A Visual Exploration of Walkability in Washington, D.C.

Team

Team 14 is comprised of:

• Corrina Calanoc
• Cynthia Ng
• Katherine Mead
• Madelyne Ventura
• Valeria Vera Lagos

Executive Summary

Walkability is the ease with which people can access amenities in a place without the use of cars. It is associated with positive outcomes in health, community, sustainability, and the economy. A report by the Institute for Transportation and Development Policy evaluated the walkability of major global cities and the only city to make the top 25 in any category was Washington, D.C. This project aims to provide a visual narrative that overviews the walks through the following research questions:

Research Questions:

1. How is walkabilty associated with socioeconomic outcomes in Washington, D.C.?
2. How is walkability associated with health outcomes in Washington, D.C.?
3. How accessible are neighborhoods in Washington, D.C. by bike?
4. What is metro ridership in Washington, D.C. like?
5. What is public sentiment around walkability in Washington, D.C.?

Description of all your code files, datasets, etc.

Code Files:

• Within /code are separate jupyter notebooks written in Python that were used to conduct separate analyses on:
  • Bike rides and bike lanes
  • WMTA rides per station
  • Walkability scores and socioeconomic outcomes data
  • Walkability scores and health outcomes data
  • Sentiment analysis of Walkability and transportation methods

These jupyter notebooks were each rendered into a .qmd file and those were compiled into one .qmd file to be later converted into an HTML.

Datasets:

• U.S. Environmental Protection Agency Walkability: A GeoPandas dataframe was created from a GeoJSON file of D.C. and joined with the Walkability Index and Community Resilience data using Census Tract IDs. All outcome columns were rescaled to 0-100 to ensure consistent scales for visualizations.
• PLACES Census: The PLACES Census dataset was filtered to only include D.C. data. The columns were also renamed for more interpretability.
• Bikeshare & Bike Lane Analysis: In the Capital Bikeshare dataset, the latitude and longitude values were standardized for each bike station and stations outside of the D.C. boundary were removed. Rows with incomplete trip values were removed as well. A map of the bike lanes in the city was brought in by a GeoJSON file.
• WMATA rideshare data: This dataset shows ridership in all WMATA Metro stations in March 2023. Six stations, three of which are located in high-walkability neighborhoods and the other three of which are located in low-walkability neighborhoods, were highlighted.
• Reddit Webscraped data: Used a webscraper to scrape Reddit posts within the /washingtondc thread. In this analysis, the text was preprocessed using the NLTK library to fine-tune a BERT sentiment analyzer and obtain sentiment scores. These scores were then analyzed by topic (Car, Walk, Bike).

Website

These visualizations as well as a more detailed write-up of the problem statement can be found at: https://valeriavl.georgetown.domains/503/

Repository structure

This repository has the following structure:

├── README.md
├── code/
├── data/
├──── /cleaned_data/
├──── /raw_data/
├── img/
└── website/

an archive folder is inside each code directory containing draft codes that are stored for future development processes