A Markov chain Monte Carlo algorithm for spatial segmentation

Nishanthi Raveendran, Georgy Sofronov

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    Abstract

    Spatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these domains is one of the important problems in spatial data analysis. Spatial segmentation is used in many different fields including epidemiology, criminology, ecology, and economics. To solve this clustering problem, we propose to use the change-point methodology. In this paper, we develop a new spatial segmentation algorithm within the framework of the generalized Gibbs sampler. We estimate the average surface profile of binary spatial data observed over a two-dimensional regular lattice. We illustrate the performance of the proposed algorithm with examples using artificially generated and real data sets.
    Original languageEnglish
    Article number58
    Pages (from-to)1-14
    Number of pages14
    JournalInformation (Switzerland)
    Volume12
    Issue number2
    DOIs
    Publication statusPublished - Feb 2021

    Bibliographical note

    Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • Markov chain Monte Carlo
    • Gibbs sampler
    • spatial segmentation
    • binary data
    • Binary data
    • Spatial segmentation

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