Identifying clusters in spatial data via sequential importance sampling

Nishanthi Raveendran, Georgy Sofronov*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. we consider a Sequential Importance Sampling approach to change-point methodology using Monte Carlo simulation to find estimates of change-points as well as parameters on each domain. Numerical experiments illustrate the effectiveness of the approach. We applied this method to artificially generated data set and compared with the results obtained via binary segmentation procedure. We also provide example with real data set to illustrate the usefulness of this method.

    Original languageEnglish
    Title of host publicationRecent advances in computational optimization
    Subtitle of host publicationresults of the Workshop on Computational Optimization WCO 2017
    EditorsStefka Fidanova
    Place of PublicationSwitzerland
    PublisherSpringer-VDI-Verlag GmbH & Co. KG
    Chapter10
    Pages175-189
    Number of pages15
    ISBN (Electronic)9783319996486
    ISBN (Print)9783319996479
    DOIs
    Publication statusPublished - 1 Jan 2019

    Publication series

    NameStudies in Computational Intelligence
    Volume795
    ISSN (Print)1860-949X

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    Raveendran, N., & Sofronov, G. (2019). Identifying clusters in spatial data via sequential importance sampling. In S. Fidanova (Ed.), Recent advances in computational optimization: results of the Workshop on Computational Optimization WCO 2017 (pp. 175-189). (Studies in Computational Intelligence; Vol. 795). Switzerland: Springer-VDI-Verlag GmbH & Co. KG. https://doi.org/10.1007/978-3-319-99648-6_10