Scoring the Divide: Race, Class, and Capital in Urban Space
For my final project, I am building on a composite mapping tool I developed using data from the Baltimore Neighborhood Indicators Alliance (BNIA). That project visualized racialized economic disparities across Baltimore neighborhoods, using indicators such as racial composition, poverty rate, household structure, and income level to score each neighborhood along a spectrum of disinvestment to privilege.
I may incorporate data from other public sources such as: the U.S. Department of Health and Human Services, the Federal Reserve Economic Data (FRED), the U.S. Census Bureau, and academic or nonprofit institutions tracking housing affordability, investment trends, and community health.
Locally, I’m interested in overlaying eviction filings (via Maryland Judiciary Case Search), housing code violations, and historical redlining boundaries. I may also explore proximity-based metrics such as access to public transit, green space, or essential services like food and medical care.
I plan to conduct temporal comparisons—looking at changes in neighborhood indicators over time (e.g., 2010–2020)—and explore clustering or correlation analysis to highlight hidden patterns. I may also experiment with PCA or unsupervised learning techniques to group neighborhoods by shared traits.
I envision an interactive, story-driven map that allows users to explore how race, policy, and capital have shaped the city’s landscape— and how those forces might continue to unfold. Ideally, the interface would offer toggled layers, tooltips, embedded media (like oral histories or archival photos), and neighborhood-level narratives. My goal is to create a digital artifact that doesn’t just inform— it invites inquiry, reflection, and action.
View My Colab Proof of Concept