rmapshaper Basics

The main advantage of the package is the availability of the topologically-aware simplification algorithm in ms_simplify (provided by the simplify tool in mapshaper). This means that shared boundaries between adjacent polygons are always kept intact, with no gaps or overlaps, even at high levels of simplification. It uses the Visvalingam simplification method.

This short vignette focuses on simplifying polygons with the ms_simplify function.

Usage

rmapshaper works with sf objects as well as geojson strings (character objects of class geo_json). It also works with Spatial classes from the sp package, though this will likely be retired in the future; users are encouraged to use the more modern sf package.

We will use the nc.gpkg file (North Carolina county boundaries) from the sf package and read it in as an sf object:

library(rmapshaper)
library(sf)

## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

file <- system.file("gpkg/nc.gpkg", package = "sf")
nc_sf <- read_sf(file)

Plot the original:

plot(nc_sf["FIPS"])

Now simplify using default parameters, then plot the simplified North Carolina counties:

nc_simp <- ms_simplify(nc_sf)
plot(nc_simp["FIPS"])

You can see that even at very high levels of simplification, the mapshaper simplification algorithm preserves the topology, including shared boundaries. The keep parameter specifies what proportion of vertices to keep:

nc_very_simp <- ms_simplify(nc_sf, keep = 0.001)
plot(nc_very_simp["FIPS"])

Compare this to the output using sf::st_simplify, where overlaps and gaps are evident:

nc_stsimp <- st_simplify(nc_sf, preserveTopology = TRUE, dTolerance = 10000) # dTolerance specified in meters
plot(nc_stsimp["FIPS"])

This time we’ll demonstrate the ms_innerlines function:

nc_sf_innerlines <- ms_innerlines(nc_sf)
plot(nc_sf_innerlines)

All of the functions are quite fast with geojson character objects. They are slower with the sf and Spatial classes due to internal conversion to/from json. If you are going to do multiple operations on large sf objects, it’s recommended to first convert to json using geojsonsf::sf_geojson(), or geojsonio::geojson_json(). All of the functions have the input object as the first argument, and return the same class of object as the input. As such, they can be chained together. For a totally contrived example, using nc_sf as created above:

library(geojsonsf)
library(rmapshaper)
library(sf)

## First convert 'states' dataframe from geojsonsf pkg to json

nc_sf %>% 
  sf_geojson() |> 
  ms_erase(bbox = c(-80, 35, -79, 35.5)) |>  # Cut a big hole in the middle
  ms_dissolve() |>  # Dissolve county borders
  ms_simplify(keep_shapes = TRUE, explode = TRUE) |> # Simplify polygon
  geojson_sf() |> # Convert to sf object
  plot(col = "blue") # plot

Using the system mapshaper

Sometimes if you are dealing with a very large spatial object in R, rmapshaper functions will take a very long time or not work at all. As of version 0.4.0, you can make use of the system mapshaper library if you have it installed. This will allow you to work with very large spatial objects.

First make sure you have mapshaper installed:

check_sys_mapshaper()

## mapshaper version 0.6.25 is installed and on your PATH

##                     mapshaper-xl 
## "/opt/homebrew/bin/mapshaper-xl"

If you get an error, you will need to install mapshaper. First install node (https://nodejs.org/en) and then install mapshaper in a command prompt with:

$ npm install -g mapshaper

Then you can use the sys argument in any rmapshaper function:

nc_simp_sys <- ms_simplify(nc_sf, sys = TRUE)

plot(nc_simp_sys[, "FIPS"])

rmapshaper Basics

Andy Teucher

2023-04-10

Usage

Using the system mapshaper