SPAR Supermarket Locations Dataset – Germany
SPAR Supermarket Locations Dataset – Germany
SPAR in Germany once operated thousands of stores but now primarily exists as a branding and purchasing partner for independent retailers and gas station convenience stores. Most former full-sized SPAR locations were integrated into the EDEKA group.
There are 289 SPAR Supermarkets as of 27 May 2026 in Germany. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all SPAR 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: 289 SPAR supermarkets 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: 27 May 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
- 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
- Opening hours
Data Quality Scorecard
- Geospatial Accuracy: 98%+ (Verified WGS84 Coordinates)
- Contact Details (Phone)37%
- Opening Hours73%
Data Preview: Sample geospatial records from the SPAR dataset in Germany
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| f3059aa... | SPAR Express (Bad Camberg) | 50.293948 | 8.271374 | 65520 | 25 Frankfurter Straße, Bad Camberg, 6... |
| 8b38c98... | SPAR Express (Herzebrock) | 51.883750 | 8.232433 | 33442 | 84 Clarholzer Straße, Herzebrock-Clar... |
| 4f7afd9... | SPAR Express (Neustadt an der Weinstraße) | 49.347377 | 8.151572 | 67434 | 58-60 Landauer Straße, Neustadt an de... |
| dd77005... | SPAR Express (Kriegshaber) | 48.386089 | 10.849139 | 86156 | 70 Kobelweg, Augsburg, 86156, Schwabe... |
| 8309d73... | SPAR Express (Singen (Hohentwiel)) | 47.764771 | 8.831621 | 78224 | 12 Hohenkrähenstraße, Singen (Hohentw... |
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 27 May 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 SPAR locations in Germany is found in Nordrhein-Westfalen (72 sites, equivalent to 0.4 SPAR supermarkets per 100,000 residents). This is followed by Bayern (45 sites; 0.34 per 100,000) and Baden-Württemberg (41 sites; 0.37 per 100,000). From a market-penetration perspective, Bremen has the highest brand density at 0.89 locations per 100,000 people (population: 675,000), making it the most saturated region for SPAR in Germany. By contrast, Thüringen records only 0.09 locations per 100,000 residents (population: 2,115,000), 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 27 Grocery 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 Grocery 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 SPAR 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 SPAR 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.
- Mobility Analysis: Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.
- Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
- Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
- Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
- Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.
- Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
- Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
Frequently Asked Questions
Q: Does the dataset include accessibility-related attributes?
A: Yes. Certain datasets include accessibility-related indicators such as wheelchair accessibility where publicly available.
Q: Can I request custom enrichment fields?
A: Yes. Custom enrichment services may be available depending on the project scope and geographic coverage requirements.
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: 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.
Q: Is the dataset standardized for analytics workflows?
A: Yes. Address formatting, administrative areas, and geospatial fields are standardized to improve consistency across analytical environments.
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.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this SPAR dataset to identify underserved regions in Germany for potential market expansion.""Identify SPAR sites with large footprints in Germany that are optimal candidates for EV charging infrastructure overlays.""Analyze the relationship between SPAR site distribution and regional economic activity across 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.