Research institutes and nonprofits
Explore health and opportunity across U.S. counties
Combine U.S. Census demographics with CDC modeled health estimates to prototype linked geographic and statistical comparisons, with transparent data definitions.
See an example project → · Evaluating for your team? Discuss a scoped pilot
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Dataset-backed demo concept
County Health and Opportunity Explorer
A research team explores county differences in household income, education, poverty, and modeled health indicators, linking a county point map to a comparison chart and source notes. This illustrative project is not a client commission or an endorsement by any data provider.
Curated source data
Census ACS 2019–2023 county demographics, CDC PLACES modeled county health estimates, and Census county centroids, joined by five-digit FIPS codes.
Example instruction
“Build a linked county centroid map and scatterplot comparing household income and estimated diabetes prevalence. Let readers choose indicators, highlight selected counties in both views, and inspect missing values and source notes. Do not imply causality.”
Target deliverable
A county-level research explorer with linked geographic and statistical comparisons
Interactions to test
- Switch between health and socioeconomic measures
- Select a county and highlight it across views
- Inspect source definitions, modeled estimates, and missingness
Gallery projects are community examples, not implementations of this brief.
Where the workflow helps
Limited development capacity
A few specialists may support many researchers, studies, and publication deadlines.
Explore several ways to present the same finding
Recurring bespoke interactions
Maps, filters, comparisons, and coordinated views can take significant custom development.
Start from working, inspectable interactive code
Accuracy and transparency
Every transformation and editorial interpretation must remain verifiable by the research team.
Keep human review and revision history in the workflow
Prototype with AI. Approve with your team.
- 1. Bring data and a brief. Give VizHub the dataset and the question you want to explore.
- 2. Try working directions. Explore alternate live interactions instead of relying only on mockups.
- 3. Review and publish. Inspect code and revisions, verify the result, then export or share it.
What to verify in a pilot
- Verify five-digit FIPS joins, denominators, missing values, and units
- Label locations as centroids, not boundary polygons
- Check accessibility, source credit, and non-causal interpretation
AI-generated code and analysis require human verification before production use.
Try this on a real project
Start with non-sensitive data and evaluate the complete workflow. Teams can also discuss a scoped 90-day design partnership.
Help us understand research institutes and nonprofits
We are testing assumptions about this workflow with practitioners. The answers below are working hypotheses, not actual interviews, testimonials, or endorsements. Expand a question group if you would like to contribute.
Questions specific to your team (7 questions)
How does a research dataset become a public tool?
Working hypothesis: Researchers validate the numbers, then collaborate with digital publishing and visualization specialists to choose an appropriate format.
How often do reports require original visualization work?
Working hypothesis: Routine charts may accompany many reports, while bespoke interactive tools occur less often and consume far more engineering effort.
What merits interactivity instead of a static chart?
Working hypothesis: Geographic comparison, filtering, exploring many indicators, and letting readers answer their own questions can justify interactivity.
What happens when a developer is unavailable?
Working hypothesis: Teams may delay a data tool, outsource implementation, reduce the scope, or publish a static alternative.
How do grants or project budgets influence scope?
Working hypothesis: Funding may be allocated at the research-project level. New software needs to reduce project effort or enable an approved deliverable.
What requirements rule out AI-generated work?
Working hypothesis: Unclear calculations, undocumented transformations, inaccessible controls, privacy concerns, or difficult-to-maintain code may make output unusable.
Which upcoming project would benefit from rapid alternative prototypes?
Working hypothesis: A public dataset needing coordinated maps and comparisons is a promising low-risk pilot, with success judged by production readiness.
Workflow, volume, pain, and budgets (10 questions)
How does a dataset become a published visualization?
Working hypothesis: The work typically spans data preparation, choosing an approach, implementation, review, and publication. The handoffs, rather than chart rendering alone, may create delays.
How many charts do teams make per week or month?
Working hypothesis: Volume varies widely. Total chart count may be a poor proxy for need: one bespoke interactive can require more effort than dozens of standard charts.
How many are standard versus custom or interactive?
Working hypothesis: Most routine graphics may be handled by established tools; custom interactions and unique layouts are a smaller but disproportionately costly category.
What tools are used today?
Working hypothesis: Excel, R, Python, Datawrapper, Flourish, D3, and AI coding tools may cover different stages. VizHub must complement rather than replace these workflows.
Where does most of the time go?
Working hypothesis: Data cleaning, story selection, implementation, stakeholder review, and publishing may each dominate depending on project type.
What was the last difficult visualization?
Working hypothesis: The most revealing example may involve linked views, nonstandard interaction, complex data, or repeated changes to a bespoke design.
Which ideas were abandoned because of cost or time?
Working hypothesis: Teams may choose a static or templated chart rather than develop an ambitious interactive tool, even when the latter could improve understanding.
What determines using a template versus building custom?
Working hypothesis: Budget, deadline, editorial goals, interactivity, accessibility, and the available developer capacity likely influence the decision.
Who requests the work, who produces it, and who approves spending?
Working hypothesis: End users, technical evaluators, and budget owners may be different people. A paid pilot needs an identified champion and sponsor.
How is visualization work funded?
Working hypothesis: The relevant baseline may be internal salary time, external development fees, project grants, or publishing budgets rather than software subscriptions alone.
Product fit, trust, purchasing, and pilot validation (9 questions)
Where would a dataset-to-prototype workflow fit?
Working hypothesis: Early exploration and custom interactive prototyping are the likeliest entry points; replacing established production systems is a higher bar.
Which capability would matter most?
Working hypothesis: Multiple genuinely different working concepts, reviewable source code, and the ability to iterate may matter more than generating a single chart.
What must be true to trust the output?
Working hypothesis: Teams will need reproducible transformations, correct calculations, accessible interactions, readable code, and human approval before publishing.
What would make adopting a new tool worthwhile?
Working hypothesis: Measured reductions in effort to a production-acceptable deliverable, not merely time to a visually appealing first draft.
What would prevent adoption?
Working hypothesis: Poor code maintainability, inaccurate analysis, workflow incompatibility, data-security requirements, and unclear costs could all stop adoption.
What real dataset could be used for a pilot?
Working hypothesis: An existing public dataset and comparable published result offer a credible, low-risk evaluation.
What would justify paying for this?
Working hypothesis: Recovered developer capacity, faster client delivery, new publishable experiences, or increased research output would need to be demonstrable.
Who can authorize a pilot?
Working hypothesis: A user may champion the tool while a team lead, department owner, or procurement process controls the purchase.
Would a $2,000, 90-day design partnership make sense?
Working hypothesis: A paid pilot could be attractive after a technical evaluation, provided the scope, support, deliverable, and success criteria are explicit.
Are these assumptions wrong or incomplete? Tell us how your team actually works.