Raiffeisen Petrol Station Locations Dataset – Germany
Raiffeisen Petrol Station Locations Dataset – Germany
There are 639 Raiffeisen 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 Raiffeisen locations, including full address details, administrative divisions, and precise WGS84 latitude/longitude coordinates - structured for GIS, retail analytics, mapping, and AI/RAG workflows.
Dataset Summary
- Dataset Coverage: 639 Raiffeisen petrol stations in Germany
- Contents: Coordinates, addresses, postal codes, administrative divisions, contact details, and popularity scores
- File Format: Fully geocoded CSV dataset (UTF-8)
- Free Sample: Instantly accessible dataset to verify structure and data quality
- Use Cases: Suitable for GIS, retail analytics, site selection, and AI/RAG workflows
- Last Updated: 7 July 2026
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.
Dataset fields included in the CSV:
- GUID
- Title
- Group
- Latitude
- Longitude
- Street No
- Street
- Area
- City
- Admin_level_1
- Admin_level_2
- Gemainde
- Federal State
- Population
- Postal Code
- Address
- Wheelchair
- Popularity Score
- Phone
- Website
- Fuel Type
- Opening hours
Data Quality Scorecard
- Geospatial Accuracy: 98%+ (Verified WGS84 Coordinates)
- Contact Details (Phone)76%
- Web Address67%
- Opening Hours76%
- Popularity Score100%
Data Preview: Sample geospatial records from the Raiffeisen dataset in Germany
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| 51930a9... | Raiffeisen Tankstelle (Soest) | 51.556349 | 8.145742 | 59494 | 23 Overweg, Soest, 59494, Arnsberg, G... |
| d344fd0... | Raiffeisen (Woltersdorf) | 52.954011 | 11.220393 | 29497 | 28 Ziegeleistraße, Woltersdorf, 29497... |
| 56387c6... | Raiffeisen Vital (Bremke) | 51.252470 | 8.200144 | 59889 | 1 Im Wennetal, Eslohe (Sauerland), 59... |
| b68219e... | Raiffeisen (Ströhen) | 52.531428 | 8.692148 | 49419 | 201 Mindener Straße, Wagenfeld, 49419... |
| d7dbcfb... | Raiffeisen Tankstelle (Wessum) | 52.102182 | 6.982960 | 48683 | 83 Eichenallee, Ahaus, 48683, Münster... |
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.
Why download from Geolocet?
- Instant download - full dataset available immediately after purchase, no waiting, no manual fulfilment
- Free sample first - verify structure, fields, and coordinate precision before you commit
- Analysis-ready CSV - clean, standardised, and compatible with Excel, Python, QGIS, Power BI, and PostgreSQL out of the box
- Regularly updated - last updated 7 July 2026
✅ Data looks right? Add to cart ↑ - or download the free sample first.
Regional Distribution Breakdown
Looking at the geographic distribution, the highest concentration of Raiffeisen locations in Germany is found in Niedersachsen (256 sites, equivalent to 3.18 Raiffeisen petrol stations per 100,000 residents). This is followed by Nordrhein-Westfalen (141 sites; 0.78 per 100,000) and Hessen (51 sites; 0.81 per 100,000). From a market-penetration perspective, Niedersachsen has the highest brand density at 3.18 locations per 100,000 people (population: 8,055,000), making it the most saturated region for Raiffeisen in Germany. By contrast, Sachsen records only 0.05 locations per 100,000 residents (population: 4,055,000), 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
Brand bundle
Top 16 Petrol Stations Brands in Germany - €480
All major chains in one standardised dataset. Best for competitive benchmarking, network analysis, and market sizing across the leading brands.
View Top Brands dataset →Full market coverage
All Petrol Stations Locations in Germany - complete POI dataset
Includes everything in the brand bundle plus independent operators, smaller chains, and local businesses not covered by the top brands. Best for full market mapping, territory planning, and white-space analysis.
View full POI dataset →Related geospatial datasets
- Administrative boundaries and full polygon dataset for Germany: Map these Raiffeisen 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 Raiffeisen 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?
- Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
- Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
- 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.
- Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
- Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
- 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.
- CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.
- B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
Frequently Asked Questions
Q: Can I combine this dataset with administrative boundaries?
A: Yes. The coordinates can be spatially joined with municipalities, census units, postal areas, and other administrative polygons.
Q: Does the dataset include unique identifiers?
A: Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.
Q: Is this dataset useful for accessibility studies?
A: Yes. Analysts can combine the coordinates with mobility, transport, and demographics datasets to evaluate accessibility and service coverage.
Q: Can this dataset support expansion planning?
A: Yes. Analysts often use the dataset to identify underserved areas, evaluate regional density, and support retail expansion decisions.
Q: Can I use this dataset for proximity analysis?
A: Yes. The geocoded coordinates are suitable for drive-time analysis, catchment modeling, nearest-neighbor analysis, and accessibility studies.
Q: Can this dataset be imported into Power BI or Tableau?
A: Yes. The CSV structure is compatible with Power BI, Tableau, Looker Studio, and other business intelligence platforms.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this Raiffeisen dataset to identify underserved regions in Germany for potential market expansion.""Analyze geographic gaps in the current Raiffeisen network and identify underserved regions in Germany with strong expansion potential.""Using this Raiffeisen data, find high-traffic retail corridors in Germany with low petrol stations density for competitive positioning."
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.