Esso Petrol Station Locations Dataset – Germany

Esso is a major brand in the German fuel market, with many of its locations managed by the EG Group under a brand partnership. It is known for its Synergy fuels and frequently features in-store partnerships with brands like BackWerk or REWE To Go.

There are 1,270 Esso 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 Esso 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 Esso dataset in Germany

ID	Location Title	Latitude	Longitude	Postal Code	Full Address
cf69174...	Esso (Bad Nauheim)	50.357447	8.738863	61231	19 Schwalheimer Straße, Bad Nauheim, ...
f38b271...	Esso (Straß)	48.418546	10.133285	89278	35 Ortsstraße, Nersingen, 89278, Schw...
2a414f4...	Esso (Anzing)	48.151298	11.851010	85646	11 Münchener Straße, Anzing, 85646, O...
914a0ed...	Esso (Marburg)	50.816384	8.751380	35041	45 Emil-von-Behring-Straße, Marburg, ...
fe6f534...	Esso (Kempten (Allgäu))	47.734538	10.310318	87439	60 Memminger Straße, Kempten (Allgäu)...

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 Esso locations in Germany is found in Bayern (369 sites, equivalent to 2.79 Esso petrol stations per 100,000 residents). This is followed by Baden-Württemberg (242 sites; 2.17 per 100,000) and Nordrhein-Westfalen (155 sites; 0.86 per 100,000). From a market-penetration perspective, Bayern has the highest brand density at 2.79 locations per 100,000 people (population: 13,230,000), making it the most saturated region for Esso in Germany. By contrast, Berlin records only 0.57 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 Esso 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 Esso 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?

• Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
• 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.
• Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
• Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
• Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.
• Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
• Mobility Analysis: Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.

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 Esso dataset to identify underserved regions in Germany for potential market expansion."
• "Identify locations where multiple Esso sites compete within overlapping catchment areas in Germany."
• "Analyze proximity between Esso locations and major highways, ring roads, or arterial transport corridors in Germany."

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.