Case Study
SNAP Data Analysis & Visualization
The goal is to evaluate whether SNAP benefits are disproportionately distributed across U.S. states relative to population, and to correct misleading narratives using better visualizations. This is an individual project I worked on for my graduate certificate program in Digital Product Design at Sheridan College, for the Usability and Data-Driven Design course.
- My Role
- Researcher
- Tools
- Tableau
- Timeline
- Sep 2020 – Dec 2020
Project Overview
DomainPublic policy and social programs — focused on SNAP (Supplemental Nutrition Assistance Program).GoalEvaluate whether SNAP benefits are disproportionately distributed across U.S. states relative to population, and correct misleading narratives using better visualizations.Methods
- Hypothesis-driven analysis — descriptive, explanatory, and predictive lenses.
- Data cleaning & normalization — preparing the dataset for fair cross-state comparisons.
- Quantitative analysis — measuring distribution against population baselines.
- Statistical reasoning — testing claims rather than taking headlines at face value.
- Data visualization in Tableau — building the charts that tell the story.
Outputs
- Charts — treemaps, scatter plots, and trendlines surfacing per-capita distribution.
- Analytical narrative — written conclusions tying findings back to the original claim.
- Methodology reflection — what the tooling enabled and where it fell short.
Selected Visualizations
