Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Self-organizing maps
Self-Organizing Maps
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Scalable Pixel-based Visual Interfaces: Challenges and Solutions
IV '06 Proceedings of the conference on Information Visualization
Spherical self-organizing map using efficient indexed geodesic data structure
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)
International Journal of Geographical Information Science
Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
A visual exploration of mobile phone users, land cover, time, and space
Pervasive and Mobile Computing
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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.