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Document database table previews

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David Adams
2026-06-09 15:04:47 -07:00
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@@ -5,6 +5,7 @@ A comprehensive geospatial research project investigating the spatial concentrat
## Documentation ## Documentation
- **[Database Tables](docs/database-tables.md)** - Complete database schema with table descriptions, column definitions, and SQL examples - **[Database Tables](docs/database-tables.md)** - Complete database schema with table descriptions, column definitions, and SQL examples
- **[Database Table Previews](docs/database-table-heads.md)** - Research-team-friendly Markdown previews showing the first rows of each documented table
- **[Research Ideas](docs/research-ideas.md)** - Future research directions, data improvements, and potential collaborations - **[Research Ideas](docs/research-ideas.md)** - Future research directions, data improvements, and potential collaborations
- **[SQL Queries](docs/query_legiscan_bills.sql)** - Pre-built legislative analysis queries - **[SQL Queries](docs/query_legiscan_bills.sql)** - Pre-built legislative analysis queries
@@ -135,7 +136,7 @@ Facilities in DBSCAN clusters differ significantly from isolated sites:
### Database ### Database
- **PostgreSQL 13+** with **PostGIS 3.x** - **PostgreSQL 13+** with **PostGIS 3.x**
- Database name: `data_centers` - Database name: `data_centers`
- See [database-tables.md](database-tables.md) for complete schema documentation - See [database-tables.md](docs/database-tables.md) for complete schema documentation and [database-table-heads.md](docs/database-table-heads.md) for readable sample rows
### Python Environment ### Python Environment
- **Python 3.10+** - **Python 3.10+**

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# Database Table Heads
Generated: 2026-06-09 15:01:04 PDT
Source order: `docs/database-tables.md`.
Each section shows up to 10 rows from the corresponding `public` table. Geometry/geography columns are summarized as WKT points or geometry type, SRID, point count, and bounding box. Long scalar values are truncated to 120 characters.
Rows are ordered by primary key where a primary key exists; otherwise the sample uses the database scan order.
To keep this shareable in Markdown, wide tables show up to 12 representative columns rather than every column.
## 01. `master_data_centers`
Estimated rows: 1,833. Columns: 30. Ordering: `master_id`.
Showing 12 of 30 columns. Omitted columns include: `curated_id`, `osm_id`, `street_address`, `postal_code`, `website`, `phone`, `nearest_airport_miles`, `has_bare_metal`, `has_iaas`, `has_internet_exchange`, `has_colocation`, `certifications`, `content_summary`, `osm_tags`, `matched_osm_tag_passes`, `match_method`, `match_distance_m`, `geom`.
| master_id | name | operator | city | state | country | latitude | longitude | power_mw | area_sqft | source | geoid |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | Verizon | | | NJ | United States | 40.54425557630532 | -74.49652096447575 | | 105786 | merged | 34023000702 |
| curated/0007805491 | Discover Financial Services New Albany | | | OH | United States | 40.10065711344692 | -82.81435808641204 | | 188209 | curated | 39049007205 |
| curated/0009474864 | Google Data Center | Google | | NC | United States | 35.89473769568108 | -81.54651452543965 | | 3407194 | merged | 37027030300 |
| curated/0013924557 | Project Alluvion | Microsoft | | IA | United States | 41.51595464169968 | -93.71171866418732 | | 10962475 | curated | 19153011028 |
| curated/0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | Meta, Inc. | Social Circle | GA | United States | 33.6563867 | -83.7186389 | | 970000 | curated | 13297110801 |
| curated/0014593270 | Apple - Maiden Data Center | | | NC | United States | 35.588771360781784 | -81.26180877040836 | | 5431080 | merged | 37035011702 |
| curated/0014930068 | HPC (880) | Sandia National Laboratories | | NM | United States | 35.04994198074901 | -106.54282203991325 | | 158463 | merged | 35001980000 |
| curated/0014997588 | Meta Henrico Data Center | Meta | | VA | United States | 37.482763284885785 | -77.23645509442149 | | 4671056 | merged | 51087201404 |
| curated/0015884451 | Barclays Datacenter | Barclays | | NJ | United States | 40.64375303338713 | -74.28548116880219 | | 94373 | merged | 34039037300 |
| curated/0016282459 | CyberNAP Glen Burnie | AiNET | | MD | United States | 39.14024170717735 | -76.60643112453295 | | 89879 | merged | 24003730404 |
## 02. `us_dc_sample_geocoded`
Estimated rows: 1,489. Columns: 40. Ordering: `id`.
Showing 12 of 40 columns. Omitted columns include: `url`, `provider_url`, `state`, `postal_code`, `street_address`, `address`, `source_address`, `phone`, `nearest_airport_miles`, `has_bare_metal`, `has_iaas`, `has_internet_exchange`, `has_colocation`, `certifications`, `content_summary`, `path`, `geocode_precision`, `geocode_status`, `geocode_match_address`, `census_status`....
| id | facility_name | provider | city | state_code | country | latitude | longitude | power_mw | area_sqft | geocode_source | geoid |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0002744301 | Verizon | | | NJ | United States | 40.54425557630532 | -74.49652096447575 | | 105786 | IM3_Existing_DataCenters | 34023000702 |
| 0007805491 | Discover Financial Services New Albany | | | OH | United States | 40.10065711344692 | -82.81435808641204 | | 188209 | IM3_Existing_DataCenters | 39049007205 |
| 0009474864 | Google Data Center | Google | | NC | United States | 35.89473769568108 | -81.54651452543965 | | 3407194 | IM3_Existing_DataCenters | 37027030300 |
| 0013924557 | Project Alluvion | Microsoft | | IA | United States | 41.51595464169968 | -93.71171866418732 | | 10962475 | IM3_Existing_DataCenters | 19153011028 |
| 0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | Meta, Inc. | Social Circle | GA | United States | 33.6563867 | -83.7186389 | | 970000 | Nominatim/OpenStreetMap | 13297110801 |
| 0014593270 | Apple - Maiden Data Center | | | NC | United States | 35.588771360781784 | -81.26180877040836 | | 5431080 | IM3_Existing_DataCenters | 37035011702 |
| 0014930068 | HPC (880) | Sandia National Laboratories | | NM | United States | 35.04994198074901 | -106.54282203991325 | | 158463 | IM3_Existing_DataCenters | 35001980000 |
| 0014997588 | Meta Henrico Data Center | Meta | | VA | United States | 37.482763284885785 | -77.23645509442149 | | 4671056 | IM3_Existing_DataCenters | 51087201404 |
| 0015884451 | Barclays Datacenter | Barclays | | NJ | United States | 40.64375303338713 | -74.28548116880219 | | 94373 | IM3_Existing_DataCenters | 34039037300 |
| 0016282459 | CyberNAP Glen Burnie | AiNET | | MD | United States | 39.14024170717735 | -76.60643112453295 | | 89879 | IM3_Existing_DataCenters | 24003730404 |
## 03. `osm_data_centers`
Estimated rows: 1,549. Columns: 22. Ordering: `id`.
Showing 12 of 22 columns. Omitted columns include: `building`, `power`, `website`, `phone`, `street_address`, `postal_code`, `matched_tags`, `tags`, `ingested_at`, `geom`.
| id | name | state | city | country | latitude | longitude | osm_type | osm_id | operator | operator_type | telecom |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| node/10014176940 | Microsoft | PR | Guaynabo | US | 18.4174359 | -66.1089895 | node | 10014176940 | Microsoft | | data_center |
| node/10537283366 | Netrality | IL | Chicago | US | 41.8728811 | -87.63344 | node | 10537283366 | Netrality | | data_center |
| node/10567817971 | Brandaleone Lab for Data and Visualization | | | US | 36.0030993 | -78.9385323 | node | 10567817971 | Duke Libraries | | data_center |
| node/10648552506 | Frontier (supercomputer) | | | US | 35.9295133 | -84.310567 | node | 10648552506 | Oak Ridge National Laboratory | | data_center |
| node/10780794741 | | CA | | US | 37.7796531 | -122.3940005 | node | 10780794741 | Ntirety | | data_center |
| node/10796113031 | VaultLogix | | | US | 33.627367 | -111.9017033 | node | 10796113031 | | | data_center |
| node/10796113033 | Profit Finder Pro Software | | | US | 33.6272477 | -111.9020476 | node | 10796113033 | | | data_center |
| node/10798644674 | DataPro USA Web Development | | | US | 33.6116624 | -111.9139825 | node | 10798644674 | | | data_center |
| node/10880725408 | e360 | CA | Concord | US | 37.9710809 | -122.0437382 | node | 10880725408 | | | data_center |
| node/10910879064 | Digital Realty ORD10 | IL | Chicago | US | 41.8533507 | -87.6183067 | node | 10910879064 | Digital Realty | | data_center |
## 04. `master_data_center_spatial_clusters`
Estimated rows: 1,831. Columns: 7. Ordering: `master_id`.
| master_id | cluster_id | is_noise | eps_km | min_samples | nearest_neighbor_km | created_at |
| --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | 0 | False | 25.0 | 5 | 0.1190382274 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0007805491 | 1 | False | 25.0 | 5 | 0.47724190925 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0009474864 | -1 | True | 25.0 | 5 | 42.317040942599995 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0013924557 | 2 | False | 25.0 | 5 | 0.45248975842 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0014271367e0e039e1b1b386315f6581 | 3 | False | 25.0 | 5 | 0.0 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0014593270 | -1 | True | 25.0 | 5 | 0.10640567666 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0014930068 | 4 | False | 25.0 | 5 | 0.18635215588 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0014997588 | 5 | False | 25.0 | 5 | 0.14982899651 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0015884451 | 0 | False | 25.0 | 5 | 7.49269729219 | 2026-05-18T01:28:48.419905+00:00 |
| curated/0016282459 | 14 | False | 25.0 | 5 | 10.471788240899999 | 2026-05-18T01:28:48.419905+00:00 |
## 05. `data_center_census_tracts_2024`
Estimated rows: 715. Columns: 58. Ordering: `geoid`.
