An alternative map of the United States based on an n-dimensional model of geographic space

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.

► Integration of diverse environmental and population variables into an n-dimensional geographic data set. ► Detailed visual examination of training effects for a very large self-organizing map (SOM). ► Spatialization of more than 200,000 U.S. Census block groups in a SOM consisting of 250,000 neurons. ► Creation of a comprehensive two-dimensional base map representing n-dimensional attribute space. ► Novel juxtapositions of geographic data visualized in attribute space and geographic space.