Geography Reference
In-Depth Information
Towards High-resolution
Self-organizing Maps of
Geographic Features
Andre Skupin and Aude Esperbe
Department of Geography, San Diego State University
This chapter introduces the use of high-resolution self-organizing maps (SOM) to represent
a large number of geographic features on the basis of their attributes. Until now, the SOM
method has been applied to geographic data for both clustering and visualization purposes.
However, the granularity of the resulting attribute space representations has been far below
the resolution at which geographic space is typically represented. We propose to construct
SOMs consisting of several hundred thousand neurons, trained with attributes of an equally
large number of geographic features, and finally visualized in standard GIS software. This is
demonstrated for a data set consisting of climate attributes attached to 200 000
US census
block groups. Further, overlays of point, line and area features onto such a high-resolution
SOM are shown.
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8.1 Introduction
This volume demonstrates the range of approaches currently pursued in the field of geo-
graphic visualization. Geographic visualization has clearly captured the public's imagina-
tion. Evolutionary changes in the creation, distribution and interaction with cartographic
depictions have powerfully converged in early realizations of the
digital earth
concept (see
Chapter 2). Further convergence of various technologies and methodologies is likely, includ-
ing trends towards high-resolution imagery (see Chapter 7 by Orford) and locations captured
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