Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the second-order properties of the point pattern, and it may be done locally by using local second-order characteristics (LISA). Some properties of this local structure solve the problem of classification into feature and clutter points. This paper proposes an estimator for local pair correlation LISA functions, discusses some of its properties and considers a particular distance to measure dissimilarities. Two classification procedures to separate feature from clutter points are described. One of them adopts multidimensional scaling and support vector machines, and the other employs bagged clustering. Simulations demonstrate the performance of the method, and it is applied to a dataset concerning earthquakes in a seismic nest located in Colombia.

空间点图案分析通常涉及在观察窗中也存在噪声的特征识别。传统上，这种识别从研究点模式的二阶特性开始，并且可以使用局部二阶特征（LISA）在本地完成。这种局部结构的某些特性解决了分类为特征点和杂乱点的问题。本文提出了一种局部对相关LISA函数的估计器，讨论了它的一些特性，并考虑了测量不相似性的特定距离。描述了两种将特征与混乱点分开的分类程序。其中一种采用多维缩放和支持向量机，另一种采用袋装聚类。仿真证明了该方法的性能，