crs-l1-london-laea-areas

Rerun

import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # Check the content to identify the columns for name and id
    print("Columns:", gdf.columns.tolist())
    print("First few rows:")
    print(gdf.head())
    
    # Calculate area in km2
    # First, ensure the projection is in a metric system (e.g., EPSG:3857 or a local British one like EPSG:27700)
    # Let's check the current CRS
    print("Current CRS:", gdf.crs)
    
    # Use EPSG:27700 (British National Grid) for accurate area measurements in meters in the UK
    # If the data is in lat/lon (WGS84), we MUST reproject it.
    if gdf.crs is None or gdf.crs.is_geographic:
        gdf_projected = gdf.to_crs(epsg=27700)
    else:
        gdf_projected = gdf.to_crs(epsg=27700)
        
    # Calculate area in square meters, then convert to km2
    gdf['area_km2'] = gdf_projected.geometry.area / 10**6
    
    # The user asked for 'id', 'name', and 'area_km2'
    # We need to figure out which columns correspond to 'id' and 'name'.
    # Based on the prompt, they might be 'id' and 'name' or something similar.
    # Common names: 'id', 'name', 'name_en', 'admin_name', etc.
    
    # Let's try to find the columns.
    # We'll use the columns we saw in the head() print if we can.
    # Since I haven't seen the head() yet, I will write code that tries to find them.
    
    # For now, let's just assume 'id' and 'name' might be there, 
    # but we will check the printed output in the next step.
    # Wait, I am writing the script. I will first inspect the file.
    
    pass

if __name__ == "__main__":
    main()

import geopandas as gpd

def main():
    gdf = gpd.read_file('london_admin.geojson')
    print("Columns:", gdf.columns.tolist())
    print("First few rows:")
    print(gdf.head())
    print("CRS:", gdf.crs)

if __name__ == "__main__":
    main()

import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # The columns are 'id', 'name', 'subtype', 'country', 'geometry'
    # The CRS is EPSG:4326 (WGS 84)
    
    # Project to EPSG:27700 (British National Grid) for accurate area in meters
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # Calculate area in km2
    # area in m2 / 1,000,000 = area in km2
    gdf['area_km2'] = gdf_projected.geometry.area / 1_000_000
    
    # Select required columns
    result = gdf[['id', 'name', 'area_km2']]
    
    # Write to CSV
    result.to_csv('borough_areas.csv', index=False)
    print("Successfully wrote borough_areas.csv")
    print(result.head())

if __name__ == "__main__":
    main()