School Funding Equity Analyzer

Name

Yanesia Norris

Project Overview

This project will use Python to analyze and visualize disparities in per-pupil public school funding in Baltimore City. It will connect funding levels to student demographics and performance metrics to expose inequities and generate data-informed insights for education policy advocacy.

Why / Purpose

School funding inequity is one of the biggest barriers to educational justice. The 2025 Maryland legislative session included significant debates over changes to the Blueprint for Maryland’s Future, including proposed delays and funding freezes that could affect the most under-resourced schools. This project aims to assess these patterns through open data and elevate equity-focused narratives with evidence.

Data Sources

• Baltimore City Public Schools Budget Data
• Maryland State Department of Education (MSDE) School Report Cards
• U.S. Census / American Community Survey (ZIP-code level demographics)
• Maryland Blueprint for the Future Funding Reports

Goals

• Use Python and pandas to calculate and compare per-pupil funding across schools
• Overlay funding data with race, poverty level, and academic performance
• Visualize disparities using matplotlib or plotly
• Optionally, create an interactive dashboard using Streamlit

Thoughts on How I’ll Do It

I plan to gather data from BCPS budgets, MSDE report cards, and census datasets, then use pandas to clean and merge the data. I’ll create bar charts or heatmaps to compare per-pupil spending across schools and demographics. If time allows, I’ll wrap the analysis in a Streamlit app so that users can select schools or ZIP codes interactively.