计算r中shapefile中变量的表面积
Calculate the surface area of a variable in a shapefile in r
我的 shapefile 代表一个大陆。它有很多多边形（因为有几个层）。
我想为不同的变量计算曲面 area/squarekm，并将结果放在一列中，即：
每个国家的总平方公里（NAME 变量）：它会给我每个国家多边形的平方公里。
每个 AEZ 的总平方公里（AEZ 变量）：它会给我每个 AEZ 区域的平方公里
等等
我在 Arcmap 中完成，但无法弄清楚如何在 R 中获得相同的结果。
我试过 Areapolygons 但它不起作用。
> 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"))
谢谢！
在 R 中加载你的多边形，确保它有一个合适的坐标系，然后使用 st_area()，returns 你的多边形中每个多边形（行）的面积。
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$geometry)
nrow(nc)
#> [1] 100
st_area(nc)
#> Units: [m^2]
#> [1] 1137107793 610916077 1423145355 694378925 1520366979 967504822
#> [7] 615794941 903423919 1179065710 1232475139 1136017416 1524295167
#> [13] 1426763054 1085709751 718024778 1893655681 524303669 1986581059
#> [19] 812132036 626215554 439637846 640597398 863142124 1276325061
#> [25] 1083947009 1697657775 1109799786 1800353048 1036247721 770426970
#> [31] 1422972995 585145178 1311460371 1224942117 800163805 1186288078
#> [37] 2194374294 1179004039 1550151186 690514844 665066784 1457728244
#> [43] 1340416729 1005633561 988219530 1163804357 2019609428 1810365923
#> [49] 944348527 1350014516 1685059736 1068120639 1691385005 2082034143
#> [55] 1447025244 943796075 2045470574 1420873777 707648814 653349704
#> [61] 1471057561 1436128964 1550970115 1186032312 788508058 1265459073
#> [67] 1829451696 1447903974 918204712 1312725482 1043733633 961860879
#> [73] 781909574 1046090580 986760532 917758923 601335294 1321974824
#> [79] 2438120829 833576485 1210382282 1738664778 1228776807 1648541762
#> [85] 1400697543 995179656 1678005426 2072031752 1228366621 519232890
#> [91] 1785013769 808690576 1978885855 2439935278 1264198838 2289052992
#> [97] 2181566551 2450830549 430798470 2166454052
Created on 2021-10-12 by the reprex package (v2.0.1)
编辑：计算数据中组内的面积
library(dplyr)
library(sf)
# I've loaded the data in your question as `df`
#
# I'll show how to calculate total areas for your group NAME,
# like you say in your question, but since there's only one
# unique value in your example data, I'll also make a dummy
# grouping variable to show the difference:
# Define dummy groups
df$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：将分组数据合并到原始数据
> 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...
我的 shapefile 代表一个大陆。它有很多多边形（因为有几个层）。
我想为不同的变量计算曲面 area/squarekm，并将结果放在一列中，即：
每个国家的总平方公里（NAME 变量）：它会给我每个国家多边形的平方公里。 每个 AEZ 的总平方公里（AEZ 变量）：它会给我每个 AEZ 区域的平方公里
等等
我在 Arcmap 中完成，但无法弄清楚如何在 R 中获得相同的结果。
我试过 Areapolygons 但它不起作用。
> 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"))
谢谢！
在 R 中加载你的多边形，确保它有一个合适的坐标系，然后使用 st_area()，returns 你的多边形中每个多边形（行）的面积。
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)
编辑：计算数据中组内的面积
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：将分组数据合并到原始数据
> 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...