DRHotNet

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.