Social Vulnerability Ireland - Copernicus data

Contents

Social Vulnerability Ireland - Copernicus data#

Processing of the Copernicus data into census output areas

Environment#

R Libraries#

Any required R libraries are imported into the kernal:

# Load R libraries
if(!require("pacman"))
    install.packages("pacman")

p_load("sf", "terra", "exactextractr")

print("Loaded Packages:")
p_loaded()

Loading required package: pacman

[1] "Loaded Packages:"

'exactextractr'
'terra'
'sf'
'pacman'

### Output directory

# create the pipeline directory if it does not exist
pipeline_dir <- file.path("../..","2_pipeline","Ireland","1b_Copernicus","2022")
if(!dir.exists(pipeline_dir)){
    dir.create(pipeline_dir, recursive = TRUE)
    print(paste0(pipeline_dir, " created"))
}

# set country variable which is used within the output filename
country <- "Ireland"

Process#

# Copernicus data folder
copernicus_data_path <- "../../0_data/Copernicus/Ireland"

# High resolution layers - IMPERVIOUSNESS, TREE COVER DENSITY
datasets <- c("IMD","TCD")

# census output areas
census_areas <- st_read("../../0_data/boundaries/Ireland/census_small_areas/2022/SMALL_AREA_2022.shp")

Reading layer `SMALL_AREA_2022' from data source 
  `/Cities/0_data/boundaries/Ireland/census_small_areas/2022/SMALL_AREA_2022.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 18919 features and 28 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 417471.5 ymin: 519663.7 xmax: 734481.1 ymax: 966896.3
Projected CRS: IRENET95 / Irish Transverse Mercator

# need to join copernicus tiles together
for(dataset in datasets){
    print(paste0("Processing: ", dataset))
    flush.console()

    # export mosaic filepath
    mosaic_output_file <- paste0(pipeline_dir, "/", country, "_", dataset, ".tif")

    # have the tiles been joined together already?
    is_mosaic_created <- file.exists(mosaic_output_file)

    if(is_mosaic_created){
        print(paste0(mosaic_output_file, " already created"))
        flush.console()
    } else {
        # Join the tiles together and export it
        # Get the files
        tiles <- list.files(copernicus_data_path, full.names = TRUE, recursive = TRUE, pattern=paste0(".*", dataset, ".*\\.tif$"))  

        # Create an image catalog
        ic <- terra::sprc(lapply(tiles, rast))

        # Mosic the tiles
        icMosaic <- mosaic(ic, filename = mosaic_output_file, fun="max", overwrite=TRUE)
        #icMosaic <- merge(ic, filename = mosaic_output_file, overwrite=TRUE) # quicker

        print(paste0(mosaic_output_file, " created"))
    }   
}

[1] "Processing: IMD"
[1] "../../2_pipeline/Ireland/1b_Copernicus/2022/Ireland_IMD.tif already created"
[1] "Processing: TCD"
[1] "../../2_pipeline/Ireland/1b_Copernicus/2022/Ireland_TCD.tif already created"

Sys.time()
census_areas_impervious <- census_areas

# Impervious surface
copRaster <- rast(paste0(pipeline_dir, "/", country, "_", "IMD", ".tif"))

# calculate the area of the raster cell (m2)
cellArea <- res(copRaster)[1]*res(copRaster)[2]

# calculate the number of cells within each output area
census_areas_impervious$cell_area_m2 <- exact_extract(copRaster, census_areas_impervious, 'count', coverage_area = TRUE, progress = TRUE)

# calculate the area of each output area that is impervious
census_areas_impervious$imp_area_m2 <- exact_extract(copRaster, census_areas_impervious, 'sum', progress = TRUE)

# calculate the percentage of each output area that is impervious
census_areas_impervious$imp_percent <- round((census_areas_impervious$imp_area_m2/census_areas_impervious$cell_area_m2)*100, 3)

# calculate Z score
census_areas_impervious$impervious <- scale(census_areas_impervious$imp_percent)

head(census_areas_impervious)
Sys.time()

[1] "2025-03-13 15:40:39 GMT"

Warning message in .local(x, y, ...):
“Polygons transformed to raster CRS (EPSG:3035)”
Cannot preload entire working area of 1395265641 cells with max_cells_in_memory = 3e+07. Raster values will be read for each feature individually.

 |======================================================================| 100%

Warning message in .local(x, y, ...):
“Polygons transformed to raster CRS (EPSG:3035)”
Cannot preload entire working area of 1395265641 cells with max_cells_in_memory = 3e+07. Raster values will be read for each feature individually.

 |======================================================================| 100%

Registered S3 method overwritten by 'geojsonsf':
  method        from   
  print.geojson geojson

