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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