Self-organizing maps
Artificial Intelligence in Geography
Artificial Intelligence in Geography
Visual Explorations in Finance
Visual Explorations in Finance
On Geometry and Transformation in Map-Like Information Visualization
Visual Interfaces to Digital Libraries [JCDL 2002 Workshop]
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
A cartographic approach to visualizing conference abstracts
IEEE Computer Graphics and Applications
An alternative map of the United States based on an n-dimensional model of geographic space
Journal of Visual Languages and Computing
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In recent years, the proliferation of multi-temporal census data products and the increased capabilities of geospatial analysis and visualization techniques have encouraged longitudinal analyses of socioeconomic census data. Traditional cartographic methods for illustrating socioeconomic change tend to rely either on comparison of multiple temporal snapshots or on explicit representation of the magnitude of change occurring between different time periods. This paper proposes to add another perspective to the visualization of temporal change, by linking multi-temporal observations to a geometric configuration that is not based on geographic space, but on a spatialized representation of n-dimensional attribute space. The presented methodology aims at providing a cognitively plausible representation of changes occurring inside census areas by representing their attribute space trajectories as line features traversing a two-dimensional display space. First, the self-organizing map (SOM) method is used to transform n-dimensional data such that the resulting two-dimensional configuration can be represented with standard GIS data structures. Then, individual census observations are mapped onto the neural network and linked as temporal vertices to represent attribute space trajectories as directed graphs. This method is demonstrated for a data set containing 254 counties and 32 demographic variables. Various transformations and visual results are presented and discussed in the paper, from the visualization of individual component planes and trajectory clusters to the mapping of different attributes onto temporal trajectories.