| name | geopandas-distance-analysis |
| description | Calculate distances between geospatial points and boundaries using GeoPandas with proper metric projections (EPSG:4087). |
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"
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")]
