Skill: wu-uk/earthquake-plate-calculation-geospatial-analysis (version 0.1.0)

--- name: geospatial-analysis description: Analyze geospatial data using geopandas with proper coordinate projections. Use when calculating distances between geographic features, performing spatial filtering, or working with plate boundaries and earthquake data. license: MIT --- # Geospatial Analysis with GeoPandas ## Overview When working with geographic data (earthquakes, plate boundaries, etc.), using geopandas with proper coordinate projections provides accurate distance calculations and efficient spatial operations. This guide covers best practices for geospatial analysis. ## Key Concepts ### Geographic vs Projected Coordinate Systems | Coordinate System | Type | Units | Use Case | |-------------------|------|-------|----------| | EPSG:4326 (WGS84) | Geographic | Degrees (lat/lon) | Data storage, display | | EPSG:4087 (World Equidistant Cylindrical) | Projected | Meters | Distance calculations | **Critical Rule**: Never calculate distances directly in geographic coordinates (EPSG:4326). Always project to a metric coordinate system first. ### Why Projection Matters ```python # ❌ 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 ```python 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 ```python 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 ```python # 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: ```python # 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 ```python # 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 ```python # 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: ```python 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 ```python # 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 ```python # 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 | Issue | Problem | Solution | |-------|---------|----------| | Distance in degrees | Using EPSG:4326 for distance calculations | Project to EPSG:4087 or similar metric CRS | | Antimeridian issues | Manual longitude adjustments (±360) | Use geopandas spatial operations, they handle it | | Slow performance | Calculating distance to each boundary point separately | Use `.unary_union` + single `.distance()` call | | Missing geometries | Some features have no geometry | Filter with `gdf[gdf.geometry.notna()]` | ## 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


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