Neural nets : applications in geography
Author(s)
Bibliographic Information
Neural nets : applications in geography
(The GeoJournal library, v. 29)
Kluwer Academic Publishers, c1994
Available at 12 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references
Description and Table of Contents
Description
Neural nets offer a new strategy for spatial analysis, and their application holds enormous potential for the geographic sciences. However, the number of studies that have utilized these techniques is limited. This lack of interest can be attributed, in part, to lack of exposure, to the use of extensive and often confusing jargon, and to the misapprehension that, without an underlying statistical model, the explanatory power of the neural net is very low. This text attacks all three issues, demonstrating a wide variety of neural net applications in geography in a simple manner, with minimal jargon. The volume presents an introduction to neural nets that describes some of the basic concepts, as well as providing a more mathematical treatise for those wanting further details on neural net architecture. The bulk of the text, however, is devoted to descriptions of neural net applications in such broad-ranging fields as census analysis, predicting the spread of AIDS, describing synoptic controls on mountain snowfall, examining the relationships between atmospheric circulation and tropical rainfall, and the remote sensing of polar cloud and sea ice characteristics.
The text illustrates neural nets employed in modes analogous to multiple regression analysis, cluster analysis, and maximum likelihood classification. Not only are the neural nets shown to be equal or superior to these more conventional methods, particularly where the relationships have a strong nonlinear component, but they are also shown to contain significant explanatory power. Several chapters demonstrate that the nets themselves can be decomposed to illuminate causative linkages between different events in both the physical and human environments.
Table of Contents
- Looks and Uses
- B.C. Hewitson, R.G. Crane. Neural Networks and Their Applications
- E.E. Clothiaux, C.M. Bachmann. Neuroclassification of Spatial Data
- S. Openshaw. Self-Organizing Maps - Application to Census Data
- K. Winter, B.C. Hewitson. Predicting Snowfall from Synoptic Circulation - a Comparison of Linear Regression and Neural Network Methodologies
- D.L. McGinnis. Neural Computing and the AIDS Pandemic - the Case of Ohio
- P.G. Gould. Precipitation Controls in Southern Mexico
- B.C. Hewitson, R.G. Crane. Classification of Arctic Cloud and Sea Ice Features in Multi-Spectral Satellite Data
- J.R. Key. Appendices: Neural Network Resources
- Fortran 77 Listing for Kohonen Self-Organizing Map.
by "Nielsen BookData"