DRHotNet: Differential Risk Hotspots in a Linear Network

Performs the identification of differential risk hotspots given a marked point pattern (Diggle 2013) <doi:10.1201/b15326> lying on a linear network (Baddeley, Rubak and Turner 2015) <doi:10.1201/b19708>. The algorithm makes use of a network-constrained version of kernel density estimation (McSwiggan, Baddeley and Nair 2017) <doi:10.1111/sjos.12255>, and then follows a statistical approach to approximate the probability of ocurrence 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 final goal is to detect microzones of the road network where the type of event indicated by the user is overrepresented, considering the network structure provided.

Version: 1.0
Depends: R (≥ 3.5.0)
Imports: spatstat, spdep, raster, maptools, sp, utils, stats
Suggests: knitr, rmarkdown
Published: 2019-06-14
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_1.0.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


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