{"title":"Contour models for physical boundaries enclosing star-shaped and approximately star-shaped polygons","authors":"Hannah M. Director, Adrian E. Raftery","doi":"10.1111/rssc.12592","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"71 5","pages":"1688-1720"},"PeriodicalIF":1.0000,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Royal Statistical Society Series C-Applied Statistics","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/rssc.12592","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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.
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.