The TRI Smoke Simulation Project is an interactive visualization and data analysis tool designed to explore the impact of toxic releases from facilities on communities across Chicago. By leveraging multiple datasets and advanced simulation techniques, this project aims to enhance understanding and foster informed decision-making for environmental justice and public health.
Smoke spread visulization ( higher the toxicity, redder the pixel)
Scaling the simulation to all TRI facilities of Chicago
Community level based aggregation
- Data is aggregated at the community level, balancing the granularity of census tracts and the broader scope of zip codes.
- Why Community Level?
- Provides a midpoint between detailed (census tract) and broader (zip code) aggregations.
- Ideal for advocacy and storytelling efforts targeted at specific communities.
- Dynamic visualizations of smoke density across a 200x200 grid.
- Ability to overlay additional datasets, such as:
- Raw facility emissions.
- Elementary school locations.
- Simulations powered by cellular automata models mimic real-world pollutant dispersion.
- Incorporates factors like:
- Wind patterns (speed and direction).
- Diffusion rates.
- Dissipation factors.
- Real-time updates allow users to observe smoke spreading dynamically.
- Supports overlaying multiple datasets, enabling participants to reassess conclusions dynamically.
- Interactive features visualize the intersection of:
- Toxic releases.
- Socioeconomic and demographic factors.
- Designed to support agentic sense-making, empowering users to explore and challenge data narratives.
- Promotes environmental justice by providing actionable insights into the effects of toxic releases on underserved communities.
- Public datasets:
- Toxic release inventory (TRI).
- Elementary schools.
- Community boundaries.
- Datasets are cleaned and preprocessed.
- Data is aggregated at the community level for consistency.
- Uses grid-based models to calculate smoke diffusion and dissipation over time.
- Factors like wind direction, speed, and terrain are included to reflect realistic pollutant spread.
- Users explore smoke density visualizations overlaid with:
- Socioeconomic data.
- Educational data.
- Additional overlays (e.g., community names) enrich the analysis.
- Serves as a powerful tool for:
- Researchers.
- Policymakers.
- Advocates.
- Addresses environmental and health challenges in marginalized communities.
- Provides an interactive and data-driven platform for meaningful engagement with complex environmental data.
- Python: Data preprocessing and static visualizations.
- GeoPandas: Geospatial data manipulation.
- Matplotlib: Initial exploratory visualizations.
- Cellular Automata Models: Smoke dispersion simulation.