CODEWITHSUNDEEP
rgisspatialr-sprgeo
Konrad
Konrad

Reputation: 18585

Simplifying polygons in rgeos and maintaining data in SpatialPolygonsDataFrame

Background

I'm interested in simplifying polygons with use of the gSimplify function available through the rgeos package.

Reproducible example

A reproducible example can be generated with use of the code below:

# Data sourcing -----------------------------------------------------------

# Download an read US state shapefiles
tmp_shps <- tempfile()
tmp_dir <- tempdir()
download.file(
    "http://www2.census.gov/geo/tiger/GENZ2014/shp/cb_2014_us_state_20m.zip",
    tmp_shps
)
unzip(tmp_shps, exdir = tmp_dir)

# Libs
require(rgdal)
require(rgeos)

# Read
us_shps <- readOGR(dsn = tmp_dir, layer = "cb_2014_us_state_20m")

# Simplified --------------------------------------------------------------

# Simplifiy
us_shps_smpl <- gSimplify(spgeom = us_shps,
                          tol = 200,
                          topologyPreserve = TRUE)

Preview

par(mfrow = c(2,1))
plot(us_shps_smpl, main = "Simplified")
plot(us_shps, main = "Original")

Simplified and original polygons

Problem

In addittion to simplifying polygons the gSimplify function changed classes of the resulting object:

>> class(us_shps)
[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"
>> class(us_shps_smpl)
[1] "SpatialPolygons"
attr(,"package")
[1] "sp"

>> names(us_shps)
[1] "STATEFP"  "STATENS"  "AFFGEOID" "GEOID"    "STUSPS"   "NAME"     "LSAD"     "ALAND"    "AWATER"  
>> names(us_shps_smpl)
 [1] "0"  "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15" "16" "17" "18" "19"
[21] "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37" "38" "39"
[41] "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51"

Questions

Upvotes: 4

Views: 2487

Answers (1)

coletl
coletl

Reputation: 803

The sf package is based entirely on data frames, so its geometry manipulations always preserve the data attached to each feature. The package hasn't caught up with all the standard spatial packages in R yet, but it's fairly easy to go back and forth between sf and sp objects when you need more functionality.

Here, st_simplify() does the work, but you'll need to project your polygons first:

library(sf)

# Download and read example data
tmp_shps <- tempfile()
tmp_dir <- tempdir()
download.file(
  "http://www2.census.gov/geo/tiger/GENZ2014/shp/cb_2014_us_state_20m.zip",
  tmp_shps
)
unzip(tmp_shps, exdir = tmp_dir)

us_shps <- st_read(paste(tmp_dir, "cb_2014_us_state_20m.shp", sep = "/"))

# st_simplify needs a projected CRS
us_shps_merc <- st_transform(us_shps, 3857)
simple_us_merc <- st_simplify(us_shps_merc)

# Change back to original CRS
simple_us <- st_transform(simple_us_merc, st_crs(us_shps))

# Change to sp object, if you like
simple_us_sp <- as(st_zm(simple_us), "Spatial")

Upvotes: 4

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