All Petrol Station Locations in the UK: Complete Geographic Dataset
All Petrol Station Locations in the UK: Complete Geographic Dataset
All Petrol Stations locations in the UK - complete national dataset
This dataset contains a complete, geocoded collection of Petrol Stations in the UK, including branded chains and independent locations. It provides a complete national coverage with 11,414 verified locations suitable for territory planning, market sizing and spatial analysis.
Last updated: 7 July 2026.
Dataset fields included in the CSV:
- GUID
- Title
- Latitude
- Longitude
- Street No
- Street
- City
- Town
- Local authority
- District
- Postal Code
- Address
- Wheelchair
- Popularity Score
- Phone
- Website
- Opening hours
Brand distribution
- Asda – 450 locations in the UK
- bp – 1,375 locations in the UK
- Esso – 1,537 locations in the UK
- Gulf – 416 locations in the UK
- JET – 378 locations in the UK
- Morrisons – 426 locations in the UK
- Sainsbury's – 318 locations in the UK
- Shell – 1,197 locations in the UK
- Tesco – 729 locations in the UK
- Texaco – 768 locations in the UK
Petrol Stations distribution by Country
- England: 8,789 petrol stations
- Northern Ireland: 796 petrol stations
- Scotland: 1,087 petrol stations
- Wales: 699 petrol stations
Direct access after purchase of the complete locations dataset for the UK
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
- Demographics dataset for the UK: Overlay demographic indicators to deeply understand the population structures and household types surrounding these petrol stations locations.
- Explore demographics data insights for the UK: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Income indicators dataset for the UK (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 the UK - 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 the UK-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 the UK
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: Can this data be combined with demographics datasets?
A: Yes. Many customers combine these locations with demographics, income, mobility, and administrative boundary datasets for deeper spatial analysis.
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.
Q: Can I analyze this dataset with AI tools?
A: Yes. The dataset is structured for compatibility with AI workflows including ChatGPT, Claude, Gemini, RAG pipelines, and Python-based analytics.
Q: Is this dataset useful for accessibility studies?
A: Yes. Analysts can combine the coordinates with mobility, transport, and demographics datasets to evaluate accessibility and service coverage.
Q: Can I preview the dataset before purchasing?
A: Yes. A free sample is available so you can evaluate the structure, fields, and geospatial quality before purchase.
Q: Can this dataset be used for academic or research purposes?
A: Yes. Researchers and universities frequently use these datasets for urban studies, geography, economics, and spatial analytics projects.
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 the UK for potential market expansion.""Measure average inter-store distance between this multi-brand locations across different regions of the UK.""Using this this multi-brand data, find high-traffic retail corridors in the UK with low petrol stations density for competitive positioning."