Auchan Supermarket Locations Dataset – Hungary

Auchan Supermarket Locations Dataset – HungaryQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Auchan operates a network of hypermarkets and supermarkets across Hungary, providing a wide selection of food and non food goods. The brand is known for its large format stores and focus on customer purchasing power through various promotional programs. There are 27 Auchan Supermarkets as of 29 May 2026 in

Auchan operates a network of hypermarkets and supermarkets across Hungary, providing a wide selection of food and non-food goods. The brand is known for its large-format stores and focus on customer purchasing power through various promotional programs.

There are 27 Auchan Supermarkets as of 29 May 2026 in Hungary. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Auchan locations, including full address details, administrative divisions, and precise WGS84 latitude/longitude coordinates - structured for GIS, retail analytics, mapping, and AI/RAG workflows.

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.

Data Quality Scorecard

Data Preview: Sample geospatial records from the Auchan dataset in Hungary

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Regional Distribution Breakdown

Looking at the geographic distribution, the highest concentration of Auchan locations in Hungary is found in Közép-Magyarország (15 sites, equivalent to 0.5 Auchan supermarkets per 100,000 residents). This is followed by Nyugat-Dunántúl (3 sites; 0.31 per 100,000) and Észak-Magyarország (3 sites; 0.28 per 100,000). From a market-penetration perspective, Közép-Magyarország has the highest brand density at 0.5 locations per 100,000 people (population: 3,020,000), making it the most saturated region for Auchan in Hungary. By contrast, Közép-Dunántúl records only 0.1 locations per 100,000 residents (population: 1,050,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Also available for Hungary

Related geospatial datasets

• Administrative boundaries and full polygon dataset for Hungary: Map these Auchan 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 Auchan 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 [email protected].

Who uses this data?

• Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
• Catchment Area Analysis: Analysts mapping 15-minute drive times to understand localized customer reach and accessibility.
• Retail Site Selection: Property developers and retail analysts identifying optimal locations, white-spaces, and avoiding cannibalization.
• Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
• Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
• Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
• Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.
• Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
• Mobility Analysis: Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.

Frequently Asked Questions

Analyze this data with AI

Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:

• "Analyze this Auchan dataset to identify underserved regions in Hungary for potential market expansion."
• "Compare the spatial distribution of Auchan locations against major competitor clusters to identify overlap and whitespace opportunities in Hungary."
• "Identify Auchan sites with large footprints in Hungary that are optimal candidates for EV charging infrastructure overlays."

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.