data-centers/output/data_center_demographic_ruca_energy_summary.md

US Data Centers — Demographic, Urban-Rural & Energy Context Analysis

Date: 2026-05-18 Notebook: cluster_analysis.ipynb Universe: 1,833 data centers in public.master_data_centers, joined to ACS-2024 demographics, USDA RUCA-2020 codes, and EIA operating-generator capacity (50 km radius, latest period 2026-02, status=OP).

Update 2026-05-18: 196 previously-null state values were backfilled from geoid (first 2 chars = state FIPS). All 1,833 DCs now have a state; all state-level numbers below reflect the corrected attribution.

---

Headline findings

1. DC tracts are richer, more educated, and more diverse than the US average. Median household income $103,623 vs. national $78,538 (+32%); 49% bachelor's+ vs. 35% (+14 pp); poverty rate 7.2% vs. 12.4%. Non-Hispanic white share is below national (50% vs. 58%), driven by Asian-heavy (mean 13% vs. 6%) and Hispanic-significant tracts.
2. The metro skew is more modest than expected: 1.11×. 89% of DCs sit in metropolitan tracts, but 80% of all US tracts are metropolitan — so DCs are only slightly more concentrated than the underlying population distribution would predict.
3. The non-metro tail is overwhelmingly hyperscale and Pacific Northwest. Of 190 DCs outside metropolitan tracts (RUCA 4–10), AWS owns 67, Meta 22, Microsoft 10, Google 4, Yahoo 2 — combined 55% of the non-metro footprint. Oregon (86) and Washington (40) alone hold 66% of non-metro DCs, anchored to the Columbia River hydropower corridor.
4. Clustered DCs are demographically distinct from isolated ones. DCs in DBSCAN clusters (n=1,583) sit in tracts with $108K median income vs. $73K for isolated DCs (n=250) — a $35K gap. Clustered DCs are more educated (+18 pp bachelor's), more diverse (–25 pp non-Hispanic white), and embedded in much denser energy infrastructure (89 vs. 40 generators within 50 km).
5. Microsoft co-locates with the largest US nuclear plant. Microsoft's Goodyear, AZ campus has 14.6 GW of generation within 50 km — including 4.2 GW from Palo Verde Nuclear, the largest in the US. Despite the campus being in a RUCA-2 "Metro high-commute" tract (not strictly metro core), the surrounding grid is the densest by capacity in our analysis.

---

Dataset coverage and joins

Source table	Rows	Join key	Coverage
master_data_centers	1,833	base	—
master_data_center_spatial_clusters	1,831	master_id	99.9%
_dc_census_tract_acs_2024	~73,000 tracts	geoid	1,807 matched (98.6%)
ruca_codes_2020_tract	85,528 tracts	tract_fips_20 = geoid	1,826 matched (99.6%)
energy_eia_operating_generator_capacity_flat	4.7M rows	ST_DWithin(geom, 50km)	1,831 DCs have ≥1 nearby gen

Energy aggregation uses period 2026-02 only with status='OP', summing nameplate_capacity_mw for operating generators within 50 km of each DC. Note: EIA capacity columns were added to this table on 2026-05-17 — prior to that the _flat table had no MW values despite its name.

---

1. Demographic profile of DC tracts (n=1,807 with non-null ACS)

Metric	DC tract (median)	DC tract (mean)	US avg	Δ mean vs. US
Median household income	$103,623	$114,543	$78,538	+$36,005
Per-capita income	$51,283	$55,725	$43,313	+$12,412
Poverty rate	7.2%	10.1%	12.4%	−2.3 pp
Unemployment rate	3.5%	4.4%	5.4%	−1.0 pp
Bachelor's+ %	49.3%	46.2%	35.0%	+11.2 pp
Broadband subscription %	94.9%	93.5%	89.0%	+4.5 pp
Non-Hispanic white %	50.2%	51.0%	58.4%	−7.4 pp
Hispanic / Latino %	12.8%	19.5%	19.5%	0.0 pp
Non-Hispanic Black %	5.9%	10.6%	12.1%	−1.5 pp
Non-Hispanic Asian %	6.4%	13.4%	6.4%	+7.0 pp

Interpretation. DC tracts skew toward high-income, highly-educated, technically connected, and racially diverse (specifically Asian-heavy). The race composition is interesting: DC tracts are less non-Hispanic white than national average, not more. This reflects DC siting in mixed-race coastal/exurban tech corridors (Bay Area, Northern Virginia, Seattle) rather than in homogeneous suburbs.

Data quality note. avg_household_size contains sentinel-value pollution (min: −666,666,666), so the mean is unusable; the median (2.55) is sensible.

