Arhelan Supermarket Locations Dataset – Poland

Arhelan Supermarket Locations Dataset – PolandQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Arhelan is a Polish family owned retail chain originating from Bielsk Podlaski, primarily active in the northeastern part of the country. It is known for its strong regional presence and has recently partnered with the Eurocash Group to further its growth. There are 100 Arhelan Supermarkets as of 30 May 2026

Arhelan is a Polish family-owned retail chain originating from Bielsk Podlaski, primarily active in the northeastern part of the country. It is known for its strong regional presence and has recently partnered with the Eurocash Group to further its growth.

There are 100 Arhelan Supermarkets as of 30 May 2026 in Poland. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Arhelan 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 Arhelan dataset in Poland

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Regional Distribution Breakdown

Looking at the geographic distribution, the highest concentration of Arhelan locations in Poland is found in Podlaskie (83 sites, equivalent to 7.28 Arhelan supermarkets per 100,000 residents). This is followed by Mazowieckie (10 sites; 0.18 per 100,000) and Warmińsko-Mazurskie (4 sites; 0.3 per 100,000). From a market-penetration perspective, Podlaskie has the highest brand density at 7.28 locations per 100,000 people (population: 1,140,000), making it the most saturated region for Arhelan in Poland. By contrast, Lubelskie records only 0.15 locations per 100,000 residents (population: 1,995,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Also available for Poland

Related geospatial datasets

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

Who uses this data?

• Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
• Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
• Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
• Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
• Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
• 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.
• Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
• CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.

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 Arhelan dataset to identify underserved regions in Poland for potential market expansion."
• "Analyze proximity between Arhelan locations and major highways, ring roads, or arterial transport corridors in Poland."
• "Create a regional ranking of Arhelan coverage efficiency using population-to-store ratios across 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.