Accessing Population Density Data via WFS using Python

For geospatial analysis, demographic context is everything. Whether you're building an AI model for urban planning or visualizing accessibility in city neighborhoods, population data is key. In this post, I’ll walk you through how I downloaded Berlin’s population density data (Einwohnerdichte 2023) using the city’s official Geoportal.

We’ll use Python and OWSLib to connect to a WFS (Web Feature Service) and download the data as a GeoJSON file.

Step-by-Step: Python Function to Fetch WFS Data

from owslib.wfs import WebFeatureService
import geopandas as gpd
import os

def download_demography_data(base_url, typename, output_path):
    os.makedirs(os.path.dirname(output_path), exist_ok=True)

    print(f"🌍 Connecting to WFS: {typename}")

    try:
        wfs = WebFeatureService(url=base_url, version="2.0.0")
        response = wfs.getfeature(typename=typename, outputFormat='application/json')

        gdf = gpd.read_file(response)

        if gdf.empty:
            print("No data found.")
        else:
            gdf.to_file(output_path, driver="GeoJSON")
            print(f"Data saved to: {output_path}")

    except Exception as e:
        print(f"Failed to download: {e}")

What’s Happening Here?

WebFeatureService: Establishes a connection to the WFS endpoint.

getfeature: Retrieves the specified layer in GeoJSON format.

GeoPandas: Loads the response and saves it locally.

os.makedirs: Ensures the output directory exists.

WFS URL and Available Layers

if __name__ == '__main__':

url = "https://gdi.berlin.de/services/wfs/ua_einwohnerdichte_2023"
wfs = WebFeatureService(url=url, version="2.0.0")

print("\nAvailable Layers:")
for layer in list(wfs.contents):
    print("🔹", layer)

You’ll see something like:

🔹 ua_einwohnerdichte_2023:einwohnerdichte2023

Downloading the Layer

# In main

# Downloading Berlin's 2023 population density layer
base_url = "https://gdi.berlin.de/services/wfs/ua_einwohnerdichte_2023"
layer = "ua_einwohnerdichte_2023:einwohnerdichte2023"
output_path = "data/berlin_population_density.geojson"

download_demography_data(base_url, layer, output_path)

Why This Matters

This data is incredibly useful for:

• Urban planning
• Accessibility mapping
• Infrastructure development
• AI/ML models that incorporate population variables

You now have a clean GeoJSON file with up-to-date demographic data, ready for QGIS, Python analysis, or integration into a web map.

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