The Spatial Data Science across Languages Community brings together developers and users from the common and emerging programming languages used for spatial data science.
Spatial data science (SDS) concerns the analysis of spatial data in various contexts. We focus broadly on geospatial and geographic space, with some applications to general image spaces, local reference frames - everything from microscopical to astronomical space.
Open source programming languages commonly used in spatial data science for analysis include Python, R and Julia. Our community is also interested in JavaScript and TypeScript, C++ and Rust. These languages are used by millions of users on a daily basis to solve spatial data problems, visualise and analyse spatial data.
A number of the challenges that we face transcend the particular programming languages. Such challenges range from: the interpretation of the underlying data; the way the data are represented in computers; visualisation; scalability and efficiency of implementations; the use of upstream libraries like GDAL, GEOS and PROJ, GIS interfaces; software distribution; and open source software community building.
The Spatial Data Science Across Languages Community aims to bring developers and users together to help build understanding and solve common problems, as well as discussing problems specific to particular language communities.
Annual Workshop
Our main activity since 2023 has been a series of annual workshops, which provide a space for bridging the various programming-language communities and establishing cross-language interaction between developers and users.
Discord
SDSL has a Discord server for discussions and communication during the workshops. Please join via https://discord.gg/HJRKEJsmrr.