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.
The popRF package can be installed directly from Github.
install.packages("devtools")
devtools::install_github("wpgp/popRF")The popRF package has a demo function popRFdemo to generate a population layer
using the WorldPop 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.
library("popRF")
popRFdemo(project_dir="/home/user/demo",
country="NPL", cores=4)
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 directorypop_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 directoryinput_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 modelres<- 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)
Population raster layer in GeoTiff format.
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.
#> 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},#> }