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:
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
Stable version
install.packages("geoops")
Dev version
remotes::install_github("sckott/geoops")
See the vignette (link here) to get started.
FIXME!!! remove rgeos stuff as that pkg is gone
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 )
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 )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
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)
geoops in R doing citation(package = 'geoops')RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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