Showing 12 of 58 columns. Omitted columns include: `countyfp`, `tractce`, `tract_name`, `namelsad`, `land_area_sqm`, `water_area_sqm`, `acs_year`, `acs_source`, `curated_only_data_center_count`, `merged_data_center_count`, `osm_only_data_center_count`, `data_center_ids`, `households`, `avg_household_size`, `high_school_or_higher_pct`, `population_16_over`, `labor_force`, `unemployed`, `unemployment_rate`, `per_capita_income`....
| geoid | acs_name | data_center_count | operators | population | median_age | median_household_income | poverty_rate | bachelor_or_higher_pct | broadband_subscription_pct | primary_industry | statefp |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 01069040205 | Census Tract 402.05; Houston County; Alabama | 1 | ["AngelTrax Mobile Video Surveillance Solutions"] | 5802 | 46.0 | 91023 | 2.3 | 49.6 | 92.6 | Educational services, and health care and social assistance | 01 |
| 01071950200 | Census Tract 9502; Jackson County; Alabama | 3 | ["Google", "Google LLC"] | 3443 | 44.8 | 46148 | 21.3 | 12.4 | 93.0 | Manufacturing | 01 |
| 01073012913 | Census Tract 129.13; Jefferson County; Alabama | 1 | ["AT&T"] | 4841 | 26.9 | 65201 | 7.6 | 40.4 | 96.2 | Educational services, and health care and social assistance | 01 |
| 01073014303 | Census Tract 143.03; Jefferson County; Alabama | 1 | | 5917 | 37.1 | 147377 | 9.0 | 71.7 | 98.3 | Educational services, and health care and social assistance | 01 |
| 01089001403 | Census Tract 14.03; Madison County; Alabama | 2 | ["Alabama Supercomputer Authority"] | 3059 | 27.6 | 68158 | 24.7 | 57.5 | 99.4 | Manufacturing | 01 |
| 01089010704 | Census Tract 107.04; Madison County; Alabama | 4 | ["Meta"] | 6752 | 44.1 | 86266 | 4.7 | 43.9 | 94.1 | Educational services, and health care and social assistance | 01 |
| 04003000301 | Census Tract 3.01; Cochise County; Arizona | 1 | ["DirecTV"] | 4125 | 52.6 | 44412 | 14.5 | 21.8 | 88.9 | Transportation and warehousing, and utilities | 04 |
| 04013061024 | Census Tract 610.24; Maricopa County; Arizona | 1 | ["Microsoft Azure"] | 2741 | 34.3 | 106458 | 8.3 | 22.9 | 92.8 | Educational services, and health care and social assistance | 04 |
| 04013061052 | Census Tract 610.52; Maricopa County; Arizona | 1 | | 8803 | 31.3 | 89191 | 3.4 | 34.2 | 97.0 | Educational services, and health care and social assistance | 04 |
| 04013061058 | Census Tract 610.58; Maricopa County; Arizona | 3 | ["Microsoft"] | 3990 | 31.6 | 100017 | 6.9 | 34.9 | 99.7 | Educational services, and health care and social assistance | 04 |
## 06. `data_center_watershed_huc8`
Estimated rows: 1,833. Columns: 5. Ordering: `master_id`.
| master_id | huc8 | huc8_name | huc8_states | huc8_area_sqkm |
| --- | --- | --- | --- | --- |
| curated/0002744301 | 02030105 | Raritan | NJ | 2863.0 |
| curated/0007805491 | 05060001 | Upper Scioto | OH | 8277.11 |
| curated/0009474864 | 03050101 | Upper Catawba | NC,SC | 6106.21 |
| curated/0013924557 | 07100006 | North Raccoon | IA | 6399.54 |
| curated/0014271367e0e039e1b1b386315f6581 | 03070103 | Upper Ocmulgee | GA | 7700.64 |
| curated/0014593270 | 03050102 | South Fork Catawba | NC,SC | 1711.71 |
| curated/0014930068 | 13020203 | Rio Grande-Albuquerque | NM | 8328.17 |
| curated/0014997588 | 02080206 | Lower James | VA | 3733.47 |
| curated/0015884451 | 02030104 | Sandy Hook-Staten Island | NJ,NY | 1838.33 |
| curated/0016282459 | 02060003 | Gunpowder-Patapsco | MD,PA | 3671.32 |
## 07. `data_center_nri_exposure`
Estimated rows: 1,833. Columns: 479. Ordering: `master_id`.
Showing 12 of 479 columns. Omitted columns include: `source`, `operator`, `city`, `country`, `longitude`, `latitude`, `geom`, `nri_status`, `STATE`, `STATEABBRV`, `STATEFIPS`, `COUNTY`, `COUNTYTYPE`, `COUNTYFIPS`, `STCOFIPS`, `TRACT`, `TRACTFIPS`, `POPULATION`, `BUILDVALUE`, `AGRIVALUE`....
| master_id | name | dc_state | NRI_ID | RISK_SCORE | RISK_RATNG | SOVI_SCORE | SOVI_RATNG | RESL_SCORE | RESL_RATNG | WFIR_RISKS | HWAV_RISKS |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | Verizon | NJ | T34023000702 | 82.4432473571 | Relatively Moderate | 53.24764556 | Relatively Moderate | 35.6782371757 | Relatively Low | 69.5123256395 | 62.2586872587 |
| curated/0007805491 | Discover Financial Services New Albany | OH | T39049007205 | 52.0185984565 | Relatively Low | 43.2346147 | Relatively Moderate | 53.7637720649 | Relatively Moderate | 35.2776093135 | 83.2916249583 |
| curated/0009474864 | Google Data Center | NC | T37027030300 | 60.7422734354 | Relatively Moderate | 65.16081265 | Relatively High | 24.7162658453 | Relatively Low | 64.5345034664 | 16.7465084132 |
| curated/0013924557 | Project Alluvion | IA | T19153011028 | 59.0774499661 | Relatively Moderate | 17.71130723 | Very Low | 85.866603483 | Very High | 73.4853079329 | 76.5706182373 |
| curated/0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | GA | T13297110801 | 26.9201954978 | Relatively Low | 53.52484748 | Relatively Moderate | 35.3453382301 | Relatively Low | 78.9768470622 | 45.1070117737 |
| curated/0014593270 | Apple - Maiden Data Center | NC | T37035011702 | 67.9117167897 | Relatively Moderate | 30.98086833 | Relatively Low | 40.2807724203 | Relatively Moderate | 76.2334558168 | 31.0882310882 |
| curated/0014930068 | HPC (880) | NM | T35001980000 | 24.5383087772 | Relatively Low | 9.209856068 | Very Low | 31.8078426727 | Relatively Low | 72.3841461239 | 44.162019162 |
| curated/0014997588 | Meta Henrico Data Center | VA | T51087201404 | 31.3367343298 | Relatively Low | 41.58917254 | Relatively Moderate | 60.2120601824 | Relatively High | 72.7860820758 | 47.838314505 |
| curated/0015884451 | Barclays Datacenter | NJ | T34039037300 | 62.9695694053 | Relatively Moderate | 22.91476633 | Relatively Low | 24.9591280654 | Relatively Low | 34.313200861 | 55.1527718194 |
| curated/0016282459 | CyberNAP Glen Burnie | MD | T24003730404 | 2.36048184748 | Very Low | 56.20505834 | Relatively Moderate | 82.3279232318 | Very High | 36.9709726137 | 43.0716430716 |
## 08. `data_center_historical_climate`
Estimated rows: 1,637. Columns: 50. Ordering: `master_id`.
Showing 12 of 50 columns. Omitted columns include: `source`, `operator`, `city`, `country`, `longitude`, `latitude`, `geom`, `daymet_dataset_version`, `gridmet_dataset_version`, `daymet_tile`, `daymet_elevation_m`, `daymet_grid_x_m`, `daymet_grid_y_m`, `gridmet_grid_latitude`, `gridmet_grid_longitude`, `observation_days`, `observation_years`, `min_daily_temperature_c`, `mean_diurnal_temperature_range_c`, `mean_relative_humidity_pct`....
| master_id | name | state | climate_period_start | climate_period_end | mean_annual_temperature_c | mean_summer_temperature_c | max_daily_temperature_c | mean_wet_bulb_temperature_c | extreme_wet_bulb_days | annual_precipitation_mm_mean | mean_wind_speed_ms |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | Verizon | NJ | 1991-01-01 | 2020-12-31 | 12.106094063926944 | 23.14475 | 39.99 | 8.787162967436066 | 8 | 1271.835333333333 | 4.2083591896331445 |
| curated/0007805491 | Discover Financial Services New Albany | OH | 1991-01-01 | 2020-12-31 | 10.999028767123287 | 22.140809782608695 | 36.65 | 7.739707851278796 | 0 | 1179.073333333333 | 4.509810184340208 |
| curated/0009474864 | Google Data Center | NC | 1991-01-01 | 2020-12-31 | 14.601237442922375 | 24.0555018115942 | 38.19 | 10.743958460260739 | 0 | 1337.2663333333333 | 3.648339112976821 |
| curated/0013924557 | Project Alluvion | IA | 1991-01-01 | 2020-12-31 | 9.937553424657532 | 22.686903985507247 | 40.02 | 6.647978315594057 | 7 | 997.7766666666668 | 4.135325789377624 |
| curated/0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | GA | 1991-01-01 | 2020-12-31 | 16.640601369863017 | 25.655347826086956 | 41.04 | 12.78379526240016 | 0 | 1407.6886666666664 | 3.336156232889214 |
| curated/0014593270 | Apple - Maiden Data Center | NC | 1991-01-01 | 2020-12-31 | 15.29845388127854 | 24.77782971014493 | 39.41 | 11.477148194394323 | 0 | 1283.5513333333333 | 3.33977003102756 |
| curated/0014930068 | HPC (880) | NM | 1991-01-01 | 2020-12-31 | 13.243185388127854 | 23.824653985507247 | 39.9 | 6.035874535137781 | 0 | 267.88033333333334 | 3.6975816754882285 |
| curated/0014997588 | Meta Henrico Data Center | VA | 1991-01-01 | 2020-12-31 | 14.962249315068494 | 24.96223731884058 | 40.06 | 11.284880957129747 | 10 | 1247.745 | 3.8984851250228147 |
| curated/0015884451 | Barclays Datacenter | NJ | 1991-01-01 | 2020-12-31 | 12.439309132420089 | 23.47996739130435 | 40.04 | 9.296053538204665 | 17 | 1323.1563333333334 | 4.481848877532397 |
| curated/0016282459 | CyberNAP Glen Burnie | MD | 1991-01-01 | 2020-12-31 | 14.028422374429223 | 24.874704710144925 | 39.33 | 10.803609721340456 | 36 | 1214.2743333333335 | 3.739450629676949 |
## 09. `data_center_usdm_drought_exposure`
Estimated rows: 1,833. Columns: 31. Ordering: `master_id`.
Showing 12 of 31 columns. Omitted columns include: `source`, `operator`, `city`, `country`, `longitude`, `latitude`, `geom`, `drought_period_start`, `drought_period_end`, `weeks_in_d0_or_worse`, `weeks_in_d1_or_worse`, `weeks_in_d2_or_worse`, `weeks_in_d3_or_worse`, `weeks_in_d4`, `pct_weeks_in_d1_or_worse`, `longest_d0_streak_weeks`, `longest_d3_streak_weeks`, `fetched_at`, `updated_at`.
| master_id | name | state | usdm_status | weeks_observed | pct_weeks_in_d0_or_worse | pct_weeks_in_d2_or_worse | pct_weeks_in_d3_or_worse | pct_weeks_in_d4 | worst_dm_category | mean_dm_category | longest_d2_streak_weeks |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | Verizon | NJ | covered | 1356 | 0.23820058997050148 | 0.038348082595870206 | 0.008112094395280236 | 0.0 | 3 | 0.1696165191740413 | 18 |
| curated/0007805491 | Discover Financial Services New Albany | OH | covered | 1356 | 0.12831858407079647 | 0.009587020648967551 | 0.0 | 0.0 | 2 | 0.04498525073746313 | 10 |
| curated/0009474864 | Google Data Center | NC | covered | 1356 | 0.3938053097345133 | 0.1261061946902655 | 0.0715339233038348 | 0.014749262536873156 | 4 | 0.45353982300884954 | 66 |
| curated/0013924557 | Project Alluvion | IA | covered | 1356 | 0.39601769911504425 | 0.07743362831858407 | 0.008112094395280236 | 0.0 | 3 | 0.3053097345132743 | 38 |
| curated/0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | GA | covered | 1356 | 0.4690265486725664 | 0.22713864306784662 | 0.10545722713864307 | 0.03023598820058997 | 4 | 0.7064896755162242 | 82 |
| curated/0014593270 | Apple - Maiden Data Center | NC | covered | 1356 | 0.4505899705014749 | 0.13200589970501475 | 0.07300884955752213 | 0.025811209439528023 | 4 | 0.5007374631268436 | 59 |
| curated/0014930068 | HPC (880) | NM | covered | 1356 | 0.7684365781710915 | 0.34365781710914456 | 0.1747787610619469 | 0.01991150442477876 | 4 | 1.0737463126843658 | 108 |
| curated/0014997588 | Meta Henrico Data Center | VA | covered | 1356 | 0.27949852507374634 | 0.043510324483775814 | 0.01032448377581121 | 0.0029498525073746312 | 4 | 0.16371681415929204 | 39 |
| curated/0015884451 | Barclays Datacenter | NJ | covered | 1356 | 0.23893805309734514 | 0.03687315634218289 | 0.008112094395280236 | 0.0 | 3 | 0.16371681415929204 | 25 |
| curated/0016282459 | CyberNAP Glen Burnie | MD | covered | 1356 | 0.26327433628318586 | 0.05825958702064897 | 0.01327433628318584 | 0.0 | 3 | 0.20648967551622419 | 24 |
## 10. `data_center_hms_smoke_exposure`
Estimated rows: 1,833. Columns: 30. Ordering: `master_id`.
Showing 12 of 30 columns. Omitted columns include: `source`, `operator`, `city`, `country`, `longitude`, `latitude`, `geom`, `smoke_period_start`, `smoke_period_end`, `days_with_any_smoke`, `days_with_light_or_worse`, `days_with_medium_or_worse`, `days_with_heavy_smoke`, `pct_days_with_light_or_worse`, `longest_any_smoke_streak_days`, `longest_medium_or_heavy_streak_days`, `fetched_at`, `updated_at`.
| master_id | name | state | hms_status | days_observed | pct_days_with_any_smoke | pct_days_with_medium_or_worse | pct_days_with_heavy_smoke | worst_density_rank | worst_density | mean_density_rank | longest_heavy_smoke_streak_days |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | Verizon | NJ | observed | 7582 | 0.11184384067528357 | 0.02176206805592192 | 0.0035610656818781325 | 3 | Heavy | 0.13716697441308362 | 3 |
| curated/0007805491 | Discover Financial Services New Albany | OH | observed | 7582 | 0.13545238723292008 | 0.02571880770245318 | 0.006067000791347929 | 3 | Heavy | 0.1672381957267212 | 4 |
| curated/0009474864 | Google Data Center | NC | observed | 7582 | 0.1144816671063044 | 0.015167501978369823 | 0.0018464785017145871 | 3 | Heavy | 0.13149564758638882 | 2 |
| curated/0013924557 | Project Alluvion | IA | observed | 7582 | 0.20034291743603272 | 0.04761276707992614 | 0.010419414402532313 | 3 | Heavy | 0.25837509891849114 | 5 |
| curated/0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | GA | observed | 7582 | 0.12674756001055132 | 0.015167501978369823 | 0.0018464785017145871 | 3 | Heavy | 0.14376154049063572 | 3 |
| curated/0014593270 | Apple - Maiden Data Center | NC | observed | 7582 | 0.11711949353732524 | 0.013848588762859404 | 0.0021102611448166712 | 3 | Heavy | 0.13307834344500133 | 2 |
| curated/0014930068 | HPC (880) | NM | observed | 7582 | 0.07122131363756265 | 0.013321023476655236 | 0.0035610656818781325 | 3 | Heavy | 0.08810340279609602 | 5 |
| curated/0014997588 | Meta Henrico Data Center | VA | observed | 7582 | 0.11764705882352941 | 0.01688208915853337 | 0.0035610656818781325 | 3 | Heavy | 0.13809021366394092 | 5 |
| curated/0015884451 | Barclays Datacenter | NJ | observed | 7582 | 0.11158005803218148 | 0.022025850699024005 | 0.0040886309680823 | 3 | Heavy | 0.1376945396992878 | 3 |
| curated/0016282459 | CyberNAP Glen Burnie | MD | observed | 7582 | 0.11263518860458982 | 0.019388024268003165 | 0.0034291743603270903 | 3 | Heavy | 0.13545238723292008 | 3 |
## 11. `data_center_election_context`
Estimated rows: 1,833. Columns: 13. Ordering: `master_id`.
