davis-mironga/Global-Forest-Trends

Data-driven analysis of Global Forest Area and Deforestation Trends (1990–2025) using FAO’s Global Forest Resources Assessment (FRA 2025). Includes global, regional, and Kenya-focused insights with Python visualizations. # Global Forest Area & Deforestation Trends (1990–2025) A data-driven exploration of global forest dynamics using FAO’s **Global Forest Resources Assessment (FRA 2025)** dataset. --- ## Overview **Goal:** Quantify and visualize how global and regional forest area changed between **1990–2025**, highlighting trends in forest loss, gains, and recovery. **Motivation:** Forests are critical for biodiversity and climate regulation. This project transforms open FAO data into actionable insights that can inform environmental policy and public awareness. --- ## Objectives - Compute **forest area trends** globally, regionally, and nationally. - Identify **top 10 countries** with the highest forest loss and gain. - Produce a **reproducible Jupyter Notebook** pipeline (Python). - Export **tidy CSVs** for BI tools (Power BI / Tableau). - Communicate findings through **visuals and summary narratives**. --- ## Data Source | Item | Description | |------|--------------| | Dataset | FAO Global Forest Resources Assessment (FRA 2025) | | Files Used | 1a_ForestArea, 1b_NaturallyRegeneratingForest, 1b_OtherPlantedForest | | Reference Years | 1990 · 2000 · 2010 · 2015 · 2020 · 2025 | | Units | Thousand hectares (kha) | | Access | fra-data.fao.org | --- ## Research Questions 1. How has global forest area changed from 1990 → 2025? 2. Which regions gained or lost the most forest area? 3. Which countries are the largest contributors to forest loss/gain? 4. (Optional) How do naturally regenerating vs planted forests compare? 5. (Case Study) How are **Kenya’s forests** evolving over the same period? --- ## Methodology ### Data Pipeline **Ingest → Clean → Standardize → Validate → Analyze → Visualize → Report** | Step | Action | |------|---------| | Ingest | Load CSVs from FRA_Years_variables automatically (no hardcoding) | | Clean & Tidy | Convert wide-format year columns to long (1990–2025) | | Standardize | Rename columns → region, iso3, country, year, forest_a …