(To run the following software packages, you need to have the Latest Java installed.)

SOMVIS: Multivariate Mapping and Visualization

SomVis Snapshot

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 the SOM 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.

Related Publication:
Guo, D., M. Gahegan, A.M. MacEachren, and B. Zhou. "Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach". Cartography and Geographic Information Science,Vol. 32, No. 2, 2005, pp. 113-132.

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.

Software Download:

  • Download the JAR file: somvis2.0.jar (Updated on 03-04-2010)
  • Download sample data files: 48states.zip . (Unzip to a local drive.)
  • Download : User Mannual .

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    • REDCAP: Regionalization with Constrained Clustering

    REDCAP Snapshot

    Regionalization is to divide a large set of spatial objects into a number of spatially contiguous regions while optimizing an objective function, which is normally a homogeneity (or heterogeneity) measure of the derived regions. This software package includes a family of six hierarchical regionalization methods, which are based on three agglomerative clustering approaches, including the single linkage, average linkage (ALK), complete linkage (CLK), and the WARD's method. The Ward's, Full-Order-CLK and the Full-Order-ALK methods all can produce significantly better results than existing regionalization appraoches. For a specific application context, it is recommended that these three methods are applied and compared.

    Related Publication:
    Guo, D. (2008). "Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (REDCAP)". International Journal of Geographical Information Science. 22(7), pp. 801-823.

    Guo, D., M. Gahegan, A.M. MacEachren, and B. Zhou. "Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach". Cartography and Geographic Information Science,Vol. 32, No. 2, 2005, pp. 113-132.

    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.

    Software Download:

  • Download the JAR file: redcap.jar (Updated on 03-04-2010)
  • Download sample data files: redcapdata.zip.
  • A brief user manual is available.


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    • VIS-STAMP: A Visualization System for Space-Time and Multivariate Patterns

    VISTAMP Snapshot

    VIS-STAMP is a geo-visual analytic software package that couples computational, visual, and cartographic methods for exploring and understanding spatio-temporal and multivariate data. It can help analysts investigate complex patterns across multivariate, spatial, and temporal dimensions via clustering, sorting, and visualization. Specifically, VIS-STAMP builds on a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices , and a geographic small multiple display.

    Related Publication:
    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.

    Guo, D. and M. Gahegan (2006). "Spatial Ordering and Encoding for Geographic Data Mining and Visualization", Journal of Intelligent Information Systems, 27(3), pp.243-266.

    Software Download:

  • Download the JAR file: visstamp.jar (Updated on 06-22-2009)
  • Download sample data files: visstamp_sampledata.zip.
  • A brief user manual is available.


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    • Flow Regionalization, Mapping, and Multivariate Visualization

    Related Publication:
    Guo, D. (2009). "Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data", IEEE Transactions on Visualization and Computer Graphics, 15(6), pp. 1041-1048 [Journal Link]

    Guo, D. (2009). "Greedy Optimization for Contiguity-Constrained Hierarchical Clustering", The Fourth International Workshop on Spatial and Spatiotemporal Data Mining, IEEE International Conference on Data Mining (ICDM 2009), Miami, FL.

    Software Download:

    (Comming soon!)

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    POLO: Program for Optimal Linear Ordering

    Polo Snapshot

    Linear ordering is to transform a set of data objects to a one dimensional order, which preserves multivariate or graph patterns as much possible. Linear ordering is widely used to discover patterns. When applied in geographical analysis, a linear ordering can be used to facilitate the detection of space-time (Guo et al. 2006), space-multivariate, and spatial interaction patterns (Guo 2007). This program (POLO) implements a linear ordering method based on the complete linkage hierarchical clustering and an optimal cluster ordering method. The method outperforms other existing ordering methods (see the evaluation provided in (Guo and Gahegan 2006)).

    Related Publication:
    Guo, D. and M. Gahegan (2006). "Spatial Ordering and Encoding for Geographic Data Mining and Visualization", Journal of Intelligent Information Systems, 27(3), pp.243-266.

    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.

    Guo, D. (2007). "Visual Analytics of Spatial Interaction Patterns for Pandemic Decision Support", International Journal of Geographical Information Science, 21(8), pp. 859-877.

    Software Download:

  • Download the JAR file: linearordering.jar (Updated on 04-08-2009)
  • Download the source code in Java: Polo.zip (Updated on 07-10-2009)
  • Download a sample multivarite data file: testdata.csv (right click and save).
  • Download a sample matrix data file: testmatrixdata.csv (right click and save).
  • A brief user manual is available.

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    • Acknowledgment: The material and software packages distributed on this website are based upon work in part supported by the National Science Foundation under Grant No. 0748813.

    Contact Information | ©2004-2009 Diansheng Guo