Regression

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How well do 40 county-level health drivers collectively explain geographic variation in this layer? An OLS+ridge regression isolates each driver's independent contribution — going beyond a single pairwise correlation.

Live signals

Healthcare watchtower

AI-synthesized signals surfacing the weakest-reach specialties, strongest health-driver correlations, and counties where disease burden most outruns clinical supply.

Synthesizing signals…

Coverage gaps

Correlation watch

County watchlists

Operations

Freshness & roadmap

Monitors source currency, schedules automatic update checks, and prioritizes the next public datasets to ingest based on analytic value and coverage gaps.

Source freshness

Next sources

Methods

How this atlas works

Geographic variation analysis modeled on the Dartmouth Atlas approach, extended with multivariable regression, clustering, international benchmarking, and live data through 2024.

Full methods & sources →

Distribution

Geographic distribution

Quantile-binned choropleth: darker = higher values. Hover for area-level detail; click any region to open its full data profile.

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Bivariate lens

Driver vs. access

Single-driver Pearson correlation against the mapped layer. Each dot is a county. For the full multivariable picture — where 40 drivers compete simultaneously — see the regression panel above.

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