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Key words: General Regression Neural Network, Scalar Dynamics, US Great Plains, Maize Production
GeoComputation is emerging as an area of emphasis within analytical geography in which inhibitory data assumptions are not made, as they are in traditional inferential statistics (Gould, 1970; Gahegan, 1999). Separating the data model from assumptions of data structure allows geocomputation techniques to be molded, to a certain degree, by the characteristics of the data themselves. Running parallel to the geocomputation emphasis is an emphasis on examining the driving forces affecting land use and land cover changes (Meyer & Turner II, 1994; Liverman et al., 1998). Driving forces such as population pressures, political institutions, and cultural values, as well as the biophysical land transformations these forces influence, occur at varying scales (Fischer et al., 1995; Easterling, 1997; Easterling et al., 1998; Kull 1998). Although Ordinary Least Squares regression has been used to model relationships between variables operating at different scales, several drawbacks are incurred due to the inherently rigid nature of the model. This paper presents a framework for addressing scalar dynamics through the implementation of a General Regression Neural Network (GRNN). The first half of the paper outlines why traditional parametric techniques are inappropriate for the analysis of multi-scale problems, and demonstrates why the GRNN is appropriate. Finally, the robustness of the GRNN approach to attacking spatial scale is made explicit through a multi-scale analysis of US Great Plains maize production.
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