Showing 12 of 13 columns. Omitted columns include: `updated_at`.
| master_id | name | city | state | election_year | office | democratic_votes | republican_votes | total_votes | turnout_or_vote_share | precinct_identifier_name | rdh_layer_title |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | Verizon | NaN | NJ | 2020 | President | 17390.0 | 6690.0 | 24080.0 | 0.7221760797342193 | Piscataway Township | VEST 2020 New Jersey precinct boundaries and election results shapefile |
| curated/0007805491 | Discover Financial Services New Albany | NaN | OH | 2020 | President | 669.0 | 469.0 | 1138.0 | 0.8687022900763359 | BFX | Ohio 2020 General Election Precinct and Election Results (Extended) |
| curated/0009474864 | Google Data Center | NaN | NC | 2020 | President | 705.0 | 1373.0 | 2078.0 | 0.33926852743022134 | NaN | VEST 2020 North Carolina precinct boundaries and election results shapefile |
| curated/0013924557 | Project Alluvion | NaN | IA | 2020 | President | 538.0 | 377.0 | 915.0 | 0.5879781420765028 | WDM112 | VEST 2020 Iowa precinct and election results |
| curated/0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | Social Circle | GA | 2020 | President | 928.0 | 2347.0 | 3275.0 | 0.2833587786259542 | 297418 | VEST 2020 Georgia precinct boundaries and election results shapefile |
| curated/0014593270 | Apple - Maiden Data Center | NaN | NC | 2020 | President | 909.0 | 2482.0 | 3391.0 | 0.26806251843114126 | NaN | VEST 2020 North Carolina precinct boundaries and election results shapefile |
| curated/0014930068 | HPC (880) | NaN | NM | 2020 | President | 80.0 | 135.0 | 215.0 | 0.37209302325581395 | 258 | VEST 2020 New Mexico precinct and election results |
| curated/0014997588 | Meta Henrico Data Center | NaN | VA | 2020 | U.S. Senate | 201.0 | 587.0 | 788.0 | 0.2550761421319797 | Elko | VEST 2020 Virginia precinct boundaries and election results shapefile |
| curated/0015884451 | Barclays Datacenter | NaN | NJ | 2020 | President | 9040.0 | 6088.0 | 15128.0 | 0.597567424643046 | Cranford Township | VEST 2020 New Jersey precinct boundaries and election results shapefile |
| curated/0016282459 | CyberNAP Glen Burnie | NaN | MD | 2020 | President | 417.0 | 491.0 | 908.0 | 0.4592511013215859 | ANNE ARUNDEL PRECINCT 02-011 | VEST 2020 Maryland precinct and election results |
## 12. `data_center_rdh_precinct_vote_matches`
Estimated rows: 3,330. Columns: 7. Ordering: `master_id`, `feature_id`.
| master_id | feature_id | layer_id | state_code | join_method | match_distance_m | matched_at |
| --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | cdeae08e0ed9070530937088 | 98741c3d7281ecf3 | NJ | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0007805491 | 3df485dfa083048eddd6742c | 001ae681b11a7403 | OH | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0007805491 | 49b7d2e77e783540b27146f9 | 8289496602c3ba6f | OH | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0007805491 | 7217cf09bb956b2663658b81 | 1dd660766654d8c9 | OH | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0009474864 | 4fccbff62a06418c9500c0db | 093bea99f4098801 | NC | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0009474864 | 68e1c4c9748ce670c466d515 | 03b4552784ad5e64 | NC | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0009474864 | cbf76052d1c2c4984fb5303d | 3cd4bcd737908e07 | NC | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0013924557 | ed3838610316a42243f0c9ad | 9e5d2513d8fda919 | IA | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0014271367e0e039e1b1b386315f6581 | 4474cd7bb10c0c9fe609799f | e7d5e5b5e514f19b | GA | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
| curated/0014271367e0e039e1b1b386315f6581 | 54c830006bdf45896d6ed1b8 | 96019051d65a5f79 | GA | point_in_precinct | 0.0 | 2026-05-22T21:56:04.013534+00:00 |
## 13. `usdm_drought_weekly`
Estimated rows: 12,080. Columns: 7. Ordering: `id`.
| id | week_date | dm_category | objectid | shape_leng | shape_area | geom |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | 2000-01-04 | 0 | 1 | 32170925.2065 | 2153934540100.0 | ST_MultiPolygon; srid=4326; points=30029; bbox=POLYGON((-122.6865729593049 25.83802464207686,-122.6865729593049 49.38... |
| 2 | 2000-01-04 | 1 | 2 | 19559980.7608 | 1092486150330.0 | ST_MultiPolygon; srid=4326; points=12359; bbox=POLYGON((-157.30627357682857 18.915496517285398,-157.30627357682857 44... |
| 3 | 2000-01-04 | 2 | 3 | 7946642.57601 | 736181293177.0 | ST_MultiPolygon; srid=4326; points=4533; bbox=POLYGON((-104.99222016805918 28.629182546534828,-104.99222016805918 44.... |
| 4 | 2000-01-11 | 0 | 1 | 32296973.4448 | 2872049646530.0 | ST_MultiPolygon; srid=4326; points=31734; bbox=POLYGON((-122.94548791097417 25.83802464207686,-122.94548791097417 49.... |
| 5 | 2000-01-11 | 1 | 2 | 12953315.7818 | 742122520570.0 | ST_MultiPolygon; srid=4326; points=9698; bbox=POLYGON((-105.02259598718679 27.447046525660603,-105.02259598718679 38.... |
| 6 | 2000-01-11 | 1 | 3 | 915837.16825 | 9626607395.74 | ST_MultiPolygon; srid=4326; points=725; bbox=POLYGON((-157.30627357682857 18.915496517285398,-157.30627357682857 21.2... |
| 7 | 2000-01-11 | 1 | 4 | 6357744.27578 | 428794634251.0 | ST_MultiPolygon; srid=4326; points=1716; bbox=POLYGON((-101.47346353234363 38.464544442435546,-101.47346353234363 44.... |
| 8 | 2000-01-11 | 2 | 5 | 1346098.76758 | 111052103778.0 | ST_MultiPolygon; srid=4326; points=351; bbox=POLYGON((-85.65307656265493 29.740860638402328,-85.65307656265493 34.192... |
| 9 | 2000-01-11 | 2 | 6 | 4617767.61288 | 453753202058.0 | ST_MultiPolygon; srid=4326; points=3650; bbox=POLYGON((-105.01457655763878 28.629159438938803,-105.01457655763878 34.... |
| 10 | 2000-01-11 | 2 | 7 | 774059.302853 | 46976398922.1 | ST_MultiPolygon; srid=4326; points=236; bbox=POLYGON((-87.48149930170945 39.494173874234626,-87.48149930170945 41.791... |
## 14. `data_center_usdm_drought_dc_week`
Estimated rows: 2,481,480. Columns: 3. Ordering: `master_id`, `week_date`.
| master_id | week_date | worst_dm |
| --- | --- | --- |
| curated/0002744301 | 2000-01-04 | -1 |
| curated/0002744301 | 2000-01-11 | -1 |
| curated/0002744301 | 2000-01-18 | -1 |
| curated/0002744301 | 2000-01-25 | -1 |
| curated/0002744301 | 2000-02-01 | -1 |
| curated/0002744301 | 2000-02-08 | -1 |
| curated/0002744301 | 2000-02-15 | -1 |
| curated/0002744301 | 2000-02-22 | -1 |
| curated/0002744301 | 2000-02-29 | -1 |
| curated/0002744301 | 2000-03-07 | -1 |
## 15. `hms_smoke_days`
Estimated rows: 7,075. Columns: 7. Ordering: `smoke_date`.
| smoke_date | source | source_file | source_url | feature_count | fetched_at | updated_at |
| --- | --- | --- | --- | --- | --- | --- |
| 2005-08-05 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 56 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-06 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 80 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-07 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 95 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-08 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 27 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-11 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 38 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-12 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 25 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-13 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 20 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-14 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 26 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-15 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 49 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
| 2005-08-16 | annual_bundle | hms_smoke2005.zip | https://satepsanone.nesdis.noaa.gov/pub/FIRE/web/HMS/Smoke_Polygons/Shapefile/Annual_Bundles/hms_smoke2005.zip | 25 | 2026-05-22T15:57:32.695621+00:00 | 2026-05-22T15:57:32.695621+00:00 |
## 16. `hms_smoke_daily`
Estimated rows: 494,284. Columns: 14. Ordering: `id`.
