Differential Risk Hotspots in a Linear Network

Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) 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) 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) . The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented.


Reference manual

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1.3 by Alvaro Briz-Redon, a month ago

Browse source code at https://github.com/cran/DRHotNet

Authors: Alvaro Briz-Redon

Documentation:   PDF Manual  

GPL-2 license

Imports graphics, grDevices, maptools, PBSmapping, raster, sp, spatstat.geom, spatstat.core, spatstat.linnet, spatstat, spdep, stats, utils

Suggests knitr, rmarkdown

See at CRAN