Appendix A — Project Proposal

Milestone 1: Proposal + Dataset

Title

Exploring Equity in Metro Transit: A Spatial Analysis of Ridership and Income

Team Members

Rishika Kundu
Hadley Wilkins
Laurice Jimu

Project Description

For this project, we’re looking at Metro Transit data and comparing it to income levels across different areas. Basically, we want to understand who is using public transit and whether access to transit is evenly distributed across neighborhoods.

We’re especially interested in whether people in lower-income areas rely more on transit, and whether those areas actually have enough stops or service to meet demand.

Research Questions

Here are the main questions we want to explore:

Is there a relationship between income levels and transit ridership?
Are transit stops more common in higher-income or lower-income areas?
Are there areas that seem underserved and could benefit from more transit options?

Why We Chose This Topic

We’re all interested in public transit and how it impacts everyday life. Transit affects things like getting to work, school, and other opportunities, so it’s important that it works well for everyone.

This project gives us a chance to look at real data and see whether transit is actually serving different communities fairly, especially in the Twin Cities.

Dataset

We plan to use the following dataset:

Metro Transit Count and Headway Data
Source: Minnesota Geospatial Commons
Link: https://gisdata.mn.gov/dataset/us-mn-state-metc-trans-transit-count-headway-sum

This dataset includes spatial data about transit routes, stops, and service levels. We’ll combine it with income data (likely from census data) so we can compare transit patterns across different neighborhoods.

Communication Plan

We’ll mainly communicate through a group text chat, which we already use regularly. We usually respond pretty quickly, but we’ll aim to reply within 24 hours during the week.

We’ll also use class time as our main meeting time to work together, and then follow up over text if anything comes up outside of class.

Team Roles

We’ll all contribute to everything, but we picked a few roles to stay organized:

Point of Contact: Laurice Jimu
(Handles communication with the instructor/preceptors)
Technical Lead: Rishika Kundu
(Keeps track of code, data, and the website)
Style Lead: Hadley Wilkins
(Makes sure everything is clean, readable, and follows code guidelines)

Conflict Resolution

If someone misses a deadline, we’ll just talk about it and figure out how to adjust so we stay on track. We want to keep communication open and not let things build up.

For decisions, we’ll try to agree as a group. If we disagree, we’ll go with what makes the most sense based on the data and our project goals.

Implementation Plan

We based our timeline on the class schedule and project milestones.

Milestone 1: Proposal + Dataset

Due: Wednesday, March 25

Finalize topic and research questions
Confirm dataset
Set up GitHub repo
Submit proposal.qmd

Milestone 2: Effective Teamwork

Due: April 6

Each person finds a resource about teamwork
Write a short summary individually

Milestone 3: Case Study

Due: April 8

Look at an example data story (NYT, FiveThirtyEight, etc.)
Analyze how it’s structured
Add write-up to case-study.qmd

Milestone 4: EDA (Exploratory Data Analysis)

Due: April 10

Each person does their own EDA
Create visualizations and summaries
Start identifying patterns

Milestone 5: Progress Presentation

Due: April 22

Put together slides
Share early findings
Get feedback from class

Milestone 6: Near-Final Project

Due: May 1

Finalize analysis and visuals
Build out website
Refine story and explanations

Milestone 7: Final Submission

Due: May 4

Final edits
Check website + links
Submit everything

GitHub Issues Plan

We’ll use GitHub Issues to keep track of tasks and stay organized.

We’ll create milestones for each phase of the project and then break them into smaller tasks (issues), like:

Set up repository
Clean dataset
Do EDA
Build visualizations
Write sections of report
Prepare presentation

Each issue will be assigned to someone and linked to a milestone so we can track progress.

Closing

We’re excited about this project because it connects data science to something real and important. Our goal is to tell a clear story about transit and equity, while also building strong data analysis and visualization skills.

