在r中计算shapefile中变量的表面积

Question

My shapefile represent a continent. It has many polygons (because of several layers).

I would like to compute the surface area/squarekm for different variables and have the results in a column ie:

Total squarekm per country (NAME variable): It would give me the square km of each country polygons. Total squarekm per AEZ (AEZ variable): It would give me the square km of each AEZ zone

etc.

I did it in Arcmap but can't figure out how to have the same results in R.

I tried with Areapolygons but it does not work.

> dput(PRIO[2:6,9,12:14, c(1,2)]) structure(list(NAME = c("Mauritania", "Mauritania", "Mauritania", "Mauritania", "Mauritania"), geometry = structure(list(structure(list( list(structure(c(-8.15539750263898, -8.5, -8.5, -8.20444499999996, -8.15539750263898, 27, 27, 27.1964674367602, 27.0274960000002, 27), .Dim = c(5L, 2L)))), class = c("XY", "MULTIPOLYGON", "sfg")), structure(list(list(structure(c(-8.5, -8.66722299999986, -8.66722299999986, -8.66722299999986, -8.66717809129804, -8.5, -8.5, 26.5, 26.5, 26.8330540000001, 26.9663889999999, 27, 27, 26.5), .Dim = c(7L, 2L)))), class = c("XY", "MULTIPOLYGON", "sfg" )), structure(list(list(structure(c(-8, -8, -8.5, -8.5, -8.15539750263898, -8.13111099999998, -8, 26.9105346374803, 26.5, 26.5, 27, 27, 26.9863850000001, 26.9105346374803), .Dim = c(7L, 2L)))), class = c("XY", "MULTIPOLYGON", "sfg")), structure(list(list(structure(c(-7.50000000000003, -7.50000000000003, -8, -8, -7.71194499999996, -7.69361099999992, -7.50000000000003, 26.6209884313231, 26.5, 26.5, 26.9105346374803, 26.7438890000001, 26.7341649999999, 26.6209884313231), .Dim = c(7L, 2L)))), class = c("XY", "MULTIPOLYGON", "sfg")), structure(list( list(structure(c(-7.29302525734133, -7.50000000000003, -7.50000000000003, -7.29302525734133, 26.5, 26.5, 26.6209884313231, 26.5), .Dim = c(4L, 2L)))), class = c("XY", "MULTIPOLYGON", "sfg"))), class = c("sfc_MULTIPOLYGON", "sfc"), precision = 0, bbox = structure(c(xmin = -8.66722299999986, ymin = 26.5, xmax = -7.29302525734133, ymax = 27.1964674367602 ), class = "bbox"), crs = structure(list(input = "WGS 84", wkt = "GEOGCRS[\\"WGS 84\\",\\n DATUM[\\"World Geodetic System 1984\\",\\n ELLIPSOID[\\"WGS 84\\",6378137,298.257223563,\\n LENGTHUNIT[\\"metre\\",1]]],\\n PRIMEM[\\"Greenwich\\",0,\\n ANGLEUNIT[\\"degree\\",0.0174532925199433]],\\n CS[ellipsoidal,2],\\n AXIS[\\"latitude\\",north,\\n ORDER[1],\\n ANGLEUNIT[\\"degree\\",0.0174532925199433]],\\n AXIS[\\"longitude\\",east,\\n ORDER[2],\\n ANGLEUNIT[\\"degree\\",0.0174532925199433]],\\n ID[\\"EPSG\\",4326]]"), class = "crs"), n_empty = 0L)), row.names = c(NA, -5L), sf_column = "geometry", agr = structure(c(NAME = NA_integer_), .Label = c("constant", "aggregate", "identity"), class = "factor"), class = c("sf", "tbl_df", "tbl", "data.frame"))

Thanks!

Answer 1

Load your multipolygon in R, make sure it has an appropriate coordinate system, then use st_area() , which returns the area of each polygon (row) in your multipolygon.

library(sf)

# Load multipolygon
nc = st_read(system.file("shape/nc.shp", package="sf"))

# Check coordinate system
st_crs(nc)
#> Coordinate Reference System:
#>   User input: NAD27 
#>   wkt:
#> GEOGCRS["NAD27",
#>     DATUM["North American Datum 1927",
#>         ELLIPSOID["Clarke 1866",6378206.4,294.978698213898,
#>             LENGTHUNIT["metre",1]]],
#>     PRIMEM["Greenwich",0,
#>         ANGLEUNIT["degree",0.0174532925199433]],
#>     CS[ellipsoidal,2],
#>         AXIS["latitude",north,
#>             ORDER[1],
#>             ANGLEUNIT["degree",0.0174532925199433]],
#>         AXIS["longitude",east,
#>             ORDER[2],
#>             ANGLEUNIT["degree",0.0174532925199433]],
#>     ID["EPSG",4267]]

plot(nc$geometry)

