Austria Petrol Stations Locations Dataset – Top 11 Petrol Station Brands
Austria Petrol Stations Locations Dataset – Top 11 Petrol Station Brands
Top 11 Petrol Station Chains Across Austria: Premium Multi-Brand Location Dataset
Unlock a complete, ready-to-use dataset featuring the leading Petrol Station brands in Austria. This expertly compiled Top Brands dataset consolidates all major Petrol Station chains in Austria, delivering a clean, standardised and analysis-ready collection of 2,368 locations. Perfect for businesses seeking high-quality data for market sizing, competitive landscape analysis, network optimisation, and strategic planning.
Last updated: 9 July 2026.
Dataset fields included in the CSV:
- GUID
- Title
- Group
- Latitude
- Longitude
- Street No
- Street
- City
- Admin_level_1
- Admin_level_2
- Gemeinde
- Region
- Population
- Postal Code
- Address
- Wheelchair
- Popularity Score
- Phone
- Website
- Opening hours
Brand distribution
- Avanti – 138 locations in Austria
- AVIA – 116 locations in Austria
- bp – 288 locations in Austria
- Diskont – 107 locations in Austria
- Eni – 330 locations in Austria
- Jet – 157 locations in Austria
- Lagerhaus Genol – 519 locations in Austria
- OMV – 243 locations in Austria
- Shell – 211 locations in Austria
- Turmöl – 259 locations in Austria
Petrol Stations distribution by Region
- Niederösterreich: 616 petrol stations
- Oberösterreich: 444 petrol stations
- Steiermark: 322 petrol stations
- Tirol: 257 petrol stations
- Kärnten: 188 petrol stations
- Salzburg: 184 petrol stations
- Wien: 155 petrol stations
- Burgenland: 123 petrol stations
- Vorarlberg: 79 petrol stations
Direct access after purchase of the complete locations dataset for Austria
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 Austria: Map these petrol stations locations against highly precise administrative polygons for territory analysis and spatial structuring.
- Demographics dataset for Austria: Overlay demographic indicators to deeply understand the population structures and household types surrounding these petrol stations locations.
- Explore demographics data insights for Austria: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Explore a rich library of Austria-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 Austria
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 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: Can I combine this dataset with administrative boundaries?
A: Yes. The coordinates can be spatially joined with municipalities, census units, postal areas, and other administrative polygons.
Q: Can I request the data in GeoJSON or Shapefile format?
A: Yes. Alternative delivery formats such as GeoJSON, Shapefile, Excel, and PostgreSQL imports are available upon request.
Q: Does the dataset include unique identifiers?
A: Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.
Q: Does the dataset include opening hours?
A: Yes, opening hours are included where publicly available and validated during the data standardization process.
Q: Are the datasets suitable for machine learning workflows?
A: Yes. The structured tabular format and standardized coordinates make the datasets suitable for machine learning and predictive analytics applications.
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 Austria for potential market expansion.""Assess how effectively the current this multi-brand network covers major commercial and residential hubs across Austria.""Calculate the total population coverage for this multi-brand in Austria using a 10km catchment radius around each coordinate."