Lidl Supermarket Locations Dataset – Hungary

Lidl Supermarket Locations Dataset – HungaryQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Lidl is a leading discount supermarket chain in Hungary, offering a mix of fresh produce, baked goods, and specialized international food weeks. It is frequently cited as one of the most trusted retail brands by Hungarian consumers. There are 226 Lidl Supermarkets as of 29 May 2026 in Hungary. This dataset is

Lidl is a leading discount supermarket chain in Hungary, offering a mix of fresh produce, baked goods, and specialized international food weeks. It is frequently cited as one of the most trusted retail brands by Hungarian consumers.

There are 226 Lidl Supermarkets as of 29 May 2026 in Hungary. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Lidl 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 Lidl dataset in Hungary

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Regional Distribution Breakdown

Looking at the geographic distribution, the highest concentration of Lidl locations in Hungary is found in Közép-Magyarország (78 sites, equivalent to 2.58 Lidl supermarkets per 100,000 residents). This is followed by Közép-Dunántúl (28 sites; 2.67 per 100,000) and Észak-Alföld (28 sites; 2.01 per 100,000). From a market-penetration perspective, Dél-Dunántúl has the highest brand density at 2.96 locations per 100,000 people (population: 845,000), making it the most saturated region for Lidl in Hungary. By contrast, Észak-Magyarország records only 1.68 locations per 100,000 residents (population: 1,070,000), 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 Lidl 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 Lidl 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?

• CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.
• Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
• Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
• Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
• Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
• 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.
• Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
• Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.

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

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.