Add comprehensive documentation: README, database tables, and research ideas

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# Database Tables Documentation
## Database Configuration
**Database Name**: `data_centers`
**Type**: PostgreSQL with PostGIS extension
**Connection**: Environment variables from `~/.zsh_secrets`
- `PGWEB_HOST`: Database host
- `PGWEB_PORT`: Database port (typically 5432)
- `PGWEB_USER`: Database user
- `PGWEB_PASSWORD`: Database password
- `PGWEB_DATABASE`: Database name (`data_centers`)
## Table Organization
Tables are organized into four categories:
1. **Core Data Center Tables** - Master inventories and source data
2. **Enrichment Tables** - Data centers joined with contextual data
3. **Base Layer Tables** - Geographic and demographic reference layers
4. **Infrastructure Tables** - Energy and connectivity infrastructure
---
## Core Data Center Tables
### `master_data_centers`
**Rows**: 1,833
**Purpose**: Canonical data center inventory - deduplicated merge of curated + OSM sources
**Key Columns**:
- `id` (INTEGER) - Unique identifier
- `name` (TEXT) - Facility name
- `address` (TEXT) - Street address
- `city` (TEXT) - City
- `state` (TEXT) - State code
- `latitude` (DOUBLE PRECISION) - Latitude
- `longitude` (DOUBLE PRECISION) - Longitude
- `geom` (GEOMETRY) - PostGIS point geometry (EPSG:4326)
- `operator` (TEXT) - Operator/owner
- `power_mw` (DOUBLE PRECISION) - Power capacity in megawatts (sparse: 5.9% populated)
- `source` (TEXT) - Data source (`curated`, `osm`, or `both`)
- `osm_id` (TEXT) - OpenStreetMap ID if applicable
- `geocode_method` (TEXT) - Geocoding provenance
**Notes**:
- 108 of 1,833 facilities have power ratings
- 45 facilities use city-precision fallback coordinates
- Operator strings have fragmentation issues ("Meta" vs. "Meta, Inc.")
### `us_dc_sample_geocoded`
**Rows**: 1,489
**Purpose**: Original curated sample with geocoding provenance (superseded by `master_data_centers`)
**Key Columns**:
- `name`, `address`, `city`, `state`, `zip`
- `latitude`, `longitude`, `geom`
- `operator`, `power_mw`
- `census_lat`, `census_lon` - Census TIGER geocode results
- `nominatim_lat`, `nominatim_lon` - Nominatim fallback results
- `geocode_source` - Which geocoder was used
### `osm_data_centers`
**Rows**: 1,549
**Purpose**: Raw OpenStreetMap-derived facilities
**Key Columns**:
- `osm_id` (TEXT) - OSM element ID
- `osm_type` (TEXT) - `node`, `way`, or `relation`
- `name` (TEXT) - OSM name tag
- `latitude`, `longitude`, `geom`
- `tags` (JSONB) - All OSM tags as JSON
- `operator` (TEXT) - Extracted from OSM tags
- `city`, `state`, `country`
**Notes**: Fetched via Overpass API with query for `telecom=data_center` or `building=data_center`
### `master_data_center_spatial_clusters`
**Rows**: 1,831
**Purpose**: DBSCAN cluster assignments for master data centers
**Key Columns**:
- All columns from `master_data_centers`
- `cluster_id` (INTEGER) - Cluster assignment (-1 = noise/singleton)
- `cluster_size` (INTEGER) - Number of facilities in cluster
- `cluster_label` (TEXT) - Human-readable cluster name
**Notes**: DBSCAN parameters: eps=15 km, min_samples=2
---
## Enrichment Tables
### `data_center_census_tracts_2024`
**Rows**: 1,815
**Purpose**: Per-facility demographics from containing Census tract
**Key Columns**:
- All columns from `master_data_centers`
- `geoid` (TEXT) - 11-digit Census tract GEOID
- `state_fips`, `county_fips`, `tract`
- **Population**: `total_population`, `population_density_sq_mi`
- **Age**: `median_age`, `under_18_pct`, `over_65_pct`
- **Race/Ethnicity**: `white_nh_pct`, `black_nh_pct`, `asian_nh_pct`, `hispanic_pct`
- **Economics**: `median_household_income`, `per_capita_income`, `poverty_rate`
- **Education**: `bachelors_or_higher_pct`, `high_school_or_higher_pct`
- **Housing**: `median_home_value`, `median_rent`, `homeownership_rate`
- **Broadband**: `broadband_pct` - Households with broadband subscription
