Exploring Area Data

Part of the SpringerBriefs in Regional Science book series (BRIEFSREGION)


Here in this chapter, we first consider the visualisation of area data before examining a number of exploratory techniques. The focus is on spatial dependence (spatial association). In other words, the techniques we consider aim to describe spatial distributions, discover patterns of spatial clustering, and identify atypical observations (outliers). Techniques and measures of spatial autocorrelation discussed in this chapter are available in a variety of software packages. Perhaps the most comprehensive is GeoDa, a free software program (downloadable from http://www.geoda.uiuc.edu). This software makes a number of exploratory spatial data analysis (ESDA) procedures available that enable the user to elicit information about spatial patterns in the data given. Graphical and mapping procedures allow for detailed analysis of global and local spatial autocorrelation results. Another valuable open software is the spdep package of the R project (downloadable from http://cran.r-project.org). This package contains a collection of useful functions to create spatial weights matrix objects from polygon contiguities, and various tests for global and spatial autocorrelation (see Bivand et al. 2008).


Area data Spatial weights matrix Contiguity-based specifications of the spatial weights matrix Distance-based specifications of the spatial weights matrix k-nearest neighbours Global measures of spatial autocorrelation Moran’s I statistic Geary’s c statistic Local measures of spatial autocorrelation G statistics LISA statistics 

Copyright information

© Manfred M. Fischer 2011

Authors and Affiliations

  1. 1.SocioEconomicsVienna University of Economics and BusinessViennaAustria
  2. 2.State Key Laboratory of Resources and Environmental Information SystemsChinese Academy of SciencesBeijingPeople’s Republic of China

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