GeoPandas Spatial Analysis

What it does

When your agent uses it

• Reprojecting a dataset to another coordinate reference system
• Joining points to polygons with a spatial join
• Clipping or dissolving boundaries before calculating areas
• Converting between shapefile, GeoJSON and GeoPackage formats

Example prompts

• “Reproject counties.shp to a metric CRS and calculate each county's area.”
• “Spatial-join the stores.geojson points to the neighborhoods polygons and count stores per neighborhood.”
• “Make a choropleth of population by district from districts.gpkg and save it as a PNG.”

Requirements

• Python with `geopandas`
• `folium` for interactive maps (optional)

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Use spatial indexing: GeoPandas creates spatial indexes automatically for most operations
2. 2Filter during read: Use bbox, mask, or where parameters to load only needed data
3. 3Use Arrow for I/O: Add use_arrow=True for 2-4x faster reading/writing
4. 4Simplify geometries: Use .simplify() to reduce complexity when precision isn't critical
5. 5Batch operations: Vectorized operations are much faster than iterating rows
6. 6Use appropriate CRS: Projected CRS for area/distance, geographic for visualization

What it can do on your machine

Read from SKILL.md and the folder at commit 79182c5. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • uv

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

GeoPandas Spatial Analysis loads about 1.8k tokens when it runs, and up to ~7.9k if it reads all its reference files. Until then it costs about 169 tokens; SKILL.md has 320 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~169

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 79182c5, republished under its MIT licence (© davila7). 320 words, ~1,762 tokens.

name

geopandas

description

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

uv pip install geopandas

# For interactive maps
uv pip install folium

# For classification schemes in mapping
uv pip install mapclassify

# For faster I/O operations (2-4x speedup)
uv pip install pyarrow

# For PostGIS database support
uv pip install psycopg2
uv pip install geoalchemy2

# For basemaps
uv pip install contextily

# For cartographic projections
uv pip install cartopy

import geopandas as gpd

# Read spatial data
gdf = gpd.read_file("data.geojson")

# Basic exploration
print(gdf.head())
print(gdf.crs)
print(gdf.geometry.geom_type)

# Simple plot
gdf.plot()

# Reproject to different CRS
gdf_projected = gdf.to_crs("EPSG:3857")

# Calculate area (use projected CRS for accuracy)
gdf_projected['area'] = gdf_projected.geometry.area

# Save to file
gdf.to_file("output.gpkg")

# Read with filtering
gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax))

# Write with Arrow acceleration
gdf.to_file("output.gpkg", use_arrow=True)

# Check CRS
print(gdf.crs)

# Reproject (transforms coordinates)
gdf_projected = gdf.to_crs("EPSG:3857")

# Set CRS (only when metadata missing)
gdf = gdf.set_crs("EPSG:4326")

# Buffer by 10 units
buffered = gdf.geometry.buffer(10)

# Simplify with tolerance
simplified = gdf.geometry.simplify(tolerance=5, preserve_topology=True)

# Get centroids
centroids = gdf.geometry.centroid

# Spatial join (intersects)
joined = gpd.sjoin(gdf1, gdf2, predicate='intersects')

# Nearest neighbor join
nearest = gpd.sjoin_nearest(gdf1, gdf2, max_distance=1000)

# Overlay intersection
intersection = gpd.overlay(gdf1, gdf2, how='intersection')

# Dissolve by attribute
dissolved = gdf.dissolve(by='region', aggfunc='sum')

# Choropleth map
gdf.plot(column='population', cmap='YlOrRd', legend=True)

# Interactive map
gdf.explore(column='population', legend=True).save('map.html')

# Multi-layer map
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
gdf1.plot(ax=ax, color='blue')
gdf2.plot(ax=ax, color='red')

# 1. Load data
gdf = gpd.read_file("data.shp")

# 2. Check and transform CRS
print(gdf.crs)
gdf = gdf.to_crs("EPSG:3857")

# 3. Perform analysis
gdf['area'] = gdf.geometry.area
buffered = gdf.copy()
buffered['geometry'] = gdf.geometry.buffer(100)

# 4. Export results
gdf.to_file("results.gpkg", layer='original')
buffered.to_file("results.gpkg", layer='buffered')

# Join points to polygons
points_in_polygons = gpd.sjoin(points_gdf, polygons_gdf, predicate='within')

# Aggregate by polygon
aggregated = points_in_polygons.groupby('index_right').agg({
    'value': 'sum',
    'count': 'size'
})

# Merge back to polygons
result = polygons_gdf.merge(aggregated, left_index=True, right_index=True)

# Read from different sources
roads = gpd.read_file("roads.shp")
buildings = gpd.read_file("buildings.geojson")
parcels = gpd.read_postgis("SELECT * FROM parcels", con=engine, geom_col='geom')

# Ensure matching CRS
buildings = buildings.to_crs(roads.crs)
parcels = parcels.to_crs(roads.crs)

# Perform spatial operations
buildings_near_roads = buildings[buildings.geometry.distance(roads.union_all()) < 50]

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (references) in cli-tool/components/skills/scientific/geopandas of davila7/claude-code-templates.

• SKILL.md
• references/crs-management.md
• references/data-io.md
• references/data-structures.md
• references/geometric-operations.md
• references/spatial-analysis.md
• references/visualization.md

Open the folder on GitHubat commit 79182c5

Used in 10 other repositories

We found 12 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 10 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

• Microck/ordinary-claude-skillsskills_all/claude-scientific-skills/scientific-skills/geopandasskills_categorized/scientific-computing/claude-scientific-skills/scientific-skills/geopandas
• aiskillstore/marketplaceskills/davila7/geopandasskills/k-dense-ai/geopandas
• lamm-mit/scienceclawskills/geopandas
• agent-skills-hub/agent-skills-hubskills/geopandas
• foryourhealth111-pixel/Vibe-Skillsbundled/skills/geopandas
• LeonChaoX/qinyan-academic-skillsskills/16-地理空间与遥感/geopandas
• wu-yc/LabClawskills/general/geopandas
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/geopandas
• BioTender-max/awesome-bio-agent-skillsskills/kdense/geopandas
• infometa/workbuddyskillsplugins/codebuddy-plugins-official/external_plugins/scientific-skills/geopandas