All Supermarket Locations in Denmark: Complete Geographic Dataset
All Supermarket Locations in Denmark: Complete Geographic Dataset
All Supermarkets locations in Denmark - complete national dataset
This dataset contains a complete, geocoded collection of Supermarkets in Denmark, including branded chains and independent locations. It provides a complete national coverage with 5,264 verified locations suitable for territory planning, market sizing and spatial analysis.
Last updated: 28 May 2026.
Dataset fields included in the CSV:
- GUID
- Title
- Latitude
- Longitude
- Street No
- Street
- City
- Kommune
- Region
- Population
- Postal Code
- Address
- Wheelchair
Brand distribution
- 7-Eleven – 152 locations in Denmark
- Coop Brugsen – 135 locations in Denmark
- Coop DagliBrugsen – 130 locations in Denmark
- Coop SuperBrugsen – 216 locations in Denmark
- føtex – 116 locations in Denmark
- Kvickly – 61 locations in Denmark
- Lidl – 172 locations in Denmark
- Meny – 118 locations in Denmark
- Min Købmand – 134 locations in Denmark
- Netto – 571 locations in Denmark
- REMA 1000 – 431 locations in Denmark
- SPAR – 149 locations in Denmark
Supermarkets distribution by Region
- Region Hovedstaden: 1,455 supermarkets
- Region Midtjylland: 1,164 supermarkets
- Region Nordjylland: 609 supermarkets
- Region Sjælland: 833 supermarkets
- Region Syddanmark: 1,198 supermarkets
Direct access after purchase of the complete locations dataset for Denmark
Download the dataset immediately in a clean, analysis-ready CSV format. Ideal for geospatial analysis, competitor benchmarking, market studies, site planning, and retail location intelligence workflows.
Evaluate the dataset before purchase
A free sample is available for download, allowing you to inspect the structure, fields, and geospatial precision before purchasing the full dataset.
Related geospatial datasets
- Administrative boundaries and full polygon dataset for Denmark: Map these supermarkets locations against highly precise administrative polygons for territory analysis and spatial structuring.
- Demographics dataset for Denmark: Overlay demographic indicators to deeply understand the population structures and household types surrounding these supermarkets locations.
- Explore demographics data insights for Denmark: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Income indicators dataset for Denmark (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 Denmark - 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 Denmark-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 Denmark
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.
Frequently Asked Questions
Q: Can I use this dataset in GIS software?
A: Yes. The dataset is suitable for GIS platforms including QGIS, ArcGIS, GeoPandas, CARTO, and other spatial analysis environments.
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 contain duplicate locations?
A: Duplicate detection and validation workflows are applied during processing to improve consistency and reduce redundant records.
Q: What file format is included with the download?
A: The dataset is delivered as a CSV file compatible with Excel, Python, R, QGIS, Power BI, Tableau, PostgreSQL, and most GIS or analytics platforms.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this this multi-brand dataset to identify underserved regions in Denmark for potential market expansion.""Assess how effectively the current this multi-brand network covers major commercial and residential hubs across Denmark.""Assess the accessibility of this multi-brand locations in Denmark based on proximity to population centers and public infrastructure."