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A Framework for Interpolating Scattered Data Using Space-Filling Curves

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Advances in Intelligent Data Analysis XV (IDA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9897))

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Abstract

The analysis of spatial data occurs in many disciplines and covers a wide variety activities. Available techniques for such analysis include spatial interpolation which is useful for tasks such as visualization and imputation. This paper proposes a novel approach to interpolation using space-filling curves. Two simple interpolation methods are described and their ability to interpolate is compared to several interpolation techniques including natural neighbour interpolation. The proposed approach requires a Monte-Carlo step that requires a large number of iterations. However experiments demonstrate that the number of iterations will not change appreciably with larger datasets.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Systems, Birkbeck College, University of London, London, UK

    David J. Weston

Authors
  1. David J. Weston
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Correspondence to David J. Weston .

Editor information

Editors and Affiliations

  1. Stockholm University , Stockholm, Sweden

    Henrik Boström

  2. Leiden University , Leiden, The Netherlands

    Arno Knobbe

  3. University of Porto , Porto, Portugal

    Carlos Soares

  4. Stockholm University , Stockholm, Sweden

    Panagiotis Papapetrou

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Weston, D.J. (2016). A Framework for Interpolating Scattered Data Using Space-Filling Curves. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_22

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  • DOI: https://doi.org/10.1007/978-3-319-46349-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46348-3

  • Online ISBN: 978-3-319-46349-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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