Spatial Filtering Applications: Selected Percentage Datasets

Daniel A. Griffith
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Daniel A. Griffith: Syracuse University

Chapter 7 in Spatial Autocorrelation and Spatial Filtering, 2003, pp 177-192 from Springer

Abstract: Abstract Chapter 1 presents an overview of a variety of georeferenced binary/percentage datasets (see §1 .4.2). These data are further analyzed in this chapter to exemplify hovv spatial filtering methodology can capture positive spatial autocorrelation for georeferenced logistic/binomial variables and to demonstrate how spatial autocorrelation affects overdispersion.1 The classical binomial probability model assumes that, for areal unit i, observed percentage pi results from summing ni independent and identically distributed binary random variables.

Keywords: Spatial Autocorrelation; Canonical Correlation Analysis; Stepwise Logistic Regression; Spatial Filter; Pepper Plant (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/978-3-540-24806-4_7

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