inst/scripts/gin_datacollect.R
In SSA-Statistical-Team-Projects/SAEplus: Package for Geospatial Analysis & Small Area Estimation Post-Diagnostics
library(foreach)
library(doParallel)
### pull in base country-level boundary files from remote source
###### starting with GUINEA
## first carve up guinea country shapefile provided by PE into polygon grids
# gin.grid <- gengrid(dsn = "tests/testdata",
# layer = "sous_prefectures",
# raster_tif = "gin_ppp_2020_UNadj_constrained.tif",
# grid_shp=T,
# featname="population",
# drop_Zero=F)
gin_shp <- sf::st_read(dsn = "//cwapov/cwapov/GIN/GEO/Boundaries",
layer = "sous_prefectures")
sf_use_s2(FALSE) ##important line to ensure flexibility in dealing with odd geometries (ALWAYS USE!)
gin_shp$area <- st_area(gin_shp) ##compute the area of each polygon
gin_shp$area <- set_units(gin_shp$area, "km^2") ##convert to sqkm
crs_dt <- rgdal::make_EPSG() ##the database of recognized coordinate reference systems in R
gin_shp <- st_transform(gin_shp, ##convert
crs = crs_dt$prj4[crs_dt$code == 3974])
gin_shp$area <- st_area(gin_shp)
gin_shp$area <- set_units(gin_shp$area, "km^2")
gin_raster <- raster("//cwapov/cwapov/GIN/GEO/Population/gin_ppp_2020_UNadj_constrained.tif")
gin_grid <- gengrid2(shp_dt = gin_shp,
grid_size = 1000,
pop_raster = gin_raster,
extract_name = "population")
sf::st_write(obj = gin_grid[,c("poly_id", "geometry")],
dsn = "tests/testdata",
layer = "gin_poppoly",
driver = "ESRI Shapefile", append = FALSE)
# ## load and clean up the shapefile a little
# gin.base <- sf::st_read(dsn = "tests/testdata",
# layer = "sous_prefectures")
#
# gin.base <- sf::st_make_valid(gin.base) #making shapefile valid for use on google earth engine
# gin.base <- gin.base[sf::st_geometry_type(gin.base$geometry) == "MULTIPOLYGON",] #we want all multipolygons
# #in this case
# #gin.base <- rmapshaper::ms_simplify(gin.base)
# gin.base <- sf::st_make_valid(gin.base)
#
# ## save the cleaned up shapefile
# sf::st_write(gin.base, dsn = "tests/testdata", layer = "sous_prefectures_valid",
# driver = "ESRI Shapefile",
# append = FALSE)
## pull nighttime lights
gin.ntl <- SAEplus::gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_boundary = "users/ifeanyiedochie/gin_poppoly",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_datestart = "2017-01-31",
gee_dateend = "2017-12-31",
gee_desc = "GIN_NTL_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
gin.no2 <- gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = "tropospheric_NO2_column_number_density",
gee_dataname = "COPERNICUS/S5P/NRTI/L3_NO2",
gee_datestart = "2018-07-31",
gee_dateend = "2018-08-31",
gee_desc = "GIN_NO2_2018",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_scale = 1113)
no2_dt <- gee_pullbigdata(shp_dsn = "tests/testdata",
shp_layer = "gin_poppoly",
gee_name = "COPERNICUS/S5P/NRTI/L3_NO2",
gee_datestart = "2018-07-31",
gee_dateend = "2018-08-31",
gee_band = "tropospheric_NO2_column_number_density",
gee_chunksize = 50,
gee_stat = "mean",
gee_scale = 1113)
##pull in the landcover data
# pull the data on Landcover
SAEplus::gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("tree-coverfraction","urban-coverfraction","grass-coverfraction",
"shrub-coverfraction","crops-coverfraction","bare-coverfraction",
"water-permanent-coverfraction","water-seasonal-coverfraction",
"moss-coverfraction"),
gee_dataname = "COPERNICUS/Landcover/100m/Proba-V-C3/Global",
gee_desc = "GIN_LC_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
SAEplus::gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("tree-coverfraction","urban-coverfraction","grass-coverfraction",
