Comparing implementations of global and local indicators of spatial association

Roger Bivand and David W. S. Wong ()
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David W. S. Wong: George Mason University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2018, vol. 27, issue 3, No 13, 716-748

Abstract: Abstract Functions to calculate measures of spatial association, especially measures of spatial autocorrelation, have been made available in many software applications. Measures may be global, applying to the whole data set under consideration, or local, applying to each observation in the data set. Methods of statistical inference may also be provided, but these will, like the measures themselves, depend on the support of the observations, chosen assumptions, and the way in which spatial association is represented; spatial weights are often used as a representational technique. In addition, assumptions may be made about the underlying mean model, and about error distributions. Different software implementations may choose to expose these choices to the analyst, but the sets of choices available may vary between these implementations, as may default settings. This comparison will consider the implementations of global Moran’s I, Getis–Ord G and Geary’s C, local $$I_i$$ I i and $$G_i$$ G i , available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages.

Keywords: Software implementations; Global spatial autocorrelation; Local spatial autocorrelation; Lattice data; 62P12; 62P20; 62P25 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (61)

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DOI: 10.1007/s11749-018-0599-x

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