domains / data / geospatial-vector-analysis / geopandas
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.
| Repository | Path |
|---|---|
| K-Dense-AI/scientific-agent-skills | skills/geopandas/SKILL.md |
| foryourhealth111-pixel/Vibe-Skills | bundled/skills/geopandas/SKILL.md |
| aipoch/medical-research-skills | scientific-skills/Data Analysis/geopandas/SKILL.md |
| brycewang-stanford/Auto-Empirical-Research-Skills | skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/geopandas/SKILL.md |
| Role | Repository | Path |
|---|---|---|
| peer | K-Dense-AI/scientific-agent-skills | skills/geopandas/SKILL.md |
| peer | foryourhealth111-pixel/Vibe-Skills | bundled/skills/geopandas/SKILL.md |
| peer | aipoch/medical-research-skills | scientific-skills/Data Analysis/geopandas/SKILL.md |
| peer | brycewang-stanford/Auto-Empirical-Research-Skills | skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/geopandas/SKILL.md |
| inferior | FreedomIntelligence/OpenClaw-Medical-Skills | skills/geopandas/SKILL.md |
# 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).