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Contour models for physical boundaries enclosing star‐shaped and approximately star‐shaped polygons

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  • Hannah M. Director
  • Adrian E. Raftery

Abstract

Boundaries on spatial fields divide regions with particular features from surrounding background areas. Methods to identify boundary lines from interpolated spatial fields are well established. Less attention has been paid to how to model sequences of connected spatial points. Such models are needed for physical boundaries. For example, in the Arctic ocean, large contiguous areas are covered by sea ice, or frozen ocean water. We define the ice edge contour as the ordered sequences of spatial points that connect to form a line around set(s) of contiguous grid boxes with sea ice present. Polar scientists need to describe how this contiguous area behaves in present and historical data and under future climate change scenarios. We introduce the Gaussian Star‐shaped Contour Model (GSCM) for modelling boundaries represented as connected sequences of spatial points such as the sea ice edge. GSCMs generate sequences of spatial points via generating sets of distances in various directions from a fixed starting point. The GSCM can be applied to contours that enclose regions that are star‐shaped polygons or approximately star‐shaped polygons. Metrics are introduced to assess the extent to which a polygon deviates from star‐shapedness. Simulation studies illustrate the performance of the GSCM in different situations.

Suggested Citation

  • Hannah M. Director & Adrian E. Raftery, 2022. "Contour models for physical boundaries enclosing star‐shaped and approximately star‐shaped polygons," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1688-1720, November.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1688-1720
    DOI: 10.1111/rssc.12592
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    References listed on IDEAS

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    1. Joshua P. French, 2014. "Confidence regions for the level curves of spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 25(7), pages 498-512, November.
    2. G. M. Jacquez & S. Maruca & M.-J. Fortin, 2000. "From fields to objects: A review of geographic boundary analysis," Journal of Geographical Systems, Springer, vol. 2(3), pages 221-241, September.
    3. Shengde Liang & Sudipto Banerjee & Bradley P. Carlin, 2009. "Bayesian Wombling for Spatial Point Processes," Biometrics, The International Biometric Society, vol. 65(4), pages 1243-1253, December.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
    6. Banerjee, Sudipto & Gelfand, Alan E., 2006. "Bayesian Wombling: Curvilinear Gradient Assessment Under Spatial Process Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1487-1501, December.
    7. Joshua P. French & Jennifer A. Hoeting, 2016. "Credible regions for exceedance sets of geostatistical data," Environmetrics, John Wiley & Sons, Ltd., vol. 27(1), pages 4-14, February.
    8. Julienne Stroeve & Mark Serreze & Marika Holland & Jennifer Kay & James Malanik & Andrew Barrett, 2012. "The Arctic’s rapidly shrinking sea ice cover: a research synthesis," Climatic Change, Springer, vol. 110(3), pages 1005-1027, February.
    9. David Bolin & Finn Lindgren, 2015. "Excursion and contour uncertainty regions for latent Gaussian models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 85-106, January.
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