Aral Petrol Station Locations Dataset – Germany

Aral is Germany's largest petrol station network and a subsidiary of bp, recognizable by its iconic blue and white branding. It is a market leader in fuel innovation and offers extensive convenience services through its REWE To Go shop partnerships.

There are 2,421 Aral Petrol Stations as of 7 July 2026 in Germany. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Aral locations, including full address details, administrative divisions, and precise WGS84 latitude/longitude coordinates - structured for GIS, retail analytics, mapping, and AI/RAG workflows.

Dataset Methodology:

This dataset is compiled from publicly available business listings, official company sources, and geospatial validation workflows. Automated quality checks and manual analyst reviews are applied to improve coordinate precision, address standardisation, duplicate detection, and overall analytical consistency.

It is periodically reviewed and updated to reflect known network changes, closures, relocations, and newly identified locations.

Data Quality Scorecard

Data Preview: Sample geospatial records from the Aral dataset in Germany

ID	Location Title	Latitude	Longitude	Postal Code	Full Address
04402d5...	Aral (Voxtrup)	52.250733	8.103426	49086	66 Meller Landstraße, Osnabrück, 4908...
f3e40af...	Aral (Werl)	51.533210	7.863697	59457	A44, Werl, 59457, Arnsberg, Germany
ea19b28...	Tankstelle (Völschow)	53.856486	13.334296	17129	2A Am großen Torfmoor, Völschow, 1712...
4f91993...	Steden GmbH (Enste)	51.353595	8.253920	59872	5 Schneidweg, Meschede, 59872, Arnsbe...
8a14f60...	Aral (Hasbergen)	52.236561	7.962421	49205	36A Osnabrücker Straße, Hasbergen, 49...

Note: Only a subset of the full dataset fields are displayed here. Download the free sample (option above) to view all fields and verify the data structure.

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Regional Distribution Breakdown

Looking at the geographic distribution, the highest concentration of Aral locations in Germany is found in Nordrhein-Westfalen (544 sites, equivalent to 3.02 Aral petrol stations per 100,000 residents). This is followed by Bayern (344 sites; 2.6 per 100,000) and Baden-Württemberg (312 sites; 2.79 per 100,000). From a market-penetration perspective, Saarland has the highest brand density at 4.47 locations per 100,000 people (population: 985,000), making it the most saturated region for Aral in Germany. By contrast, Berlin records only 0.89 locations per 100,000 residents (population: 3,690,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Learn more about the brand network in our report: View Report

Also available for Germany

Related geospatial datasets

• Administrative boundaries and full polygon dataset for Germany: Map these Aral locations against highly precise administrative polygons for territory analysis and spatial structuring.
• Demographics dataset for Germany: Overlay demographic indicators to deeply understand the population structures and household types surrounding these Aral locations.
• Explore demographics data insights for Germany: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
• Income indicators dataset for Germany (small-area income analysis): Download an analysis-ready dataset on income distribution across small geographic units - ideal for segmentation, customer analytics, location planning, and socio-economic scoring.
• Explore a rich library of Germany-specific datasets on our dedicated country page: detailed demographics, wealth indicators, multi-level boundaries, and a broad spectrum of retail POIs. View demographics, retail POI, and administrative boundary datasets for Germany

Need the data in another format?

We can deliver this dataset in alternative formats upon request (GeoJSON, Shapefile, Excel, PostgreSQL import files, etc.). Contact us at contact@geolocet.com.

Who uses this data?

• Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
• Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
• Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
• Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.
• CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.
• Catchment Area Analysis: Analysts mapping 15-minute drive times to understand localized customer reach and accessibility.
• Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.

Frequently Asked Questions

Analyze this data with AI

Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:

• "Analyze this Aral dataset to identify underserved regions in Germany for potential market expansion."
• "Identify high-income or high-density residential zones in Germany that currently lack nearby Aral locations."
• "Identify fast-growing suburban areas in Germany that currently have limited access to nearby petrol stations."

Disclaimer: All brand logos and trademarks displayed are the property of their respective owners and are used strictly for identification purposes. This product consists of geospatial location data only; no images, logos, or trademark rights are included in the downloadable files.