Fines EV Charging Station Locations Dataset – Bulgaria

Fines EV Charging Station Locations Dataset – BulgariaQuick links: Dataset Summary Methodology Download Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Fines Charging is one of the biggest charge point operators in Bulgaria, positioning itself as the country's fastest EV charging network with multiple high power chargers and a constantly expanding network offering convenient activation via app or RFID chip. There are 273 Fines EV Charging Stations as of 2 June 2026 in

Fines Charging is one of the biggest charge point operators in Bulgaria, positioning itself as the country's fastest EV charging network with multiple high-power chargers and a constantly expanding network offering convenient activation via app or RFID chip.

There are 273 Fines EV Charging Stations as of 2 June 2026 in Bulgaria. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Fines 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 Preview: Sample geospatial records from the Fines dataset in Bulgaria

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 Fines locations in Bulgaria is found in Бургас (45 sites, equivalent to 11.69 Fines ev charging stations per 100,000 residents). This is followed by София-Град (32 sites; 2.47 per 100,000) and Пловдив (22 sites; 3.46 per 100,000). From a market-penetration perspective, Бургас has the highest brand density at 11.69 locations per 100,000 people (population: 385,000), making it the most saturated region for Fines in Bulgaria. By contrast, Силистра records only 1.11 locations per 100,000 residents (population: 90,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Also available for Bulgaria

Related geospatial datasets

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

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?

• Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
• Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
• Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
• 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.
• Retail Site Selection: Property developers and retail analysts identifying optimal locations, white-spaces, and avoiding cannibalization.
• CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.

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 Fines dataset to identify underserved regions in Bulgaria for potential market expansion."
• "Evaluate the balance between urban and rural coverage within the current Fines network in Bulgaria."
• "Identify locations where multiple Fines sites compete within overlapping catchment areas in Bulgaria."

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.