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

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Hannah M. Director, Adrian E. Raftery
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引用次数: 0

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.

星形多边形和近似星形多边形的物理边界的轮廓模型
空间场的边界将具有特定特征的区域与周围的背景区域分开。从插值空间场中识别边界线的方法已经建立。如何对空间点的连通序列进行建模一直受到较少的关注。物理边界需要这样的模型。例如,在北冰洋,大片连续的区域被海冰或冰冻的海水覆盖。我们将冰边缘轮廓定义为空间点的有序序列,这些点围绕存在海冰的一组连续网格框连接形成一条线。极地科学家需要描述这片连续区域在当前和历史数据以及未来气候变化情景下的表现。我们引入了高斯星形轮廓模型(GSCM),用于将边界表示为空间点(如海冰边缘)的连接序列。GSCMs从固定的起始点出发,通过不同方向的距离生成集生成空间点序列。GSCM可以应用于包围星形多边形或近似星形多边形区域的轮廓。引入度量来评估多边形偏离星形的程度。仿真研究表明了GSCM在不同情况下的性能。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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