An engineer's notebook for spatial science. Every step is auditable, every coefficient is interrogable, every claim has a confidence interval.
Every prediction has a local model. Coefficients vary smoothly across space; uncertainty travels with them. Spatial heterogeneity is a feature, not noise.
The pipeline learns under directed causal structure. Edges have direction, signs are physically grounded, and counterfactuals respect the graph.
Increase canopy 12%. Lower impervious 8%. Get a posterior mean, σ, and a pixel-wise uncertainty raster — never a single number.
A simplified urban-heat run on Philadelphia census tracts. Every stage is reproducible from project.yml.
Four stages, hand-numbered. The same flow whether you're modeling drought across a watershed or noise around a runway.
Drop a project.yml, point at your raster + vector data, set CRS and time window. Or fork one of 13 templates.
SPARC proposes a causal graph from the correlogram; you accept, reject, or rewrite. Physics constraints (signs, monotones) are first-class.
Locally-weighted models train in parallel across the grid. Posterior traces stream live. χ² and σ gate every stage.
Compare adaptation scenarios on a single map. Export pixel-wise uncertainty rasters and a fully reproducible audit trail.
Tell us your domain and your CRS. We'll send a build with the right template pre-loaded and a sidecar binary that talks to your GPU.