**Source**: ACS 2024 5-year estimates
**Notes**:
- 18 of 1,833 facilities failed tract join (geocoding issues)
- Data from `_dc_census_tract_acs_2024` base table
### `data_center_watershed_huc8`
**Rows**: 1,833
**Purpose**: Per-facility watershed assignment
**Key Columns**:
- All columns from `master_data_centers`
- `huc8` (TEXT) - 8-digit Hydrologic Unit Code
- `watershed_name` (TEXT) - Watershed name
- `watershed_area_sq_km` (DOUBLE PRECISION)
- `states` (TEXT) - States intersecting watershed
**Source**: USGS Watershed Boundary Dataset
**Notes**: 257 unique HUC8 watersheds contain at least one data center
### `data_center_nri_exposure`
**Rows**: 1,833
**Purpose**: Per-facility FEMA National Risk Index hazard exposure scores
**Key Columns**:
- All columns from `master_data_centers`
- `nri_id` (TEXT) - Census tract GEOID (matches `geoid` from demographics)
- `risk_score` (DOUBLE PRECISION) - Overall NRI risk score
- `social_vulnerability` (DOUBLE PRECISION) - Social vulnerability index
- **Hazard-specific risk scores** (18 hazards):
- `avalanche_risk`, `coastal_flooding_risk`, `cold_wave_risk`
- `drought_risk`, `earthquake_risk`, `hail_risk`
- `heat_wave_risk`, `hurricane_risk`, `ice_storm_risk`
- `landslide_risk`, `lightning_risk`, `riverine_flooding_risk`
- `strong_wind_risk`, `tornado_risk`, `tsunami_risk`
- `volcanic_activity_risk`, `wildfire_risk`, `winter_weather_risk`
**Source**: FEMA National Risk Index (December 2025 release)
### `data_center_rdh_precinct_vote_matches`
**Rows**: Varies
**Purpose**: Per-facility precinct-level election results
**Key Columns**:
- Data center identifiers
- `precinct_name`, `precinct_id`
- `election_year`, `office`
- `candidate`, `party`, `votes`
- `vote_share_pct`
**Source**: Redistricting Data Hub precinct shapefiles
**Notes**: Spatial join to voting precincts (point-in-polygon)
---
## Base Layer Tables
### `_dc_census_tract_acs_2024`
**Rows**: 85,382
**Purpose**: ACS 2024 demographics for all Census tracts in states with data centers
**Key Columns**:
- `geoid` (TEXT) - 11-digit tract GEOID (PRIMARY KEY)
- `name` (TEXT) - Tract name
- `state_fips`, `county_fips`, `tract`
- **Full ACS 5-year estimates** (85+ columns):
- Population by age, sex, race/ethnicity
- Households, families, housing units
- Income, poverty, education, employment
- Housing values, rents, costs
- Broadband, computer access
- Commuting, vehicles
**Source**: Census ACS 2024 5-year estimates API
**Notes**: Universe limited to 46 states with data centers (excludes DC-free states)
### `_dc_census_tract_boundaries_2024`
**Rows**: 85,058
**Purpose**: TIGER 2024 tract polygons for data center states
**Key Columns**:
- `geoid` (TEXT) - 11-digit tract GEOID
- `name` (TEXT) - Tract name
- `state_fips`, `county_fips`, `tract_code`
- `geom` (GEOMETRY) - Polygon geometry (EPSG:4326)
- `area_land_sq_m` (DOUBLE PRECISION) - Land area in square meters
- `area_water_sq_m` (DOUBLE PRECISION) - Water area in square meters
**Source**: Census TIGER/Line 2024
### `ruca_codes_2020_tract`
**Rows**: 85,528
**Purpose**: USDA Rural-Urban Commuting Area codes for metro/rural classification
**Key Columns**:
- `geoid` (TEXT) - 11-digit tract GEOID (matches Census tracts)
- `ruca_code` (TEXT) - Primary RUCA code (1-10)
- `ruca_category` (TEXT) - Simplified category:
- `Metropolitan` (codes 1-3)
- `Micropolitan` (codes 4-6)
- `Small town` (codes 7-9)
- `Rural` (code 10)
- `ruca_description` (TEXT) - Full RUCA code description
- `population_2020` (INTEGER)
**Source**: USDA Economic Research Service RUCA 2020
**Notes**:
- Based on 2020 Census tracts and 2010-2020 commuting patterns
- 7 data centers failed RUCA join (Puerto Rico / non-US)
### `watershed_huc8`
**Rows**: 2,139
**Purpose**: USGS HUC8 subbasin polygons for water-stress analysis
**Key Columns**:
- `huc8` (TEXT) - 8-digit Hydrologic Unit Code (PRIMARY KEY)
- `name` (TEXT) - Watershed name
- `geom` (GEOMETRY) - Polygon geometry (EPSG:4326)
- `area_sq_km` (DOUBLE PRECISION)
