Tesla EV Charging Station Locations Dataset – Poland

Tesla EV Charging Station Locations Dataset – PolandQuick links: Dataset Summary Methodology Download Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Tesla operates a proprietary network of high speed electric vehicle chargers, known as Superchargers, strategically distributed across Poland. This infrastructure facilitates long distance travel for Tesla owners, with select locations increasingly being opened to non Tesla electric vehicles. There are 79 Tesla EV

Tesla operates a proprietary network of high-speed electric vehicle chargers, known as Superchargers, strategically distributed across Poland. This infrastructure facilitates long-distance travel for Tesla owners, with select locations increasingly being opened to non-Tesla electric vehicles.

There are 79 Tesla 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 Tesla 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 Tesla 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 Tesla locations in Poland is found in Dolnośląskie (13 sites, equivalent to 0.45 Tesla ev charging stations per 100,000 residents). This is followed by Małopolskie (12 sites; 0.35 per 100,000) and Zachodniopomorskie (11 sites; 0.68 per 100,000). From a market-penetration perspective, Zachodniopomorskie has the highest brand density at 0.68 locations per 100,000 people (population: 1,620,000), making it the most saturated region for Tesla in Poland. By contrast, Podlaskie records only 0.09 locations per 100,000 residents (population: 1,140,000), 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 Tesla 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 Tesla 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?

• Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
• 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.
• Retail Site Selection: Property developers and retail analysts identifying optimal locations, white-spaces, and avoiding cannibalization.
• Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
• Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
• CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.
• B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).

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 Tesla dataset to identify underserved regions in Poland for potential market expansion."
• "Analyze geographic gaps in the current Tesla network and identify underserved regions in Poland with strong expansion potential."
• "Analyze which Tesla locations in Poland are positioned in highly competitive retail zones versus standalone markets."

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.