In this paper, we want to support the idea of using a family of indices of similarity, that we call the Simpson’s family indices or nestedness-based similarity functions (NBSF) for comparing operational geographic units (OGUs) (phytosociological relevés, animal traps, watersheds, administrative units, industrial areas, islands etc.). In these cases, similarity-dissimilarity depends, in addition to factors that induce replacement, also on factors that produce reduction or increment in the number of features within the same typology of OGUs (e.g., extent, reduction of fertility, anthropogenic pressure etc.). To keep into consideration this aspect, the indices are defined to be equal to 1 when the OGUs are completely nested. The results of the application to four simulated data sets prove that, when the data set does not show clear nested pattern, the use of NBSF produces results similar to the nestedness-free similarity functions, however since NBSF clearly detect nested situations, we should prefer their use in the circumstances where we think important to put in evidence nestedness. In conclusion, we support the idea of using both types of indices in order to improve the knowledge about the structure of any data set.

中文翻译：

---

关于使用基于嵌套的相似性函数（NBSF）来分类和/或排序操作地理单位（OGU）

在本文中，我们希望支持使用相似性指数系列的想法，我们称其为辛普森氏族指数或基于嵌套的相似性函数（NBSF），用于比较运营地理单位（OGU）（植物社会学相关性，动物陷阱，分水岭，行政单位，工业区，岛屿等）。在这些情况下，相似性-不相似性，除了诱发替换的因素外，还取决于在同一OGU类型内特征数量减少或增加的因素（例如，程度，生育力降低，人为压力等）。 。为了考虑这一方面，当OGU完全嵌套时，将索引定义为等于1。应用于四个模拟数据集的结果证明，当数据集没有显示清晰的嵌套模式时，NBSF的使用产生的结果与无嵌套相似度函数相似，但是，由于NBSF可以清楚地检测到嵌套情况，因此，在我们认为重要的是要提供证据嵌套性的情况下，我们应该优先使用它们。总之，我们支持使用两种类型的索引的想法，以提高对任何数据集结构的了解。