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: 2.3
Depends: R (≥ 3.5.0)
Imports: graphics, grDevices, PBSmapping, raster, sp, spatstat.geom, spatstat.linnet, spatstat (≥ 2.0-0), spdep, stats, utils
Suggests: knitr, rmarkdown
Published: 2023-07-16
DOI: 10.32614/CRAN.package.DRHotNet
Author: Alvaro Briz-Redon
Maintainer: Alvaro Briz-Redon <alvaro.briz at uv.es>
License: GPL-2
NeedsCompilation: no
CRAN checks: DRHotNet results


Reference manual: DRHotNet.pdf


Package source: DRHotNet_2.3.tar.gz
Windows binaries: r-devel: DRHotNet_2.3.zip, r-release: DRHotNet_2.3.zip, r-oldrel: DRHotNet_2.3.zip
macOS binaries: r-release (arm64): DRHotNet_2.3.tgz, r-oldrel (arm64): DRHotNet_2.3.tgz, r-release (x86_64): DRHotNet_2.3.tgz, r-oldrel (x86_64): DRHotNet_2.3.tgz
Old sources: DRHotNet archive


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