The Health Gap: Uncovering Hidden Relationships in Wellness"

1. Inspiration Our inspiration for this project stems from the understanding that health disparities, influenced by factors such as income, education, and access to healthcare, often go unnoticed. Richmond, Virginia, is an area where these disparities are particularly pronounced, and our goal is to bring attention to these hidden inequities through data visualization.

2. What it does This project visualizes health disparities in Richmond, VA, by mapping various health indicators such as mortality, impervious surfaces, and canopy coverage. By using Python to create heat maps and Spearman's rank correlation graphs, it reveals relationships between urban factors (like green spaces) and health outcomes (like life expectancy).

3. How we built it We used publicly available datasets to explore correlations between health outcomes and environmental factors in Richmond. The program outputs heat maps to visualize geographic disparities and generates Spearman’s rank correlation graphs to identify relationships between different variables. Python was the primary tool for data analysis and visualization.

4. Challenges we ran into A key challenge was time constraints, which limited our ability to explore a broader range of health indicators. Additionally, due to data availability, we had to work with pseudo data for visualization purposes, though the program is designed to work with any public dataset.

5. Accomplishments that we're proud of We are proud of the heat maps and correlation graphs we've created, which clearly demonstrate the relationship between urban factors like green spaces and health outcomes. Despite the time limitations, we were able to generate actionable insights and proposed solutions to address health disparities in Richmond.

6. What we learned We learned how to work with large, complex datasets and how to visualize data to reveal hidden trends. This project also taught us the power of geographic data visualization in understanding health disparities and the importance of using data-driven insights to propose real-world solutions.

7. What's next for "The Health Gap: Uncovering Hidden Relationships in Wellness" The next steps include expanding the dataset to explore more correlations, refining our visualizations, and testing the program with real-world data. Additionally, we plan to collaborate with local policymakers and health organizations to implement the solutions we've proposed, such as increasing vegetation and park spaces in underserved areas of Richmond.

Built With

• python