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Comparing implementations of global and local indicators of spatial association

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... Full description

Journal Title: Test (Madrid Spain), 2018-07-27, Vol.27 (3), p.716-748
Main Author: Bivand, Roger S
Other Authors: Wong, David W. S
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
ID: ISSN: 1133-0686
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title: Comparing implementations of global and local indicators of spatial association
format: Article
creator:
  • Bivand, Roger S
  • Wong, David W. S
subjects:
  • Applications programs
  • Economics
  • Finance
  • general
  • Insurance
  • Management
  • Mathematics and Statistics
  • Original Paper
  • Software
  • Statistical inference
  • Statistical methods
  • Statistical Theory and Methods
  • Statistics
  • Statistics for Business
ispartof: Test (Madrid, Spain), 2018-07-27, Vol.27 (3), p.716-748
description: 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 and G i , available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages.
language: eng
source:
identifier: ISSN: 1133-0686
fulltext: no_fulltext
issn:
  • 1133-0686
  • 1863-8260
url: Link


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descriptionFunctions 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 and G i , available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages.
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abstractFunctions 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 and G i , available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages.
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doi10.1007/s11749-018-0599-x
orcididhttps://orcid.org/0000-0003-2392-6140