Comparing implementations of global and local indicators of spatial association

• Roger S. Bivand ^[1] ; David W. S. Wong ^[2]
  1. [1] Norwegian School of Economics

     Norwegian School of Economics

     Noruega

     
  2. [2] George Mason University

     George Mason University

     Estados Unidos

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 27, Nº. 3, 2018, págs. 716-748
• Idioma: inglés
• DOI: 10.1007/s11749-018-0599-x
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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 Ii and Gi , available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages.