2023-03-07, 14:00–14:30, Auditorium
We discuss differences and correspondences between the R and Python spatial software ecosystems, upstream libraries, and efforts and opportunities to further mutually benefit each other.
R and Python are both used in spatial data science mostly for transient processing: data is read, results are computed, and numbers, tables or figures are created after which the process stops. Both benefit largely from the same set of upstream libraries for reading and writing (GDAL/OGR), geometric operations and spatial indexes (e.g. GEOS, s2geometry, h3), and coordinate conversion and transformation (PROJ), and this commonality creates a large amount of consensus across scientific and industry applications. Both also benefit from large but different sets of capacity that the respective language ecosystem brings. The ability to use both in an integrated setting, R in Python (r2py) or Python in R (reticulate) also increases, as do systems built to cater both languages e.g. for analysing satellite imagery archives (openEO) or scientific publishing (quarto). We will discuss the status quo, and future potential. A joint focus on emerging cloud-ready data formats (including Zarr, GeoParquet, GeoArrow) brings further synergies and might bring zero-copy data transfer between the languages.