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Himanshu Naidu edited this page Jul 12, 2024 · 7 revisions

Welcome to the DSSG2024_transit_equity wiki!

Project description

We are using ORCA (one regional card for all) fare card data from the Puget Sound region’s 7 transit organizations. Our goal is to reveal insights about transit use for low-income riders and other disadvantaged groups and identify areas for improvement. More specifically, our research questions are

  1. Are there bus shelters at transfer hot spot locations for low income riders?
  2. Is service for low income riders as good as for other demographics?
  3. How do transit networks differ between card demographics?
  4. What are the different types of users based on transit behaviors? Our data exists in a postgres database housed on a server at the UW TRAC office and Ryan Avery can give you access as a user.

Code

Our Python code is in the following repositories:

  1. Transit Equity Public Repository
  • This is the public repository that can be installed as a local package. Installation instructions are given in the ReadMe file.
  1. Transit Equity Private Repository
  • This is the private repository that contains the notebooks for all the analysis done to answer different questions.
  • Some files have been segregated by folders with team member names: rebecca, note_siman, ishan
  • Other files have been segregated in folders with analysis purpose: notebooks (general notebooks from Himanshu), notebooks_low_income (To answer Low Income question) Install the conda environment in orca.yaml (public repository) to work with both the packages. Make sure to refer the env.example file to ensure the correct .env configuration.

Methods

Primary programming language: Python. Libraries: Pandas, numpy, matplotlib, etc. Database: ORCA PostgreSQL DB (With DBeaver, DataGrip and SQL Alchemy), Census for getting census information (income, household size, commute mode, etc). GIS: Geopandas library, Folium maps, Network Analysis: Networkx and pyvis for network analyses and visualization.

Data access

We have a tutorial on how to access the database at the following Github repository. Please consult the DSSG team or Dr. Ryan Avery for access to these repositories Setup Transit Equity

Details for the database schema is given in the following repository. Please consult Dr. Ryan Avery for access to these repositories . Orca Analysis SQL

Tasks and questions

  • How are we serving the disabled communities? (Can we get paratransit data)?
  • How can we scale up analyses so that we are able to use the full dataset?
  • How can transit and land use be better integrated?
  • How can we improve the per-transaction census block prediction algorithm by Dr. Ryan Avery?
    • How can we extend the algorithm for per-user census block prediction algorithm.
  • Network – explore additional relevant network metrics to refine question of how networks differ

Final Notes

A lot of important information is provided in the Notion Workspace: Please consult the DSSG team for access to the workspace.

Clone this wiki locally