- `states` (TEXT) - Comma-separated state codes
- `dc_count` (INTEGER) - Number of data centers in watershed
**Source**: USGS Watershed Boundary Dataset
**Notes**:
- 257 of 2,139 watersheds contain at least one data center
- Top 15 watersheds contain 50% of all US data centers
### `nri_census_tracts`
**Rows**: ~84,000
**Purpose**: Full FEMA National Risk Index by Census tract
**Key Columns**:
- `nri_id` (TEXT) - Census tract GEOID
- `state_name`, `county_name`, `tract_name`
- **460+ columns** including:
- Overall risk scores and ratings
- Expected annual loss (dollars and building value %)
- Social vulnerability components (15 factors)
- Community resilience score
- Individual hazard risk scores (18 hazards)
- Exposure, annualized frequency, historic loss ratios per hazard
**Source**: FEMA National Risk Index v2.1 (December 2025)
**Notes**:
- Massive table with comprehensive natural hazard risk data
- Join to data centers via `geoid` field
- See [FEMA NRI Technical Documentation](https://hazards.fema.gov/nri/)
---
## Infrastructure Tables
### Energy Infrastructure
#### `energy_eia_operating_generator_capacity_flat`
**Rows**: 4.7 million
**Purpose**: EIA generator inventory with lat/lon/MW (monthly 2008-2026)
**Key Columns**:
- `plant_id` (INTEGER) - EIA plant ID
- `generator_id` (TEXT) - Generator unit ID
- `plant_name` (TEXT)
- `latitude`, `longitude`, `geom`
- `state`, `county`
- `utility_name`, `operator_name`
- `nameplate_capacity_mw` (DOUBLE PRECISION)
- `technology` (TEXT) - Generation technology
- `energy_source_1`, `energy_source_2` - Primary fuel codes
- `operating_month`, `operating_year` - When unit became operational
- `status` (TEXT) - Operating, standby, retired, etc.
- `report_month`, `report_year` - Data snapshot date
**Source**: EIA Form 860 via API
**Notes**:
- "Flat" means denormalized for fast spatial queries
- Each generator-month is a row (4.7M rows from monthly snapshots)
- Use for proximity analysis (e.g., "all generators within 50 km of data center")
#### `energy_eia_facility_fuel_flat`
**Rows**: Varies
**Purpose**: Monthly generation by plant/fuel
**Key Columns**:
- `plant_id`, `plant_name`
- `report_month`, `report_year`
- `energy_source` (TEXT) - Fuel code
- `net_generation_mwh` (DOUBLE PRECISION)
- `fuel_consumed_mmbtu` (DOUBLE PRECISION)
**Source**: EIA Form 923 via API
#### `energy_eia_seds_flat`
**Rows**: 2.57 million
**Purpose**: Annual state energy consumption/production (1960-2024)
**Key Columns**:
- `state_code` (TEXT)
- `year` (INTEGER)
- `msn` (TEXT) - Mnemonic series names (e.g., `TETCB` = total energy consumption)
- `value` (DOUBLE PRECISION) - Energy in trillion BTU
- `unit` (TEXT)
- `description` (TEXT) - Human-readable MSN description
**Source**: EIA State Energy Data System (SEDS)
**Notes**:
- Annual aggregates by state
- Use for state-level energy context analysis
---
### Connectivity Infrastructure
#### `internet_cables`
**Rows**: 693
**Purpose**: Submarine cable routes
**Key Columns**:
- `cable_id` (TEXT) - Unique cable identifier
- `cable_name` (TEXT) - Official cable name
- `geom` (GEOMETRY) - LineString geometry (EPSG:4326)
- `rfs_year` (INTEGER) - Ready For Service year
- `length_km` (DOUBLE PRECISION)
- `owners` (TEXT[]) - Array of owner names
- `landing_points` (TEXT[]) - Array of landing point names
**Source**: TeleGeography-style cable database
**Notes**:
- 693 unique submarine cables
- Geometry is approximate route (not exact seabed path)
#### `internet_cable_landing_points`
**Rows**: 3,361
**Purpose**: Cable landing points (where cables come ashore)
**Key Columns**:
- `landing_point_id` (TEXT) - Unique identifier
- `name` (TEXT) - Landing point name
- `city`, `country`
- `latitude`, `longitude`, `geom`
- `cables` (TEXT[]) - Array of cable names landing at this point
- `cable_count` (INTEGER)
**Source**: TeleGeography-style cable database
**Notes**:
- Used for proximity analysis (how close are data centers to cable landings?)
