E.ON EV Charging Station Locations Dataset – Poland

E.ON EV Charging Station Locations Dataset – PolandQuick links: Dataset Summary Methodology Download Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI E. ON Polska is part of the multinational energy company E. ON, providing electricity and gas to customers in Poland. The company is also actively involved in electromobility, operating a network of public electric vehicle chargers and offering smart charging solutions for homes and businesses. There are 25 E. ON EV

E.ON Polska is part of the multinational energy company E.ON, providing electricity and gas to customers in Poland. The company is also actively involved in electromobility, operating a network of public electric vehicle chargers and offering smart charging solutions for homes and businesses.

There are 25 E.ON EV Charging Stations as of 2 June 2026 in Poland. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all E.ON 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 E.ON dataset in Poland

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 E.ON locations in Poland is found in Mazowieckie (18 sites, equivalent to 0.33 E.ON ev charging stations per 100,000 residents). This is followed by Śląskie (2 sites; 0.05 per 100,000) and Dolnośląskie (1 sites; 0.03 per 100,000). From a market-penetration perspective, Mazowieckie has the highest brand density at 0.33 locations per 100,000 people (population: 5,505,000), making it the most saturated region for E.ON in Poland. By contrast, Wielkopolskie records only 0.03 locations per 100,000 residents (population: 3,495,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Also available for Poland

Related geospatial datasets

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

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?

• Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
• Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
• Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
• Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
• Mobility Analysis: Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.
• 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.
• 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 E.ON dataset to identify underserved regions in Poland for potential market expansion."
• "Map the concentration of E.ON locations relative to major retail centers and shopping districts in Poland."
• "Rank the top-performing urban areas in Poland for future E.ON 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.