SKU: 24835470241

Netto Supermarket Locations Dataset – Poland

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Description

Netto Supermarket Locations Dataset – PolandQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Netto is a discount supermarket chain in Poland owned by the Danish Salling Group. It significantly increased its market share in 2021 by acquiring the majority of the former Tesco stores in Poland. There are 715 Netto Supermarkets as of 30 May 2026 in Poland. This dataset is compiled and maintained by

Netto is a discount supermarket chain in Poland owned by the Danish Salling Group. It significantly increased its market share in 2021 by acquiring the majority of the former Tesco stores in Poland.

There are 715 Netto Supermarkets as of 30 May 2026 in Poland. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Netto 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: 715 Netto supermarkets in Poland
  • 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: 30 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
  • Admin_level_1
  • Admin_level_2
  • 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)96%
  • Web Address96%
  • Opening Hours96%
  • Popularity Score100%

Data Preview: Sample geospatial records from the Netto dataset in Poland

ID Location Title Latitude Longitude Postal Code Full Address
5d62c13... Netto (Chojnice) 53.693752 17.564243 89-600 3A Plac Niepodległości, Chojnice, 89-...
020d9b8... Netto (Rogoźno) 52.752477 16.991065 64-610 1C Fabryczna, Rogoźno, 64-610, Powiat...
04ce616... Netto (Chorzów) 50.287065 18.949898 41-530 57 Hajducka, Chorzów, 41-530, Powiat ...
0d50b03... Netto (Jelonek) 52.486039 16.855753 62-002 12 Obornicka, Jelonek, 62-002, Powiat...
2ab2140... Netto (Międzyrzecz) 52.587162 15.495521 66-300 35 Garnizonowa, Międzyrzecz, 66-300, ...

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 30 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 Netto locations in Poland is found in Śląskie (93 sites, equivalent to 2.17 Netto supermarkets per 100,000 residents). This is followed by Wielkopolskie (92 sites; 2.63 per 100,000) and Zachodniopomorskie (88 sites; 5.43 per 100,000). From a market-penetration perspective, Zachodniopomorskie has the highest brand density at 5.43 locations per 100,000 people (population: 1,620,000), making it the most saturated region for Netto in Poland. By contrast, Podkarpackie records only 0.39 locations per 100,000 residents (population: 2,060,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Also available for Poland

Brand bundle

Top 21 Grocery Brands in Poland - €400

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 Poland - 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 →

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?

  • Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
  • 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.
  • Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
  • B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
  • Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
  • Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.

Frequently Asked Questions

Q: Are postal codes included for all locations?

A: Postal codes are included wherever available and validated as part of the standardization workflow.

Q: Can I use this dataset for proximity analysis?

A: Yes. The geocoded coordinates are suitable for drive-time analysis, catchment modeling, nearest-neighbor analysis, and accessibility studies.

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 coordinate reference system is used?

A: Coordinates are provided in the global WGS84 geographic coordinate system (EPSG:4326).

Q: Does the dataset include unique identifiers?

A: Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.

Q: Can this dataset be imported into Power BI or Tableau?

A: Yes. The CSV structure is compatible with Power BI, Tableau, Looker Studio, and other business intelligence platforms.

Analyze this data with AI

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

  • "Analyze this Netto dataset to identify underserved regions in Poland for potential market expansion."
  • "Identify regions in Poland where Netto has a disproportionately strong or weak presence relative to population density."
  • "Identify fast-growing suburban areas in Poland that currently have limited access to nearby supermarkets."

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.

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Reviewed in the United States on May 27, 2026
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Reviewed in the United States on May 25, 2026
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Easy to assemble, does the job, material is shiny nylon and shows creases, minor defects, sloppy
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