Geospatial Analysis

Why Projection Matters

# ❌ INCORRECT: Calculating distance in EPSG:4326
# This treats degrees as if they were equal distances everywhere on Earth
gdf = gpd.GeoDataFrame(..., crs="EPSG:4326")
distance = point1.distance(point2)  # Wrong! Returns degrees, not meters

# ✅ CORRECT: Project to metric CRS first
gdf_projected = gdf.to_crs("EPSG:4087")
distance_meters = point1_proj.distance(point2_proj)  # Correct! Returns meters
distance_km = distance_meters / 1000.0

Loading Geospatial Data

From GeoJSON Files

import geopandas as gpd

# Load GeoJSON files directly
gdf_plates = gpd.read_file("plates.json")
gdf_boundaries = gpd.read_file("boundaries.json")

From Regular Data with Coordinates

from shapely.geometry import Point
import geopandas as gpd

# Convert coordinate data to GeoDataFrame
data = [
    {"id": 1, "lat": 35.0, "lon": 140.0, "value": 5.5},
    {"id": 2, "lat": 36.0, "lon": 141.0, "value": 6.0},
]

geometry = [Point(row["lon"], row["lat"]) for row in data]
gdf = gpd.GeoDataFrame(data, geometry=geometry, crs="EPSG:4326")

Spatial Filtering

Finding Points Within a Polygon

# Get the polygon of interest
target_poly = gdf_plates[gdf_plates["Name"] == "Pacific"].geometry.unary_union

# Filter points that fall within the polygon
points_inside = gdf_points[gdf_points.within(target_poly)]

print(f"Found {len(points_inside)} points inside the polygon")

Using .unary_union for Multiple Geometries

When you have multiple polygons or lines that should be treated as one:

# Combine multiple boundary segments into one geometry
all_boundaries = gdf_boundaries.geometry.unary_union

# Or filter first, then combine
pacific_boundaries = gdf_boundaries[
    gdf_boundaries["Name"].str.contains("PA")
].geometry.unary_union

Distance Calculations

Point to Line/Boundary Distance

# 1. Load your data
gdf_points = gpd.read_file("points.json")
gdf_boundaries = gpd.read_file("boundaries.json")

# 2. Project to metric coordinate system
METRIC_CRS = "EPSG:4087"
points_proj = gdf_points.to_crs(METRIC_CRS)
boundaries_proj = gdf_boundaries.to_crs(METRIC_CRS)

# 3. Combine boundary segments if needed
boundary_geom = boundaries_proj.geometry.unary_union

# 4. Calculate distances (returns meters)
gdf_points["distance_m"] = points_proj.geometry.distance(boundary_geom)
gdf_points["distance_km"] = gdf_points["distance_m"] / 1000.0

Finding Furthest Point

# Sort by distance and get the furthest point
furthest = gdf_points.nlargest(1, "distance_km").iloc[0]

print(f"Furthest point: {furthest['id']}")
print(f"Distance: {furthest['distance_km']:.2f} km")

Common Workflow Pattern

Here's a complete example for analyzing earthquakes near plate boundaries:

import geopandas as gpd
from shapely.geometry import Point

# 1. Load data
earthquakes_data = [...]  # Your earthquake data
gdf_plates = gpd.read_file("plates.json")
gdf_boundaries = gpd.read_file("boundaries.json")

# 2. Create earthquake GeoDataFrame
geometry = [Point(eq["longitude"], eq["latitude"]) for eq in earthquakes_data]
gdf_eq = gpd.GeoDataFrame(earthquakes_data, geometry=geometry, crs="EPSG:4326")

# 3. Spatial filtering - find earthquakes in specific plate
target_plate = gdf_plates[gdf_plates["Code"] == "PA"].geometry.unary_union
earthquakes_in_plate = gdf_eq[gdf_eq.within(target_plate)].copy()

# 4. Calculate distances (project to metric CRS)
METRIC_CRS = "EPSG:4087"
eq_proj = earthquakes_in_plate.to_crs(METRIC_CRS)

# Filter and combine relevant boundaries
plate_boundaries = gdf_boundaries[
    gdf_boundaries["Name"].str.contains("PA")
].to_crs(METRIC_CRS).geometry.unary_union

# Calculate distances
earthquakes_in_plate["distance_km"] = eq_proj.geometry.distance(plate_boundaries) / 1000.0

# 5. Find the furthest earthquake
furthest_eq = earthquakes_in_plate.nlargest(1, "distance_km").iloc[0]

Filtering by Attributes

# Filter by name or code
pacific_plate = gdf_plates[gdf_plates["PlateName"] == "Pacific"]
pacific_plate_alt = gdf_plates[gdf_plates["Code"] == "PA"]

# Filter boundaries involving a specific plate
pacific_bounds = gdf_boundaries[
    (gdf_boundaries["PlateA"] == "PA") | 
    (gdf_boundaries["PlateB"] == "PA")
]

# String pattern matching
pa_related = gdf_boundaries[gdf_boundaries["Name"].str.contains("PA")]

Performance Tips

1. Filter before projecting: Reduce data size before expensive operations
2. Project once: Convert to metric CRS once, not in loops
3. Use .unary_union: Combine geometries before distance calculations
4. Copy when modifying: Use .copy() when creating filtered DataFrames

# Good: Filter first, then project
small_subset = gdf_large[gdf_large["region"] == "Pacific"]
small_projected = small_subset.to_crs(METRIC_CRS)

# Avoid: Projecting large dataset just to filter
# gdf_projected = gdf_large.to_crs(METRIC_CRS)
# small_subset = gdf_projected[gdf_projected["region"] == "Pacific"]

Common Pitfalls

When NOT to Use Manual Calculations

Avoid implementing your own:

• Haversine distance formulas (use geopandas projections instead)
• Point-in-polygon checks (use .within())
• Iterating through boundary points (use .distance() with .unary_union)

These manual approaches are slower, more error-prone, and less accurate than geopandas methods.

Best Practices Summary

1. ✅ Load GeoJSON with gpd.read_file()
2. ✅ Use .within() for spatial filtering
3. ✅ Project to metric CRS (EPSG:4087) before distance calculations
4. ✅ Combine geometries with .unary_union before distance calculation
5. ✅ Use .distance() method for point-to-geometry distances
6. ✅ Use .nlargest() / .nsmallest() for finding extreme values
7. ❌ Never calculate distances in EPSG:4326
8. ❌ Avoid manual Haversine implementations
9. ❌ Don't iterate through individual boundary points