nrow(nc)
#> [1] 100

st_area(nc)
#> Units: [m^2]
#>   [1] 1137107793  610916077 1423145355  694378925 1520366979  967504822
#>   [7]  615794941  903423919 1179065710 1232475139 1136017416 1524295167
#>  [13] 1426763054 1085709751  718024778 1893655681  524303669 1986581059
#>  [19]  812132036  626215554  439637846  640597398  863142124 1276325061
#>  [25] 1083947009 1697657775 1109799786 1800353048 1036247721  770426970
#>  [31] 1422972995  585145178 1311460371 1224942117  800163805 1186288078
#>  [37] 2194374294 1179004039 1550151186  690514844  665066784 1457728244
#>  [43] 1340416729 1005633561  988219530 1163804357 2019609428 1810365923
#>  [49]  944348527 1350014516 1685059736 1068120639 1691385005 2082034143
#>  [55] 1447025244  943796075 2045470574 1420873777  707648814  653349704
#>  [61] 1471057561 1436128964 1550970115 1186032312  788508058 1265459073
#>  [67] 1829451696 1447903974  918204712 1312725482 1043733633  961860879
#>  [73]  781909574 1046090580  986760532  917758923  601335294 1321974824
#>  [79] 2438120829  833576485 1210382282 1738664778 1228776807 1648541762
#>  [85] 1400697543  995179656 1678005426 2072031752 1228366621  519232890
#>  [91] 1785013769  808690576 1978885855 2439935278 1264198838 2289052992
#>  [97] 2181566551 2450830549  430798470 2166454052
Created on 2021-10-12 by the reprex package (v2.0.1)

Edit: To calculate area within groups in your data

library(dplyr)
library(sf)

# I've loaded the data in your question as `df`
#
# I'll show how to calculate total areas for your group NAME,
# like you say in your question, but since there's only one
# unique value in your example data, I'll also make a dummy
# grouping variable to show the difference:

# Define dummy groups
df$id <- c(1,1,2,2,3)

# First, calculate the area of each polygon in your multipolygon
df$area <- st_area(df)

# Group by NAME and calculate a total area for each group.
# We expect this to return one area value, because there is only one group.

df %>% group_by(NAME) %>% summarize(st_union(geometry), area_NAME = sum(area))
#> Simple feature collection with 1 feature and 2 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -8.667223 ymin: 26.5 xmax: -7.293025 ymax: 27.19647
#> Geodetic CRS:  WGS 84
#> # A tibble: 1 x 3
#>   NAME                                           `st_union(geometry)`  area_NAME
#>   <chr>                                                 <POLYGON [°]>      [m^2]
#> 1 Mauritania ((-7.5 26.62099, -7.693611 26.73416, -7.711945 26.74389~     5.59e9

# Now group by the dummy variable and calculate a total area for each group.
# In this case, we have three groups (1,2,3), so we expect three area values.

df %>% group_by(id) %>% summarize(st_union(geometry), area_id = sum(area))
#> Simple feature collection with 3 features and 2 fields
#> Geometry type: GEOMETRY
#> Dimension:     XY
#> Bounding box:  xmin: -8.667223 ymin: 26.5 xmax: -7.293025 ymax: 27.19647
#> Geodetic CRS:  WGS 84
#> # A tibble: 3 x 3
#>      id                                           `st_union(geometry)`   area_id
#>   <dbl>                                                 <GEOMETRY [°]>     [m^2]
#> 1     1 MULTIPOLYGON (((-8.204445 27.0275, -8.5 27.19647, -8.5 27, -8~    1.30e9
#> 2     2 POLYGON ((-7.693611 26.73416, -7.711945 26.74389, -8 26.91053~    4.15e9
#> 3     3 POLYGON ((-7.5 26.62099, -7.5 26.5, -7.293025 26.5, -7.5 26.6~    1.39e8
Created on 2021-10-12 by the reprex package (v2.0.1)

Edit2: Merge grouped data to original data

> df2 <- df %>% group_by(id) %>% summarize(st_union(geometry), area_id = sum(area))

> merge(df, st_drop_geometry(df2), by = "id", all.x = TRUE)
Simple feature collection with 5 features and 4 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -8.667223 ymin: 26.5 xmax: -7.293025 ymax: 27.19647
Geodetic CRS:  WGS 84
  id       NAME             area          area_id
1  1 Mauritania  371871356 [m^2] 1295023668 [m^2]
2  1 Mauritania  923152312 [m^2] 1295023668 [m^2]
3  2 Mauritania 2683469487 [m^2] 4153042391 [m^2]
4  2 Mauritania 1469572903 [m^2] 4153042391 [m^2]
5  3 Mauritania  138546017 [m^2]  138546017 [m^2]
                        geometry
1 MULTIPOLYGON (((-8.155398 2...
2 MULTIPOLYGON (((-8.5 26.5, ...
3 MULTIPOLYGON (((-8 26.91053...
4 MULTIPOLYGON (((-7.5 26.620...
5 MULTIPOLYGON (((-7.293025 2...