# Yirgacheffe: A Declarative Approach to Geospatial Data

*2025-10-01 — paper*

Authors: Michael Winston Dales, Alison Eyres, Patrick Ferris, Francesca A. Ridley, Simon Tarr, Anil Madhavapeddy


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.


DOI: 10.1145/3759536.3763806
Classification: short
Venue: Proceedings of the 2nd ACM SIGPLAN International Workshop on Programming for the Planet
URL: https://dl.acm.org/doi/10.1145/3759536.3763806
Projects: Planetary Computing

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---
Canonical: https://anil.recoil.org/papers/2025-yirgacheffe
Type: paper
Tags: spatial, python, declarative, biodiversity, conference, plancomp