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

  • Daniel GriffithEmail author
Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.The University of Texas at DallasRichardsonUSA

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