- **Key finding**: Data centers are NOT systematically closer to cables than ordinary US cities
#### `internet_city_dominance`
**Rows**: 4,552
**Purpose**: City-level IPs/capacity (internet hub strength proxy)
**Key Columns**:
- `city` (TEXT)
- `country` (TEXT)
- `latitude`, `longitude`, `geom`
- `ip_addresses` (INTEGER) - Number of routable IP addresses
- `capacity_rank` (INTEGER) - Relative capacity ranking
**Source**: Internet topology datasets
**Notes**: Proxy for "internet hub" strength (not directly used in main analyses)
---
### Broadband
#### `fcc_bdc_location_provider_aggregates`
**Rows**: Varies
**Purpose**: FCC BDC provider availability aggregated by county/tract
**Key Columns**:
- `geoid` (TEXT) - County or tract GEOID
- `geography_level` (TEXT) - `county` or `tract`
- `provider_count` (INTEGER)
- `technology_counts` (JSONB) - Count by technology type
- `max_download_mbps`, `max_upload_mbps`
**Source**: FCC Broadband Data Collection (BDC)
#### `fcc_bdc_broadband_connection_table`
**Rows**: Varies
**Purpose**: Per-data-center broadband provider availability
**Key Columns**:
- Data center identifiers
- `provider_id`, `provider_name`
- `technology` (TEXT)
- `max_advertised_download_speed`, `max_advertised_upload_speed`
- `low_latency` (BOOLEAN)
**Source**: FCC BDC, joined to data center locations
**Notes**: Built by `build_fcc_bdc_broadband_connection_table.py`
---
## Commonly Used Joins
### Data Center to Demographics
```sql
SELECT
dc.*,
ct.median_household_income,
ct.bachelors_or_higher_pct,
ct.broadband_pct
FROM master_data_centers dc
JOIN data_center_census_tracts_2024 ct
ON dc.id = ct.id;
```
### Data Center to Watershed
```sql
SELECT
dc.*,
w.huc8,
w.watershed_name
FROM master_data_centers dc
JOIN data_center_watershed_huc8 dw ON dc.id = dw.id
JOIN watershed_huc8 w ON dw.huc8 = w.huc8;
```
### Data Center to Energy Infrastructure (50 km radius)
```sql
SELECT
dc.id,
dc.name,
SUM(eg.nameplate_capacity_mw) AS total_capacity_50km
FROM master_data_centers dc
JOIN energy_eia_operating_generator_capacity_flat eg
ON ST_DWithin(
dc.geom::geography,
eg.geom::geography,
50000 -- 50 km in meters
)
WHERE eg.status = 'OP' -- Operating only
GROUP BY dc.id, dc.name;
```
### Data Center to FEMA Hazard Risk
```sql
SELECT
dc.*,
nri.risk_score,
nri.wildfire_risk,
nri.drought_risk,
nri.heat_wave_risk
FROM master_data_centers dc
JOIN data_center_census_tracts_2024 ct ON dc.id = ct.id
JOIN nri_census_tracts nri ON ct.geoid = nri.nri_id;
```
---
## Table Naming Conventions
- **`master_*`** - Canonical, deduplicated tables (use these for analysis)
- **`data_center_*`** - Data center-specific enrichment tables
- **`_dc_*`** - Base layers scoped to data center states (underscore prefix = private/internal)
- **`energy_eia_*`** - EIA energy data
- **`internet_*`** - Connectivity infrastructure
- **`fcc_bdc_*`** - FCC Broadband Data Collection
---
## Indexes and Performance
All tables have spatial indexes on `geom` columns for fast spatial joins:
```sql
CREATE INDEX idx_tablename_geom ON tablename USING GIST(geom);
```
Key `geoid` columns are indexed for fast demographic joins:
```sql
CREATE INDEX idx_tablename_geoid ON tablename(geoid);
```
---
## Maintenance Notes
### Updating Data Centers
1. Run `load_postgis_osm_data_centers.py` to refresh OSM data
2. Run `build_master_data_centers.py` to rebuild master table
3. Run enrichment scripts to update joins
### Updating Demographics
1. Update `_dc_census_tract_acs_2024` from Census API
2. Run `create_data_center_census_tract_table.py --replace-final`
### Updating Energy Data
```bash
python3 ingest_eia_energy_layers.py --category power --update
```
---
## Schema Export
To export the full schema:
```bash
pg_dump -h $PGWEB_HOST -U $PGWEB_USER -d data_centers --schema-only > schema.sql
```
To list all tables:
```sql
SELECT schemaname, tablename, pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename))
FROM pg_tables
WHERE schemaname = 'public'
ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;
```
---
## Contact
For database access or questions, contact the repository owner.