A comprehensive geospatial research project investigating the spatial concentration, infrastructure dependencies, and socioeconomic/environmental impacts of US data center locations.

Documentation

Database Tables - Complete database schema with table descriptions, column definitions, and SQL examples
Research Ideas - Future research directions, data improvements, and potential collaborations

Project Overview

This repository implements a PostGIS-based analytical framework that integrates multiple data sources to examine:

Spatial concentration patterns: Where are data centers located and why?
Infrastructure dependencies: How do data centers relate to submarine cables, power grids, and watersheds?
Equity and impact: Do data center host communities bear localized burdens while benefits are nationally dispersed?
Demographics: Who lives in data center-hosting census tracts?
Environmental exposure: What are the water, energy, and natural hazard exposures?

Key Research Question

Do data centers represent "concentrated costs / dispersed benefits"? Host communities bear localized infrastructure burdens (power, water, land use) while cloud computing benefits are nationally dispersed.

Major Findings

Spatial Concentration

State level: Top 5 states (VA, TX, CA, OR, OH) hold 51% of all US data centers
- Virginia alone: 20.6% (378 of 1,833 facilities)
Tract level: Top 1% of data center-hosting census tracts hold 14.6% of all facilities
- Only 0.86% of data center-state residents live in a hosting tract
- Per-capita burden is 115× higher in host tracts vs. state average
Watershed level: Half of all US data centers sit in just 15 of 2,139 HUC8 watersheds
- Single watershed (Middle Potomac-Catoctin / Loudoun County): 12.8% of US facilities

Demographics of Host Communities

Compared to the US average, data center host communities are:

Wealthier: Median household income $103,623 (vs. $78,538, +32%)
More educated: 49% bachelor's+ (vs. 35%, +14 pp)
More diverse: 50% non-Hispanic white (vs. 58%), driven by high Asian share (13% vs. 6%)
Better connected: 94.9% broadband (vs. 89%)

Infrastructure Insights

89% of data centers are in metropolitan tracts (vs. 80% of all US tracts)
Non-metro data centers (11%) are dominated by hyperscalers:
- AWS (67), Meta (22), Microsoft (10), Google (4) = 55% of non-metro facilities
- 66% are in Oregon + Washington (Columbia River hydro corridor)
Energy infrastructure: 4 states have >2/3 of generation within 50 km of a data center:
- New Jersey: 83%, Nevada: 75%, Tennessee: 70%, Oregon: 68%

Submarine Cables

Data centers are NOT systematically closer to cables than ordinary US cities
Only 21.4% of data centers are within 100 km of a submarine cable landing point
Largest clusters (Ashburn VA, Columbus OH, Iowa) are inland, driven by fiber/power/tax incentives, not cables

Data Sources

Primary Data Center Inventories

Curated Sample: 1,489 facilities from web scraping + manual curation, geocoded via Census TIGER + Nominatim
OpenStreetMap: 1,549 OSM features tagged as data centers (via Overpass API)
IM3 Model Data: PNNL's Integrated Multisector Multiscale Modeling existing facilities
Master Table: 1,833 deduplicated facilities merging all sources

Geospatial Context Layers

US Census: 2024 TIGER tract boundaries, ACS 2024 5-year demographics (85k+ tracts)
USDA RUCA 2020: Rural-Urban Commuting Area codes for metro/micropolitan/rural classification
USGS HUC8 Watersheds: 2,139 subbasin polygons for water-stress analysis
FEMA NRI: National Risk Index with 18 natural hazard risk scores by census tract

Infrastructure Layers

Submarine Cables: 693 cables, 3,361 landing points (TeleGeography-style)
EIA Energy Data: Operating generator capacity (4.7M monthly records, 2008-2026), facility fuel, state energy data
FCC Broadband Data: Provider availability by location/block

Additional Data

RDH Precinct Vote Data: Election results for political-economy analysis
NOAA HMS Smoke Data: Wildfire smoke exposure (2005-2025)
USDM Drought Data: Drought severity
Utility Rate Tracker: State-level electricity rate increases

Repository Structure

Core Python Scripts

Data Ingestion

load_postgis_data_centers.py - Load curated data center CSV into PostGIS
load_postgis_osm_data_centers.py - Fetch OSM data centers via Overpass API
build_master_data_centers.py - Deduplicate & merge curated + OSM sources
load_postgis_internet_cables.py - Load submarine cables and landing points
ingest_eia_energy_layers.py - Ingest EIA energy data via API
build_watershed_huc8_tables.py - Load USGS HUC8 watersheds

Enrichment

create_data_center_census_tract_table.py - Join data centers to Census tracts with ACS demographics
build_fcc_bdc_broadband_connection_table.py - Build per-facility broadband provider table
build_fcc_bdc_location_provider_aggregates.py - Aggregate FCC BDC data by county/tract

