crs-l1-london-laea-areas

Rerun

import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # Inspect the columns to ensure we use the right ones
    print("Columns:", gdf.columns.tolist())
    print("First few rows:")
    print(gdf.head())
    
    # The task asks for 'id', 'name', and 'area_km2'. 
    # We need to calculate the area in km2.
    
    # Ensure the geometry is in a projected CRS for accurate area calculation.
    # EPSG:27700 (British National Grid) is appropriate for London.
    if gdf.crs is None:
        # If no CRS is provided, we'll assume it's WGS84 (common for geojson) 
        # and reproject it.
        gdf.set_crs(epsg=4326, inplace=True)
    
    # Project to British National Grid (EPSG:27700)
    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 task specifies: one row per feature containing its id, name, and area_km2.
    # Let's check which columns correspond to 'id' and 'name'.
    # Usually, it's 'id' or 'id' or 'name' or 'name' or 'NAME'.
    # Based on common patterns, I'll look for them.
    
    # Let's try to find the most likely candidates for 'id' and 'name'
    # if they are not exactly 'id' and 'name'.
    id_col = None
    name_col = None
    
    for col in gdf.columns:
        if col.lower() == 'id':
            id_col = col
        if col.lower() in ['name', 'nm', 'name_en', 'label']:
            name_col = col
            
    if id_col is None or name_col is None:
        # If not found, we might need to rely on index or check the printout
        # Let's see the head printout in the logs first.
        pass

    # Let's re-examine the columns in the script once we see the printout.
    # For now, I'll write the code to be more robust.

    # Assuming standard names for now, but I'll refine it after seeing the output.
    # Actually, let's just use the output from the print.

if __name__ == "__main__":
    main()

import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # Ensure the geometry is in a projected CRS for accurate area calculation.
    # EPSG:27700 (British National Grid) is appropriate for London.
    if gdf.crs is None:
        # If no CRS is provided, we'll assume it's WGS84 (common for geojson) 
        # and reproject it.
        gdf.set_crs(epsg=4326, inplace=True)
    
    # Project to British National Grid (EPSG:27700)
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # Calculate area in square kilometers
    gdf['area_km2'] = gdf_projected.geometry.area / 10**6
    
    # The task specifies: one row per feature containing its id, name, and area_km2.
    # Columns are 'id', 'name', 'subtype', 'country', 'geometry'
    result = gdf[['id', 'name', 'area_km2']]
    
    # Write to CSV
    result.to_csv('borough_areas.csv', index=False)
    print("Successfully created borough_areas.csv")

if __name__ == "__main__":
    main()