geoops

geoops does spatial operations on GeoJSON.

geoops is inspired by the JS library turf (http://turfjs.org/). It's tagline is Advanced geospatial analysis for browsers and node. Turf works only with GeoJSON, as does geoops. I don't know JS that well, but it's easy enough to understand the language, so I've been porting Turf to C++ wrapped up in R. The C++ so we can have fast performance. We've also wrapped the Turf JS library itself in the package lawn (https://github.com/ropensci/lawn), but we should be able to get better performance out of C++.

geoops has a ways to go to include all the methods that Turf has, but we'll get there eventually.

All data is expected to be in WGS-84.

We use a library from Niels Lohmann (https://github.com/nlohmann/json) for working with JSON in C++.

See also:

• geojson: https://github.com/ropensci/geojson

Package API:

#>  - geo_bearing
#>  - geo_midpoint
#>  - geo_bbox_polygon
#>  - geo_pointgrid
#>  - geo_area
#>  - geo_get_coords
#>  - version
#>  - geo_nearest
#>  - geo_along
#>  - geo_distance
#>  - geo_destination
#>  - geo_trianglegrid
#>  - geo_planepoint
#>  - geo_line_distance

Installation

Stable version

install.packages("geoops")

Dev version

remotes::install_github("sckott/geoops")

library("geoops")

See the vignette (link here) to get started.

comparison to rgeos

distance

pt1 <- '{"type":"Feature","properties":{"marker-color":"#f00"},"geometry":{"type":"Point","coordinates":[-75.343,39.984]}}'
pt2 <- '{"type":"Feature","properties":{"marker-color":"#0f0"},"geometry":{"type":"Point","coordinates":[-75.534,39.123]}}'
library(rgeos)
rgeospt1 <- rgeos::readWKT("POINT(0.5 0.5)")
rgeospt2 <- rgeos::readWKT("POINT(2 2)")

microbenchmark::microbenchmark(
  rgeos = rgeos::gDistance(rgeospt1, rgeospt2),
  geoops = geoops::geo_distance(pt1, pt2, units = "miles"),
  times = 10000L
)
#> Unit: microseconds
#>    expr    min      lq     mean median      uq      max neval
#>   rgeos 24.096 25.5240 29.45499 26.835 27.8475 1923.960 10000
#>  geoops 27.826 29.3715 32.47806 30.294 31.4760 3126.253 10000

nearest

point1 <- '{"type":["Feature"],"properties":{"marker-color":["#0f0"]},"geometry":{"type":["Point"],"coordinates":[28.9658,41.0101]}}'
point2 <- '{"type":["FeatureCollection"],"features":[{"type":"Feature","properties":{},"geometry":{"type":"Point","coordinates":[28.9739,41.0111]}},{"type":"Feature","properties":{},"geometry":{"type":"Point","coordinates":[28.9485,41.0242]}},{"type":"Feature","properties":{},"geometry":{"type":"Point","coordinates":[28.9387,41.0133]}}]}'
points <- '{"type":"FeatureCollection","features":[{"type":"Feature","properties":{},"geometry":{"type":"Point","coordinates":[28.9739,41.0111]}},{"type":"Feature","properties":{},"geometry":{"type":"Point","coordinates":[28.9485,41.0242]}},{"type":"Feature","properties":{},"geometry":{"type":"Point","coordinates":[28.9387,41.0133]}}]}'
g1 <- readWKT("MULTILINESTRING((34 54, 60 34), (0 10, 50 10, 100 50))")
g2 <- readWKT("POINT(100 30)")

microbenchmark::microbenchmark(
  rgeos = rgeos::gNearestPoints(g1, g2),
  geoops = geoops::geo_nearest(point1, points),
  times = 10000L
)
#> Unit: microseconds
#>    expr     min       lq     mean   median       uq       max neval
#>   rgeos 435.978 452.3660 535.2996 461.2875 495.9435 44073.248 10000
#>  geoops  99.798 110.8965 129.7795 127.7090 132.6170  5960.852 10000

Example use case

expand

Get some GeoJSON data, a FeatureCollection of Polygons

file <- system.file("examples/zillow_or.geojson", package = "geoops")
x <- paste0(readLines(file), collapse = "")

Break each polygon into separate JSON string

library("jqr")
polys <- unclass(jq(x, ".features[]"))

Using geo_area, calculate the area of the polygon

areas <- vapply(polys, geo_area, 1, USE.NAMES = FALSE)

Visualize area of the polygons as a histogram

hist(areas, main = "")

Visualize some of the polygons, all of them

library(leaflet)
leaflet() %>%
  addProviderTiles(provider = "OpenStreetMap.Mapnik") %>%
  addGeoJSON(geojson = x) %>%
  setView(lng = -123, lat = 45, zoom = 7)

Just one of them

leaflet() %>%
  addProviderTiles(provider = "OpenStreetMap.Mapnik") %>%
  addGeoJSON(geojson = polys[1]) %>%
  setView(lng = -122.7, lat = 45.48, zoom = 13)

Meta

• Please report any issues or bugs.
• License: MIT
• Get citation information for geoops in R doing citation(package = 'geoops')
• Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.