Exploring Area Data

Manfred Fischer () and Jinfeng Wang ()
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Jinfeng Wang: Chinese Academy of Sciences

Chapter Chapter 2 in Spatial Data Analysis, 2011, pp 15-29 from Springer

Abstract: Abstract 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).

Keywords: 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 (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/978-3-642-21720-3_2

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