This repository contains a collection of vector processing benchmarks for Python and R packages. The tests cover the most common operations such as loading and saving a geopackage file, sampling points in a polygon, creating buffers, transformating the coordinate system (CRS), calculating the distance between points, and intersecting geometries.
Note that all operations were performed in the Cartesian coordinate system excluding s2 package, where calculations were performed on the sphere (this may affects the longer calculation times). For more information, see the "Spherical geometry in sf using s2geometry" article and presentation at the FOSS4G 2021 conference.
It makes a huge difference to the operations performance in {geopandas} if the {pygeos} library is installed.
Its absence significantly slows down data processing.
{pygeos} is installed by default with version 2.0 of {Shapely}.
The detailed results are available at https://kadyb.github.io/vector-benchmark/report.html.
For high-performance data frames processing in R, check data.table and collapse.
You may also be interested in the raster processing benchmarks.
Python:
R:
- Generate the data from
data/folder in R. - Run all benchmarks using batch script (
run_benchmarks.sh) or single benchmarks files.
Batch script
cd vector-benchmark
./run_benchmarks.sh
Single benchmark
Rscript sf/buffer.R
python3 geopandas/buffer.py
The dataset is synthetically generated and consists of 300,000 points in a planar coordinate system.
- CPU: Intel Xeon CPU E5-2620 v2 @ 2.10GHz
- RAM: 64 GB
- OS: Pop!_OS 20.04 LTS