ChargeIn EV Charging Station Locations Dataset – Poland

ChargeIn EV Charging Station Locations Dataset – PolandQuick links: Dataset Summary Methodology Download Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI ChargeIn is a growing network of electric vehicle charging stations operating within Poland. It provides accessible charging infrastructure for EV drivers, supporting the transition towards sustainable transport with user friendly solutions. There are 67 ChargeIn EV Charging Stations as of 2 June 2026 in Poland. This

ChargeIn is a growing network of electric vehicle charging stations operating within Poland. It provides accessible charging infrastructure for EV drivers, supporting the transition towards sustainable transport with user-friendly solutions.

There are 67 ChargeIn 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 ChargeIn 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 ChargeIn 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 ChargeIn locations in Poland is found in Wielkopolskie (14 sites, equivalent to 0.4 ChargeIn ev charging stations per 100,000 residents). This is followed by Mazowieckie (10 sites; 0.18 per 100,000) and Dolnośląskie (7 sites; 0.24 per 100,000). From a market-penetration perspective, Wielkopolskie has the highest brand density at 0.4 locations per 100,000 people (population: 3,495,000), making it the most saturated region for ChargeIn in Poland. By contrast, Małopolskie records only 0.03 locations per 100,000 residents (population: 3,430,000), 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 ChargeIn 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 ChargeIn 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?

• Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
• Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
• Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
• 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).
• 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.
• 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.

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 ChargeIn dataset to identify underserved regions in Poland for potential market expansion."
• "Identify locations where multiple ChargeIn sites compete within overlapping catchment areas in Poland."
• "Using this ChargeIn data, find high-traffic retail corridors in Poland with low ev charging stations density for competitive positioning."

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.