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.

Version: 1.1
Depends: R (≥ 3.5.0)
Imports: graphics, grDevices, maptools, PBSmapping, raster, sp, spatstat, spdep, stats, utils
Suggests: knitr, rmarkdown
Published: 2019-09-15
Author: Alvaro Briz-Redon
Maintainer: Alvaro Briz-Redon <alvaro.briz at uv.es>
License: GPL-2
NeedsCompilation: no
CRAN checks: DRHotNet results

Downloads:

Reference manual: DRHotNet.pdf
Package source: DRHotNet_1.1.tar.gz
Windows binaries: r-devel: DRHotNet_1.0.zip, r-release: DRHotNet_1.0.zip, r-oldrel: DRHotNet_1.0.zip
OS X binaries: r-release: DRHotNet_1.0.tgz, r-oldrel: DRHotNet_1.0.tgz
Old sources: DRHotNet archive

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