landscapeR

A landscape simulator for R

https://github.com/dariomasante/landscaper

Science Score: 26.0%

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  • codemeta.json file
    Found codemeta.json file
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  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
  • Committers with academic emails
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  • Scientific vocabulary similarity
    Low similarity (14.7%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

A landscape simulator for R

Basic Info
  • Host: GitHub
  • Owner: dariomasante
  • Language: HTML
  • Default Branch: master
  • Size: 3.03 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 1
  • Open Issues: 2
  • Releases: 5
Created over 10 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog

README.md

landscapeR

A landscape simulator for R. This package is aimed at simulating categorical landscapes on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). The purpose is to provide a tool to tweak or create the landscape while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscape in a theorethical space. All basic GIS operations are handled by the raster package.

URL: https://cran.r-project.org/package=landscapeR
Reference manual: landscapeR.pdf

Citation: Thomas, A., Masante, D., Jackson, B., Cosby, B., Emmett, B., Jones, L. (2020). Fragmentation and thresholds in hydrological flow-based ecosystem services. Ecological Applications. https://doi.org/10.1002/eap.2046

To install, open a R session and select 'landscapeR' from the packages list, or type in the console: r install.packages("landscapeR") Alternatively to install from source: - download the source file (.tar.gz) to the R working directory (or any other directory) - start an R session - run the following commands in the console: ``` r

Install the required packages

install.packages("raster", dependencies=T, clean=T)

Install landscapeR (full path to the file, if not in the R working directory)

install.packages("~/landscapeR_1.3.tar.gz", repos = NULL, type="source") Here it follows a set of examples, using landscapeR functions to generate various landscape configurations. Similar examples are showed in the [vignette](http://htmlpreview.github.com/?https://github.com/dariomasante/landscapeR/blob/master/landscapeR.html). Let's start loading the required packages and making an empty landscape (by transforming a matrix into a geographical obkect): {r, message=FALSE, warning=FALSE} library(landscapeR)

Create an empty landscape

library(terra) m = matrix(0, 33, 33) r = rast(m) ext(r) = c(0, 10, 0, 10) ```

makePatch

This is the basic function to create a single patch. For instance: {r, eval=FALSE} rr = makePatch(r, size=500, rast=TRUE) plot(rr)

Some more features can be specified about the patch. For example, the following will create a patch with value 3, starting from the centre cell of the raster: {r} patchSize = 500 newVal = 3 centre = 545 rr = makePatch(r, patchSize, centre, val=newVal, rast=TRUE) plot(rr)

Forbidden cells can be specified by value, so the patch will occupy only the allowed background. The following will generate a new patch with value 5 and size 100 inside the existing patch: {r, warning=FALSE} rr = makePatch(rr, 100, bgr=newVal, rast=TRUE, val=5) plot(rr)

makeClass

makeClass generates a group of patches, as specified by its arguments. Example: {r, warning=FALSE} num = 5 size = 15 rr = makeClass(r, num, size) plot(rr)

Patches are allowed to be contiguous, so they may appear as a single patch in those instances: {r, warning=FALSE} num = 75 size = 10 rr = makeClass(r, num, size) plot(rr)

Each patch size and seed starting position can be specified as well: {r} num = 5 size = c(1,5,10,20,50) pts = c(1, 33, 1089, 1057, 545) rr = makeClass(r, num, size, pts) plot(rr)

expandClass

Expand (and shrinks) classes starting from an existing landscape. Building on the previous: {r} rr = expandClass(rr, 1, 250) plot(rr)

This function can be used to mimic shapes, by providing a skeleton: {r} m[,17] = 1 r = rast(m) ext(r) = c(0, 10, 0, 10) par(mfrow=c(1,2)) plot(r) rr = expandClass(r, 1, 200) plot(rr)

makeLine

Create a linear patch, setting direction and convolution. The higher the convolution degree, the weaker the linear shape (and direction). ```{r} par(mfrow=c(1,2)) rr = makeLine(r, size=50, direction=90, rast=TRUE, spt=545, convol=0.25) plot(rr)

plot(makeLine(r, size=50, direction=90, rast=TRUE, spt=545, convol=0.6)) ```

GitHub Events

Total
  • Issue comment event: 3
  • Push event: 4
  • Pull request event: 4
Last Year
  • Issue comment event: 3
  • Push event: 4
  • Pull request event: 4

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 55
  • Total Committers: 3
  • Avg Commits per committer: 18.333
  • Development Distribution Score (DDS): 0.436
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Dario Masante d****e 31
dariomasante d****e@g****m 22
MASANTE Dario d****e@e****u 2
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: over 2 years ago

All Time
  • Total issues: 2
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • dariomasante (2)
Pull Request Authors
  • LMurphy186232 (2)
Top Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 324 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 8
  • Total maintainers: 1
cran.r-project.org: landscapeR

Categorical Landscape Simulation Facility

  • Versions: 8
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 324 Last month
Rankings
Dependent repos count: 19.9%
Forks count: 28.4%
Dependent packages count: 28.5%
Stargazers count: 35.0%
Average: 35.5%
Downloads: 65.7%
Maintainers (1)
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • Rcpp >= 1.0.3 imports
  • raster * imports
  • knitr * suggests
  • markdown * suggests