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    A framework for interpolating scattered data using space-filling curves

    Weston, David J. (2016) A framework for interpolating scattered data using space-filling curves. In: Boström, H. and Knobbe, A. and Soares, C. and Papapetrou, P. (eds.) Advances in Intelligent Data Analysis XV. Lecture Notes in Computer Science 9897. New York, U.S.: Springer International Publishing, pp. 249-260. ISBN 9783319463483.

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    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.


    Item Type: Book Section
    Additional Information: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings. The final publication is available at Springer via the link above.
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Data Analytics, Birkbeck Institute for
    Depositing User: David Weston
    Date Deposited: 23 Nov 2016 10:59
    Last Modified: 09 Aug 2023 12:38


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