Geospatial distance analysis

Geospatial Distance Analysis

This skill provides instructions for calculating distances between geospatial features accurately using GeoPandas and appropriate coordinate reference systems (CRS).

1. Projecting for Distance Calculations

Standard WGS84 (EPSG:4326) coordinates (latitude/longitude) are not suitable for direct distance calculations in meters or kilometers because degrees of longitude vary in length.

Choosing a Projection

• Equidistant Projections: Use for accurate distance measurements.
• World Equidistant Cylindrical (EPSG:4087): Good for global datasets.
• Local UTM Zones: Best for high precision in specific local areas.
• Custom Azimuthal Equidistant: Best for distances from a single point of interest.

Implementation in GeoPandas

import geopandas as gpd

# Load data in WGS84
gdf = gpd.read_file("data.json")
gdf.crs = "EPSG:4326"

# Reproject to a metric CRS (e.g., World Equidistant Cylindrical)
gdf_metric = gdf.to_crs("EPSG:4087")

2. Calculating Point-to-Geometry Distances

To find the distance from a point to the nearest part of a boundary (line or polygon):

# Assuming 'points_gdf' and 'boundary_gdf' are in the same metric CRS
# 'boundary' should be a single geometry (e.g., the union of all boundary segments)
boundary_geom = boundary_gdf.union_all()

# Calculate distance for each point
points_gdf['distance_meters'] = points_gdf.geometry.distance(boundary_geom)
points_gdf['distance_km'] = points_gdf['distance_meters'] / 1000.0

3. Handling Antimeridian Issues

When working with global data (like the Pacific Plate), geometries may cross the Antimeridian (180° longitude).

• Use geopandas.tools.wrap_longitude or ensure your CRS handles the wrap-around correctly.
• For calculations across the Pacific, consider a CRS centered on 180° longitude if necessary, though EPSG:4087 usually handles the coordinate range -180 to 180.

4. Performance Tips

• Use .union_all() (or .unary_union in older versions) to combine many boundary segments into a single geometry before calculating distances. This is much faster than iterating.
• For very large datasets, use spatial indexes (gdf.sindex).