Aggregating spatial feature objects¶
The following makes use of the sf package. You may need to install it from
CRAN using the code
install.packages("sf")
if you want to run this on your computer. (The
package is already installed on the notebook container, however.)
library(sf)
Linking to GEOS 3.9.0, GDAL 3.2.2, PROJ 7.2.1
The files “south-america-1990.RData”, “ged101.RData”, and “cshapes-1990.RData” where created by an earlier example.
load("south-america-1990.RData")
load("ged191.RData")
load("cshapes-1990.RData")
ged191_ellips <- st_transform(ged191,st_crs(cshapes.1990))
# Civilian deaths per country
aggregate(ged191_ellips["deaths_civilians"],by=SthAmCountries,sum)
although coordinates are longitude/latitude, st_intersects assumes that they are planar
Simple feature collection with 12 features and 1 field
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -109.4461 ymin: -55.90223 xmax: -34.79292 ymax: 12.59027
CRS: +proj=longlat +ellps=WGS84
First 10 features:
deaths_civilians geometry
0 20 MULTIPOLYGON (((-58.17262 6...
1 NA MULTIPOLYGON (((-55.12796 5...
3 792 MULTIPOLYGON (((-66.11835 1...
6 0 MULTIPOLYGON (((-71.8632 -4...
7 0 MULTIPOLYGON (((-62.19884 -...
8 164 MULTIPOLYGON (((-52.16862 -...
9 NA MULTIPOLYGON (((-109.4092 -...
10 15 MULTIPOLYGON (((-91.65224 -...
11 0 MULTIPOLYGON (((-57.67267 -...
12 1021 MULTIPOLYGON (((-69.49973 -...
# Civilian deaths per country, with country names
within(
aggregate(ged191_ellips["deaths_civilians"],by=SthAmCountries,sum),
country <- SthAmCountries$CNTRY_NAME)
although coordinates are longitude/latitude, st_intersects assumes that they are planar
Simple feature collection with 12 features and 2 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -109.4461 ymin: -55.90223 xmax: -34.79292 ymax: 12.59027
CRS: +proj=longlat +ellps=WGS84
First 10 features:
deaths_civilians geometry country
0 20 MULTIPOLYGON (((-58.17262 6... Guyana
1 NA MULTIPOLYGON (((-55.12796 5... Suriname
3 792 MULTIPOLYGON (((-66.11835 1... Venezuela
6 0 MULTIPOLYGON (((-71.8632 -4... Argentina
7 0 MULTIPOLYGON (((-62.19884 -... Bolivia
8 164 MULTIPOLYGON (((-52.16862 -... Brazil
9 NA MULTIPOLYGON (((-109.4092 -... Chile
10 15 MULTIPOLYGON (((-91.65224 -... Ecuador
11 0 MULTIPOLYGON (((-57.67267 -... Paraguay
12 1021 MULTIPOLYGON (((-69.49973 -... Peru
st_circ <- function(x,dist.km){
dist.degr <- 360*dist.km/40007.863
st_buffer(st_geometry(x),dist=dist.degr)
}
Bogota.region <- st_circ(Bogota,dist.km=200)
Colombia.rest <- st_difference(st_geometry(Colombia),Bogota.region)
Warning in st_buffer.sfc(st_geometry(x), dist = dist.degr):
st_buffer does not correctly buffer longitude/latitude data
dist is assumed to be in decimal degrees (arc_degrees).
although coordinates are longitude/latitude, st_difference assumes that they are planar
# Civilian deaths in the Bogota region and the rest of Colombia
as.data.frame(
aggregate(ged191_ellips["deaths_civilians"],
by=c(Bogota.region,Colombia.rest),
sum))
although coordinates are longitude/latitude, st_intersects assumes that they are planar
deaths_civilians geometry
1 1021 POLYGON ((-72.30035 4.6, -7...
2 4994 MULTIPOLYGON (((-81.70473 1...
- R file: spatial-aggregates.R
- Rmarkdown file: spatial-aggregates.Rmd
- Jupyter notebook file: spatial-aggregates.ipynb
- Interactive version of the Jupyter notebook (shuts down after 60s):
- Interactive version of the Jupyter notebook (sign in required):