⚡ Electric Charging Stations Analysis in Italy 🇮🇹

This project focuses on analyzing the distribution, accessibility, and strategic positioning of electric vehicle (EV) charging stations in Italy, with particular emphasis on the Lombardy region. Through a combination of spatial analysis, statistical correlation, and priority mapping, the project identifies critical gaps and proposes data-driven insights for infrastructure improvements.

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🎯 Objectives

• Assess the distribution of EV charging stations across Italy.
• Evaluate accessibility and saturation in different regions.
• Identify strategic areas lacking sufficient infrastructure.
• Develop a Priority Map to guide future installations.

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📊 Datasets Used

1. EV Charging Stations (2023)
   Sourced via Overpass Turbo – up to December 31, 2023.

2. Electric Vehicle Circulation by Region
   Data from Piattaforma Unica Nazionale

3. Population by Region (ISTAT)
   Official statistics from ISTAT (Italian National Institute of Statistics).

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🧪 Methodology & Analyses

1. 🔴 Mapping Charging Station Density

• A thematic map was created to visualize charging station density per region.
• Normalization of charging station numbers based on regional surface area.
• Key findings:
  • Regions with the highest number of stations: Lombardy (515), Veneto (470), Friuli Venezia Giulia (195).
  • Regions with the lowest number of stations: Sardinia (27), Basilicata (9).
    
    
  

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2. 🧩 Spatial Clustering of Charging Stations

• Tool: DBSCAN algorithm via QGIS.
• Goal: Visualize clusters of EV stations to identify areas of high density.
• Parameters: Min Points: 2 | Max Distance: 5 km
• Findings: Northern regions like Lombardy show higher cluster concentrations (e.g., 465 clusters), while southern areas like Basilicata show minimal presence (only 5 clusters).

  

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3. 🔗 Correlation between Stations & EV Circulation

• Method: Pearson correlation coefficient.
• Result: A strong positive correlation (r = 0.71, p-value = 0.0005) suggesting that better infrastructure may boost EV adoption.

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4. 🔄 Saturation Analysis by Region

• Formula: Saturation = EVs / Charging Stations
• Interpretation:
  • High saturation = potential congestion, need for expansion.
  • Low saturation = balanced infrastructure.
• Insight:
  • Highest: Trentino-Alto Adige → 35,746 EVs / 151 stations.
  • Lowest: Friuli-Venezia Giulia → 3,632 EVs / 195 stations.
    
    
  

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5. 🌍 Land Use Raster Analysis

• Data: CORINE Land Cover 2018 from Copernicus.
• Goal: Identify the type of land where stations are located.
• Result:
  • Urban areas: 72.8%
  • Rural/Extra-urban: 27.2%

  

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6. 📍 Points of Interest Analysis (Strategic Zones)

• Approach: Heatmap distribution by land use.
• Insight: Stations are concentrated in limited strategic zones – mostly in:
  • Urban Areas
  • Ports
  • Roads & Railways
    
    
  

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7. 🛣️ Motorway Network Analysis – Focus on Lombardy

• Tool: QuickOSM (for motorway network).
• Goal: Assess the presence of charging stations along highways.
• Findings:
  • Only 64 out of 515 stations in Lombardy are on motorways.
  • Motorways rank below rural zones like pastures and forests in station density.
• Conclusion: Reinforces the need for boosting long-distance EV travel infrastructure.

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8. 🕒 Accessibility Analysis (Ischrones – Lombardy)

• Tool: ORS Tools in QGIS
• Method: Generate 5 and 10-minute driving isochrones around each station.
• Result:
  • 72.9% of Lombardy is covered (≤10 min drive).
  • 27.1% remains uncovered.
    
    
  

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9. 🏙️ Urban Gaps – Lombardy

• Urban areas represent strategic installation points due to higher demand.
• Findings:
  • 72% of urban zones in Lombardy are covered.
  • 28% uncovered – including entire cities like Pavia.
    
    
  

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10. 🗺️ Urban Gaps – Italy-Wide

• Extended the previous analysis nationwide.
• Regions with highest urban coverage:
  • Valle d’Aosta (79%), Friuli-Venezia Giulia (72%), Lombardia (72%)
• Lowest coverage:
  • Basilicata (16%), Sardinia (19%), Molise (20%)

  

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11. 🔴 Priority Map for Future Installations

• Objective: Identify regions with the highest urgency for new EV stations.

• Formula: Priority = 0.4 * (Saturation_norm) + 0.3 * (EV Fleet_norm) + 0.15 * (Stations_norm) + 0.15 * (Population_norm)

• • Visualization: Bubble Chart

• Red + Large = High Priority

• Blue + Small = Low Priority

• Top Priority: Trentino-Alto Adige (0.75)

• Lowest Priority: Friuli-Venezia Giulia (0.04)
  
  

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✅ Final Results & Conclusions

Topic	Summary
Strategic Locations	Urban areas dominate in station presence, but some cities are still unserved.
Saturation	Some regions appear well-equipped, but high saturation reveals service strain.
Coverage	Isochrone analysis highlights key urban gaps, essential for planning.
Future Outlook	The Priority Map helps pinpoint where new stations are urgently needed.

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📁 Project Structure

├── ExportedDatasets/ #Dataset used for analysis
├── images/  #images for  README.md
├── AnalisiCluster.ipynb/              # Python script for cluster analysis
├── AnalisiPearson.ipynb/              # Python script for Pearson analysis
├── AnalisiRaster.ipynb/              # Python script for Raster & Point of interest analysis
├── E-Mobility-Access.qgz           # QGIS Project File
├── Layer.zip           # QGIS Project File
├── README.md             # Project documentation (this file)

👨‍💻 Author

Mario Cicalese 🔗 LinkedIn • GitHub

Irene Gaita 🔗 LinkedIn • GitHub