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Review geographic intelligence surfaces

These geo surfaces use ZIP-to-locality and GPCI mappings to expose reimbursement gradient and locality concentration risk before regional planning decisions.

ZIP mappings: 42,956 Localities: 105 High-risk localities: 66

Source snapshot: cms.zip-locality.2026@2026-03-31T18:58:47.554Z · Schema: intelligence.geo.zip-reimbursement-gradient.v1

O12 · ZIP Reimbursement Gradient

Locality-level gradient by composite GPCI with a sample of high-deviation ZIP mappings.

Derived support Coverage 95.02% Join success 95.02%

ZIP-to-locality linkage is strong but should be interpreted as planning context for local reimbursement gradients.

Support: 40,816 of 42,956 entities met support thresholds.

Snapshot cms.zip-locality.2026@2026-03-31T18:58:47.554Z · Schema intelligence.geo.zip-reimbursement-gradient.v1 · Generated 4/6/2026

ZIP-to-locality reimbursement gradient from composite GPCI mapping.

  • ZIP rows require valid locality and state assignment
  • GPCI composite must be available for mapped locality
  • Gradient reflects geographic payment index context, not payer contract outcomes.
  • ZIP mapping is directional planning context and should be validated with local service mix.

Distribution + intensity

Bubble size represents weighted context volume.

Bubble chart plotting Composite GPCI against Gradient (%) for 32 points. Bubble size represents weighted context volume.
  • 32 bubble points shown.
  • SAN JOSE-SUNNYVALE-SANTA CLARA (SAN BENITO CNTY) has the highest composite gpci at 1.252.
  • SAN JOSE-SUNNYVALE-SANTA CLARA (SAN BENITO CNTY) has the highest gradient (%) at 25.21.
Composite GPCI and Gradient (%) bubble chart table
LabelComposite GPCIGradient (%)Bubble size
SAN JOSE-SUNNYVALE-SANTA CLARA (SAN BENITO CNTY)1.25225.215
ALASKA*1.25225.1946
SAN JOSE-SUNNYVALE-SANTA CLARA (SANTA CLARA CNTY)1.24724.7119.333
SAN FRANCISCO-OAKLAND-BERKELEY (MARIN CNTY)1.22722.656.833
SAN FRANCISCO-OAKLAND-BERKELEY (SAN FRANCISCO/SAN MATEO/ALAMEDA/CONTRA COSTA CNTY)1.22522.5342.333
NAPA1.16816.85
VALLEJO1.16616.625
NYC SUBURBS/LONG ISLAND1.15415.4273.333
QUEENS1.13613.5714
MANHATTAN1.13113.1127.5
SEATTLE (KING CNTY)1.12812.8421.667
DC + MD/VA SUBURBS1.11711.753.167
NORTHERN NJ1.11111.0871.5
METROPOLITAN BOSTON1.11111.0643
SANTA ROSA-PETALUMA1.10910.928.5
SANTA CRUZ-WATSONVILLE1.0999.865
LOS ANGELES-LONG BEACH-ANAHEIM (LOS ANGELES/ORANGE CNTY)1.0979.7115.167
SAN DIEGO-CHULA VISTA-CARLSBAD1.0949.3731.667
OXNARD-THOUSAND OAKS-VENTURA1.0898.898
ARKANSAS0.913-8.69121.167
SACRAMENTO-ROSEVILLE-FOLSOM1.088.0236.5
SANTA MARIA-SANTA BARBARA1.0787.796.833
MISSISSIPPI0.922-7.7790.167
ALABAMA0.923-7.71142.333
SALINAS1.0767.585.667
MIAMI1.0767.5724.167
POUGHKPSIE/N NYC SUBURBS1.0767.5554
REST OF NEW JERSEY1.0686.7653.167
IOWA0.936-6.37180
TENNESSEE0.939-6.15135.667
OKLAHOMA0.939-6.06132
NEBRASKA0.94-6.04105.333

O13 · Locality Compression Risk Map

State-level locality concentration where a small number of localities carry a large share of ZIP mappings. Rankings exclude single-locality states (trivial 100% share).

Derived support Coverage 96.33% Join success 96.33%

Locality concentration metrics are supported but remain derived context due to geographic aggregation limits.

Support: 105 of 109 entities met support thresholds.

Snapshot cms.zip-locality.2026@2026-03-31T18:58:47.554Z · Schema intelligence.geo.locality-compression-risk.v1 · Generated 4/6/2026

State-level ZIP concentration by locality to flag compressed geographic dependency zones.

  • State and locality grouping must meet minimum ZIP support
  • Derived concentration scores require explicit aggregation boundary
  • Compression risk is geographic concentration context, not market-share or payer access certainty.
  • Interpret with local referral and service-line context before operational decisions.

Ranked trend profile

Line chart showing ranked locality share (%) values across 18 points.
  • 18 ranked points shown.
  • VA has the highest locality share (%) at 89.66.
  • CA has the lowest locality share (%) at 25.31.
Locality share (%) ranked trend table
RankLabelLocality share (%)
1VA89.66
2LA85.12
3EM84.73
4PA83.83
5WA82.53
6ME80.99
7MI78.92
8OR76.2
9TX74.91
10GA73.35
11FL71.44
12IL66.38
13MA65.69
14WM63.16
15NJ57.35
16NY54.92
17MD54.29
18CA25.31

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Boundary

ZIP-to-locality reimbursement gradient from composite GPCI mapping.

Helps regional planning teams see where reimbursement context diverges from baseline geography.

  • Gradient reflects geographic payment index context, not payer contract outcomes.
  • ZIP mapping is directional planning context and should be validated with local service mix.
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