Project Team 18: Public Transportation

COSI 116A - Information Visualization, Fall 2024

Team Members:

• Jason Chen
• Capo Wang
• Tri Phan
• Isaac Zygmuntowicz

Instructor: Prof. Dylan Cashman
Course: Brandeis University, COSI 116A

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Motivation

After the global COVID-19 pandemic, people's habits and economic activities changed drastically. Public transportation was one of the most affected sectors due to social distancing and safety measures.

This project visualizes New York State public transportation data from March 2020 to October 2024. Our work explores:

• The decline and recovery of public transportation usage.
• The impact of COVID-19 cases on ridership trends.
• Spatial patterns of subway ridership across New York City.

The project provides insights for government agencies and public transportation offices to better understand usage trends and plan future developments.

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Visualizations

Our final visualization combines three interactive visualizations:

1. Heat Map

   • Displays subway ridership trends across different stations in New York City over time.
   • Users can hover over stations to view detailed data.

2. Line Chart

   • Shows the overall public transportation ridership and COVID-19 cases over time.
   • Supports brushing and filtering to explore trends for specific periods.

3. Bar Chart

   • Compares different public transportation types as a percentage of pre-COVID ridership.
   • Allows users to filter the line chart by selecting transportation types.

Together, these visualizations provide an interactive and comprehensive view of public transportation trends.

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Design Process

Our project went through multiple iterations to ensure clarity and interactivity:

• Initial Goals:

  • Show public transportation trends over time.
  • Visualize spatial subway ridership patterns.

• Challenges:

  • Handling and cleaning large datasets.
  • Designing visualizations that balance clarity and detail.

• Decisions:

  • The line chart captures overall trends and integrates COVID-19 data to show correlations.
  • The bar chart highlights recovery across different transportation types compared to pre-COVID levels.
  • The heat map focuses on subway ridership due to its dominance and clear spatial representation.

Each visualization was designed to complement the others, enabling deeper exploration through interactive brushing, filtering, and highlighting.

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GitHub Pages

The project is published on GitHub Pages. Access the live visualization here:

https://cosi116a-brandeis-infovis-fall23.github.io/cosi-116a-f24-final-project-repository-Asurazpr/

Data Sources

The project uses the following datasets:

• Subway Ridership Data:

  • Combines early 2020 Turnstile data with later subway ridership records.
  • Data processing included standardizing station names and merging datasets for consistency.

• MTA Daily Ridership Data:

  • Provides daily ridership counts for various transportation types (Subway, Buses, LIRR, etc.).
  • Source: MTA Data Portal.

• COVID-19 Cases:

  • Statewide COVID-19 testing data for New York.

These datasets were cleaned, merged, and analyzed to ensure accurate and meaningful visualizations.

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Task Analysis

Our primary goals for the project were as follows:

1. Visualize trends in public transportation ridership:

   • Show how ridership changed over time during and after the COVID-19 pandemic.

2. Explore spatial patterns:

   • Highlight subway ridership by station across New York City using a heat map.

3. Compare ridership types:

   • Use a bar chart to compare ridership recovery percentages for different transportation modes.

4. Analyze correlations:

   • Explore the relationship between COVID-19 cases and ridership trends using a line chart.

The interactive features allow users to filter, brush, and explore specific time periods and transportation types.

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Conclusion

This project effectively visualizes the significant impact of the COVID-19 pandemic on public transportation in New York State. By integrating a heat map, a line chart, and a bar chart, we provide a comprehensive, interactive, and user-friendly exploration of ridership trends.

Key Insights:

• Subway ridership declined drastically during the early phases of the pandemic but began to recover over time.
• Correlations between rising COVID-19 cases and declining public transportation usage were observed.
• Certain transportation types recovered at different rates compared to pre-COVID levels.

The project offers meaningful insights for policymakers, urban planners, and transportation agencies to better understand and address changes in public transportation usage.

Acknowledgments

• Instructor: Prof. Dylan Cashman
• Tools Used:
  • D3.js
  • Leaflet.js
• Data Sources: MTA Data Portal, New York State COVID-19 Data, Subway Ridership data.