Attribute space visualization of demographic change

This paper introduces an approach for closer integration of self-organizing maps into the visualization of spatio-temporal phenomena in GIS. It is proposed to provide a more explicit representation of changes occurring inside socio-economic units by representing their attribute space trajectories as line features traversing a two-dimensional display space. A self-organizing map consisting of several thousand neurons is first used to create a high-resolution representation of attribute space in two dimensions. Then, multi-year observations are mapped onto the neural network and linked to form trajectories. This method is implemented for a data set containing 254 counties and 34 demographic variables. Various visual results are presented and discussed in the paper, from the visualizations of individual component planes to the mapping of voting behavior onto temporal trajectories.

[1]  Manfred M. Fischer,et al.  GeoComputational modelling : techniques and applications , 2001 .

[2]  André Skupin,et al.  From metaphor to method: cartographic perspectives on information visualization , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

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

[4]  Masahiro Takatsuka.  An application of the Self-Organizing Map and interactive 3-D visualization to geospatial data , 2001 .

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

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

[7]  André Skupin,et al.  VISUALIZING THE ICA – A CONTENT-BASED APPROACH , 2005 .

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

[9]  Sara Irina Fabrikant,et al.  Spatialization Methods: A Cartographic Research Agenda for Non-geographic Information Visualization , 2003 .

[10]  Sara Irina Fabrikant,et al.  Cognitively Plausible Information Visualization , 2005 .

[11]  A. Skupin.  SPATIAL METAPHORS FOR VISUALIZING INFORMATION SPACES , 1997 .

[12]  A. Skupin,et al.  A picture from a thousand words [information visualization] , 2004, Comput. Sci. Eng..

[13]  Samuel Kaski,et al.  Self organization of a massive text document collection , 1999 .

[14]  André Skupin,et al.  Cartographic Considerations for Map-Like Interfaces to Digital Libraries , 2001 .

[15]  Stan Openshaw,et al.  Census users' handbook , 1995 .

[16]  André Skupin,et al.  SPATIAL METAPHORS FOR VISUALIZING VERY LARGE DATA ARCHIVES , 2003 .

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

[18]  A. MacEachren,et al.  Research Challenges in Geovisualization , 2001, KN - Journal of Cartography and Geographic Information.

[19]  A. P. Brigham,et al.  Association of American Geographers , 1935, Nature.

[20]  André Skupin,et al.  The world of geography: Visualizing a knowledge domain with cartographic means , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[22]  David M. Mark,et al.  Features, Objects, and Other Things: Ontological Distinctions in the Geographic Domain , 2001, COSIT.

[23]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

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

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