Forest Area Analysis

Analyze the changes in forest area over time and explore the impact of economic, demographic, and urbanization factors using data-driven methods and visualizations.

Table of Contents

• Forest Area Analysis
  • Table of Contents
  • Project Overview
  • Datasets
  • Project Structure
  • Setup Instructions
  • Usage
  • Example Analysis

Project Overview

This project investigates trends in forest area globally and regionally, correlating them with GDP per capita, population density, and urban expansion. The analysis is performed in a Jupyter Notebook and includes data cleaning, merging, visualization, and statistical insights.

Datasets

• forestation.csv: Forest area data by country and year.
• gdp-per-capita-maddison.csv: Historical GDP per capita data.
• population-density.csv: Population density by country and year.
• urban-area-long-term.csv: Urban area expansion data.

Project Structure

• forest_area_analysis.ipynb: Main Jupyter Notebook for analysis and visualization.
• requirements.txt: Python dependencies.
• *.csv: Data files used in the analysis.

Setup Instructions

1. Ensure you have Python and Jupyter Notebook installed.
2. Install the required Python packages:

   pip install -r requirements.txt

3. Open forest_area_analysis.ipynb in Jupyter Notebook or VS Code.

Usage

Run the notebook cells sequentially to:

• Load and clean datasets
• Merge and analyze data
• Visualize trends (e.g., forest area decline, GDP correlation)
• Draw insights from the results

Example Analysis

The notebook provides:

• Line plots of forest area over time
• Correlation heatmaps between forest area, GDP, and population density
• Urbanization impact visualizations
• Statistical summaries and key findings