"shrub-coverfraction","crops-coverfraction","bare-coverfraction",
"water-permanent-coverfraction","water-seasonal-coverfraction",
"moss-coverfraction"),
gee_dataname = "COPERNICUS/Landcover/100m/Proba-V-C3/Global",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_LC_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
## pull the data on impervious surface
SAEplus::gee_pullimage(email = "ifeanyi.edochie@gmail.com",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = "change_year_index",
gee_dataname = "Tsinghua/FROM-GLC/GAIA/v10",
gee_desc = "GIN_IS_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
# include other GEE data on CO, global human modification, gridmet drought data
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("CO_column_number_density", "H2O_column_number_density",
"cloud_height"),
gee_dataname = "COPERNICUS/S5P/NRTI/L3_CO",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_CO_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
# pull some precipitation data as well
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("total_precipitation"),
gee_dataname = "ECMWF/ERA5/DAILY",
gee_datestart = "2018-07-01",
gee_dateend = "2018-09-30",
gee_desc = "GIN_PP_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("total_precipitation"),
gee_dataname = "ECMWF/ERA5/DAILY",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_PP_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
## add some drought data as well
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("pdsi", "z"),
gee_dataname = "GRIDMET/DROUGHT",
gee_datestart = "2018-07-01",
gee_dateend = "2018-09-30",
gee_desc = "GIN_drought_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("pdsi", "z"),
gee_dataname = "GRIDMET/DROUGHT",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_drought_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
#######################################################################################################
## pull in the building data
wpopbuilding_pull(iso = "GIN", wpversion = "v2.0",
ldrive_dsn = "D:/Ify/Guinea_SAE")
## pull in the electricity data
gin.elect <- gengrid(dsn = "tests/testdata",
layer = "gin_poppoly",
raster_tif = "GIN_Electricity_2018.tif",
grid_shp = F,
featname="elect_cons",
drop_Zero=F)
## pull building data
#### writing a simple function to pull data from a set of tif files using gengrid() and then combining
#### the columns
gin_buildingtifs <- list.files(path = "tests/testdata", pattern = "GIN_buildings")
gin_building.dt <- mult_gengrid(tif_namelist = gin_buildingtifs,
location = "tests/testdata",
feature_name = c("count", "cv_length",
"imagery_year", "mean_length",
"total_length", "cv_area",
"density", "mean_area",
"total_area", "urban"),
parallel = T,
numCores = length(gin_buildingtifs))
## combine all extracted columns into one data.frame/data.table object
multi_merge_DT <- function(sf1, sf2){
obj <- sf1[sf2, on = c("id", "population")]
return(obj)
}
bld_polygon.dt <- list()
for (i in seq_along(gin_building.dt)){
bld_polygon.dt[i] <- gin_building.dt[[i]]["polygon_dt"]
}
###remove geometry column from all but one polygon_dt
remove_varfromdtlist <- function(list_dt = bld_polygon.dt,
varname = "geometry"){
for (i in 1:(length(list_dt)-1)){
selected_names <- colnames(list_dt[[i]])[!(colnames(list_dt[[i]]) %in% varname)]
list_dt[[i]] <- list_dt[[i]][,selected_names, with = F]
}
return(list_dt)
}
bld_polygon.dt <- remove_varfromdtlist()
rm(gin_building.dt) ##remove the gin_building.dt object
bld_polygon.dt <- Reduce(multi_merge_DT, bld_polygon.dt) ##combine all building data