Showing 12 of 14 columns. Omitted columns include: `source_url`, `fetched_at`.
| id | smoke_date | density | density_rank | satellite | start_utc | end_utc | source | source_file | geom | start_raw | end_raw |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T12:00:00+00:00 | 2005-08-05T17:00:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=7; bbox=POLYGON((-121.468 50.35,-121.468 50.581,-121.111 50.581,-121.111 50.35,-12... | 2005217 1200 | 2005217 1700 |
| 2 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T12:00:00+00:00 | 2005-08-05T17:00:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=9; bbox=POLYGON((-115.228 47.047,-115.228 47.387,-114.673 47.387,-114.673 47.047,-... | 2005217 1200 | 2005217 1700 |
| 3 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T12:00:00+00:00 | 2005-08-05T17:00:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=8; bbox=POLYGON((-114.301 46.027,-114.301 46.276,-113.998 46.276,-113.998 46.027,-... | 2005217 1200 | 2005217 1700 |
| 4 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T11:45:00+00:00 | 2005-08-05T17:15:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=8; bbox=POLYGON((-78.781 37.401,-78.781 37.51,-78.693 37.51,-78.693 37.401,-78.781... | 2005217 1145 | 2005217 1715 |
| 5 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T16:45:00+00:00 | 2005-08-05T19:45:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=9; bbox=POLYGON((-124.16 42.581,-124.16 42.747,-123.905 42.747,-123.905 42.581,-12... | 2005217 1645 | 2005217 1945 |
| 6 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T16:45:00+00:00 | 2005-08-05T19:45:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=9; bbox=POLYGON((-120.965 47.31,-120.965 47.566,-120.67 47.566,-120.67 47.31,-120.... | 2005217 1645 | 2005217 1945 |
| 7 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T16:45:00+00:00 | 2005-08-05T19:45:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=14; bbox=POLYGON((-121.517 50.304,-121.517 50.744,-120.784 50.744,-120.784 50.304,... | 2005217 1645 | 2005217 1945 |
| 8 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T16:45:00+00:00 | 2005-08-05T19:45:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=13; bbox=POLYGON((-114.377 46.041,-114.377 46.286,-113.669 46.286,-113.669 46.041,... | 2005217 1645 | 2005217 1945 |
| 9 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T19:15:00+00:00 | 2005-08-05T22:45:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=11; bbox=POLYGON((-93.056 30.692,-93.056 30.95,-92.61 30.95,-92.61 30.692,-93.056 ... | 2005217 1915 | 2005217 2245 |
| 10 | 2005-08-05 | Unspecified | 1 | GOES | 2005-08-05T19:15:00+00:00 | 2005-08-05T22:45:00+00:00 | annual_bundle | hms_smoke2005.zip | ST_MultiPolygon; srid=4326; points=10; bbox=POLYGON((-92.674 33.601,-92.674 33.713,-92.5 33.713,-92.5 33.601,-92.674 ... | 2005217 1915 | 2005217 2245 |
## 17. `data_center_hms_smoke_dc_day`
Estimated rows: 13,910,574. Columns: 4. Ordering: `master_id`, `smoke_date`.
| master_id | smoke_date | max_density_rank | polygon_hits |
| --- | --- | --- | --- |
| curated/0002744301 | 2005-08-05 | 0 | 0 |
| curated/0002744301 | 2005-08-06 | 0 | 0 |
| curated/0002744301 | 2005-08-07 | 0 | 0 |
| curated/0002744301 | 2005-08-08 | 0 | 0 |
| curated/0002744301 | 2005-08-11 | 0 | 0 |
| curated/0002744301 | 2005-08-12 | 0 | 0 |
| curated/0002744301 | 2005-08-13 | 0 | 0 |
| curated/0002744301 | 2005-08-14 | 0 | 0 |
| curated/0002744301 | 2005-08-15 | 0 | 0 |
| curated/0002744301 | 2005-08-16 | 0 | 0 |
## 18. `rdh_precinct_vote_layers`
Estimated rows: 56. Columns: 11. Ordering: `layer_id`.
| layer_id | state_code | title | format | datasetid | source_url | filename | local_path | spatial_path | metadata | loaded_at |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 001ae681b11a7403 | OH | Ohio 2020 Primary Election Precinct and Election Results | SHP | 33850 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Ferj_precinct%2Foh_prim_20_prec.zip?username=... | oh_prim_20_prec.zip | data/rdh_precinct_vote/raw/OH/oh_prim_20_prec.zip | data/rdh_precinct_vote/extracted/001ae681b11a7403/oh_prim_20_prec/oh_prim_20_st_prec.shp | {"columns": ["UNIQUE_ID", "COUNTYFP", "STATEFP20", "COUNTYFP20", "VTDST20", "PRECINCT20", "GEOID20", "NAME20", "REG_V... | 2026-05-20T16:25:26.396218+00:00 |
| 03b4552784ad5e64 | NC | North Carolina 2020 General Election Precinct and Election Results (Extended) | SHP | 33725 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Ferj_precinct%2Fnc_gen_20_prec.zip?username=Y... | nc_gen_20_prec.zip | data/rdh_precinct_vote/raw/NC/nc_gen_20_prec.zip | data/rdh_precinct_vote/extracted/03b4552784ad5e64/nc_gen_20_cong_prec.shp | {"columns": ["UNIQUE_ID", "COUNTYFP", "ENR_DESC", "COUNTY_NAM", "COUNTY_ID", "CONG_DIST", "GCON01DBUT", "GCON01RSMI",... | 2026-05-20T16:24:34.085021+00:00 |
| 03ec783efd4c2651 | WV | VEST 2020 West Virginia precinct and election results | SHP | 36595 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Fdata_partners%2Fvest%2F2020%2Fwv_vest_20.zip... | wv_vest_20.zip | data/rdh_precinct_vote/raw/WV/wv_vest_20.zip | data/rdh_precinct_vote/extracted/03ec783efd4c2651/wv_vest_20.shp | {"columns": ["STATEFP", "COUNTYFP", "VTDST", "NAME", "NAMELSAD", "G20PRERTRU", "G20PREDBID", "G20PRELJOR", "G20PREMHA... | 2026-05-20T16:27:43.153607+00:00 |
| 06f81d92346b902b | MT | VEST 2020 Montana precinct and election results | SHP | 23396 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Fdata_partners%2Fvest%2F2020%2Fmt_vest_20.zip... | mt_vest_20.zip | data/rdh_precinct_vote/raw/MT/mt_vest_20.zip | data/rdh_precinct_vote/extracted/06f81d92346b902b/mt_vest_20.shp | {"columns": ["STATEFP10", "COUNTYFP10", "COUNTY", "NAME", "SOSPRECINC", "G20PRERTRU", "G20PREDBID", "G20PRELJOR", "G2... | 2026-05-20T16:24:29.335586+00:00 |
| 093bea99f4098801 | NC | North Carolina 2020 Primary Election Precinct and Election Results | SHP | 34057 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Ferj_precinct%2Fnc_prim_20_prec.zip?username=... | nc_prim_20_prec.zip | data/rdh_precinct_vote/raw/NC/nc_prim_20_prec.zip | data/rdh_precinct_vote/extracted/093bea99f4098801/nc_prim_20_prec/nc_prim_20_cong_prec.shp | {"columns": ["UNIQUE_ID", "COUNTYFP", "ENR_DESC", "COUNTY_NAM", "COUNTY_ID", "CONG_DIST", "PCON01RBAC", "PCON01RGLI",... | 2026-05-20T16:24:41.914603+00:00 |
