A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)

The research reported here integrates computational, visual and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivari...

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Bibliographic Details
Published inIEEE transactions on visualization and computer graphics Vol. 12; no. 6; pp. 1461 - 1474
Main Authors Guo, D., Chen, J., MacEachren, A.M., Liao, K.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.11.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Cartography
Companies
Computer Graphics
Computer Simulation
Data Interpretation, Statistical
Data mining
Data visualization
Design methodology
Displays
Geographic Information Systems
Geography
geovisualization
Humans
Information Storage and Retrieval - methods
Information visualization
Mathematical analysis
Mathematical models
Matrices
Methods
Multivariate Analysis
multivariate and spatio-temporal data
Order disorder
ordering
Pattern analysis
self-organizing map (SOM)
small multiples
Software
Sorting
Spatiotemporal phenomena
Studies
Time varying systems
User-Computer Interface
Visual
visual analytics
Visualization
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Summary:The research reported here integrates computational, visual and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial and temporal dimensions via clustering, sorting and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 contest data set, which contains time-varying, geographically referenced and multivariate data for technology companies in the US
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ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2006.84