All Petrol Station Locations in Italy: Complete Geographic Dataset
All Petrol Station Locations in Italy: Complete Geographic Dataset
All Petrol Stations locations in Italy - complete national dataset
This dataset contains a complete, geocoded collection of Petrol Stations in Italy, including branded chains and independent locations. It provides a complete national coverage with 35,150 verified locations suitable for territory planning, market sizing and spatial analysis.
Last updated: 7 July 2026.
Dataset fields included in the CSV:
- GUID
- Title
- Group
- Latitude
- Longitude
- Street No
- Street
- City
- Admin_level_1
- Admin_level_2
- Commune
- Region
- Population
- Postal Code
- Address
- Wheelchair
- Phone
- Website
- Fuel Type
- Opening hours
Brand distribution
- AS 24 – 192 locations in Italy
- Beyfin – 118 locations in Italy
- Costantin – 118 locations in Italy
- EnerGas – 157 locations in Italy
- Eni (Agip) – 4,523 locations in Italy
- Esso – 2,441 locations in Italy
- Europam – 182 locations in Italy
- Ewa – 163 locations in Italy
- IP – 5,060 locations in Italy
- Keropetrol – 154 locations in Italy
- Q8 – 3,085 locations in Italy
- Retitalia – 233 locations in Italy
- Shell – 183 locations in Italy
- Star Oil – 137 locations in Italy
- Tamoil – 1,592 locations in Italy
- Vega – 140 locations in Italy
Direct access after purchase of the complete locations dataset for Italy
Download the dataset immediately in a clean, analysis-ready CSV format. Ideal for geospatial analysis, competitor benchmarking, market studies, site planning, and retail location intelligence workflows.
Evaluate the dataset before purchase
A free sample is available for download, allowing you to inspect the structure, fields, and geospatial precision before purchasing the full dataset.
Related geospatial datasets
- Administrative boundaries and full polygon dataset for Italy: Map these petrol stations 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 petrol stations 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 contact@geolocet.com.
Frequently Asked Questions
Q: Is this dataset useful for franchise analysis?
A: Yes. The dataset can support franchise territory planning, network expansion analysis, and regional performance benchmarking.
Q: Can this dataset be used for logistics planning?
A: Yes. Many customers use these datasets to optimize delivery territories, distribution networks, route planning, and last-mile logistics analysis.
Q: Can this dataset support expansion planning?
A: Yes. Analysts often use the dataset to identify underserved areas, evaluate regional density, and support retail expansion decisions.
Q: Does the dataset include opening hours?
A: Yes, opening hours are included where publicly available and validated during the data standardization process.
Q: Is the dataset immediately downloadable after purchase?
A: Yes. The full dataset becomes available for instant digital download immediately after purchase.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this this multi-brand dataset to identify underserved regions in Italy for potential market expansion.""Analyze the clustering behavior of this multi-brand locations to determine whether the network prioritizes convenience, coverage, or saturation strategies.""Cross-reference these petrol stations with urban transit data to score each location's accessibility for non-driving customers."