Advances in Spatial Science

Techniques and Applications

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

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Hardcover | 6 June 2001

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Hardcover
300 Pages

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23.5 x 15.88 x 2.54

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Information Retrieval Earth Sciences, Geography, Environment, Planning Geography Geographical Information Systems GIS & Remote Sensing Travel & Holidays Economics Computer Science Information Technology General Issue Business Applications

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Published: 8th December 2010

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Preface
GeoComputational Modelling - Techniques and Applications: Prologue	p. 1
Concepts, Modelling Tools and Key Issues
Computational Neural Networks - Tools for Spatial Data Analysis
Introduction	p. 15
Why Computational Neural Networks?	p. 17
Definition of a Computational Neural Network	p. 20
Properties of the Processing Elements	p. 21
Network Topologies	p. 24
Learning in a Computational Neural Network	p. 27
A Taxonomy of Computational Neural Networks	p. 29
Outlook - How Do Neurocomputing Techniques Differ?	p. 34
Evolving Computational Neural Networks Through Evolutionary Computation
Introduction	p. 35
Evolving Computational Neural Network Architectures	p. 37
EPNetp40
Experimental Studies	p. 48
Evolutionary Learning and Optimization	p. 60
A Population of ECNNs as an Ensemble	p. 61
Conclusions	p. 69
Neural andEvolu^mhary Computauon Methods for Spatial Classification and Knowledge Acquisition
Introduction	p. 71
Spatial Classification by Multilayer Feedforward Neural Networks	p. 73
Spatial Classification by Other Unidirectional Neural Networks	p. 78
Spatial Classification by Recurrent Neural Networks	p. 80
Clustering by Scale-Space Algorithms	p. 80
Rule Learning by a Radial Basis Function Neural Network	p. 83
Rule Learning by a Hybrid Fuzzy Neural Network	p. 89
Rule Acquisition by Genetic Algorithms - The SCION System	p. 94
Fuzzy Rule Acquisition by Genetic Algorithms -The GANGO System	p. 100
Conclusions	p. 107
Cellular Dynamics: Modelling Urban Growth as a Spatial Epidemic
Defining Urban Growth as Sprawl	p. 109
Growth as an Epidemic: Spatially Aggregate Models	p. 112
Simplifications and Extensions to the Aggregate Model	p. 116
Growth as Spatial Diffusion: Spatially Disaggregate Models	p. 122
A Computable Structure Based on Cellular Automata	p. 125
The Dynamics of Urban Regeneration	p. 130
Classifying Urban Growth through Morphology	p. 134
Conclusions: Applications and Policy	p. 139
Spatial Application Domains
Spatial Pattern Recognition in Remote Sensing by Neural Networks
Introduction	p. 145
Artificial and Biological Neural Networks	p. 146
Recent Developments in Remote Sensing	p. 147
Uses of Neural Networks in Remote Sensing	p. 148
Creation of Neural Network Input Vectors	p. 150
Neural Networks in Unsupervised Classification of Remote Sensing Data	p. 150
Neural Networks in Supervised Classification of Remote Sensing Data	p. 154
'Soft Computing' Approaches Using Neural Networks	p. 157
Managing Complexity	p. 159
Hybrid Analysis Methodologies	p. 162
Conclusions	p. 164
Fuzzy ARTMAP - A Neural Classifier for Multispectral Image Classification
Introduction	p. 165
Adaptive Resonance Theory and ART 1	p. 166
The ARTMAP Neural Network Architecture	p. 173
Generalization to Fuzzy ARTMAP	p. 177
The Spectral Pattern Recognition Problem	p. 180
Fuzzy ARTMAP Simulations and Classification Results	p. 181
Summary and Conclusions	p. 188
Neural Spatial Interaction Models
Introduction	p. 195
The Model Class under Consideration	p. 196
Training Neural Spatial Interaction Models: Classical Techniques	p. 200
A New Global Search Approach for Network Training: The Differential Evolution Model	p. 205
Selecting Neural Spatial Interaction Models: The Model Choice Issue	p. 208
Evaluating the Generalization Performance of a Neural Spatial Interaction Model	p. 214
Conclusion and Outlook	p. 218
A Neural Network Approach for Mobility Panel Analysis
Introduction	p. 220
The German Mobility Panel	p. 221
Classical Panel Analysis	p. 223
Application of Computational Neural Networks to the German Mobility Panel	p. 223
Analysis of the Variable LOG[DAU_SUM]	p. 228
Analysis of the Variable NUTZPKW	p. 232
Conclusions and Outlook	p. 234
References	p. 236
List of Figures	p. 255
List of Tables	p. 259
Subject Index	p. 261
Author Index	p. 271
List of Contributors	p. 277
Table of Contents provided by Publisher. All Rights Reserved.

Advances in Spatial Science

Techniques and Applications

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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