Title: An inductive attack on spatial scale | ||||||||

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> > | Date: 23 August 2000 | |||||||

Authors: Nate Currit | ||||||||

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< < | Link: http://www.geocomputation.org/2000/GC011/Gc011.htm | |||||||

> > | Link: http://www.geocomputation.org/2000/GC011/Gc011.htm | |||||||

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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 (LUCC) (Meyer and Turner II 1994; Liverman, Moran 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, Rojkov et al. 1995; Easterling 1997; Easterling and 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.
Keywords: General Regression Neural Network, Scalar Dynamics, Geocomputation, US Great Plains, Maize Production | ||||||||

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< < | Reference: Proceedings of the Fifth International Conference on GeoComputation, University of Greenwich's School of Earth and Environmental Sciences, Kent, UK, 23 - 25 August 2000. Papers published on CD-ROM. Produced by: R.J.Abrahart and B.H.Carlisle. Publisher: "GeoComputation CD-ROM". ISBN 0-9533477-2-9 | |||||||

> > | Reference: Proceedings of the Fifth International Conference on GeoComputation, University of Greenwich's School of Earth and Environmental Sciences, Kent, UK, 23 - 25 August 2000. Papers published on CD-ROM. Produced by: R.J.Abrahart and B.H.Carlisle. Publisher: "GeoComputation CD-ROM". ISBN 0-9533477-2-9 | |||||||

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Title: An inductive attack on spatial scale
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 (LUCC) (Meyer and Turner II 1994; Liverman, Moran 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, Rojkov et al. 1995; Easterling 1997; Easterling and 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. Keywords: General Regression Neural Network, Scalar Dynamics, Geocomputation, US Great Plains, Maize Production Reference: Proceedings of the Fifth International Conference on GeoComputation, University of Greenwich's School of Earth and Environmental Sciences, Kent, UK, 23 - 25 August 2000. Papers published on CD-ROM. Produced by: R.J.Abrahart and B.H.Carlisle. Publisher: "GeoComputation CD-ROM". ISBN 0-9533477-2-9 -- TWikiAdminUser - 2010-06-16 |

Title: An inductive attack on spatial scale
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 (LUCC) (Meyer and Turner II 1994; Liverman, Moran 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, Rojkov et al. 1995; Easterling 1997; Easterling and 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. Keywords: General Regression Neural Network, Scalar Dynamics, Geocomputation, US Great Plains, Maize Production -- TWikiAdminUser - 2010-06-16 |

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