EV Analysis - GIS Portfolio Project

A comprehensive spatial analysis project focused on Electric Vehicle (EV) infrastructure, adoption patterns, and related socio-economic factors using Geographic Information Systems (GIS).

Project Overview

This project aims to analyze and visualize various aspects of electric vehicle adoption and infrastructure through spatial data analysis. It combines demographic, economic, infrastructure, and satellite data to provide insights into EV accessibility and adoption patterns.

Project Structure

ev-analysis/
│
├── data/                    # Processed data ready for analysis
│   ├── boundaries/         # Geographic boundary files (administrative regions, etc.)
│   ├── demographics/       # Population and demographic data
│   ├── economic/          # Economic indicators and data
│   ├── infrastructure/    # EV charging stations, roads, utilities
│   └── satellite/         # Satellite imagery and derived products
│
├── raw_data/               # Original, unprocessed data files
│
├── scripts/                # Analysis scripts and code
│   ├── cache/             # Cached intermediate results
│   └── outputs/           # Script-generated outputs
│       ├── analysis/      # Analysis results and statistics
│       ├── maps/          # Generated maps
│       └── reports/       # Generated reports
│
├── outputs/                # Final project outputs
│   ├── maps/              # Publication-ready maps
│   └── reports/           # Final reports and documentation
│
└── docs/                   # Project documentation

Features (Planned)

• Spatial Analysis: Analyze EV infrastructure distribution and accessibility
• Demographic Integration: Correlate EV adoption with demographic factors
• Economic Analysis: Examine relationships between economic indicators and EV infrastructure
• Satellite Data Processing: Utilize satellite imagery for land use and urban planning insights
• Interactive Mapping: Generate interactive and static maps for visualization
• Comprehensive Reporting: Automated report generation with key findings

Getting Started

Prerequisites

• Python 3.8 or higher
• GIS software (QGIS/ArcGIS)
• Required Python packages (see requirements.txt - to be added)

Installation

1. Clone the repository

git clone [repository-url]
cd ev-analysis

2. Set up virtual environment (recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt  # To be created

Data Sources

Lahore Census Data - 2023 OSM Data

Contributing

This is a portfolio project. For any questions or collaboration opportunities, please contact the author.

License

MIT

Author

Haris - GIS Portfolio Project