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GeoPandas Spatial Data Guide

A rigorous GeoPandas 1.1.4 playbook with correctness gates for CRS, geometry validity, join cardinality and exports, plus six local audit CLIs.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Pins a reproducible GeoPandas 1.1.4 environment (Python 3.12 with exact package versions) via uv.
  • Enforces eight correctness gates before trusting a result: provenance/IDs, geometry state, CRS semantics, units, transform quality, topology/precision, cardinality, and an output contract.
  • Encodes hard-won rules on sjoin predicate directionality, overlay/clip/dissolve pitfalls, GeoParquet schema 1.0 vs 1.1, antimeridian handling, and safe PostGIS access.
  • Applies a privacy contract: exact coordinates, addresses, parcels and trajectories are sensitive; no automatic URL, /vsi*, archive loading or geocoding.
  • Ships six deterministic local CLIs for inventory, reprojection planning, validity reporting, join auditing, export planning and sensitive-coordinate release gating.

Who it's for

  • Data analysts and researchers handling administrative, infrastructure or mobility vector data.
  • Developers migrating GeoPandas 0.14-era code to the 1.x/Shapely 2 stack.
  • Teams that must generalize or redact location data before publishing.

Examples

  1. "Audit this GeoPackage's geometry and CRS" → geometry_validity_report.py classifies null/empty/invalid/mixed geometries and compares pre/post repair.
  2. "Spatially join EPSG:4326 points to zone polygons" → pick a justified projected CRS, state the predicate, then spatial_join_audit.py reports unmatched and multiplied rows.
  3. "Export the result to GeoParquet" → export_plan.py produces a schema/CRS/stable-ID contract and requires reopening the artifact to verify counts.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/geopandas/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/geopandas folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/geopandas/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sas && mkdir -p ~/.claude/skills && cp -r /tmp/sas/skills/geopandas ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal in your working directory.
  2. Clone the repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills directory: mkdir -p ~/.claude/skills
  4. Copy the skill: cp -r scientific-agent-skills/skills/geopandas ~/.claude/skills/
  5. Build the environment: run uv venv --python 3.12, then paste the uv pip install ... block from SKILL.md.
  6. Restart Claude Code and try a prompt like "Use GeoPandas to audit this shapefile".
  7. Verify the bundled tools with python ~/.claude/skills/geopandas/scripts/vector_inventory.py --help.
View source on GitHub ↗License: MIT