Global Climate Change Insights: An Interactive Visualization of Environmental Data

Repository Structure

See the structure here

Run the app

To tun the app you can simple clone our repository and run docker-compose up --build from the root directory. After containers are ready the page is available on the localhost:8080.

Visualizations

Heatmap shows CO₂ emissions by countries

Interactive globes with CO₂ and forests data

Graph illustrating temperature trends spanning more than 140 years

CO₂ emissions graph with available counrty/region selection

Correlations of different metrics

Graph featuring options to select regions and metrics

Renewable energy top

Project Description

Goal

The goal of this project is to create an interactive web application that visualizes global climate change data, enabling users to explore trends, correlations, and impacts of environmental factors such as temperature, carbon emissions, and natural disasters over time.

Vision

The application will tell the story of how climate change has evolved globally, highlighting key trends and anomalies. It will help users understand the relationship between human activities (e.g., CO2 emissions) and environmental changes (e.g., rising temperatures, melting ice caps).

Target Audience

• General Public: To raise awareness about climate change.
• Researchers and Students: To explore and analyze climate data.
• Policy Makers: To understand trends and make data-driven decisions.

Key Questions for Users

1. How have global temperatures changed over the past century?
2. What is the correlation between CO2 emissions and temperature rise?
3. How have natural disasters (e.g., wildfires, hurricanes) increased over time?
4. Which countries contribute the most to global emissions?

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Dataset Description

Data Sources

• Primary Sources:
  • NASA: Historical temperature records, CO2 levels, and sea level rise.
  • World Bank Open Data: Country-specific emissions and economic indicators.

Dataset Overview

• Variables: Temperature anomalies, CO2 levels, sea level rise, natural disaster frequency, country-specific emissions, GDP.
• Time Period: 1900–2023.
• Geographic Coverage: Global (country-level granularity where possible).
• Size: ~10,000–20,000 records after preprocessing.

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Visualization Layout

Architecture

1. Data Pipeline:
   • Scraping: Collect data using Scrapy (NASA, NOAA, World Bank).
   • Cleaning/Preprocessing: Pandas for handling missing values and restructuring.
   • Exploration: Matplotlib for EDA.
   • Delivery: Flask API to serve processed JSON data.
   • Visualization: Interactive panels built with Three.js and D3.js.

Interactive Features

• Interactive Charts: Points hovering allows to see more detailed information.
• Filtering: By year, region, or variable.
• Animations: Smooth transitions during updates.