Analysis

analyze_dc_tract_concentration.py - Tract-level cost concentration analysis (Gini, HHI, demographic deltas)
analyze_cables_concentration.py - Test if data centers cluster near submarine cables
make_data_center_map.py - Generate Leaflet map of data centers
make_internet_cables_map.py - Generate Leaflet map of data centers + cables

Key Jupyter Notebooks

spatial_clustering_master_data_centers.ipynb - DBSCAN clustering of data centers
cluster_analysis.ipynb - Main demographic/energy/watershed analysis
fema_nri_data_centers.ipynb - Join data centers to FEMA hazard scores
rdh_precinct_vote_data_centers.ipynb - Join data centers to election data
usdm_drought_data_centers.ipynb - Drought exposure analysis
hms_smoke_data_centers.ipynb - Wildfire smoke exposure
enhanced_data_center_cluster_map.ipynb - Generate enhanced cluster visualization

Output Files

output/data_center_demographic_ruca_energy_summary.md - Flagship analysis report
cables_concentration_report.md - Cable proximity + cost/benefit concentration analysis
data_center_map.html - Basic data center locations (Leaflet)
data_centers_cables_map.html - Data centers + submarine cables (Leaflet)
output/enhanced_master_data_center_spatial_clusters_map.html - Enhanced cluster visualization

Technical Architecture

Database

PostgreSQL 13+ with PostGIS 3.x
Database name: data_centers
See database-tables.md for complete schema documentation

Python Environment

Python 3.10+
Key libraries: psycopg2, geopandas, shapely, scikit-learn, pandas, numpy, requests, folium

Data Formats

CSV (raw data exports)
GeoJSON (watershed/cluster geometries)
Shapefiles (Census, USGS, FEMA inputs)
HTML (interactive Leaflet maps)

Configuration

Credentials stored in ~/.zsh_secrets, loaded via environment variables:

PGWEB_*: PostgreSQL connection
EIA_API_KEY: EIA energy data
FCC_USERNAME, FCC_API_KEY: FCC broadband data
RDH_USERNAME, RDH_PASSWORD: Redistricting Data Hub
CENSUS_API_KEY: Census ACS API

Quick Start

Basic Rebuild Sequence

# 1. Load base data center data
python3 load_postgis_data_centers.py
python3 load_postgis_osm_data_centers.py
python3 build_master_data_centers.py

# 2. Enrich with context layers
python3 create_data_center_census_tract_table.py --replace-final
python3 load_postgis_internet_cables.py
python3 ingest_eia_energy_layers.py --category power
python3 build_watershed_huc8_tables.py

# 3. Run analyses
python3 analyze_dc_tract_concentration.py > output/tract_analysis.txt
python3 analyze_cables_concentration.py > output/cables_analysis.txt

# 4. Execute notebooks
jupyter notebook cluster_analysis.ipynb

Generate Maps

python3 make_data_center_map.py
python3 make_internet_cables_map.py

Key Outputs

Research Reports

Demographic, Energy & Watershed Analysis: output/data_center_demographic_ruca_energy_summary.md
Submarine Cable Proximity: cables_concentration_report.md

Interactive Maps

Data center locations with cluster assignments
Submarine cable routes and landing points
Energy infrastructure proximity
Watershed boundaries with data center counts

Data Exports

master_data_center_spatial_cluster_points.csv - Data center points with cluster IDs
master_data_center_spatial_cluster_summary.csv - Cluster-level statistics
output/master_data_center_huc8_watersheds.geojson - Watershed polygons
output/master_data_center_map_context.csv - Full context for mapping
output/master_data_center_state_energy_context.csv - State-level energy statistics

Data Quality Notes

Incomplete power ratings: Only 5.9% of data centers have power ratings (108/1,833)
Operator fragmentation: String variations ("Meta" vs. "Meta, Inc.") inflate distinct-operator counts
45 facilities use city-precision fallback coordinates (approximate tract assignment)
7 facilities failed RUCA join (Puerto Rico / non-US)
Broadband subscribers are a coarse benefit proxy (actual cloud users are global)

Research Ideas & Future Work

See research-ideas.md for detailed next steps and potential research directions.

Project Status

This is a mature, publication-ready geospatial analysis infrastructure combining authoritative government datasets (Census, EIA, USGS, FEMA) with novel data center location data to test political economy and environmental justice hypotheses.

The "concentrated costs / dispersed benefits" hypothesis is operationalized and tested with rigorous spatial statistics (Gini coefficients, HHI indices, Mann-Whitney tests).

License

Research data compiled from public sources. Please cite appropriately if used in publications.

Contact

For questions about this research project, please contact the repository owner.

README.md Unescape Escape

US Data Centers - Geospatial Research Infrastructure