Kaufland Supermarket Locations Dataset – Poland
Kaufland Supermarket Locations Dataset – Poland
Kaufland is a leading hypermarket chain in Poland and part of the German Schwarz Gruppe alongside Lidl. It offers a massive selection of food and non-food items, positioning itself as a one-stop-shop for Polish families.
There are 264 Kaufland Supermarkets as of 30 May 2026 in Poland. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Kaufland 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: 264 Kaufland supermarkets in Poland
- 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: 30 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
- City
- Admin_level_1
- Admin_level_2
- Municipality
- Region
- Population
- Postal Code
- Address
- Wheelchair
- Popularity Score
- Phone
- Website
- Opening hours
Data Quality Scorecard
- Geospatial Accuracy: 98%+ (Verified WGS84 Coordinates)
- Contact Details (Phone)96%
- Web Address96%
- Opening Hours96%
- Popularity Score100%
Data Preview: Sample geospatial records from the Kaufland dataset in Poland
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| 971af5c... | Kaufland (Jasło) | 49.753382 | 21.484050 | 38-200 | 24 Lwowska, Jasło, 38-200, Powiat jas... |
| e20b58f... | Kaufland (Rzeszów) | 50.050222 | 21.989563 | 35-222 | 14 A Aleja Generała Leopolda Okulicki... |
| 49b47e5... | Kaufland (Bełchatów) | 51.359385 | 19.385212 | 97-400 | 2 Świętej Faustyny Kowalskiej, Bełcha... |
| 7b6000d... | Kaufland (Stare Miasto) | 52.435139 | 16.927043 | 61-696 | 42 Aleje Solidarności, Poznań, 61-696... |
| 2f5e072... | Kaufland (Legnica) | 51.208133 | 16.213639 | 59-220 | 1 Iwaszkiewicza, Legnica, 59-220, Pow... |
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 30 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 Kaufland locations in Poland is found in Śląskie (42 sites, equivalent to 0.98 Kaufland supermarkets per 100,000 residents). This is followed by Mazowieckie (34 sites; 0.62 per 100,000) and Wielkopolskie (24 sites; 0.69 per 100,000). From a market-penetration perspective, Opolskie has the highest brand density at 1.08 locations per 100,000 people (population: 930,000), making it the most saturated region for Kaufland in Poland. By contrast, Kujawsko-Pomorskie records only 0.45 locations per 100,000 residents (population: 1,980,000), indicating a potential white-space opportunity for network expansion or competitor analysis.
Also available for Poland
Brand bundle
Top 21 Grocery Brands in Poland - €400
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 Poland - 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 Poland: Map these Kaufland locations against highly precise administrative polygons for territory analysis and spatial structuring.
- Demographics dataset for Poland: Overlay demographic indicators to deeply understand the population structures and household types surrounding these Kaufland locations.
- Explore demographics data insights for Poland: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Income indicators dataset for Poland (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 Poland-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 Poland
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?
- Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
- Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
- Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
- Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
- Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
- CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.
Frequently Asked Questions
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: Are postal codes included for all locations?
A: Postal codes are included wherever available and validated as part of the standardization workflow.
Q: How accurate are the coordinates?
A: Coordinates undergo automated validation and manual quality review processes to improve positional accuracy and analytical reliability.
Q: Are the datasets suitable for machine learning workflows?
A: Yes. The structured tabular format and standardized coordinates make the datasets suitable for machine learning and predictive analytics applications.
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 Kaufland dataset to identify underserved regions in Poland for potential market expansion.""Identify locations where multiple Kaufland sites compete within overlapping catchment areas in Poland.""Map the concentration of Kaufland locations relative to major retail centers and shopping districts in Poland."
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.