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Geospatial intersects with data.table, geos and sf

Vincent Thorne · Posted 09 Feb 2022

Working with large spatial data sets in R, I was delighted to discover Grant McDermott’s post on how to combine data.table and geos for blazing-fast spatial operations compared to the standard sf (keeping in mind that geos assumes planar geometries). The benchmark for the spatial operations he presents are very convincing, so decided to give it a go on the data set I’m currently working with, the infamous NYC taxi trip records.

In the example below, I am matching each trip pickup coordinates (points) with one of 263 taxi zones (polygons). In the end, I want to know how many taxi trips started in each zones. To find out to which taxi zone each pair of coordinates belongs, I use st_intersect from the sf package, and geos_intersects_matrix from geos. Surprisingly, the benchmark reveals that when intersecting points and polygons sf is about 40% faster compared to the data.table+geos team.

I am in no position to say what is happening behind the scenes here, but found it interesting to report and contrast with Grant’s findings: the advantages of using data.table+geos might depend on the type of spatial operation. There might also be a better way to implement the data.table+geos version in this case that I overlooked — if you have ideas, please let me know!

library(sf)
library(geos)
library(data.table)
library(microbenchmark)
options(timeout=10000)

rm(list = ls())
gc()

url_trips = "https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2015-01.csv"

download.file(url = url_trips, destfile = "trip_records.csv", mode = "wb") # takes some time and is pretty heavy!
trips.dt = fread("trip_records.csv", nrows = 100000) # enough rows to benchmark performance

# Keep only pickup (origin) coordinate columns
cols_drop = grep("pickup_l", names(trips.dt), value = T, invert = T)
trips.dt[, (cols_drop) := NULL]
trips.dt = trips.dt[!(pickup_longitude == 0 | pickup_latitude == 0)] # obviously invalid coordinates

# Generate geom
trips.dt[, `:=`(o_geom = geos_make_point(pickup_longitude, 
                                         pickup_latitude, 
                                         crs=4326))]

# Generate sf
trips.sf = st_as_sf(trips.dt)
trips.sf = st_transform(trips.sf, 4326)

# Get polygons to intersect
url_zones = "https://s3.amazonaws.com/nyc-tlc/misc/taxi_zones.zip"
download.file(url_zones, destfile = "zones_polygons.zip", mode = "wb")
unzip("zones_polygons.zip", exdir = file.path("zones"))

# sf version
zones.sf = st_read(file.path("zones", "taxi_zones.shp"))
zones.sf = st_transform(zones.sf, 4326)

# geom version
zones.dt = as.data.table(zones.sf)[, geom := as_geos_geometry(geometry, crs = 4326)]
zones.dt[, geometry := NULL]

# Functions to test
geos.dt = function(x) {
  geos_intersects_matrix(zones.dt[, geom], trips.dt[, o_geom])
}

sf = function(x) {
  st_intersects(zones.sf, trips.sf)
}

# Benchmark
microbenchmark(geos = geos.dt(),
               sf = sf(),
               times = 10)

# Unit: seconds
#  expr      min       lq     mean   median       uq      max neval
#  geos 3.406296 3.823752 4.388384 4.226851 4.992151 6.192083    10
#    sf 2.431363 2.493557 2.894264 2.870200 3.118318 3.941235    10