

Author

Listed:
  • Daniel Griffith

    (The University of Texas at Dallas)

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.

Suggested Citation

  • Daniel Griffith, 2010. "Spatial Filtering and Missing Georeferenced Data Imputation: A Comparison of the Getis and Griffith Methods," Advances in Spatial Science, in: Luc Anselin & Sergio J. Rey (ed.), Perspectives on Spatial Data Analysis, chapter 0, pages 227-233, Springer.
    • Handle: RePEc:spr:adspcp:978-3-642-01976-0_16
    DOI: 10.1007/978-3-642-01976-0_16
    as

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