--- title: "Project Proposal" format: html --- # Milestone 1: Proposal + Dataset ## Title **Exploring Equity in Metro Transit: A Spatial Analysis of Ridership and Income** ## Team Members - Rishika Kundu - Hadley Wilkins - Laurice Jimu ## Project Description For this project, we’re looking at Metro Transit data and comparing it to income levels across different areas. Basically, we want to understand who is using public transit and whether access to transit is evenly distributed across neighborhoods. We’re especially interested in whether people in lower-income areas rely more on transit, and whether those areas actually have enough stops or service to meet demand. ## Research Questions Here are the main questions we want to explore: 1. Is there a relationship between income levels and transit ridership? 2. Are transit stops more common in higher-income or lower-income areas? 3. Are there areas that seem underserved and could benefit from more transit options? ## Why We Chose This Topic We’re all interested in public transit and how it impacts everyday life. Transit affects things like getting to work, school, and other opportunities, so it’s important that it works well for everyone. This project gives us a chance to look at real data and see whether transit is actually serving different communities fairly, especially in the Twin Cities. ## Dataset We plan to use the following dataset: - **Metro Transit Count and Headway Data** - Source: Minnesota Geospatial Commons - Link: <https://gisdata.mn.gov/dataset/us-mn-state-metc-trans-transit-count-headway-sum> This dataset includes spatial data about transit routes, stops, and service levels. We’ll combine it with income data (likely from census data) so we can compare transit patterns across different neighborhoods. ## Communication Plan We’ll mainly communicate through a group text chat, which we already use regularly. We usually respond pretty quickly, but we’ll aim to reply within 24 hours during the week. We’ll also use class time as our main meeting time to work together, and then follow up over text if anything comes up outside of class. ## Team Roles We’ll all contribute to everything, but we picked a few roles to stay organized: - **Point of Contact:** Laurice Jimu (Handles communication with the instructor/preceptors) - **Technical Lead:** Rishika Kundu (Keeps track of code, data, and the website) - **Style Lead:** Hadley Wilkins (Makes sure everything is clean, readable, and follows code guidelines) ## Conflict Resolution If someone misses a deadline, we’ll just talk about it and figure out how to adjust so we stay on track. We want to keep communication open and not let things build up. For decisions, we’ll try to agree as a group. If we disagree, we’ll go with what makes the most sense based on the data and our project goals. ## Implementation Plan We based our timeline on the class schedule and project milestones. ### Milestone 1: Proposal + Dataset **Due: Wednesday, March 25** - Finalize topic and research questions - Confirm dataset - Set up GitHub repo - Submit proposal.qmd --- ### Milestone 2: Effective Teamwork **Due: April 6** - Each person finds a resource about teamwork - Write a short summary individually --- ### Milestone 3: Case Study **Due: April 8** - Look at an example data story (NYT, FiveThirtyEight, etc.) - Analyze how it’s structured - Add write-up to case-study.qmd --- ### Milestone 4: EDA (Exploratory Data Analysis) **Due: April 10** - Each person does their own EDA - Create visualizations and summaries - Start identifying patterns --- ### Milestone 5: Progress Presentation **Due: April 22** - Put together slides - Share early findings - Get feedback from class --- ### Milestone 6: Near-Final Project **Due: May 1** - Finalize analysis and visuals - Build out website - Refine story and explanations --- ### Milestone 7: Final Submission **Due: May 4** - Final edits - Check website + links - Submit everything --- ## GitHub Issues Plan We’ll use GitHub Issues to keep track of tasks and stay organized. We’ll create milestones for each phase of the project and then break them into smaller tasks (issues), like: - Set up repository - Clean dataset - Do EDA - Build visualizations - Write sections of report - Prepare presentation Each issue will be assigned to someone and linked to a milestone so we can track progress. ## Closing We’re excited about this project because it connects data science to something real and important. Our goal is to tell a clear story about transit and equity, while also building strong data analysis and visualization skills.