DRHotNet - Differential Risk Hotspots in a Linear Network
Performs the identification of differential risk hotspots
(Briz-Redon et al. 2019) <doi:10.1016/j.aap.2019.105278> along
a linear network. Given a marked point pattern lying on the
linear network, the method implemented uses a
network-constrained version of kernel density estimation
(McSwiggan et al. 2017) <doi:10.1111/sjos.12255> to approximate
the probability of occurrence across space for the type of
event specified by the user through the marks of the pattern
(Kelsall and Diggle 1995) <doi:10.2307/3318678>. The goal is to
detect microzones of the linear network where the type of event
indicated by the user is overrepresented.