geopolycensus_dt <- bld_polygon.dt ## rename bld_polygon.dt to a name that will hold all geospatial data
rm(bld_polygon.dt) ## remove the bld_polygon.dt object after renaming it
## include the GEE data and electricty data into the geopolycensus.dt
geopolycensus_dt <- gin.elect$polygon_dt[,c("id", "elect_cons")][geopolycensus_dt, on = "id"] ## add electricity
#### function to read the set of GEE shapefiles and return a list of datasets
readmerge_gee <- function(folder = "//cwapov/cwapov/GIN/GEO/GEE", ##folder location
identifier = "GIN_"){ ##identifiable
gee_dt <- list.files(path = folder, pattern = identifier)
drop_chr <- function(x){
x <- substr(x, start = 1, stop = nchar(x) - 4)
return(x)
}
gee_dt <- unique(unlist(lapply(gee_dt, drop_chr)))
st_readlist <- function(X){
obj <- sf::st_read(dsn = folder, layer = X)
obj <- as.data.table(obj)
colnames(obj)[colnames(obj) %in% "mean"] <- X
return(obj)
}
gee_dt <- lapply(gee_dt, st_readlist)
return(gee_dt)
}
gee_dt <- readmerge_gee() ##run readmerge_gee to combine the pulled GEE data
##remove geometry from the gee_dt list for all but one so that we dont have duplicates after merge
gee_dt <- remove_varfromdtlist(list_dt = gee_dt)
#join gee data using rbindlist
gee_dt <- do.call(cbind, gee_dt)
gee_dt <- gee_dt[ , which( !duplicated( t( gee_dt ) ) ), with = FALSE ]
geopolycensus_dt <- gee_dt[geopolycensus_dt, on = c("id", "population")]
## include electricity data to geopolycensus
geopolycensus_dt <- gin.elect$polygon_dt[geopolycensus_dt, on = c("id", "population")]
## clean up the global environment
rm(gee_dt)
rm(gin.elect)
## write the data to file
saveRDS(geopolycensus_dt, file = "tests/testdata/gin_geopolycensus.RDS")
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library(foreach)
library(doParallel)
### pull in base country-level boundary files from remote source
###### starting with GUINEA
## first carve up guinea country shapefile provided by PE into polygon grids
# gin.grid <- gengrid(dsn = "tests/testdata",
# layer = "sous_prefectures",
# raster_tif = "gin_ppp_2020_UNadj_constrained.tif",
# grid_shp=T,
# featname="population",
# drop_Zero=F)
gin_shp <- sf::st_read(dsn = "//cwapov/cwapov/GIN/GEO/Boundaries",
layer = "sous_prefectures")
sf_use_s2(FALSE) ##important line to ensure flexibility in dealing with odd geometries (ALWAYS USE!)
gin_shp$area <- st_area(gin_shp) ##compute the area of each polygon
gin_shp$area <- set_units(gin_shp$area, "km^2") ##convert to sqkm
crs_dt <- rgdal::make_EPSG() ##the database of recognized coordinate reference systems in R
gin_shp <- st_transform(gin_shp, ##convert
crs = crs_dt$prj4[crs_dt$code == 3974])
gin_shp$area <- st_area(gin_shp)
gin_shp$area <- set_units(gin_shp$area, "km^2")
gin_raster <- raster("//cwapov/cwapov/GIN/GEO/Population/gin_ppp_2020_UNadj_constrained.tif")
gin_grid <- gengrid2(shp_dt = gin_shp,
grid_size = 1000,
pop_raster = gin_raster,
extract_name = "population")
sf::st_write(obj = gin_grid[,c("poly_id", "geometry")],
dsn = "tests/testdata",
layer = "gin_poppoly",
driver = "ESRI Shapefile", append = FALSE)
# ## load and clean up the shapefile a little
# gin.base <- sf::st_read(dsn = "tests/testdata",
# layer = "sous_prefectures")
#
# gin.base <- sf::st_make_valid(gin.base) #making shapefile valid for use on google earth engine
# gin.base <- gin.base[sf::st_geometry_type(gin.base$geometry) == "MULTIPOLYGON",] #we want all multipolygons
# #in this case
# #gin.base <- rmapshaper::ms_simplify(gin.base)
# gin.base <- sf::st_make_valid(gin.base)
#
# ## save the cleaned up shapefile
# sf::st_write(gin.base, dsn = "tests/testdata", layer = "sous_prefectures_valid",
# driver = "ESRI Shapefile",
# append = FALSE)
## pull nighttime lights
gin.ntl <- SAEplus::gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_boundary = "users/ifeanyiedochie/gin_poppoly",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_datestart = "2017-01-31",
