rmapshaper Basics

Andy Teucher

2016-06-29

rmapshaper is a package providing access to the awesome mapshaper tool by Matthew Bloch, which has both a Node.js command-line tool as well as an interactive web tool.

I wrote this package so that I could use mapshaper’s Visvalingam simplification method in R. There is, as far as I know, no other R package that performs topologically-aware multi-polygon simplification. (This means that shared boundaries between adjacent polygons are always kept intact, with no gaps or overlaps, even at high levels of simplification).

At this time, rmapshaper provides the following functions:

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

Usage

rmapshaper works with geojson strings (character objects of class geo_json) and list geojson objects of class geo_list. These classes are defined in the geojsonio package. It also works with Spatial classes from the sp package.

We will use the states dataset from the geojsonio package and first turn it into a geo_json object:

library(geojsonio)
## 
## We recommend using rgdal v1.1-1 or greater, but we don't require it
## rgdal::writeOGR in previous versions didn't write
## multipolygon objects to geojson correctly.
## See https://stat.ethz.ch/pipermail/r-sig-geo/2015-October/023609.html
## 
## Attaching package: 'geojsonio'
## The following object is masked from 'package:base':
## 
##     pretty
library(rmapshaper)
library(sp)

states_json <- geojson_json(states, geometry = "polygon", group = "group")
## Assuming 'long' and 'lat' are longitude and latitude, respectively

For ease of illustration via plotting, we will convert to a SpatialPolygonsDataFrame:

states_sp <- geojson_sp(states_json)

## Plot the original
plot(states_sp)

Now simplify using default parameters, then plot the simplified states

states_simp <- ms_simplify(states_sp)
plot(states_simp)

You can see that even at very high levels of simplification, the mapshaper simplification algorithm preserves the topology, including shared boudaries:

states_very_simp <- ms_simplify(states_sp, keep = 0.001)
plot(states_very_simp)

Compare this to the output using rgeos::gSimplify, where overlaps and gaps are evident:

library(rgeos)
## rgeos version: 0.3-19, (SVN revision 524)
##  GEOS runtime version: 3.5.0-CAPI-1.9.0 r4084 
##  Linking to sp version: 1.2-3 
##  Polygon checking: TRUE
states_gsimp <- gSimplify(states_sp, tol = 1, topologyPreserve = TRUE)
plot(states_gsimp)

All of the functions are quite fast with geo_json character objects and geo_list list objects. They are slower with the Spatial classes due to internal conversion to/from json. If you are going to do multiple operations on large Spatial objects, it’s recommended to first convert to json using geojson_list or geojson_json from the geojsonio package. 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 states_sp as created above:

library(geojsonio)
library(rmapshaper)
library(sp)
library(magrittr)

## First convert 'states' dataframe from geojsonio pkg to json
states_json <- geojson_json(states, lat = "lat", lon = "long", group = "group", 
                            geometry = "polygon")

states_json %>% 
  ms_erase(bbox = c(-107, 36, -101, 42)) %>% # Cut a big hole in the middle
  ms_dissolve() %>% # Dissolve state borders
  ms_simplify(keep_shapes = TRUE, explode = TRUE) %>% # Simplify polygon
  geojson_sp() %>% # Convert to SpatialPolygonsDataFrame
  plot(col = "blue") # plot