| 0c9eb5f131f0d5a7 | MI | Michigan 2020 General Election Precinct and Election Results (Extended) | SHP | 36077 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Ferj_precinct%2Fmi_gen_20_prec.zip?username=Y... | mi_gen_20_prec.zip | data/rdh_precinct_vote/raw/MI/mi_gen_20_prec.zip | data/rdh_precinct_vote/extracted/0c9eb5f131f0d5a7/mi_st_20_prec.shp | {"columns": ["UNIQUE_ID", "COUNTYFP", "PRECINCTID", "COUNTYFIPS", "cousubname", "elexpre", "G20AM1NO", "G20AM1YES", "... | 2026-05-20T16:24:01.188530+00:00 |
| 0d2368617ecb8a90 | NE | VEST 2020 Nebraska precinct boundaries and election results | SHP | 24199 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Fdata_partners%2Fvest%2F2020%2Fne_vest_20.zip... | ne_vest_20.zip | data/rdh_precinct_vote/raw/NE/ne_vest_20.zip | data/rdh_precinct_vote/extracted/0d2368617ecb8a90/ne_vest_20.shp | {"columns": ["COUNTY", "NAME", "G20PRERTRU", "G20PREDBID", "G20PRELJOR", "G20USSRSAS", "G20USSDJAN", "G20USSLSIA", "g... | 2026-05-20T16:24:54.404563+00:00 |
| 137ca493b7e6859d | NV | VEST 2020 Nevada precinct boundaries and election results shapefile | SHP | 23751 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Fdata_partners%2Fvest%2F2020%2Fnv_vest_20.zip... | nv_vest_20.zip | data/rdh_precinct_vote/raw/NV/nv_vest_20.zip | data/rdh_precinct_vote/extracted/137ca493b7e6859d/nv_vest_20.shp | {"columns": ["STATEFP", "COUNTYFP", "COUNTY", "VTDST", "NAME", "G20PREDBID", "G20PRERTRU", "G20PRELJOR", "G20PREIBLA"... | 2026-05-20T16:25:04.650847+00:00 |
| 1c295ef7a5ca9e75 | VA | Virginia 2020 General Election Precinct and Election Results (Extended) | SHP | 36165 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Ferj_precinct%2Fva_gen_20_prec.zip?username=Y... | va_gen_20_prec.zip | data/rdh_precinct_vote/raw/VA/va_gen_20_prec.zip | data/rdh_precinct_vote/extracted/1c295ef7a5ca9e75/va_gen_20_st_cong_prec.shp | {"columns": ["UNIQUE_ID", "COUNTYFP", "LOCALITY", "VTDST", "PRECINCT", "CONG_DIST", "G20PREDBID", "G20PRELJOR", "G20P... | 2026-05-20T13:05:47.686733+00:00 |
| 1c4b6b10d62350ab | NM | VEST 2020 New Mexico precinct and election results | SHP | 32704 | https://redistrictingdatahub.org/wp-json/download/file/web_ready_stage%2Fdata_partners%2Fvest%2F2020%2Fnm_vest_20.zip... | nm_vest_20.zip | data/rdh_precinct_vote/raw/NM/nm_vest_20.zip | data/rdh_precinct_vote/extracted/1c4b6b10d62350ab/nm_vest_20.shp | {"columns": ["STATEFP", "COUNTYFP", "COUNTYNAME", "VTDST", "NAME", "G20PREDBID", "G20PRERTRU", "G20PRELJOR", "G20PREG... | 2026-05-20T16:25:00.803232+00:00 |
## 19. `rdh_precinct_vote_features`
Estimated rows: 260,953. Columns: 6. Ordering: `feature_id`.
| feature_id | layer_id | state_code | source_row | properties | geom |
| --- | --- | --- | --- | --- | --- |
| 00001ee7d4edd43dbd774cc8 | 2ddb7e8b308af66e | UT | 2134 | {"CountyID": "26", "G20ATGDSKO": "61", "G20ATGLBAU": "8", "G20ATGRREY": "159", "G20AUDCOST": "20", "G20AUDRDOU": "167... | ST_MultiPolygon; srid=4326; points=573; bbox=POLYGON((-111.52424487897268 40.477892051471144,-111.52424487897268 40.5... |
| 00006aeed0fa0fadee0665d8 | 24c27a4e4d0a783a | OK | 262 | {"COUNTYFP": "109", "COUNTY_NAM": "Oklahoma", "G20COCLHAG": "290", "G20COCRHIE": "946", "G20PREDBID": "415", "G20PREI... | ST_MultiPolygon; srid=4326; points=132; bbox=POLYGON((-97.6543008528392 35.49329338479545,-97.6543008528392 35.507888... |
| 00015585705bd61161fc6fb8 | d0f34610aa1b4513 | WI | 3892 | {"CNTY_FIPS": "55139", "CNTY_NAME": "WINNEBAGO", "CTV": "T", "G20PRECBLA": "1", "G20PREDBID": "157", "G20PREICAR": "1... | ST_MultiPolygon; srid=4326; points=107; bbox=POLYGON((-88.63494382054726 44.15531286305138,-88.63494382054726 44.1850... |
| 00018e188804ab223d3fe67d | 001ae681b11a7403 | OH | 5074 | {"COUNTYFP": "035", "COUNTYFP20": "035", "GEOID20": "39035018CBP", "NAME20": "MAYFIELD HEIGHTS-00-B", "P20PREDBEN": "... | ST_MultiPolygon; srid=4326; points=18; bbox=POLYGON((-81.46835899999999 41.520042,-81.46835899999999 41.530711,-81.46... |
| 0001d95aec8088a258b4cbf8 | 1c295ef7a5ca9e75 | VA | 742 | {"CONG_DIST": "8", "COUNTYFP": "059", "G20PREDBID": "1111.0", "G20PRELJOR": "6.0", "G20PREOWRI": "23.0", "G20PRERTRU"... | ST_MultiPolygon; srid=4326; points=112; bbox=POLYGON((-77.05839399999999 38.721784,-77.05839399999999 38.733346999999... |
| 0002094741e64fe1174479bf | b274db08233b3488 | CA | 11095 | {"ADDIST": "56", "BEDIST": "4", "CDDIST": "36", "CNTY_CODE": "33", "COUNTY": "Riverside", "FIPS_CODE": "6065", "G20PR... | ST_MultiPolygon; srid=4326; points=1223; bbox=POLYGON((-116.30127818534983 33.73807788000914,-116.30127818534983 33.8... |
| 00024ffb305d4cb048b8c4da | 299459035d6a5f81 | TX | 4951 | {"CNTY": "409", "CNTYKEY": "205", "COLOR": "2", "G20PREDBID": "134", "G20PREGHAW": "0", "G20PRELJOR": "2", "G20PREOWR... | ST_MultiPolygon; srid=4326; points=1067; bbox=POLYGON((-97.771343000238 27.86559399961167,-97.771343000238 28.0596060... |
| 000284c52de245ad9500727a | b274db08233b3488 | CA | 8062 | {"ADDIST": "4", "BEDIST": "2", "CDDIST": "5", "CNTY_CODE": "28", "COUNTY": "Napa", "FIPS_CODE": "6055", "G20PREAFUE":... | ST_MultiPolygon; srid=4326; points=220; bbox=POLYGON((-122.60430471286963 38.578543542126795,-122.60430471286963 38.5... |
| 00030724f179135b773cdf2f | 65fba79e3ea24d7e | KS | 2204 | {"COUNTYFP": "167", "G20PREDBID": "11", "G20PRELJOR": "2", "G20PRERTRU": "78", "G20USSDBOL": "16", "G20USSLBUC": "1",... | ST_MultiPolygon; srid=4326; points=510; bbox=POLYGON((-99.039186 38.958084,-99.039186 39.133359999999996,-98.815737 3... |
| 000331172a4640dd67e9a30f | 9e5d2513d8fda919 | IA | 1107 | {"COUNTY": "Story", "DISTRICT": "AMES3-2", "G20PRECBLA": "0", "G20PREDBID": "781", "G20PREGHAW": "3", "G20PREIPIE": "... | ST_MultiPolygon; srid=4326; points=130; bbox=POLYGON((-93.68695235712704 42.00842657102323,-93.68695235712704 42.0182... |
## 20. `_dc_census_tract_acs_2024`
Estimated rows: 85,382. Columns: 45. Ordering: `geoid`.
