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Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis

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Discovery Science (DS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9356))

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Abstract

Knowledge discovery in scientific and research datasets is an extremely challenging problem due to the high dimensionality, heterogeneity, and complex relationships within the data. When these datasets also includes temporal and geospatial components, the challenges in analyzing the data become even more difficult. A number of visualization approaches have been developed and studied to support the exploration and analysis among such datasets, including parallel coordinate plots, dimensional subsetting, geovisualization, and multiple coordinated views. In this research, we combine and enhance these approaches in a system called Geo-Coordinated Parallel Coordinates (GCPC), with the goal of supporting interactive exploration, analytical reasoning, and knowledge discovery.

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Acknowledgements

The authors wish to thank the Too Big To Ignore (TBTI) project for their support and dataset they provided. This work was supported in part by grant from Social Sciences and Humanities Research Council (SSHRC) held by the second author.

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Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, Canada

    Maha El Meseery & Orland Hoeber

Authors
  1. Maha El Meseery
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  2. Orland Hoeber
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Corresponding author

Correspondence to Orland Hoeber .

Editor information

Editors and Affiliations

  1. University of Ottawa, Ottawa, Ontario, Canada

    Nathalie Japkowicz

  2. Faculty of Computer Science, Dalhousie University, HALIFAX, Nova Scotia, Canada

    Stan Matwin

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El Meseery, M., Hoeber, O. (2015). Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-24282-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24281-1

  • Online ISBN: 978-3-319-24282-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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