---

2. Geographic concentration (top 15 states)

State	DC count	Total power_mw (where known)	Median HH income	Median bachelor's %	Median % white	Notes
VA	378	255	$141,250	62.6%	42.5%	Loudoun / DC-Alley dominance (20.6% of all US DCs)
TX	162	597	$88,228	46.2%	32.0%	DFW + Austin + San Antonio
CA	147	130	$164,928	56.4%	22.4%	Bay Area + LA basin
OR	145	125	$72,719	20.0%	63.2%	Columbia River hydro corridor (rural)
OH	103	135	$128,875	47.0%	74.5%	Columbus boom — fastest-rising market
WA	93	70	$91,623	21.9%	40.3%	Quincy/Wenatchee + Seattle
AZ	69	54	$85,335	35.2%	51.6%	Phoenix/Goodyear hyperscale
IA	65	0	$93,393	34.3%	88.1%	88% white (rural Midwest)
NJ	62	98	$147,321	59.4%	32.9%	NYC-metro carrier hotels
IL	61	128	$96,191	52.9%	52.0%	Chicago metro
GA	50	241	$101,176	51.4%	31.6%	Atlanta + high-power rural builds
NY	48	47	$77,465	47.6%	74.8%	NYC + upstate
NV	41	0	$93,409	31.2%	34.6%	Reno + Las Vegas
TN	32	0	—	—	54.8%	Nashville + Memphis (newly visible after state backfill)
NC	31	56	$82,708	44.7%	59.6%	Charlotte + Catawba (nuclear-adjacent)

Virginia alone holds 20.6% of all US DCs (378 of 1,833), with the most affluent tract profile in the top 15 — a Loudoun County effect. The state backfill substantially elevated Ohio (76 → 103) and Texas (135 → 162), pushing TX into the #2 slot. The previously-uncounted Tennessee (32) now appears in the top 15.

Oregon and Washington tracts look notably different from the urban-heavy states (lower income, lower education, lower broadband, higher Hispanic share), reflecting their rural Columbia River siting.

---

3. Spatially clustered DCs vs. isolated DCs

DBSCAN cluster assignment from master_data_center_spatial_clusters (1,583 clustered, 250 isolated):

Metric (median)	Isolated	In cluster	Δ
Median household income	$73,500	$108,359	+$34,859
Bachelor's+ %	33.2	51.2	+18.0 pp
Poverty rate	11.6	6.9	−4.7 pp
Non-Hispanic white %	71.0	45.9	−25.1 pp
EIA generators within 50 km	40	89	+49
EIA capacity within 50 km (MW)	2,176	3,300	+1,125

Reading. A clustered data center sits, at the median, in a tract that is ~$35K richer, 18 pp more educated, and 25 pp less non-Hispanic white than an isolated one — and is surrounded by twice as much energy infrastructure (and 50% more generation capacity). The isolated set looks like rural / small-town America (whiter, poorer, less educated); the clustered set looks like coastal exurban tech corridors.

---

4. RUCA (urban-rural) distribution

National baseline of all US tracts: 80% Metropolitan, 9% Micropolitan, 3% Small town, 8% Rural.

RUCA band	DCs	DC %	US tract %	Over-index
Metropolitan (1–3)	1,636	89.3%	80.1%	1.11×
Micropolitan (4–6)	98	5.3%	9.0%	0.59×
Small town (7–9)	15	0.8%	2.9%	0.28×
Rural (10)	77	4.2%	7.6%	0.55×
Unknown / missed match	7	0.4%	—	—

Reading. The metro skew is real but only mild — 1.11×. The eye-catching pattern is that rural tracts (RUCA 10) hold more DCs than micropolitan or small-town combined, because the hyperscale greenfield model deliberately bypasses small-city economies in favor of remote, cheap-power, low-population sites.

Per-RUCA-code drilldown

RUCA	Description	DCs	Median HH income	Median pop density	Median EIA gens (50km)
1	Metro core	1,425	$110,333	1,859 / sq mi	97
2	Metro high-commute	206	$105,404	96	49
3	Metro low-commute	5	$119,495	22	23
4	Micropolitan core	54	$63,698	312	53
5	Micropolitan high-commute	22	$72,465	191	51
6	Micropolitan low-commute	22	$72,719	69	59
7	Small town core	14	$87,522	2,336	40
8	Small town high-commute	1	$69,074	36	41
10	Rural area	77	$93,820	12	42

Two surprises:

• Rural DCs (RUCA 10) sit in tracts with $93.8K median income — higher than micropolitan DCs ($63.7K–$72.7K). The rural DC sites are not poor rural America; they are wealthy-by-rural-standards counties chosen for power and water access.
• Micropolitan-core DCs (RUCA 4) have the lowest median income at $63.7K — the closest thing to "economic-development DC siting" in the dataset.

---

5. Non-metro deep dive (190 DCs, RUCA 4–10)

Operators

Operator	Non-metro DCs
Amazon Web Services	67
(null operator)	50
Meta	20 (+ 2 as "Meta, Inc.")
Microsoft	10
Google	4
Rowan Green Data	4
NTT	2
Yahoo	2
Amazon AWS (dupe)	2

The five hyperscalers (AWS, Meta, Microsoft, Google, Yahoo) account for 105 of 190 non-metro DCs (55%). If the 50 null-operator rows skew similarly hyperscale (likely — they're disproportionately in OR/WA), the share is probably closer to 75%.

