This function generates simulated realisations of
the Switzer-type point process on a network,
as described in Baddeley et al (2017).
The linear network is first divided into pieces by a random
mechanism:
if cuts="points",
a Poisson process of breakpoints with intensity lambdacut
is generated on the network, and these breakpoints separate the
network into connected pieces.
if cuts="lines", a Poisson line process in the plane
with intensity lambdacut is generated; these lines divide
space into tiles; the network is divided into subsets associated
with the tiles. Each subset may not be a connected sub-network.
In each piece of the network, a random intensity is generated
using the random variable generator rintens (the default is
a negative exponential random variable with rate 1). Given the
intensity value, a Poisson process is generated with the specified
intensity.
The intensity of the final process is determined by the mean
of the values generated by rintens. If rintens=rexp (the
default), then the parameter rate specifies the inverse of the
intensity.