A sf: 6 × 33
	OBJECTID	SA_GUID_20	SA_GUID__1	SA_PUB2011	SA_PUB2016	SA_PUB2022	SA_GEOGID_	SA_CHANGE_	SA_URBAN_A	SA_URBAN_1	geometry	⋯	COUNTY_ENG	COUNTY_GAE	CSO_LEA	SHAPE_Leng	SHAPE_Area	geometry	cell_area_m2	imp_area_m2	imp_percent	impervious
	<int>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<chr>	⋯	<chr>	<chr>	<chr>	<dbl>	<dbl>	<MULTIPOLYGON [m]>	<dbl>	<dbl>	<dbl>	<dbl[,1]>
1	1	4c07d11e-0a4f-851d-e053-ca3ca8c0ca7f	4c07d11e-0a4f-851d-e053-ca3ca8c0ca7f	017010016	017010016	017010016	A017010016	0	1	Carlow	MULTIPOLYGON (((673146.5 67...	⋯	CARLOW	Ceatharlach	CARLOW	2123.346	204944.2	MULTIPOLYGON (((673146.5 67...	204991.3	75945.95	37.048	0.4175754
2	2	4c07d11e-0a3a-851d-e053-ca3ca8c0ca7f	4c07d11e-0a3a-851d-e053-ca3ca8c0ca7f	017010046	017010046	017010046	A017010046	4	1	Carlow	MULTIPOLYGON (((673956.3 67...	⋯	CARLOW	Ceatharlach	CARLOW	2891.271	288225.4	MULTIPOLYGON (((673956.3 67...	288290.4	57333.25	19.887	-0.2955289
3	3	4c07d11e-0a4e-851d-e053-ca3ca8c0ca7f	4c07d11e-0a4e-851d-e053-ca3ca8c0ca7f	017010037	017010037	017010037	A017010037	0	1	Carlow	MULTIPOLYGON (((673720.6 67...	⋯	CARLOW	Ceatharlach	CARLOW	2436.986	208426.9	MULTIPOLYGON (((673720.6 67...	208474.4	69274.20	33.229	0.2588815
4	4	4c07d11e-0a25-851d-e053-ca3ca8c0ca7f	4c07d11e-0a25-851d-e053-ca3ca8c0ca7f	017010005	017010005	017010005	A017010005	0	1	Carlow	MULTIPOLYGON (((671923.3 67...	⋯	CARLOW	Ceatharlach	CARLOW	2887.091	348798.6	MULTIPOLYGON (((671923.3 67...	348880.3	109519.96	31.392	0.1825472
5	5	4c07d11e-0a57-851d-e053-ca3ca8c0ca7f	4c07d11e-0a57-851d-e053-ca3ca8c0ca7f	017010036	017010036	017010036	A017010036	0	1	Carlow	MULTIPOLYGON (((673711.8 67...	⋯	CARLOW	Ceatharlach	CARLOW	2844.410	429349.1	MULTIPOLYGON (((673711.8 67...	429446.9	209251.55	48.726	0.9028403
6	6	4c07d11e-2df2-851d-e053-ca3ca8c0ca7f	4c07d11e-2df2-851d-e053-ca3ca8c0ca7f	017010009	017010009	017010009	A017010009	4	1	Carlow	MULTIPOLYGON (((673488.5 67...	⋯	CARLOW	Ceatharlach	CARLOW	1058.290	59299.2	MULTIPOLYGON (((673488.5 67...	59312.7	18759.59	31.628	0.1923539

[1] "2025-03-13 15:52:17 GMT"

# output the impervious data as a geojson
st_write(census_areas_impervious, file.path(pipeline_dir, "census_areas_IMD.geojson"), delete_dsn = TRUE)

Deleting source `../../2_pipeline/Ireland/1b_Copernicus/2022/census_areas_IMD.geojson' using driver `GeoJSON'
Writing layer `census_areas_IMD' to data source 
  `../../2_pipeline/Ireland/1b_Copernicus/2022/census_areas_IMD.geojson' using driver `GeoJSON'
Writing 18919 features with 32 fields and geometry type Multi Polygon.

Sys.time()
census_areas_tree_cover <- census_areas

# TREE COVER DENSITY
# cell size = 100 m2, therefore a percentage value directly equals m2, i.e. 100% coverage = 100 m2 area of impervious surface
copRaster <- rast(paste0(pipeline_dir, "/", country, "_", "TCD", ".tif"))

# calculate the area of the raster cell (m2)
cellArea <- res(copRaster)[1]*res(copRaster)[2]

# calculate the number of cells within each output area
census_areas_tree_cover$cell_area_m2 <- exact_extract(copRaster, census_areas_tree_cover, 'count', coverage_area = TRUE, progress = TRUE)

# calculate the area of each output area that has tree cover
census_areas_tree_cover$tcd_area_m2 <- exact_extract(copRaster, census_areas_tree_cover, 'sum', progress = TRUE)

# calculate the percentage of each output area that has tree cover
census_areas_tree_cover$tcd_percent <- round((census_areas_tree_cover$tcd_area_m2/census_areas_tree_cover$cell_area_m2)*100, 3)

