In the field of spatial statistics, the analysis of point patterns on linear networks has received recent attention. This involves the analysis of points in a spatial domain (most often a Euclidean space, but also modelled on non-convex polygon-type windows) that coincide on a linear network. In spatial statistics a linear network is modelled as a set of lines in a spatial domain connected at their ends or intersecting along their lengths, resembling characteristics of mathematical graph theory. Recent techniques focus on spatial points that fall on the linear network, namely in the network space. Limited research has been conducted for spatial data that falls on the Euclidean space the linear network is embedded in; points in the vicinity of the network and not necessarily lying directly on the linear network. There has been a recent increase in spatial statistics methodology for spatial linear networks. The focus has been for points lying on a linear network, such as density estimation of points on a network, distance metrics and second-order analysis, space-time analysis of points on a network, regression for points on a network, summary statistics, directed networks, and a review. The review by Baddeley et al highlights important issues when dealing with a network space. The first is the intrinsic lack of homogeneity of the lines in the network, a common assumption in spatial analysis. The spatial analysis done must therefore account for this with a suitable distance metric. Computational challenges will also be encountered. The researchers listed in the references are big change makers in spatial statistics, indicating the direction of research currently and only recently. Directional statistics is a branch of statistics that deals with data that are measured on a circular scale, such as angles, directions, or phases. In contrast to traditional statistics, which is concerned with data on a linear scale, directional statistics takes into account the periodicity of the circular scale. The classical statistical models are not capable of handling the periodicity of directional data and new approaches need to be developed. There is very little literature about using directional methods in spatial statistics. The only available literature is about spatial and spatio-temporal circular processes with application in meteorology, in particular wave direction. The application of directional statistics in spatial linear networks has yet to be explored.