Showing 12 of 45 columns. Omitted columns include: `population_16_over`, `labor_force`, `unemployed`, `unemployment_rate`, `industry_total_workers`, `industry_agriculture_mining_workers`, `industry_construction_workers`, `industry_manufacturing_workers`, `industry_wholesale_trade_workers`, `industry_retail_trade_workers`, `industry_transportation_warehousing_utilities_workers`, `industry_information_workers`, `industry_finance_real_estate_workers`, `industry_professional_management_admin_workers`, `industry_education_health_social_workers`, `industry_arts_entertainment_food_workers`, `industry_other_services_workers`, `industry_public_administration_workers`, `median_household_income`, `per_capita_income`....
| geoid | acs_name | statefp | countyfp | tractce | population | median_age | households | avg_household_size | high_school_or_higher_pct | bachelor_or_higher_pct | broadband_subscription_pct |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 01001020100 | Census Tract 201; Autauga County; Alabama | 01 | 001 | 020100 | 1781 | 37.7 | 736 | 2.42 | 90.6 | 25.0 | 97.4 |
| 01001020200 | Census Tract 202; Autauga County; Alabama | 01 | 001 | 020200 | 1941 | 34.8 | 592 | 3.00 | 90.3 | 19.6 | 81.8 |
| 01001020300 | Census Tract 203; Autauga County; Alabama | 01 | 001 | 020300 | 3425 | 35.8 | 1235 | 2.75 | 86.9 | 12.2 | 95.7 |
| 01001020400 | Census Tract 204; Autauga County; Alabama | 01 | 001 | 020400 | 3948 | 44.4 | 1638 | 2.41 | 94.6 | 27.5 | 95.1 |
| 01001020501 | Census Tract 205.01; Autauga County; Alabama | 01 | 001 | 020501 | 4444 | 38.8 | 1907 | 2.33 | 96.8 | 47.6 | 99.5 |
| 01001020502 | Census Tract 205.02; Autauga County; Alabama | 01 | 001 | 020502 | 3563 | 35.1 | 1370 | 2.60 | 95.4 | 37.7 | 97.1 |
| 01001020503 | Census Tract 205.03; Autauga County; Alabama | 01 | 001 | 020503 | 3368 | 41.1 | 1406 | 2.30 | 93.6 | 42.4 | 98.6 |
| 01001020600 | Census Tract 206; Autauga County; Alabama | 01 | 001 | 020600 | 3458 | 41.3 | 1393 | 2.48 | 85.7 | 30.9 | 92.5 |
| 01001020700 | Census Tract 207; Autauga County; Alabama | 01 | 001 | 020700 | 3184 | 36.0 | 1224 | 2.57 | 90.4 | 23.1 | 89.5 |
| 01001020801 | Census Tract 208.01; Autauga County; Alabama | 01 | 001 | 020801 | 3247 | 40.2 | 1120 | 2.90 | 90.9 | 36.5 | 97.5 |
## 21. `_dc_census_tract_boundaries_2024`
Estimated rows: 85,058. Columns: 15. Ordering: `gid`.
Showing 12 of 15 columns. Omitted columns include: `lsad`, `aland`, `awater`.
| gid | geoid | name | geom | statefp | countyfp | tractce | geoidfq | namelsad | stusps | namelsadco | state_name |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | 06077005127 | 51.27 | ST_MultiPolygon; srid=4326; points=10; bbox=POLYGON((-121.289906 37.826373,-121.289906 37.842728,-121.274765 37.84272... | 06 | 077 | 005127 | 1400000US06077005127 | Census Tract 51.27 | CA | San Joaquin County | California |
| 2 | 06077003406 | 34.06 | ST_MultiPolygon; srid=4326; points=9; bbox=POLYGON((-121.309004 38.020908,-121.309004 38.028239,-121.29487 38.028239,... | 06 | 077 | 003406 | 1400000US06077003406 | Census Tract 34.06 | CA | San Joaquin County | California |
| 3 | 06077004402 | 44.02 | ST_MultiPolygon; srid=4326; points=12; bbox=POLYGON((-121.273865 38.10113,-121.273865 38.116276,-121.242758 38.116276... | 06 | 077 | 004402 | 1400000US06077004402 | Census Tract 44.02 | CA | San Joaquin County | California |
| 4 | 06077005108 | 51.08 | ST_MultiPolygon; srid=4326; points=14; bbox=POLYGON((-121.228856 37.797395,-121.228856 37.811969,-121.216447 37.81196... | 06 | 077 | 005108 | 1400000US06077005108 | Census Tract 51.08 | CA | San Joaquin County | California |
| 5 | 06077000401 | 4.01 | ST_MultiPolygon; srid=4326; points=10; bbox=POLYGON((-121.31334 37.956222,-121.31334 37.966403,-121.29819 37.966403,-... | 06 | 077 | 000401 | 1400000US06077000401 | Census Tract 4.01 | CA | San Joaquin County | California |
| 6 | 06077003310 | 33.10 | ST_MultiPolygon; srid=4326; points=11; bbox=POLYGON((-121.323174 38.020931,-121.323174 38.028264,-121.304941 38.02826... | 06 | 077 | 003310 | 1400000US06077003310 | Census Tract 33.10 | CA | San Joaquin County | California |
| 7 | 37037020600 | 206 | ST_MultiPolygon; srid=4326; points=304; bbox=POLYGON((-79.402377 35.517434,-79.402377 35.686784,-79.08809 35.686784,-... | 37 | 037 | 020600 | 1400000US37037020600 | Census Tract 206 | NC | Chatham County | North Carolina |
| 8 | 37105030102 | 301.02 | ST_MultiPolygon; srid=4326; points=58; bbox=POLYGON((-79.230631 35.441489,-79.230631 35.512961,-79.189538 35.512961,-... | 37 | 105 | 030102 | 1400000US37105030102 | Census Tract 301.02 | NC | Lee County | North Carolina |
| 9 | 37165010101 | 101.01 | ST_MultiPolygon; srid=4326; points=50; bbox=POLYGON((-79.527903 34.704143,-79.527903 34.746661,-79.462012 34.746661,-... | 37 | 165 | 010101 | 1400000US37165010101 | Census Tract 101.01 | NC | Scotland County | North Carolina |
| 10 | 02050000300 | 3 | ST_MultiPolygon; srid=4326; points=801; bbox=POLYGON((-160.985378 60.49432,-160.985378 62.292644,-153.00164 62.292644... | 02 | 050 | 000300 | 1400000US02050000300 | Census Tract 3 | AK | Bethel Census Area | Alaska |
## 22. `ruca_codes_2020_tract`
Estimated rows: 85,528. Columns: 27. Ordering: `tract_fips_20`.
Showing 12 of 27 columns. Omitted columns include: `urban_area_code_20`, `urban_area_name_20`, `urban_core`, `urban_core_type`, `primary_ruca`, `primary_ruca_description`, `primary_destination_code`, `primary_destination_name`, `secondary_ruca`, `secondary_ruca_description`, `secondary_destination_code`, `secondary_destination_name`, `population`, `land_area`, `pop_density`.
| tract_fips_20 | tract_fips_23 | county_fips_23 | county_code_23 | county_name_23 | tract_code_20 | tract_name_20 | county_fips_20 | county_code_20 | county_name_20 | state_fips_20 | state_name_20 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 01001020100 | 01001020100 | 01001 | 001 | Autauga County | 020100 | Census Tract 201 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020200 | 01001020200 | 01001 | 001 | Autauga County | 020200 | Census Tract 202 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020300 | 01001020300 | 01001 | 001 | Autauga County | 020300 | Census Tract 203 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020400 | 01001020400 | 01001 | 001 | Autauga County | 020400 | Census Tract 204 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020501 | 01001020501 | 01001 | 001 | Autauga County | 020501 | Census Tract 205.01 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020502 | 01001020502 | 01001 | 001 | Autauga County | 020502 | Census Tract 205.02 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020503 | 01001020503 | 01001 | 001 | Autauga County | 020503 | Census Tract 205.03 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020600 | 01001020600 | 01001 | 001 | Autauga County | 020600 | Census Tract 206 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020700 | 01001020700 | 01001 | 001 | Autauga County | 020700 | Census Tract 207 | 01001 | 001 | Autauga County | 01 | Alabama |
| 01001020801 | 01001020801 | 01001 | 001 | Autauga County | 020801 | Census Tract 208.01 | 01001 | 001 | Autauga County | 01 | Alabama |
## 23. `watershed_huc8`
Estimated rows: 2,139. Columns: 12. Ordering: `huc8`.
