We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological errors. The intrinsic lack of homogeneity in a network militates against the traditional methods of spatial statistics based on stationary processes. Topics include kernel density estimation, relative risk estimation, parametric and non-parametric modelling of intensity, second-order analysis using the K-function and pair correlation function, and point process model construction. An important message is that the choice of distance metric on the network is pivotal in the theoretical development and in the analysis of real data. Challenges for statistical computation are discussed and open-source software is provided.

我们回顾了有关统计方法的最新研究，这些方法用于分析线网络上点的空间模式，例如沿道路网络的道路事故位置。由于几何上的复杂性，对此类数据的分析极具挑战性，我们描述了几种常见的方法错误。网络中固有的缺乏同质性与传统的基于平稳过程的空间统计方法背道而驰。主题包括核密度估计，相对风险估计，强度的参数和非参数建模，使用K的二阶分析函数和对相关函数，以及点过程模型的构建。一个重要的信息是，网络上距离度量的选择对于理论发展和实际数据分析至关重要。讨论了统计计算的挑战，并提供了开源软件。