Geopandas distance analysis

Skill cxcscmu/SkillLearnBench/skills/b1-one-shot-claude-sonnet-4-6/earthquake-plate-calculation/geopandas-distance-analysis

[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.

Install

npx -y skills add cxcscmu/SkillLearnBench --skill geopandas-distance-analysis

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Calculate distances between geospatial points and boundaries using GeoPandas with proper metric projections (EPSG:4087).

SKILL.md

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GeoPandas Distance Analysis

Setup

pip install geopandas shapely

Core Workflow

1. Load GeoJSON files

import geopandas as gpd
from shapely.geometry import Point

gdf_plates = gpd.read_file("PB2002_plates.json")
gdf_boundaries = gpd.read_file("PB2002_boundaries.json")

2. Create GeoDataFrame from coordinate list

geometry = [Point(lon, lat) for lon, lat in zip(lons, lats)]
gdf_points = gpd.GeoDataFrame(data, geometry=geometry, crs="EPSG:4326")

3. Spatial filtering (points within polygon)

target_poly = gdf_plates[gdf_plates["Code"] == "PA"].geometry.unary_union
inside = gdf_points[gdf_points.within(target_poly)].copy()

4. Project to metric CRS before distance calculation

METRIC_CRS = "EPSG:4087"  # World Equidistant Cylindrical (meters)
inside_proj = inside.to_crs(METRIC_CRS)
boundary_proj = gdf_boundaries.to_crs(METRIC_CRS).geometry.unary_union
inside["distance_km"] = inside_proj.geometry.distance(boundary_proj) / 1000.0

5. Find furthest point

result = inside.nlargest(1, "distance_km").iloc[0]
print(f"ID: {result['id']}, Distance: {result['distance_km']:.2f} km")

Key Rules

NEVER calculate distances in EPSG:4326 (degrees != meters)
Always project to EPSG:4087 or EPSG:3857 before .distance()
Use .unary_union to combine multiple boundary segments into one geometry
Use .within() for point-in-polygon tests (handles antimeridian better than manual checks)
Filter boundaries by PlateA/PlateB before combining: gdf_boundaries[(gdf_boundaries["PlateA"]=="PA") | (gdf_boundaries["PlateB"]=="PA")]

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