Introduction: What is a Self‐Organizing Map?

In the quest to understand and address important issues of the modern era, from environmental degradation to economic development, enormous amounts of geographic data are being generated. With the increasing adoption of such technologies as hyper-spectral remote sensing or wireless sensor networks, the growth rate of data volumes continues to rise. Granularity of geographic data is increasing both in geometric space (i.e. more features and finer cell sizes), and in attribute space (i.e. more attributes and finer measurements of attribute values), leaving us with truly n-dimensional data. We are thus increasingly faced with a data-rich environment, in which traditional inference methods are either failing or have become obstacles in the search for geographic structures, relationships, and meaning. With respect to statistical analysis, some problems of traditional approaches, especially regarding spatial autocorrelation, are increasingly being addressed (Fotheringham et al., 2000, 2002; Rogerson, 2001). However, many see the need for a paradigmatic shift in how geographic data are analysed and this push for a new direction is gaining strength, as indicated by the emergence of such disciplinary labels as geocomputation (Fischer and Leung, 2001; Longley, 1998; Openshaw and Abrahart, 2000) or geographic data mining (Miller and Han, 2001).

[1]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[2]  Paul A. Longley,et al.  Geocomputation: a primer , 1998 .

[3]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[4]  B. Pijanowski,et al.  Using neural networks and GIS to forecast land use changes: a Land Transformation Model , 2002 .

[5]  M. Charlton,et al.  Quantitative geography : perspectives on spatial data analysis by , 2001 .

[6]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[7]  Jay F. Nunamaker,et al.  Verifying the Proximity and Size Hypothesis for Self-Organizing Maps , 2000, J. Manag. Inf. Syst..

[8]  H. Ritter.  Self-Organizing Maps on non-euclidean Spaces , 1999 .

[9]  Stan Openshaw,et al.  Artificial intelligence in geography , 1997 .

[10]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[11]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[12]  John F. Roddick,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[13]  J. H. Schuenemeyer,et al.  Statistical Methods For Geographers , 1986 .

[14]  Bernd Fritzke.  Growing self-organizing networks—history, status quo, and perspectives , 1999 .

[15]  André Skupin.  A NOVEL MAP PROJECTION USING AN ARTIFICIAL NEURAL NETWORK , 2003 .

[16]  Jorma Laaksonen,et al.  SOM_PAK: The Self-Organizing Map Program Package , 1996 .

[17]  André Skupin,et al.  Visualizing Demographic Trajectories with Self-Organizing Maps , 2005, GeoInformatica.

[18]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[19]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[20]  Peter H. A. Sneath,et al.  Numerical Taxonomy: The Principles and Practice of Numerical Classification , 1973 .

[21]  George K. Knopf,et al.  Representing High-Dimensional Data Sets as Closed Surfaces , 2002, Inf. Vis..

[22]  Manfred M. Fischer,et al.  Computational Neural Networks — Tools for Spatial Data Analysis , 2001 .

[23]  André Skupin,et al.  A cartographic approach to visualizing conference abstracts , 2002 .

[24]  Masahiro Takatsuka,et al.  Geodesic self-organizing map , 2005, IS&T/SPIE Electronic Imaging.

[25]  Mark Gahegan,et al.  Introducing geovista studio: an integrated suite of visualization and computational methods for expl , 2002 .

[26]  André Skupin,et al.  On Geometry and Transformation in Map-Like Information Visualization , 2002, Visual Interfaces to Digital Libraries.