California EV Charging Equity Analysis

Overview

This project analyzes whether public EV charging infrastructure is keeping pace with electric vehicle adoption across California counties, and examines the role income plays in that relationship.

Research Question

Are lower-income California counties being underserved by public charging infrastructure relative to their EV adoption rates?

Data Sources

• EV Registrations: California DMV via data.ca.gov (2025)
• Charging Stations: Alternative Fuels Data Center, U.S. DOE (2025)
• Per Capita Income: Bureau of Economic Analysis, CAINC1 (2024)
• Zip-to-County Crosswalk: US State County Zip (GitHub)

Tools & Technologies

• SQL / SQLite - data loading, querying, and aggregation
• Python - pandas, statsmodels, scikit-learn, matplotlib, seaborn
• Tableau Public - interactive dashboard

Key Findings

• EV adoption scale is the dominant predictor of charging infrastructure (RF importance: 97.2%), not income
• Income was statistically insignificant in OLS regression (p = 0.278) once EV adoption was controlled for
• High-income counties like Marin and Santa Clara remain among the most underserved by ports per EV
• Random Forest outperformed OLS in predictive accuracy (RMSE: 0.578 vs 1.443)

Dashboard

California EV Charging Equity — Tableau Public

Project Structure

ev_charging_equity/
├── setup_db.py          # loads CSVs into SQLite database
├── queries.py           # SQL queries for data extraction
├── analysis.ipynb       # full analysis pipeline
├── ev_equity_clean.csv  # cleaned county-level analysis table
├── ev_equity.db         # SQLite database
└── README.md

How to Run

1. Clone the repo
2. Create a virtual environment: python3 -m venv venv
3. Activate it: source venv/bin/activate
4. Install dependencies: pip install pandas jupyterlab numpy matplotlib seaborn scikit-learn statsmodels
5. Run setup_db.py to build the database
6. Open analysis.ipynb in Jupyter