research-article

Using Multivariate Clustering to Characterize Ecoregion Borders

Authors:

William W. Hargrove,

Forrest M. HoffmanAuthors Info & Claims

Computing in Science and Engineering, Volume 1, Issue 4

Pages 18 - 25

https://doi.org/10.1109/5992.774837

Published: 01 July 1999 Publication History

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Abstract

The authors' clustering technique unambiguously locates, characterizes, and visualizes ecoregions and their borders. When coded with similarity colors, it can produce planar map views with sharpness contours that are visually rich in ecological information and represent integrated visualizations of complex and massive environmental data sets.

References

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W.W. Hargrove and R.J. Luxmoore, "A New High-Resolution National Map of Vegetation Ecoregions Produced Empirically Using Multivariate Spatial Clustering," 1998; www.esri.com/library/userconf/proc98/PROCEED/TO350/PAP333/P333.htm (current June 1999).

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W.W. Hargrove and F.M. Hoffman, "National Clustering," 1998; www.esd.ornl.gov/projects/clustering/ (current June 1999).

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Stepinski TNiesterowicz JJasiewicz J(2015)Pattern-based Regionalization of Large Geospatial Datasets Using Complex Object-based Image AnalysisProcedia Computer Science10.1016/j.procs.2015.05.49151:C(2168-2177)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.procs.2015.05.491
Thomee BRae ASchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Uncovering locally characterizing regions within geotagged dataProceedings of the 22nd international conference on World Wide Web10.1145/2488388.2488500(1285-1296)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2488388.2488500
Steinhaeuser KChawla NGanguly A(2011)Comparing predictive power in climate dataProceedings of the 12th international conference on Advances in spatial and temporal databases10.5555/2035253.2035259(39-55)Online publication date: 24-Aug-2011
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Using Multivariate Clustering to Characterize Ecoregion Borders
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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cover image Computing in Science and Engineering

Computing in Science and Engineering Volume 1, Issue 4

July 1999

84 pages

ISSN:1521-9615

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IEEE Educational Activities Department

United States

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Published: 01 July 1999

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Stepinski TNiesterowicz JJasiewicz J(2015)Pattern-based Regionalization of Large Geospatial Datasets Using Complex Object-based Image AnalysisProcedia Computer Science10.1016/j.procs.2015.05.49151:C(2168-2177)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.procs.2015.05.491
Thomee BRae ASchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Uncovering locally characterizing regions within geotagged dataProceedings of the 22nd international conference on World Wide Web10.1145/2488388.2488500(1285-1296)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2488388.2488500
Steinhaeuser KChawla NGanguly A(2011)Comparing predictive power in climate dataProceedings of the 12th international conference on Advances in spatial and temporal databases10.5555/2035253.2035259(39-55)Online publication date: 24-Aug-2011
https://dl.acm.org/doi/10.5555/2035253.2035259
Steinhaeuser KChawla NGanguly A(2010)An exploration of climate data using complex networksACM SIGKDD Explorations Newsletter10.1145/1882471.188247612:1(25-32)Online publication date: 9-Nov-2010
https://dl.acm.org/doi/10.1145/1882471.1882476
Steinhaeuser KChawla NGanguly AOmitaomu OGanguly AGama JVatsavai RChawla NGaber M(2009)An exploration of climate data using complex networksProceedings of the Third International Workshop on Knowledge Discovery from Sensor Data10.1145/1601966.1601973(23-31)Online publication date: 28-Jun-2009
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Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index

Using Linked Micromap Plots to Characterize Omernik Ecoregions

Visualizing the Impact of Geographical Variations on Multivariate Clustering

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