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GeoComputational Modelling : Techniques and Applications - Manfred M. Fischer

GeoComputational Modelling

Techniques and Applications

By: Manfred M. Fischer (Editor), Yee Leung (Editor)

Hardcover

Published: 6th June 2001
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Geocomputation may be viewed as the application of a computational science paradigm to study a wide range of problems in geographical systems contexts.
This volume presents a clear, comprehensive and thoroughly state-of-the-art overview of current research, written by leading figures in the field.
It provides important insights into this new and rapidly developing field and attempts to establish the principles, and to develop techniques for solving real world problems in a wide array of application domains with a catalyst to greater understanding of what geocomputation is and what it entails.
The broad coverage makes it invaluable reading for resarchers and professionals in geography, environmental and economic sciences as well as for graduate students of spatial science and computer science.

Preface
GeoComputational Modelling - Techniques and Applications: Prologuep. 1
Concepts, Modelling Tools and Key Issues
Computational Neural Networks - Tools for Spatial Data Analysis
Introductionp. 15
Why Computational Neural Networks?p. 17
Definition of a Computational Neural Networkp. 20
Properties of the Processing Elementsp. 21
Network Topologiesp. 24
Learning in a Computational Neural Networkp. 27
A Taxonomy of Computational Neural Networksp. 29
Outlook - How Do Neurocomputing Techniques Differ?p. 34
Evolving Computational Neural Networks Through Evolutionary Computation
Introductionp. 35
Evolving Computational Neural Network Architecturesp. 37
EPNetp40
Experimental Studiesp. 48
Evolutionary Learning and Optimizationp. 60
A Population of ECNNs as an Ensemblep. 61
Conclusionsp. 69
Neural andEvolu^mhary Computauon Methods for Spatial Classification and Knowledge Acquisition
Introductionp. 71
Spatial Classification by Multilayer Feedforward Neural Networksp. 73
Spatial Classification by Other Unidirectional Neural Networksp. 78
Spatial Classification by Recurrent Neural Networksp. 80
Clustering by Scale-Space Algorithmsp. 80
Rule Learning by a Radial Basis Function Neural Networkp. 83
Rule Learning by a Hybrid Fuzzy Neural Networkp. 89
Rule Acquisition by Genetic Algorithms - The SCION Systemp. 94
Fuzzy Rule Acquisition by Genetic Algorithms -The GANGO Systemp. 100
Conclusionsp. 107
Cellular Dynamics: Modelling Urban Growth as a Spatial Epidemic
Defining Urban Growth as Sprawlp. 109
Growth as an Epidemic: Spatially Aggregate Modelsp. 112
Simplifications and Extensions to the Aggregate Modelp. 116
Growth as Spatial Diffusion: Spatially Disaggregate Modelsp. 122
A Computable Structure Based on Cellular Automatap. 125
The Dynamics of Urban Regenerationp. 130
Classifying Urban Growth through Morphologyp. 134
Conclusions: Applications and Policyp. 139
Spatial Application Domains
Spatial Pattern Recognition in Remote Sensing by Neural Networks
Introductionp. 145
Artificial and Biological Neural Networksp. 146
Recent Developments in Remote Sensingp. 147
Uses of Neural Networks in Remote Sensingp. 148
Creation of Neural Network Input Vectorsp. 150
Neural Networks in Unsupervised Classification of Remote Sensing Datap. 150
Neural Networks in Supervised Classification of Remote Sensing Datap. 154
'Soft Computing' Approaches Using Neural Networksp. 157
Managing Complexityp. 159
Hybrid Analysis Methodologiesp. 162
Conclusionsp. 164
Fuzzy ARTMAP - A Neural Classifier for Multispectral Image Classification
Introductionp. 165
Adaptive Resonance Theory and ART 1p. 166
The ARTMAP Neural Network Architecturep. 173
Generalization to Fuzzy ARTMAPp. 177
The Spectral Pattern Recognition Problemp. 180
Fuzzy ARTMAP Simulations and Classification Resultsp. 181
Summary and Conclusionsp. 188
Neural Spatial Interaction Models
Introductionp. 195
The Model Class under Considerationp. 196
Training Neural Spatial Interaction Models: Classical Techniquesp. 200
A New Global Search Approach for Network Training: The Differential Evolution Modelp. 205
Selecting Neural Spatial Interaction Models: The Model Choice Issuep. 208
Evaluating the Generalization Performance of a Neural Spatial Interaction Modelp. 214
Conclusion and Outlookp. 218
A Neural Network Approach for Mobility Panel Analysis
Introductionp. 220
The German Mobility Panelp. 221
Classical Panel Analysisp. 223
Application of Computational Neural Networks to the German Mobility Panelp. 223
Analysis of the Variable LOG[DAU_SUM]p. 228
Analysis of the Variable NUTZPKWp. 232
Conclusions and Outlookp. 234
Referencesp. 236
List of Figuresp. 255
List of Tablesp. 259
Subject Indexp. 261
Author Indexp. 271
List of Contributorsp. 277
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9783540419686
ISBN-10: 3540419683
Series: Advances in Spatial Science
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 275
Published: 6th June 2001
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.5 x 15.5  x 2.54
Weight (kg): 1.32