SOMVIS: Multivariate Mapping and Visualization
SomVis is an integrated software tool that is able to: (1) perform multivariate analysis, dimensional reduction, and data reduction (summarizing a large number of input data items in a moderate number of clusters) with the Self-Organizing Map (SOM); (2) encode theSOM result with a systematically designed color scheme; (3) visualize the multivariate patterns with a modified Parallel Coordinate Plot (PCP) display and a geographic map (GeoMap); and (4) support human interactions to explore and examine patterns. Computational and visual methods, once combined together, can mitigate each other’s weakness and collaboratively discover complex patterns in large geographic datasets, in an effective and efficient way.
Multivariate mapping of global climate change, see Jin and Guo. (2009) and Guo, D. (2009).
- Guo, D., M. Gahegan, A.M. MacEachren, and B. Zhou. (2005) "Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach". Cartography and Geographic Information Science,Vol. 32, No. 2, pp. 113-132. (Publications that cited this paper)
- Guo, D., J. Chen, A. M. MacEachren, and K. Liao (2006), "A Visualization System for Spatio- Temporal and Multivariate Patterns (VIS-STAMP)", IEEE Transactions on Visualization and Computer Graphics, 12(6), pp. 1461-1474. (Publications that cited this paper)
- Jin, H. & D. Guo. (2009) "Understanding Climate Change Patterns with Multivariate Geovisualization". In Proceedings - IEEE International Conference on Data Mining Workshops, 217-222. Miami, FL, USA: IEEE Press.
- Guo, D. (2009) "Multivariate Spatial Clustering and Geovisualization". In Geographic Data Mining and Knowledge Discovery, edited by H. J. Miller and J. Han. London and New York, Taylor & Francis, pp. 325-345.
Java is needed to run the following software. You can verify if Java is already installed on your computer at this link: http://java.com/en/download/installed.jsp.