Coop Supermarket Locations Dataset – Hungary
Coop Supermarket Locations Dataset – Hungary
Coop is one of Hungary's largest retailers, operating as a cooperative of independent regional stores with a strong presence in smaller towns and villages. It emphasizes local community support and provides essential groceries through its different store formats like Coop ABC and Coop Szuper.
There are 1,648 Coop Supermarkets as of 29 May 2026 in Hungary. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Coop 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: 1,648 Coop supermarkets in Hungary
- 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: 29 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
- 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)55%
- Web Address39%
- Opening Hours88%
- Popularity Score100%
Data Preview: Sample geospatial records from the Coop dataset in Hungary
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| 6d67318... | Coop (Zalaegerszeg) | 46.844930 | 16.848189 | 8900 | 2 Kovács Károly tér, Zalaegerszeg, 89... |
| 216504d... | Super Coop (Szob) | 47.817480 | 18.871782 | 2628 | 11 Arany János utca, Szob, 2628, Hungary |
| c3e33d4... | Coop Abc (Nagyszentjános) | 47.711147 | 17.870697 | 9072 | 16 Fő utca, Nagyszentjános, 9072, Hun... |
| 971dd77... | Coop (Tihany) | 46.913046 | 17.886547 | 8237 | 1 Dózsa György utca, Tihany, 8237, Hu... |
| 48c5e22... | Coop Mini (Szilasliget) | 47.573082 | 19.269538 | 2145 | 51 József Attila utca, Kerepes, 2145,... |
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 29 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 Coop locations in Hungary is found in Észak-Magyarország (358 sites, equivalent to 33.46 Coop supermarkets per 100,000 residents). This is followed by Észak-Alföld (286 sites; 20.58 per 100,000) and Nyugat-Dunántúl (238 sites; 24.54 per 100,000). From a market-penetration perspective, Észak-Magyarország has the highest brand density at 33.46 locations per 100,000 people (population: 1,070,000), making it the most saturated region for Coop in Hungary. By contrast, Közép-Magyarország records only 5.66 locations per 100,000 residents (population: 3,020,000), indicating a potential white-space opportunity for network expansion or competitor analysis.
Also available for Hungary
Brand bundle
Top 8 Grocery Brands in Hungary - €175
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 Hungary - 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 Hungary: Map these Coop locations against highly precise administrative polygons for territory analysis and spatial structuring.
- Demographics dataset for Hungary: Overlay demographic indicators to deeply understand the population structures and household types surrounding these Coop locations.
- Explore demographics data insights for Hungary: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Income indicators dataset for Hungary (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.
- Income distribution and socio-economic patterns in Hungary - expert analysis: A detailed blog post explaining income geography, disparities, and how income-based indicators can support location intelligence and market analytics.
- Explore a rich library of Hungary-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 Hungary
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?
- 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.
- Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
- Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
- Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
- Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
- Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
- 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 use this dataset for competitor benchmarking?
A: Yes. The dataset is frequently used to compare retail footprints, market density, and regional presence against competing brands.
Q: Can this dataset be used for logistics planning?
A: Yes. Many customers use these datasets to optimize delivery territories, distribution networks, route planning, and last-mile logistics analysis.
Q: Is this dataset suitable for market analysis?
A: Yes. The dataset is designed for retail analysis, competitor benchmarking, site selection, market coverage studies, and geospatial intelligence workflows.
Q: Does the dataset include accessibility-related attributes?
A: Yes. Certain datasets include accessibility-related indicators such as wheelchair accessibility where publicly available.
Q: Does the dataset contain duplicate locations?
A: Duplicate detection and validation workflows are applied during processing to improve consistency and reduce redundant records.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this Coop dataset to identify underserved regions in Hungary for potential market expansion.""Identify strategic locations in Hungary where new Coop sites could maximize geographic coverage while minimizing overlap.""Identify isolated Coop sites in Hungary that may face operational inefficiencies due to low regional clustering."
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.