geo-infer-math
Spatial statistics, topology, and graph theory for geospatial analysis. Use when computing Moran's I, spatial autocorrelation, geodesic distances, graph connectivity, kernel density estimation, or any mathematical operation on geographic data.
Best use case
geo-infer-math is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Spatial statistics, topology, and graph theory for geospatial analysis. Use when computing Moran's I, spatial autocorrelation, geodesic distances, graph connectivity, kernel density estimation, or any mathematical operation on geographic data.
Teams using geo-infer-math should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-MATH/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-math Compares
| Feature / Agent | geo-infer-math | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Spatial statistics, topology, and graph theory for geospatial analysis. Use when computing Moran's I, spatial autocorrelation, geodesic distances, graph connectivity, kernel density estimation, or any mathematical operation on geographic data.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GEO-INFER-MATH
## Instructions
Foundation module with zero internal dependencies. Provides mathematical primitives consumed by all other modules.
### Core Capabilities
- **Spatial statistics**: Moran's I, Geary's C, Getis-Ord G*, LISA, semivariograms
- **Topology**: Voronoi tessellation, Delaunay triangulation, spatial indexing
- **Graph theory**: Network analysis, shortest paths, centrality measures
- **Kernel density**: Gaussian, Epanechnikov, adaptive bandwidth KDE
- **Distance metrics**: Haversine, Vincenty, geodesic on WGS84 ellipsoid
### Key Imports
```python
from geo_infer_math.core.spatial_statistics import MoranI, GearysC, GetisOrd
from geo_infer_math.core.topology import VoronoiTessellation, DelaunayTriangulation
from geo_infer_math.core.graph_theory import SpatialGraph, CentralityAnalyzer
from geo_infer_math.core.kernel_density import KernelDensityEstimator
```
## Examples
```python
from geo_infer_math.core.spatial_statistics import MoranI
import numpy as np
values = np.random.randn(100)
weights = np.random.rand(100, 100)
moran = MoranI(values, weights)
result = moran.compute()
print(f"Moran's I: {result.statistic}, p-value: {result.p_value}")
```
## Guidelines
- All distance calculations default to WGS84 ellipsoid
- Weight matrices should be row-standardized for spatial statistics
- This module has no external geo-dependencies — pure numpy/scipy
- Test: `uv run python -m pytest GEO-INFER-MATH/tests/ -v`
### Integrations
- **BAYES** → Spatial statistics feeding Bayesian priors
- **SPACE** → H3 spatial weights for autocorrelation
- **SPM** → Statistical parametric map computation
- **AI** → Spatial feature engineering for ML
- **EDU** → Spatial statistics teaching exercises