In this mini-dissertation we discuss the spatial relationship between point processes and a linear network.
As a starting point, we discuss basic spatial point processes and tests for first-order homogeneity. Following
that, we discuss second-order properties of point processes in the form of Ripley's K-function for unmarked
point patterns and the cross-K function for marked point patterns. We then get to the main focus of this
mini-dissertation, that is, the spatial relationship between points and linear structures, particularly linear
networks. Recently developed is a method to characterise the spatial relationship between points and
linear networks by Comas et al. , similar to Ripley's K-function for point-to-point relationships. The
non-stationarity of a linear network is of particular interest in how it affects the measurement of this spatial
relationship, which has not been explicitly investigated in the literature before. To investigate this we
consider the Poisson line process and how one might simulate a non-stationary line process. Furthermore,
we discuss a mechanism to extend tests of first-order homogeneity of point patterns to line patterns. The
non-stationary line process is used to model linear networks in the simulations conducted to determine
the effect of this non-stationarity on the developed method, which was not covered in the original article
. The methodology is developed and tested on a real data set.
Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020.