# calculate Z score
census_areas_tree_cover$tree_cover_density <- -scale(census_areas_tree_cover$tcd_percent)

head(census_areas_tree_cover)
Sys.time()

[1] "2025-03-13 15:53:31 GMT"

Warning message in .local(x, y, ...):
“Polygons transformed to raster CRS (EPSG:3035)”
Cannot preload entire working area of 1395265641 cells with max_cells_in_memory = 3e+07. Raster values will be read for each feature individually.

 |======================================================================| 100%

Warning message in .local(x, y, ...):
“Polygons transformed to raster CRS (EPSG:3035)”
Cannot preload entire working area of 1395265641 cells with max_cells_in_memory = 3e+07. Raster values will be read for each feature individually.

 |======================================================================| 100%

A sf: 6 × 33
	OBJECTID	SA_GUID_20	SA_GUID__1	SA_PUB2011	SA_PUB2016	SA_PUB2022	SA_GEOGID_	SA_CHANGE_	SA_URBAN_A	SA_URBAN_1	geometry	⋯	COUNTY_ENG	COUNTY_GAE	CSO_LEA	SHAPE_Leng	SHAPE_Area	geometry	cell_area_m2	tcd_area_m2	tcd_percent	tree_cover_density
	<int>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<chr>	⋯	<chr>	<chr>	<chr>	<dbl>	<dbl>	<MULTIPOLYGON [m]>	<dbl>	<dbl>	<dbl>	<dbl[,1]>
1	1	4c07d11e-0a4f-851d-e053-ca3ca8c0ca7f	4c07d11e-0a4f-851d-e053-ca3ca8c0ca7f	017010016	017010016	017010016	A017010016	0	1	Carlow	MULTIPOLYGON (((673146.5 67...	⋯	CARLOW	Ceatharlach	CARLOW	2123.346	204944.2	MULTIPOLYGON (((673146.5 67...	204991.3	202.7830	0.099	0.7831503
2	2	4c07d11e-0a3a-851d-e053-ca3ca8c0ca7f	4c07d11e-0a3a-851d-e053-ca3ca8c0ca7f	017010046	017010046	017010046	A017010046	4	1	Carlow	MULTIPOLYGON (((673956.3 67...	⋯	CARLOW	Ceatharlach	CARLOW	2891.271	288225.4	MULTIPOLYGON (((673956.3 67...	288290.4	961.1264	0.333	0.7459016
3	3	4c07d11e-0a4e-851d-e053-ca3ca8c0ca7f	4c07d11e-0a4e-851d-e053-ca3ca8c0ca7f	017010037	017010037	017010037	A017010037	0	1	Carlow	MULTIPOLYGON (((673720.6 67...	⋯	CARLOW	Ceatharlach	CARLOW	2436.986	208426.9	MULTIPOLYGON (((673720.6 67...	208474.4	968.9902	0.465	0.7248895
4	4	4c07d11e-0a25-851d-e053-ca3ca8c0ca7f	4c07d11e-0a25-851d-e053-ca3ca8c0ca7f	017010005	017010005	017010005	A017010005	0	1	Carlow	MULTIPOLYGON (((671923.3 67...	⋯	CARLOW	Ceatharlach	CARLOW	2887.091	348798.6	MULTIPOLYGON (((671923.3 67...	348880.3	26616.5039	7.629	-0.4154939
5	5	4c07d11e-0a57-851d-e053-ca3ca8c0ca7f	4c07d11e-0a57-851d-e053-ca3ca8c0ca7f	017010036	017010036	017010036	A017010036	0	1	Carlow	MULTIPOLYGON (((673711.8 67...	⋯	CARLOW	Ceatharlach	CARLOW	2844.410	429349.1	MULTIPOLYGON (((673711.8 67...	429446.9	11372.1279	2.648	0.3773941
6	6	4c07d11e-2df2-851d-e053-ca3ca8c0ca7f	4c07d11e-2df2-851d-e053-ca3ca8c0ca7f	017010009	017010009	017010009	A017010009	4	1	Carlow	MULTIPOLYGON (((673488.5 67...	⋯	CARLOW	Ceatharlach	CARLOW	1058.290	59299.2	MULTIPOLYGON (((673488.5 67...	59312.7	107.7513	0.182	0.7699382

[1] "2025-03-13 16:07:43 GMT"

Export#

# output the tree cover density data as a geojson
st_write(census_areas_tree_cover, file.path(pipeline_dir, "census_areas_TCD.geojson"), delete_dsn = TRUE)

Deleting source `../../2_pipeline/Ireland/1b_Copernicus/2022/census_areas_TCD.geojson' using driver `GeoJSON'
Writing layer `census_areas_TCD' to data source 
  `../../2_pipeline/Ireland/1b_Copernicus/2022/census_areas_TCD.geojson' using driver `GeoJSON'
Writing 18919 features with 32 fields and geometry type Multi Polygon.

END