gee_dateend = "2017-12-31",
gee_desc = "GIN_NTL_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
gin.no2 <- gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = "tropospheric_NO2_column_number_density",
gee_dataname = "COPERNICUS/S5P/NRTI/L3_NO2",
gee_datestart = "2018-07-31",
gee_dateend = "2018-08-31",
gee_desc = "GIN_NO2_2018",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_scale = 1113)
no2_dt <- gee_pullbigdata(shp_dsn = "tests/testdata",
shp_layer = "gin_poppoly",
gee_name = "COPERNICUS/S5P/NRTI/L3_NO2",
gee_datestart = "2018-07-31",
gee_dateend = "2018-08-31",
gee_band = "tropospheric_NO2_column_number_density",
gee_chunksize = 50,
gee_stat = "mean",
gee_scale = 1113)
##pull in the landcover data
# pull the data on Landcover
SAEplus::gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("tree-coverfraction","urban-coverfraction","grass-coverfraction",
"shrub-coverfraction","crops-coverfraction","bare-coverfraction",
"water-permanent-coverfraction","water-seasonal-coverfraction",
"moss-coverfraction"),
gee_dataname = "COPERNICUS/Landcover/100m/Proba-V-C3/Global",
gee_desc = "GIN_LC_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
SAEplus::gee_datapull(email = "ifeanyi.edochie@gmail.com",
gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("tree-coverfraction","urban-coverfraction","grass-coverfraction",
"shrub-coverfraction","crops-coverfraction","bare-coverfraction",
"water-permanent-coverfraction","water-seasonal-coverfraction",
"moss-coverfraction"),
gee_dataname = "COPERNICUS/Landcover/100m/Proba-V-C3/Global",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_LC_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
## pull the data on impervious surface
SAEplus::gee_pullimage(email = "ifeanyi.edochie@gmail.com",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = "change_year_index",
gee_dataname = "Tsinghua/FROM-GLC/GAIA/v10",
gee_desc = "GIN_IS_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE")
# include other GEE data on CO, global human modification, gridmet drought data
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("CO_column_number_density", "H2O_column_number_density",
"cloud_height"),
gee_dataname = "COPERNICUS/S5P/NRTI/L3_CO",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_CO_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
# pull some precipitation data as well
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("total_precipitation"),
gee_dataname = "ECMWF/ERA5/DAILY",
gee_datestart = "2018-07-01",
gee_dateend = "2018-09-30",
gee_desc = "GIN_PP_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("total_precipitation"),
gee_dataname = "ECMWF/ERA5/DAILY",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_PP_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
## add some drought data as well
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("pdsi", "z"),
gee_dataname = "GRIDMET/DROUGHT",
gee_datestart = "2018-07-01",
gee_dateend = "2018-09-30",
gee_desc = "GIN_drought_2018JulSep",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
SAEplus::gee_datapull(gee_boundary = "users/ifeanyiedochie/sous_prefectures_valid",
gee_polygons = "users/ifeanyiedochie/gin_poppoly",
gee_band = c("pdsi", "z"),
gee_dataname = "GRIDMET/DROUGHT",
gee_datestart = "2019-04-01",
