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Description
Nahkauf Supermarket Locations Dataset – GermanyQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Nahkauf is a proximity store banner operated by the REWE Group, specifically designed to provide grocery supplies in rural areas and small urban neighborhoods. These stores are typically run by independent merchants under the REWE umbrella. There are 547 Nahkauf Supermarkets as of 27 May 2026 in Germany. This
Nahkauf is a proximity store banner operated by the REWE Group, specifically designed to provide grocery supplies in rural areas and small urban neighborhoods. These stores are typically run by independent merchants under the REWE umbrella.
There are 547 Nahkauf Supermarkets as of 27 May 2026 in Germany. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Nahkauf 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: 547 Nahkauf 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)92%
- Web Address85%
- Opening Hours94%
- Popularity Score100%
Data Preview: Sample geospatial records from the Nahkauf dataset in Germany
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| 5b155f4... | Nahkauf (Waldeck) | 51.205646 | 9.062347 | 34513 | 5 Marktplatz, Waldeck, 34513, Kassel,... |
| c951110... | Nahkauf (Bezirk Steglitz-Zehlendorf) | 52.429126 | 13.346598 | 12249 | 64 Dessauerstraße, Berlin, 12249, Ger... |
| a2f3f08... | Nahkauf (Niedernhall) | 49.294790 | 9.616757 | 74676 | 36 Hauptstraße, Niedernhall, 74676, S... |
| b0cb46e... | Nahkauf (Bockum) | 51.344840 | 6.593132 | 47800 | 170 Friedrich-Ebert-Straße, Krefeld, ... |
| abd0a85... | Nahkauf (Stadtlengsfeld) | 50.783384 | 10.127694 | 36466 | 13 Obertor, Dermbach, 36466, Germany |
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 Nahkauf locations in Germany is found in Hessen (106 sites, equivalent to 1.68 Nahkauf supermarkets per 100,000 residents). This is followed by Baden-Württemberg (72 sites; 0.64 per 100,000) and Bayern (61 sites; 0.46 per 100,000). From a market-penetration perspective, Thüringen has the highest brand density at 2.36 locations per 100,000 people (population: 2,115,000), making it the most saturated region for Nahkauf in Germany. By contrast, Bremen records only 0.15 locations per 100,000 residents (population: 675,000), 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 Nahkauf 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 Nahkauf 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 [email protected].
Who uses this data?
- B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
- Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
- Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
- Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
- Catchment Area Analysis: Analysts mapping 15-minute drive times to understand localized customer reach and accessibility.
- 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.
Frequently Asked Questions
Q: Can I preview the dataset before purchasing?
A: Yes. A free sample is available so you can evaluate the structure, fields, and geospatial quality before purchase.
Q: Does the dataset contain duplicate locations?
A: Duplicate detection and validation workflows are applied during processing to improve consistency and reduce redundant records.
Q: Can this dataset be used for academic or research purposes?
A: Yes. Researchers and universities frequently use these datasets for urban studies, geography, economics, and spatial analytics projects.
Q: Can I request custom enrichment fields?
A: Yes. Custom enrichment services may be available depending on the project scope and geographic coverage requirements.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this Nahkauf dataset to identify underserved regions in Germany for potential market expansion.""Analyze geographic gaps in the current Nahkauf network and identify underserved regions in Germany with strong expansion potential.""Identify fast-growing suburban areas in Germany that currently have limited access to nearby supermarkets."
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.
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