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I wish to map a high-resolution grid of population counts of Korea and extract from it the Seoul Capital Area (SCA) at the same level of resolution. The population data comes in two forms: raster (population_kor_2018-10-01_geotiff.zip) or CSV centroid data (population_kor_2018-10-01.csv.zip). The SCA administrative boundaries come from GADM (R (sf) file).

Am I doing this right? in the right order? Is it not correct to reproject the raster before converting to polygons since the variable (population) is areal thus should be recalculated? I have also seen it recommended to reproject polygons rather than the raster.

library(raster)
library(rgdal)
library(sf)

#Load raster of population
kor_dens <- raster("population_kor_2018-10-01.tif")
kor_dens@crs #CRS is WGS84

#Load SCA admin boundaries
sca_gadm <- readRDS("gadm36_KOR_1_sf.rds")
st_crs(sca_gadm)$epsg #CRS is WGS84

#Crop raster down to SCA first to save on computing (rectangular outcome)
kor_dens <- crop(kor_dens, extent(sca_gadm))

#Reproject raster to planar CRS, which adjusts population values
kor_dens <- projectRaster(pop_rast, crs=CRS('+init=EPSG:5179'))

#Reproject SCA boundaries to planar CRS
sca_gadm <- st_transform(sca_gadm, 5179)

#Mask raster, which replaces values outside the area of interest with NA
kor_dens <- mask(kor_dens, as(sca_gadm$geometry, 'Spatial'))

#Convert raster to polygons, where cells with NA are not converted
kor_dens <- rasterToPolygons(kor_dens)

The resulting polygon object is massive at 9.3 Gb, so I'm still not sure this is the way to go for statistical analysis.

EDIT: I've replaced the code entirely, inspired by @BrunoConteLeite's answer.

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I would change your approach by using the velox package that does super fast and efficient raster aggregation. I would also avoid re-projecting the raster object as this step is super CPU demanding, but instead, reproject the polygon to the former's projection.

Try:

library(raster)
library(velox)
library(rgdal)
library(sf)

#Load raster
kor_dens <- raster("population_kor_2018-10-01.tif")

sca_gadm <- readRDS("gadm36_KOR_1_sf.rds")
sca_gadm <- sca_gadm[sca_gadm$NAME_1 %in% c("Seoul", "Incheon", "Gyeonggi-do"),]
# make a sp object with which velox works
sca_gadm_sp <- as(sca_gadm, 'Spatial')
# reproject it to the raster's CRS:
sca_gadm_sp <- spTransform(sca_gadm_sp, kor_dens@crs)

# aggregating it:
kor_dens <- crop(kor_dens,extent(sca_gadm_sp)) # cropping the raster over the area of interest
kor_dens <- velox(kor_dens) # making it a velox object
final.df <- kor_dens$extract(sp = sca_gadm_sp, fun = function(x) sum(x, na.rm = TRUE), df=T, small = T) # summing the count of population within the admin boundaries

final.df is the data frame of interest.

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  • Thanks! I want to keep the data in a grid of the same level of resolution (not aggregated) exploitable for statistical analysis (with spml or spdep I guess although I haven't got that far yet). I guess I need the data in vector format for this, not raster format. Sorry if my question isn't clear as I'm a beginner at this.
    – syre
    May 28 '20 at 3:32
  • sca_gadm is already in the same CRS (WGS84) as the raster so I guess there is no need for reprojection in your approach? However, I think I need planar coordinates for later stages.
    – syre
    May 28 '20 at 3:43
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    Please note that the velox package is depreciated. May 28 '20 at 5:38
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    An alternative to velox is the exactextractr package. It is actually faster than velox and coercing to a different object class is not necessary as the exact_extract function operates on raster class objects. Also, since coercion is not necessary for exact_extract, cropping the raster data to the extent of the polygons is not necessary and would actually slow things down. Jun 1 '20 at 16:04
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    It will be notability faster to project the polygons to the same CSR as the raster, defining a new polygon object and then join the tabular results back to the original polygons. Although, it is good practice to define a common projection and get all of your data correctly aligned to a standard analysis extend. This is part of good data management and ensures reproducibility down the line. Jun 2 '20 at 14:00

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