Are There Better Alternatives to Standard Rose Diagrams
Are There Better Alternatives to Standard Rose Diagrams
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标准玫瑰图有更好的替代品吗
DOI：
10.1306/2dc408fc-0e47-11d7-8643000102c1865d
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发表时间：
2000
影响因子：
2
通讯作者：
N. Wells
中科院分区：
文献类型：
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作者：
N. Wells
摘要标准玫瑰图是一种最受欢迎的描述方向的方法，因为它们易于理解，但它们有两个严重的问题。首先，关于类宽度和起始位置的任意决定可以显著地改变结果图，尽管变化的程度被低估了。其次，当玫瑰图被正确地缩放到类频率的平方根时，它们可能难以评估。值得考虑的可能解决方案包括：（1）用点的射线表示1°类（“点图”，允许线性缩放），以及（2）通过将数据集的所有可能玫瑰图组合到一个图中来克服任意性（“求和数据集”）。解决方案#1可以通过为每个数据绘制一个点（“电晕点图”）或为高于或低于平均隶属度的每个值绘制一个点（“平均偏差点图”）来工作。这两种图类型都可以显示原始数据或汇总数据。绘制一度类宽图并将所有可能的玫瑰图求和（组合），提供了方向图的唯一非任意版本，但求和图稍微平滑了数据，从而额外加权了相似但不相同的数据点的集群。我的建议是发布一个原始数据的点图，以及另一个汇总结果。均值-偏差图和因子平均求和数据集配合得特别好。
ABSTRACT Standard rose diagrams are a favorite method of depicting orientations because of their ease of comprehension, but they are known to have two serious problems. First, arbitrary decisions about class width and starting position can dramatically alter the resulting diagram, although the degree of variation has been underappreciated. Second, when rose diagrams are correctly scaled to the square root of the class frequency, they can be awkward to evaluate. Possible solutions that deserve consideration include (1) representing 1° classes with rays of dots ("dot diagrams", allowing linear scaling), and (2) overcoming arbitrariness by combining all possible rose diagrams for a dataset into one diagram ("summed datasets"). Solution #1 can work by plotting either one dot per datum ("corona dot diagrams") or one dot for each value above or below the mean membership ("mean-deviation dot diagrams"). Both plot types can show either raw or summed data. Drawing one-degree class-width diagrams and summing (combining) all possible rose diagrams offers the only non-arbitrary versions of orientation diagrams, but summing diagrams smoothes the data slightly, thereby additionally weighting clusters of similar but not identical data points. My recommendation is to publish a dot plot of the raw data, and another of the summed results. Mean-deviation diagrams and factor-averaged summed datasets work together particularly well.