CHEDVI

Health equity is not just a set of figures. It is a question of how conditions differ from one neighborhood to another—and how people can examine those differences. CHEDVI turns dispersed public data into an explorable platform for Camden.

The challenge

Health outcomes and the social conditions that shape them are often reported separately. That makes it harder for residents, researchers, and decision-makers to see where disparities appear and to ask useful questions about their context. CHEDVI brings those views together at the neighborhood level.

What the platform makes possible

The live site gives visitors several routes into the data rather than a single static report:

• Compare places. Explore Camden neighborhoods through profiles, rankings, and a dual-map dashboard.
• Compare dimensions. Select health outcomes and social determinants side by side on synchronized maps.
• Investigate relationships. Use interactive charts and an underlying data table to examine patterns behind the maps.
• Move from data to questions. Read insights and resource pages that put the visualizations in a community and policy context.

Try the interactive dashboard ↗

Data and approach

The platform identifies the 2022 US Census Bureau and CDC PLACES datasets as its sources. It presents neighborhood comparisons across measures such as income, poverty, food access, diabetes, asthma, and other health outcomes. The design connects a place, a health measure, and a social condition so a visitor can explore a question instead of receiving only a headline statistic.

My contribution

I originated the idea for CHEDVI and led both the project and the platform build, taking it from concept to a live public-facing product. It was collaborative work with colleagues in data science, prevention science, and public policy. The full project team is credited on the CHEDVI site.

What exists now

CHEDVI is live. Visitors can open the dashboard, examine neighborhood profiles and rankings, and move between visual analysis and written insights. It shows how a complex public-interest question can become a working, navigable data product.