Package: roads 1.2.1

Josie Hughes

roads: Road Network Projection

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using 'igraph' <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

Authors:Sarah Endicott [aut], Kyle Lochhead [aut], Josie Hughes [aut, cre], Patrick Kirby [aut], Her Majesty the Queen in Right of Canada as represented by the Minister of the Environment [cph], Province of British Columbia [cph]

roads_1.2.1.tar.gz
roads_1.2.1.zip(r-4.7-any)roads_1.2.1.zip(r-4.6-any)roads_1.2.1.zip(r-4.5-any)
roads_1.2.1.tgz(r-4.6-any)roads_1.2.1.tgz(r-4.5-any)
roads_1.2.1.tar.gz(r-4.7-any)roads_1.2.1.tar.gz(r-4.6-any)
roads_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
roads/json (API)

# Install 'roads' in R:
install.packages('roads', repos = c('https://landscitech.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/landscitech/roads/issues

Pkgdown/docs site:https://landscitech.github.io

Datasets:

On CRAN:

Conda:

6.16 score 5 stars 32 scripts 531 downloads 9 exports 33 dependencies

Last updated from:7fb87c74b0. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK267
source / vignettesOK238
linux-release-x86_64OK258
macos-release-arm64OK267
macos-oldrel-arm64OK198
windows-develOK197
windows-releaseOK225
windows-oldrelOK210
wasm-releaseOK143

Exports:getDistFromSourcegetLandingsFromTargetgradePenaltyFnplotRoadsprepExDataprojectRoadsrasterizeLinerasterToLineSegmentssimpleCostFn

Dependencies:classclassIntclicpp11data.tableDBIdplyre1071genericsglueigraphKernSmoothlatticelifecyclemagrittrMASSMatrixpillarpkgconfigproxyR6Rcpprlangs2sfterratibbletidyselectunitsutf8vctrswithrwk

roads Package
Introduction | Setup | Resource development scenario | 1. Weights Raster and Weight Function | 2. Existing road network layer | 3. Landings layer(s) | Output format | Simulation of new roads development | Simulation methods | 1. Snapping | 2. Least Cost Paths (LCP) | 3. Iterative Least Cost Paths (ILCP) | 4. Minimum Spanning Tree (MST) with Least Cost Paths (LCP) | One-time versus multi-temporal simulation | One-time simulation | Multi-temporal simulation | Multiple landings per harvest block | Note

Last update: 2026-01-16
Started: 2021-11-09

Using the grade penalty function
References

Last update: 2024-06-26
Started: 2024-05-16

Reconstruct road development history

Last update: 2024-03-26
Started: 2022-10-31

Readme and manuals

Help Manual

Help pageTopics
Data from the CLUS exampleCLUSexample
Grade penalty example datadem_example
Demonstration set of 10 input scenariosdemoScen
Get landing target points within harvest blocksgetLandingsFromTarget
Grade penalty edge weight functiongradePenaltyFn
Plot projected roadsplotRoads
Prepare example dataprepExData
Project road networkprojectRoads projectRoads,ANY,ANY,ANY,ANY,ANY,ANY,ANY,ANY,list-method projectRoads,ANY,ANY,ANY,ANY,ANY,ANY,ANY,ANY,missing-method
Convert raster to linesrasterToLineSegments
Simple cost edge weight functionsimpleCostFn