Bipartite projections have become a common way to measure spatial networks. They are now used in many subfields of geography, and are among the most common ways to measure the world city network, where intercity links are inferred from firm co-location patterns. Bipartite projections are attractive because a network can be indirectly inferred from readily available data. However, spatial bipartite projections are difficult to analyze because the links in these networks are weighted, and larger weights do not necessarily indicate stronger or more important connections. Methods for extracting the backbone of bipartite projections offer a solution by using statistical models for identifying the links that have statistically significant weights. In this article, we introduce the open-source backbone R package, which implements several backbone models, and demonstrate its key features by using it to measure a world city network.

中文翻译：

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使用 R 骨干包从二分投影分析空间网络

二分投影已成为测量空间网络的常用方法。它们现在被用于地理学的许多子领域，并且是衡量世界城市网络的最常用方法之一，其中城际联系是从牢固的协同定位模式推断出来的。二分预测很有吸引力，因为可以从现成的数据中间接推断出网络。然而，空间二分投影很难分析，因为这些网络中的链接是加权的，较大的权重并不一定表示更强或更重要的连接。提取二分投影主干的方法通过使用统计模型来识别具有统计显着权重的链接提供了一种解决方案。在本文中，我们介绍了开源主干 R包，它实现了几个主干模型，并通过使用它来测量世界城市网络来展示其关键特性。