英文:
median raster value for polygons by attribute that is not unique
问题
给定这个栅格和多边形数据集：
f <- system.file("ex/elev.tif", package="terra")r <- rast(f)fs <- system.file("ex/lux.shp", package="terra")v <- vect(fs)
如何提取多边形 v 中具有相同 "NAME_1" 值的栅格单元的中位数值，并将其添加到 v 的属性中？
请注意，"NAME_1" 在 v 中不是唯一的：
v# class : SpatVector# geometry : polygons# dimensions : 12, 6 (geometries, attributes)# extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax)# source : lux.shp# coord. ref. : lon/lat WGS 84 (EPSG:4326)# names : ID_1 NAME_1 ID_2 NAME_2 AREA POP# type : <num> <chr> <num> <chr> <num> <int># values : 1 Diekirch 1 Clervaux 312 18081# 1 Diekirch 2 Diekirch 218 32543# 1 Diekirch 3 Redange 259 18664
英文:
Given this this raster and polygon dataset
f <- system.file("ex/elev.tif", package="terra")r <- rast(f)fs <- system.file("ex/lux.shp", package="terra")v <- vect(fs)
How can I extract the median value of the raster cells for polygons with the same value of "NAME_1" in v, and add this to the attributes of v?
Please note that "NAME_1" is not unique in v:
v# class : SpatVector# geometry : polygons# dimensions : 12, 6 (geometries, attributes)# extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax)# source : lux.shp# coord. ref. : lon/lat WGS 84 (EPSG:4326)# names : ID_1 NAME_1 ID_2 NAME_2 AREA POP# type : <num> <chr> <num> <chr> <num> <int># values : 1 Diekirch 1 Clervaux 312 18081# 1 Diekirch 2 Diekirch 218 32543# 1 Diekirch 3 Redange 259 18664
答案1
得分: 1
为了计算所有几何体的中位数，您可以执行以下操作：
library(terra)r <- rast(system.file("ex/elev.tif", package="terra"))v <- vect(system.file("ex/lux.shp", package="terra"))v$median = extract(r, v, fun=median, na.rm=TRUE, ID=FALSE)v# class : SpatVector# geometry : polygons# dimensions : 12, 7 (几何体, 属性)# extent : 5.74414, 6.528252, 49.44781, 50.18162 (最小x, 最大x, 最小y, 最大y)# source : lux.shp# coord. ref. : lon/lat WGS 84 (EPSG:4326)# names : ID_1 NAME_1 ID_2 NAME_2 AREA POP median# type : <num> <chr> <num> <chr> <num> <int> <num># values : 1 Diekirch 1 Clervaux 312 18081 467.1# 1 Diekirch 2 Diekirch 218 32543 333.9# 1 Diekirch 3 Redange 259 18664 377.4
由于"NAME_1"对于多个几何体具有相同的值，我们需要多做一些工作。您可以选择执行以下操作之一：
a <- aggregate(v, "NAME_1")e <- extract(r, a, fun=median, na.rm=TRUE)e$NAME_1 <- a$NAME_1[e$ID]e$ID <- NULLve <- merge(v, e, by="NAME_1")
或者
ee <- extract(r, v, na.rm=TRUE)ee$NAME_1 <- v$NAME_1[ee$ID]ee <- aggregate(ee[,2,drop=FALSE], ee[,"NAME_1",drop=FALSE], median, na.rm=TRUE)vee <- merge(v, ee, by="NAME_1")values(vee)# NAME_1 ID_1 ID_2 NAME_2 AREA POP elevation#1 Diekirch 1 1 Clervaux 312 18081 422.0#2 Diekirch 1 2 Diekirch 218 32543 422.0#3 Diekirch 1 3 Redange 259 18664 422.0#4 Diekirch 1 4 Vianden 76 5163 422.0#5 Diekirch 1 5 Wiltz 263 16735 422.0#6 Grevenmacher 2 6 Echternach 188 18899 288.5#7 Grevenmacher 2 7 Remich 129 22366 288.5#8 Grevenmacher 2 12 Grevenmacher 210 29828 288.5#9 Luxembourg 3 8 Capellen 185 48187 314.0#10 Luxembourg 3 9 Esch-sur-Alzette 251 176820 314.0#11 Luxembourg 3 10 Luxembourg 237 182607 314.0#12 Luxembourg 3 11 Mersch 233 32112 314.0
英文:
To compute the median for all geometries, you can do
library(terra)r <- rast(system.file("ex/elev.tif", package="terra") )v <- vect(system.file("ex/lux.shp", package="terra"))v$median = extract(r, v, fun=median, na.rm=TRUE, ID=FALSE)v# class : SpatVector# geometry : polygons# dimensions : 12, 7 (geometries, attributes)# extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax)# source : lux.shp# coord. ref. : lon/lat WGS 84 (EPSG:4326)# names : ID_1 NAME_1 ID_2 NAME_2 AREA POP median# type : <num> <chr> <num> <chr> <num> <int> <num># values : 1 Diekirch 1 Clervaux 312 18081 467.1# 1 Diekirch 2 Diekirch 218 32543 333.9# 1 Diekirch 3 Redange 259 18664 377.4
Since "NAME_1" has the same value for multiple geometries, we have to do a bit more work. You can do either
a <- aggregate(v, "NAME_1")e <- extract(r, a, fun=median, na.rm=TRUE)e$NAME_1 <- a$NAME_1[e$ID]e$ID <- NULLve <- merge(v, e, by="NAME_1")
or
ee <- extract(r, v, na.rm=TRUE)ee$NAME_1 <- v$NAME_1[ee$ID]ee <- aggregate(ee[,2,drop=FALSE], ee[,"NAME_1",drop=FALSE], median, na.rm=TRUE)vee <- merge(v, ee, by="NAME_1")values(vee)# NAME_1 ID_1 ID_2 NAME_2 AREA POP elevation#1 Diekirch 1 1 Clervaux 312 18081 422.0#2 Diekirch 1 2 Diekirch 218 32543 422.0#3 Diekirch 1 3 Redange 259 18664 422.0#4 Diekirch 1 4 Vianden 76 5163 422.0#5 Diekirch 1 5 Wiltz 263 16735 422.0#6 Grevenmacher 2 6 Echternach 188 18899 288.5#7 Grevenmacher 2 7 Remich 129 22366 288.5#8 Grevenmacher 2 12 Grevenmacher 210 29828 288.5#9 Luxembourg 3 8 Capellen 185 48187 314.0#10 Luxembourg 3 9 Esch-sur-Alzette 251 176820 314.0#11 Luxembourg 3 10 Luxembourg 237 182607 314.0#12 Luxembourg 3 11 Mersch 233 32112 314.0
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