domains / data / geospatial-vector-analysis / geopandas

geopandas

Skill card

combined

The four copies cover the same library but diverge on scope, version, and safety. K-Dense-AI is a strict, version-pinned (1.2.0) audit workflow with local CLI tools and a heavy privacy/safety contract. brycewang-stanford targets 1.1.3, includes PySAL/spatial statistics, and uses a decision-tree reference structure. foryourhealth111 and aipoch are general usage guides with standard installation and code examples. Because K-Dense's strict audit steps and brycewang's spatial statistics focus are material steps missing from the others, crowning one would hide real alternatives.

Repositories

RepositoryPath
K-Dense-AI/scientific-agent-skillsskills/geopandas/SKILL.md
foryourhealth111-pixel/Vibe-Skillsbundled/skills/geopandas/SKILL.md
aipoch/medical-research-skillsscientific-skills/Data Analysis/geopandas/SKILL.md
brycewang-stanford/Auto-Empirical-Research-Skillsskills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/geopandas/SKILL.md

Other copies

RoleRepositoryPath
peerK-Dense-AI/scientific-agent-skillsskills/geopandas/SKILL.md
peerforyourhealth111-pixel/Vibe-Skillsbundled/skills/geopandas/SKILL.md
peeraipoch/medical-research-skillsscientific-skills/Data Analysis/geopandas/SKILL.md
peerbrycewang-stanford/Auto-Empirical-Research-Skillsskills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/geopandas/SKILL.md
inferiorFreedomIntelligence/OpenClaw-Medical-Skillsskills/geopandas/SKILL.md

Combined skill

# GeoPandas

## Environment Setup
- **Standard**: `uv pip install geopandas` (plus optional `pyarrow`, `folium`, `mapclassify`).
- **Strict/Audit**: Use Python 3.12+ with pinned versions (e.g., `geopandas==1.2.0`, `shapely==2.1.2`) and local audit tools if provenance is critical.

## Core Workflow
1. **Load Data**: Use `gpd.read_file` for vector formats (GeoJSON, GPKG, SHP). Use `use_arrow=True` for performance if `pyarrow` is installed.
2. **CRS Management**: 
   - Check `gdf.crs`.
   - Use `set_crs` only if metadata is missing and known.
   - Use `to_crs` to transform coordinates. Always reproject to a local/equal-area CRS before calculating area or distance.
3. **Spatial Analysis**:
   - **Joins**: Use `gpd.sjoin` for predicate-based joins and `gpd.sjoin_nearest` for proximity.
   - **Overlay**: Use `gpd.overlay` for intersection/union/difference.
   - **Statistics**: For spatial autocorrelation or regression, load PySAL references.
4. **Visualization**: Use `.plot()` for static maps or `.explore()` for interactive folium maps.

## Safety & Correctness
- **Provenance**: Record source hashes and CRS for every derived artifact.
- **Privacy**: Generalize coordinates and avoid publishing exact addresses or small-area joins.
- **Validation**: Check for invalid geometries and ensure CRS units match the operation (e.g., meters for buffer).