Dpiù Supermarket Locations Dataset – Italy

Dpiù Supermarket Locations Dataset – ItalyQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Dpi is an Italian discount supermarket chain owned by the Rialto group, primarily located in Northern and Central Italy. It focuses on essential grocery shopping with a strong emphasis on value for money and simplified store layouts. There are 317 Dpi Supermarkets as of 28 May 2026 in Italy. This dataset is

Dpiù is an Italian discount supermarket chain owned by the Rialto group, primarily located in Northern and Central Italy. It focuses on essential grocery shopping with a strong emphasis on value for money and simplified store layouts.

There are 317 Dpiù Supermarkets as of 28 May 2026 in Italy. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Dpiù 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 Dpiù dataset in Italy

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 Dpiù locations in Italy is found in Pop - Liguria (1510143 sites). This is followed by Pop - Abruzzo (1269118 sites) and Lombardia (64 sites; 0.64 per 100,000). From a market-penetration perspective, Valle D'Aosta has the highest brand density at 2.5 locations per 100,000 people (population: 120,000), making it the most saturated region for Dpiù in Italy. By contrast, Lazio records only 0.37 locations per 100,000 residents (population: 5,705,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Learn more about the brand network in our report: View Report

Also available for Italy

Related geospatial datasets

• Administrative boundaries and full polygon dataset for Italy: Map these Dpiù locations against highly precise administrative polygons for territory analysis and spatial structuring.
• Demographics dataset for Italy: Overlay demographic indicators to deeply understand the population structures and household types surrounding these Dpiù locations.
• Explore demographics data insights for Italy: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
• Income indicators dataset for Italy (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 Italy-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 Italy

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?

• 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.
• 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.
• Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
• Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
• Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
• B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
• Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.

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 Dpiù dataset to identify underserved regions in Italy for potential market expansion."
• "Cross-reference these supermarkets with urban transit data to score each location's accessibility for non-driving customers."
• "Rank the top-performing urban areas in Italy for future Dpiù expansion based on existing location density and regional population."

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.