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https://github.com/wpgp/popRF

Random Forest-informed Population Disaggregation R package
https://github.com/wpgp/popRF

disaggregation population random-forest

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Random Forest-informed Population Disaggregation R package

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## popRF: Random Forest-informed Population Disaggregation R package



High resolution, recent data on human population distributions are important for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets.  

`popRF` is a population modelling R package utilizing Random Forests to inform a dasymetric redistribution of census-based population count data. A description of using Random Forests machine learning method  in `popRF` is described in [Stevens et al](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107042). 



### Installation

The `popRF` package can be installed directly from Github.



``` r

install.packages("devtools")

devtools::install_github("wpgp/popRF")

```



### Demo 



The `popRF` package has a demo function `popRFdemo` to generate a population layer

using the [WorldPop](https://www.worldpop.org) geospatial covariates and

subnational census-based population estimates for 230 countries. 

All necessary covariates will be downloaded and used to disaggregat population.

All input datasets use a geographical coordinate system (GCS) with WGS 1984

datum (EPSG:4326) in Geotiff format at a resolution of 3 arc-second

(0.00083333333 decimal degree, approximately 100m at the equator).



The following script will produce a population layer for Nepal (NPL) using 4 cores.



``` r

library("popRF")



popRFdemo(project_dir="/home/user/demo",

          country="NPL", 

          cores=4)

 

```



### Basic Usage



``` r

library("popRF")



# Specifying a name of the file from which the unique area ID and corresponding 

# population values are to be read from. The file should contain two columns 

# comma-separated with the value of administrative ID and population without

# columns names. If it does not contain an absolute path, the file name is 

# relative to the current working directory



pop_table <- list("NPL"="/user/npl_population.csv")



# Specifying a nested list of named list(s), i.e. where each element of the

# first list is a named list object with atomic elements. The name of

# each named list corresponds to the 3-letter ISO code of a specified

# country. The elements within each named list define the specified

# input covariates to be used in the random forest model, i.e. the name

# of the covariates and the corresponding, if applicable and local, path

# to them. If the path is not a full path, it is assumed to be relative

# to the current working directory



input_cov <- list(

                  "NPL"= list(

                             "cov1" = "covariate1.tif",

                             "cov2" = "covariate2.tif"

                     )

                  )

                     

# Specifying a named list where each element of the list defines the

# path to the input mastergrid(s), i.e. the template gridded raster(s)

# that contains the unique area IDs as their value. The name(s)

# corresponds to the 3-letter ISO code(s) of a specified country(ies).

# Each corresponding element defines the path to the mastergrid(s). If

# the path is local and not a full path, it is assumed to be relative to

# the current working directory                     



input_mastergrid <- list("NPL" = "npl_mastergrid.tif")



# Specifying a named list where each element of the list defines the path

# to the input country-specific watermask. The name corresponds to the

# 3-letter ISO code of a specified country. Each corresponding element

# defines the path to the watermask, i.e. the binary raster that

# delineates the presence of water (1) and non-water (0), that is used

# to mask out areas from modelling. If the path is local and not a full

# path, it is assumed to be relative to the current working directory.



input_watermask <- list("NPL" = "npl_watermask.tif")



# Specifying a named list where each element of the list defines the path

# to the input raster(s) containing the pixel area. The name corresponds

# to the 3-letter ISO code of a specified country. Each corresponding

# element defines the path to the raster whose values indicate the area

# of each unprojected (WGS84) pixel. If the path is local and not a full

# path, it is assumed to be relative to the current working directory.



input_px_area <- list("NPL" = "npl_px_area.tif")



# Running a model



res <- popRF(pop=pop_table,

             cov=input_cov,

             mastergrid=input_mastergrid,

             watermask=input_watermask,

             px_area=input_px_area,

             output_dir="/user/output",

             cores=4)



# Plot populataion raster

plot(res$pop)



# Plot Error via Trees

plot(res$popfit)

```






### Outputs



Population raster layer in GeoTiff format.



## Contributions



Contributions are welcome. Please raise or respond to an issue, or create a new

branch to develop a feature/modification and submit a pull request.



## Acknowledgements



``` r

#> citation("popRF")



#> To cite popRF in publications use:

#> 

#> Bondarenko M., Nieves J.J., Forrest R.S., Andrea E.G., Jochem C., Kerr D., and Sorichetta A. (2021): popRF: Random Forest-informed Population

#> Disaggregation R package, _Comprehensive R Archive Network (CRAN)_, url:https://cran.r-project.org/package=popRF.

#>

#> A BibTeX entry for LaTeX users is

#> 

#> @Manual{,

#>  title = {popRF: Random Forest-informed Population Disaggregation R package.},

#>  author = {Maksym Bondarenko and Jeremiah J Nieves and Forrest R. Stevens and Andrea E. Gaughan and Chris Jochem and David Kerr and Alessandro Sorichetta},

#>  year = {2021},

#>  journal = {Comprehensive R Archive Network (CRAN)},

#>  url = {https://cran.r-project.org/package=popRF},

#>  language = {English},

#> }

  

```



### License

[GNU General Public License v3.0 (GNU GPLv3)](https://www.gnu.org/licenses/gpl-3.0.en.html)