States (post-backfill)

State	Non-metro DCs
Oregon	86
Washington	40
Texas	9
New Mexico	7
North Carolina	6
Pennsylvania	5
Wisconsin	4
New York	3
Tennessee	3
Georgia	3

Oregon + Washington = 126 (66%) of all non-metro DCs. This is the Columbia River basin: Prineville / Hermiston / Boardman / The Dalles (OR) and Quincy / East Wenatchee / Moses Lake (WA). The pull is hydroelectric power (cheap, low-carbon, abundant) and cool dry climate (free-cooling).

The state backfill clarified the rest of the non-metro tail: Texas (9) and Pennsylvania (5) were previously hidden in the null bucket. These likely represent shale-gas-adjacent builds (Permian and Marcellus respectively).

---

6. Energy footprint by operator (using EIA capacity within 50 km)

Aggregated across DCs in RUCA 2–10 (i.e. anything outside dense metro core, n=401):

Operator	DCs	States	Total nearby capacity (GW)	Median per site (GW)	Hydro (GW)	Nuclear (GW)	NG (GW)	Solar (GW)	Wind (GW)
AWS	93	5	397	4.8	66	2.5	201	4.6	114
(Unknown)	118	26	339	2.3	86	35	135	23	19
Meta	51	11	120	2.0	4.9	0	61	16	0.3
Microsoft	26	6	113	3.4	28	13	39	9.1	8.1
Google	31	5	100	3.9	14	0	43	3.6	4.7
Apple	5	2	4	0.6	1.6	0	1.1	0.9	0.4
Yahoo	2	1	7	3.5	6.4	0	0	0	0.7

Distinct hyperscaler strategies, visible in the fuel mix:

• AWS has aggregated 114 GW of wind exposure across its 93 sites — by far the most renewable-coupled portfolio. Also heavy hydro (66 GW) from its OR/WA footprint and 201 GW of natural gas as baseline.
• Microsoft has the highest nuclear exposure (12.6 GW) — almost entirely from its Goodyear, AZ campuses near Palo Verde Nuclear.
• Meta has the most solar (16 GW) among the named hyperscalers, but minimal nuclear or wind — consistent with its New Mexico (Los Lunas) and Iowa builds.
• Google is split — moderate hydro and natural gas, modest renewables.

Largest non-metro grid neighborhoods (top sites by surrounding capacity)

DC	Operator	Location	Nearby capacity	Fuel highlight
PHX70 / PHX-10 / PHX-11	Microsoft (Azure)	Goodyear, AZ (RUCA 2)	14.0–14.6 GW	4.2 GW nuclear (Palo Verde) + 6.4 GW gas + 2.2 GW solar
Stream PHX-1	Stream Data Centers	Goodyear, AZ	13.4 GW	Same Palo Verde / gas mix
T5 Charlotte Campus	T5	Kings Mountain, NC (RUCA 6)	12.9 GW	4.9 GW nuclear (Catawba) + 5.5 GW gas + 1.5 GW coal
Apple Maiden	Apple	Maiden, NC (RUCA 2)	9.1 GW	2.4 GW nuclear + 4.6 GW gas
Percheron DC	Rowan Green Data	(Texas, RUCA 10)	6.7 GW	3.0 GW wind + 0.9 GW hydro + 2.4 GW gas

---

Data quality flags

1. 196 of 1,833 DCs (10.7%) have null state Resolved 2026-05-18 by backfilling from geoid first-2-chars (state FIPS).
2. master_data_centers.power_mw is populated for only 108 / 1,833 DCs (5.9%). Useless as a sizing metric without imputation or alternative source. Nearby EIA capacity is the more reliable proxy (used as the per-DC scale in this analysis). A grant-funded scrape of Baxtel / Data Center Map would close this gap.
3. 50 of 190 non-metro DCs (26%) have null operator. Likely hyperscalers based on geography (OR/WA) but unconfirmed.
4. Operator-string fragmentation: "Meta" vs. "Meta, Inc."; "Amazon Web Services" vs. "Amazon AWS" vs. "amazon web services"; "Microsoft" vs. "Microsoft Azure". Inflates distinct-operator counts and fragments per-operator totals.
5. avg_household_size column has sentinel pollution (min: −666,666,666). Use median or filter before using.
6. 7 DCs failed RUCA join — Puerto Rico tracts or non-US locations; trivial.
7. EIA generator coordinates had a longitude sign error for 2008-01 through 2010-11 (~11K rows with positive lower-48 longitudes). The flat-table build at ingest_eia_energy_layers.py:839-870 corrects this in longitude and geom, so spatial joins are unaffected.

---

Suggested next steps

1. Backfill power_mw from Baxtel / Data Center Map (paid scrape — grant work).
2. Operator-string deduplication — collapse "Meta"/"Meta, Inc.", "AWS" variants, etc., before any per-operator analysis.
3. Watershed (HUC8) join — public.watershed_huc8 is loaded but unused; would let us look at water stress overlap, particularly for the 190 non-metro DCs.
4. State-level energy demand context — im3_state_projected_moderate_50 and seds_state_msn_year are loaded; joining these would let us compute "DC nearby capacity as share of state grid" rather than absolute MW.
5. Opposition cases overlay — opposition_cases_geocoded is loaded but unused; could test whether cluster-vs-isolated demographic differences predict community opposition.