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An alternative map of the United States based on an n-dimensional model of geographic space
- Source :
-
Journal of Visual Languages & Computing . Aug2011, Vol. 22 Issue 4, p290-304. 15p. - Publication Year :
- 2011
-
Abstract
- Abstract: Geographic features have traditionally been visualized with fairly high amount of geometric detail, while relationships among these features in attribute space have been represented at a much coarser resolution. This limits our ability to understand complex high-dimensional relationships and structures existing in attribute space. In this paper, we present an alternative approach aimed at creating a high-resolution representation of geographic features with the help of a self-organizing map (SOM) consisting of a large number of neurons. In a proof-of-concept implementation, we spatialize 200,000+ U.S. Census block groups using a SOM consisting of 250,000 neurons. The geographic attributes considered in this study reflect a more holistic representation of geographic reality than in previous studies. The study includes 69 attributes regarding population statistics, land use/land cover, climate, geology, topography, and soils. This diversity of attributes is informed by our desire to build a comprehensive two-dimensional base map of n-dimensional geographic space. The paper discusses how standard GIS methods and neural network processing are combined towards the creation of an alternative map of the United States. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 1045926X
- Volume :
- 22
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Languages & Computing
- Publication Type :
- Academic Journal
- Accession number :
- 63185227
- Full Text :
- https://doi.org/10.1016/j.jvlc.2011.03.004