论文标题
使用最近的邻居量在线性网络上的点过程中的特征检测
Feature detection in point processes on linear networks using nearest neighbour volumes
论文作者
论文摘要
我们考虑在线性网络上的点过程中存在混乱中的特征检测问题。我们将先前研究中开发的分类方法扩展到了这种更复杂的几何环境，在这种情况下，点过程变化的经典属性和数据可视化不是直观的。我们在线性网络中使用k-th最近的邻居量分布进行此方法。结果，我们的方法适合分析由特征和混乱组成的点模式，作为在同一线性网络上的两个叠加泊松过程。为了说明该方法，我们提出了道路交通事故的模拟和例子，这些事故导致哥伦比亚两个城市受伤或死亡。
We consider the feature detection problem in the presence of clutter in point processes on linear networks. We extend the classification method developed in previous studies to this more complex geometric context, where the classical properties of a point process change and data visualization are not intuitive. We use the K-th nearest neighbour volumes distribution in linear networks for this approach. As a result, our method is suitable for analysing point patterns consisting of features and clutter as two superimposed Poisson processes on the same linear network. To illustrate the method, we present simulations and examples of road traffic accidents that resulted in injuries or deaths in two cities in Colombia.
