DEV Community

Ochwada Linda
Ochwada Linda

Posted on

2 1 1 1 1

Accessing Population Density Data via WFS using Python

#data #wfs #python #gis

Source: https://pixabay.com/photos/ilight-ilightmarine-abstract-2185506/
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}")
Enter fullscreen mode Exit fullscreen mode

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)
Enter fullscreen mode Exit fullscreen mode

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)
Enter fullscreen mode Exit fullscreen mode

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.

👉 More on Blogs
👉 Reach Out

profile
AWS
 Promoted

AWS reinvent image

🔐 Unlock exclusive savings to AWS re:Invent when you register before November 7!

Act now to save $150 using the following discount code: DEVEXJVu6vUt. This special discount is only available for the first 100 tickets booked on or before November 7th.

BONUS!!! Lock in your tickets! Receive $250 Delta Air Lines flight vouchers when you book using this discount code.

*Voucher will be sent to the email you use to register for re:Invent.
Register now

Top comments (0)

profile
Sonar
 Promoted

Sonar image

Explore the coding personalities of leading LLMs

Sonar’s new report on leading LLMs explores the critical tradeoffs between performance and security. Explore the distinct coding personalities of models like OpenAI’s GPT-4o and Claude Sonnet 4 to determine the best AI strategy for your team.

Read now