# spatial-statistics

> Claude/Codex skill by [@muend](https://omgskills.com/library/muend/index.md) · 35 stars · Updated 2026-10-17

Always invoke before testing a geographic pattern for clustering, hotspots, dependence, or explanatory regression, even when aggregation or ordinary OLS is proposed as routine. Covers Moran's I, LISA, Getis-Ord Gi\*, weights, MAUP and scale sensitivity for areas/grids, residual dependence, and spatial lag/error/GWR/MGWR models. Use ML standards for predictive evaluation and geostatistics for continuous surfaces from sparse samples.

Tags: agent-skills, ai, ai-agents, claude-code, codex, geoai, geospatial, gis, github-copilot, google-earth-engine, lidar, machine-learning, postgis, remote-sensing, spatial-analysis, spatial-data-science

## Install

```sh
git clone https://github.com/muend/geoai-skills /tmp/geoai-skills && ln -s /tmp/geoai-skills/skills/spatial-statistics ~/.claude/skills/spatial-statistics
```

## From README

Spatial Statistics Purpose: answer "is it clustered, where, and why" with defensible inference. The core discipline: spatial data violates independence assumptions, so standard statistics silently overstate significance — every analysis here starts with weights design and ends with residual diagnostics. Spatial weights (W) — the analysis IS the weights Every result downstream depends on W; choose it for substantive reasons and run a sensitivity check with one alternative: | Weights | Use when | |---|---| | Queen/Rook contiguity | Irregular polygons (admin units, parcels) | | K-nearest neighbors | Points; islands present (contiguity leaves them unconnected) | | Distance band | Physical process with known range | | Kernel (distance-decayed) | Smooth influence, GWR-style local models | Always report: weights type, parameters, number of islands, and whether results survive an alternative W.

## Links

- HTML page: https://omgskills.com/library/muend/geoai-skills/skills/spatial-statistics/
- GitHub: https://github.com/muend/geoai-skills
- Author: [@muend](https://omgskills.com/library/muend/index.md)

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