Self-Organising Maps: Applications in Geographic Information Science

Self-Organising Maps: Applications in GI Science brings together the latest geographical research where extensive use has been made of the SOM algorithm, and provides readers with a snapshot of these tools that can then be adapted and used in new research projects. The book begins with an overview of the SOM technique and the most commonly used (and freely available) software; it is then sectioned to look at the different uses of the technique, namely clustering, data mining and cartography, from a range of application-areas in the biophysical and socio-economic environments.

• Only book that takes SOM algorithm to the GIS and Geography research communities
• The Editors draw together expert contributors from the UK, Europe, USA, New Zealand, and South Africa
• Covers a range of techniques in clustering, data mining cartography, all featuring an appropriate case study

Self-Organising Maps: Applications in Geographic Information Science brings together innovative geographic research involving use of the self-organising map (SOM) method. The range of applications in this book demonstrates how innovative artificial intelligence and neural network methods such as SOMs offer novel opportunities to geospatial experts and how these tools can be adapted for specific geographic needs. The book begins with an overview of the SOM technique and the most commonly used (and often freely available) software. It then looks at different uses of the technique, namely clustering, data mining, and cartography, from a range of application areas in physical and socio-economic environments.

This book will be an invaluable reference to researchers in the field who are looking for a comprehensive overview of the subject, by providing a clear explanation and understanding of this novel technique. Advanced undergraduates and post-graduates undertaking a research project in GIScience techniques will also find this book useful.

• A comprehensive overview of cutting-edge research on applying the classic SOM method to a range of GIScience applications.

• Chapters cover a range of techniques including clustering and data mining and all include appropriate case studies to enhance understanding.

• Presents balanced coverage of physical and socio-economic applications.

• Includes contributions from leading international researchers in the field.