Yirgacheffe: A Declarative Approach to Geospatial Data (Video, PROPL 2025) Michael Winston Dales, Alison Eyres, Patrick Ferris, Francesca A. Ridley, Simon Tarr, and Anil Madhavapeddy (University of Cambridge, UK; University of Cambridge, UK; University of Cambridge, UK; Newcastle University, UK; IUCN, UK; University of Cambridge, UK)

Abstract: We present Yirgacheffe, a declarative geospatial library that allows spatial algorithms to be implemented concisely, supports parallel execution, and avoids common errors by automatically handling data (large geospatial rasters) and resources (cores, memory, GPUs). Our primary user domain comprises ecologists, where a typical problem involves cleaning messy occurrence data, overlaying it over tiled rasters, combining layers, and deriving actionable insights from the results. We describe the successes of this approach towards driving key pipelines related to global biodiversity and describe the capability gaps that remain, hoping to motivate more research into geospatial domain-specific languages.

Article: https://doi.org/10.1145/3759536.3763806

ORCID: https://orcid.org/0009-0003-0832-4114, https://orcid.org/0000-0001-7866-7559, https://orcid.org/0000-0002-0778-8828, https://orcid.org/0000-0001-6068-7519, https://orcid.org/0000-0001-8464-1240, https://orcid.org/0000-0001-8954-2428

Video Tags: Declarative, Geospatial, Python, Biodiversity, splashws25proplmain-p63-p, doi:10.1145/3759536.3763806, orcid:0009-0003-0832-4114, orcid:0000-0001-7866-7559, orcid:0000-0002-0778-8828, orcid:0000-0001-6068-7519, orcid:0000-0001-8464-1240, orcid:0000-0001-8954-2428

Presentation at the PROPL 2025 conference, October 12–18, 2025, https://conf.researchr.org/home/icfp-splash-2025/propl-2025 Sponsored by ACM SIGPLAN, ACM SIGAda,