Lewiatan Supermarket Locations Dataset – Poland

Lewiatan Supermarket Locations Dataset – PolandQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Lewiatan is one of the largest and most successful Polish franchise networks, consisting of thousands of independent neighborhood stores. It is a cornerstone of local Polish retail, emphasizing local products and community presence. There are 3,315 Lewiatan Supermarkets as of 30 May 2026 in Poland. This

Lewiatan is one of the largest and most successful Polish franchise networks, consisting of thousands of independent neighborhood stores. It is a cornerstone of local Polish retail, emphasizing local products and community presence.

There are 3,315 Lewiatan Supermarkets as of 30 May 2026 in Poland. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Lewiatan 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 Lewiatan dataset in Poland

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.

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Regional Distribution Breakdown

Looking at the geographic distribution, the highest concentration of Lewiatan locations in Poland is found in Śląskie (461 sites, equivalent to 10.75 Lewiatan supermarkets per 100,000 residents). This is followed by Mazowieckie (410 sites; 7.45 per 100,000) and Małopolskie (284 sites; 8.28 per 100,000). From a market-penetration perspective, Podlaskie has the highest brand density at 17.28 locations per 100,000 people (population: 1,140,000), making it the most saturated region for Lewiatan in Poland. By contrast, Łódzkie records only 3.75 locations per 100,000 residents (population: 2,345,000), 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 Lewiatan 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 Lewiatan 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?

• Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
• Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
• Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
• Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
• Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
• Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
• Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.

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 Lewiatan dataset to identify underserved regions in Poland for potential market expansion."
• "Evaluate how evenly Lewiatan locations are distributed across provinces, districts, or municipalities in Poland."
• "Identify the most central Lewiatan locations in Poland to serve as hubs for a last-mile delivery network."

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.