Food System Emissions Explorer

An interactive Shiny for Python application built from the same datasets and analysis pipeline used in the DSS notebook project on food-system greenhouse gas emissions.

Project Focus

This app turns the notebook analysis into a portfolio-ready interactive dashboard. It combines three datasets:

  • EDGARfood.csv for country-level emissions over time
  • Food_Product_Emissions.csv for food-product emissions across the supply chain
  • GLEAM_LivestockEmissions.csv for regional livestock emissions by gas type

The dashboard lets visitors explore long-run global trends, regional patterns, top-emitting countries, and the highest-emission food products without reading through the full notebook.

Why This Fits The Portfolio

This project reflects the Data Science and Society portfolio brief closely:

  • The website shell is built with Quarto and published as a static site.
  • The interactive component is built with Shiny for Python.
  • The app code uses object-oriented components to separate loading, analysis, visualization, and UI concerns.
  • The dashboard showcases a real data science project rather than sample data.

Interactive Demo

Interact with the live Shiny application below:

Posit Connect blocks iframe embedding for this app, so open the dashboard directly.

Open Food System Demo

Features

  • Global trend view: Explore total food-system emissions from 1990 to 2015.
  • Regional trend view: Compare regions or focus on a single region such as Asia.
  • Top-emitter country view: See the highest-emitting country within the chosen region over time.
  • Food-product ranking: Inspect which products contribute most from land to retail.
  • GLEAM livestock overview: Compare CO2, CH4, and N2O emissions across regions.
  • Data preview: Inspect the cleaned EDGAR data used in the app.

Running the App Locally

To run this application on your machine:

# Install app dependencies
pip install -r projects/data/requirements.txt

# Start the Shiny app
cd projects/data
../../.venv/bin/python -m shiny run app.py --port 8000

Then visit http://127.0.0.1:8000 in your browser.

Project Details

Technology Stack: - Python - Shiny for Python - Pandas for data manipulation - Plotly for interactive visualizations - pycountry-convert for country-to-region mapping

Data direction: this dashboard is derived from the DSS notebook analysis on food-system emissions, forecasting, and regional comparisons.