An interactive web application for visualizing urban heat patterns in Durham, NC and exploring the cooling effects of tree canopy coverage.

This tool allows users to:

• Visualize temperature data across Durham with an interactive heat map
• See existing tree canopy coverage overlaid on temperature data
• Paint hypothetical tree scenarios to see cooling effects in real-time
• Compare different tree densities (sparse to urban forest levels)
• Navigate through time to see temperature variations

This project addresses the Data-Driven EnviroLab x School of Data Science & Society: Climate Action in North Carolina challenge from HackNC 2025.

North Carolina faces rising temperatures, extreme rainfall, and other climate threats. In Durham, some neighborhoods experience up to 7°F higher heat during the day due to fewer trees and more paved surfaces, putting vulnerable residents at greater risk. This tool creates a solution that helps reduce heat stress, improve resilience, and support local climate action by visualizing the cooling impact of strategic tree placement.

The project with the greatest potential impact will win a prize and may be shared with local councils and community boards.

• Real-time cooling calculations using data-driven linear regression model (-0.0148°C per 1% tree coverage)
• Dynamic paint radius that adjusts based on zoom level for precision or broad coverage
• Multiple tree density options from street trees (10%) to urban forest (50%)
• Live statistics showing mean, min, max temperatures that update with scenarios
• Time series data for temporal analysis

# Setup environment
python -m venv env
source env/bin/activate
pip install -r requirements.txt

# Run the application
python main.py

Then open http://localhost:5000 in your browser.

• Source: High-resolution weather grid data provided by Data-Driven EnviroLab

• Source: TreeMap Live Canopy Cover dataset (percent coverage)

• Method: Data-driven linear regression analysis based on observed relationships in Durham temperature and canopy data
• Relationship: -0.0148°C cooling per 1% increase in tree coverage

• Python for backend and Javascript/HTML/CSS for frontend
• Mapbox GL JS API for interactive map rendering and data visualization
• Open-Meteo API to fetch current temperature data for comparison
• ChatGPT and Claude used to understand new packages/libraries and assist with implementation

• Explore climate adaptation and heat mitigation strategies via tree canopy coverage
• Educational tool for understanding urban heat islands and climate equity
• Community engagement in tree planting initiatives and green urban planning

Built for HackNC 2025