gee_dateend = "2019-06-30",
gee_desc = "GIN_drought_2019AprJun",
ldrive_dsn = "//cwapov/cwapov/GIN/GEO/GEE",
gee_crs = "EPSG:4326")
#######################################################################################################
## pull in the building data
wpopbuilding_pull(iso = "GIN", wpversion = "v2.0",
ldrive_dsn = "D:/Ify/Guinea_SAE")
## pull in the electricity data
gin.elect <- gengrid(dsn = "tests/testdata",
layer = "gin_poppoly",
raster_tif = "GIN_Electricity_2018.tif",
grid_shp = F,
featname="elect_cons",
drop_Zero=F)
## pull building data
#### writing a simple function to pull data from a set of tif files using gengrid() and then combining
#### the columns
gin_buildingtifs <- list.files(path = "tests/testdata", pattern = "GIN_buildings")
gin_building.dt <- mult_gengrid(tif_namelist = gin_buildingtifs,
location = "tests/testdata",
feature_name = c("count", "cv_length",
"imagery_year", "mean_length",
"total_length", "cv_area",
"density", "mean_area",
"total_area", "urban"),
parallel = T,
numCores = length(gin_buildingtifs))
## combine all extracted columns into one data.frame/data.table object
multi_merge_DT <- function(sf1, sf2){
obj <- sf1[sf2, on = c("id", "population")]
return(obj)
}
bld_polygon.dt <- list()
for (i in seq_along(gin_building.dt)){
bld_polygon.dt[i] <- gin_building.dt[[i]]["polygon_dt"]
}
###remove geometry column from all but one polygon_dt
remove_varfromdtlist <- function(list_dt = bld_polygon.dt,
varname = "geometry"){
for (i in 1:(length(list_dt)-1)){
selected_names <- colnames(list_dt[[i]])[!(colnames(list_dt[[i]]) %in% varname)]
list_dt[[i]] <- list_dt[[i]][,selected_names, with = F]
}
return(list_dt)
}
bld_polygon.dt <- remove_varfromdtlist()
rm(gin_building.dt) ##remove the gin_building.dt object
bld_polygon.dt <- Reduce(multi_merge_DT, bld_polygon.dt) ##combine all building data
geopolycensus_dt <- bld_polygon.dt ## rename bld_polygon.dt to a name that will hold all geospatial data
rm(bld_polygon.dt) ## remove the bld_polygon.dt object after renaming it
## include the GEE data and electricty data into the geopolycensus.dt
geopolycensus_dt <- gin.elect$polygon_dt[,c("id", "elect_cons")][geopolycensus_dt, on = "id"] ## add electricity
#### function to read the set of GEE shapefiles and return a list of datasets
readmerge_gee <- function(folder = "//cwapov/cwapov/GIN/GEO/GEE", ##folder location
identifier = "GIN_"){ ##identifiable
gee_dt <- list.files(path = folder, pattern = identifier)
drop_chr <- function(x){
x <- substr(x, start = 1, stop = nchar(x) - 4)
return(x)
}
gee_dt <- unique(unlist(lapply(gee_dt, drop_chr)))
st_readlist <- function(X){
obj <- sf::st_read(dsn = folder, layer = X)
obj <- as.data.table(obj)
colnames(obj)[colnames(obj) %in% "mean"] <- X
return(obj)
}
gee_dt <- lapply(gee_dt, st_readlist)
return(gee_dt)
}
gee_dt <- readmerge_gee() ##run readmerge_gee to combine the pulled GEE data
##remove geometry from the gee_dt list for all but one so that we dont have duplicates after merge
gee_dt <- remove_varfromdtlist(list_dt = gee_dt)
#join gee data using rbindlist
gee_dt <- do.call(cbind, gee_dt)
gee_dt <- gee_dt[ , which( !duplicated( t( gee_dt ) ) ), with = FALSE ]
geopolycensus_dt <- gee_dt[geopolycensus_dt, on = c("id", "population")]
## include electricity data to geopolycensus
geopolycensus_dt <- gin.elect$polygon_dt[geopolycensus_dt, on = c("id", "population")]
## clean up the global environment
rm(gee_dt)
rm(gin.elect)
## write the data to file
saveRDS(geopolycensus_dt, file = "tests/testdata/gin_geopolycensus.RDS")
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