| huc8 | name | states | areaacres | areasqkm | loaddate | sourceoriginator | sourcedatadesc | sourcefeatureid | metasourceid | tnmid | geom |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 01010002 | Allagash | ME | 790334.93 | 3198.37 | 2012-06-11 | | | | | {C930D1F1-68F0-472A-A4EB-5F73B61330EA} | ST_MultiPolygon; srid=4326; points=15997; bbox=POLYGON((-69.76273221499994 46.2352810790001,-69.76273221499994 47.097... |
| 01010003 | Fish | ME | 569876.46 | 2306.21 | 2018-05-04 | | | | | {AE921AE3-D259-4655-ABA5-67477C88A328} | ST_MultiPolygon; srid=4326; points=20136; bbox=POLYGON((-68.96623380899996 46.683741004000055,-68.96623380899996 47.3... |
| 01010004 | Aroostook | CN,ME | 1561913.71 | 6320.85 | 2012-06-11 | | | | | {17CF1648-B6CE-498E-A826-EC6CB47FB1C9} | ST_MultiPolygon; srid=4326; points=23045; bbox=POLYGON((-69.16016810299996 46.196373031000036,-69.16016810299996 47.1... |
| 01010005 | Meduxnekeag | CN,ME | 328370.43 | 1328.87 | 2012-06-11 | | | | | {530F8058-881E-45F5-9F80-D7027E49B810} | ST_MultiPolygon; srid=4326; points=6476; bbox=POLYGON((-68.13147749799995 45.89917515700006,-68.13147749799995 46.456... |
| 01010006 | Headwaters Saint John River | CN,ME | 774740.34 | 3135.27 | 2018-05-04 | | | | | {23F97AA3-4C4C-438B-8095-AAECBD7B85C9} | ST_MultiPolygon; srid=4326; points=13877; bbox=POLYGON((-70.43220942899998 45.986810322000025,-70.43220942899998 46.8... |
| 01010007 | Big Black River-Saint John River | CN,ME | 950037.23 | 3844.67 | 2018-05-04 | | | | | {0831C9CC-36F8-4251-9A19-414313C5078C} | ST_MultiPolygon; srid=4326; points=13547; bbox=POLYGON((-70.20554245899999 46.53396527800005,-70.20554245899999 47.45... |
| 01010008 | St. Francis River-Saint John River | CN,ME | 1489332.74 | 6027.12 | 2018-05-04 | | | | | {3DAA3FB3-E53A-43B9-A16D-D3B3619D3B02} | ST_MultiPolygon; srid=4326; points=17211; bbox=POLYGON((-69.46546432399995 46.96859477000008,-69.46546432399995 48.09... |
| 01010009 | Little River-Saint John River | CN,ME | 1029296.77 | 4165.42 | 2018-05-04 | | | | | {D4E96931-8FD5-4C44-AF23-C2A3C558A773} | ST_MultiPolygon; srid=4326; points=11052; bbox=POLYGON((-68.52576651599998 46.80976282400012,-68.52576651599998 47.90... |
| 01010010 | Becaguimec Stream-Saint John River | CN,ME | 624456.56 | 2527.09 | 2012-06-11 | | | | | {8F48101E-393E-4960-9A96-6097A52EE9D3} | ST_MultiPolygon; srid=4326; points=6633; bbox=POLYGON((-68.00928863599995 46.12960339600005,-68.00928863599995 46.813... |
| 01010011 | Keswick River-Saint John River | CN,ME | 899412.41 | 3639.8 | 2017-08-10 | | | | | {AAB3BA4E-77CF-4233-B100-94B3DBE345AE} | ST_MultiPolygon; srid=4326; points=5964; bbox=POLYGON((-67.84526302399996 45.7019823910001,-67.84526302399996 46.3038... |
## 24. `nri_census_tracts`
Estimated rows: 85,154. Columns: 467. Ordering: database scan order.
Showing 12 of 467 columns. Omitted columns include: `AGRIVALUE`, `AREA`, `RISK_VALUE`, `RISK_SCORE`, `RISK_RATNG`, `RISK_SPCTL`, `EAL_SCORE`, `EAL_RATNG`, `EAL_SPCTL`, `EAL_VALT`, `EAL_VALB`, `EAL_VALP`, `EAL_VALPE`, `EAL_VALA`, `ALR_VALB`, `ALR_VALP`, `ALR_VALA`, `ALR_NPCTL`, `ALR_VRA_NP`, `SOVI_SCORE`....
| NRI_ID | STATE | STATEABBRV | STATEFIPS | COUNTY | COUNTYTYPE | COUNTYFIPS | STCOFIPS | TRACT | TRACTFIPS | POPULATION | BUILDVALUE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| T01001020100 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020100 | 01001020100 | 1775 | 327304655.0 |
| T01001020400 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020400 | 01001020400 | 4246 | 714630002.0 |
| T01001020200 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020200 | 01001020200 | 2055 | 522608396.0 |
| T01001020300 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020300 | 01001020300 | 3216 | 652996417.0 |
| T01001020501 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020501 | 01001020501 | 4322 | 739207890.0 |
| T01001020502 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020502 | 01001020502 | 3284 | 514565009.0 |
| T01001020503 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020503 | 01001020503 | 3616 | 648328253.0 |
| T01001020600 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020600 | 01001020600 | 3729 | 675852573.0 |
| T01001020700 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020700 | 01001020700 | 3383 | 712676246.0 |
| T01001020801 | Alabama | AL | 01 | Autauga | County | 001 | 01001 | 020801 | 01001020801 | 3143 | 807494674.0 |
## 25. `energy_eia_operating_generator_capacity_flat`
Estimated rows: 4,725,434. Columns: 24. Ordering: database scan order.
Showing 12 of 24 columns. Omitted columns include: `state_name`, `entity_id`, `entity_name`, `sector`, `sector_name`, `energy_source_desc`, `prime_mover_code`, `balancing_authority_code`, `balancing_authority_name`, `net_summer_capacity_mw`, `net_winter_capacity_mw`, `raw_properties`.
| plant_id | generator_id | plant_name | state_id | status | energy_source_code | nameplate_capacity_mw | latitude | longitude | gid | geom | period |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 2 | 1 | Bankhead Dam | AL | OP | WAT | 45.0 | 33.218889 | -87.579722 | 1 | POINT(-87.579722 33.218889) | 2008-01 |
| 3 | 1 | Barry | AL | OP | BIT | 153.1 | 31.004167 | -88.013889 | 2 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | 2 | Barry | AL | OP | BIT | 153.1 | 31.004167 | -88.013889 | 3 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | 3 | Barry | AL | OP | BIT | 272.0 | 31.004167 | -88.013889 | 4 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | 4 | Barry | AL | OP | BIT | 403.7 | 31.004167 | -88.013889 | 5 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | 5 | Barry | AL | OP | BIT | 788.8 | 31.004167 | -88.013889 | 6 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | A1CT | Barry | AL | OP | NG | 170.1 | 31.004167 | -88.013889 | 7 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | A1ST | Barry | AL | OP | NG | 195.2 | 31.004167 | -88.013889 | 8 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | A2C1 | Barry | AL | OP | NG | 170.1 | 31.004167 | -88.013889 | 9 | POINT(-88.013889 31.004167) | 2008-01 |
| 3 | A2C2 | Barry | AL | OP | NG | 170.1 | 31.004167 | -88.013889 | 10 | POINT(-88.013889 31.004167) | 2008-01 |
## 26. `energy_eia_facility_fuel_flat`
_No matching table found in `public` for `public.energy_eia_facility_fuel_flat`._
## 27. `energy_eia_seds_flat`
Estimated rows: 2,575,437. Columns: 10. Ordering: database scan order.
| gid | period | year | series_id | series_description | state_id | state_name | value | unit | raw_properties |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | AK | Alaska | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 2 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | AL | Alabama | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 3 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | AR | Arkansas | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 4 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | AZ | Arizona | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 5 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | CA | California | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 6 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | CO | Colorado | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 7 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | CT | Connecticut | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 8 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | DC | District of Columbia | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 9 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | DE | Delaware | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
| 10 | 1960 | 1960 | ABICB | Aviation gasoline blending components consumed by the industrial sector | FL | Florida | 0.0 | Billion Btu | {"period": "1960", "seriesDescription": "Aviation gasoline blending components consumed by the industrial sector", "s... |
## 28. `internet_cables`
Estimated rows: 693. Columns: 14. Ordering: `feature_id`.
Showing 12 of 14 columns. Omitted columns include: `properties`, `geom`.
| feature_id | name | cable_id | color | owners | rfs_year | decommission_year | length_raw | length_km | cable_type | url | extra_urls |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 2africa-0 | 2Africa | 2africa | #939597 | Bayobab, China Mobile, Meta, Orange, Telecom Egypt, Vodafone, WIOCC, center3 | 2024 | | 45,000 km | 45000 | | https://www.2africacable.net/ | ["https://www.submarinenetworks.com/en/systems/asia-europe-africa/2africa"] |
| 2africa-1 | 2Africa | 2africa | #b5258f | Bayobab, China Mobile, Meta, Orange, Telecom Egypt, Vodafone, WIOCC, center3 | 2024 | | 45,000 km | 45000 | | https://www.2africacable.net/ | ["https://www.submarinenetworks.com/en/systems/asia-europe-africa/2africa"] |
| 5-villages-6-islands-0 | 5 Villages 6 Islands | 5-villages-6-islands | #694099 | Tokyo Metropolitan Government | 2019 | | 355 km | 355 | | | [] |
| acs-alaska-oregon-network-akorn-0 | ACS Alaska-Oregon Network (AKORN) | acs-alaska-oregon-network-akorn | #4c4fa1 | Alaska Communications | 2009 | | 3,000 km | 3000 | | https://www.alaskacommunications.com | [] |
| aden-djibouti-0 | Aden-Djibouti | aden-djibouti | #08acdb | Djibouti Telecom, Orange, Sparkle, Tata Communications, TeleYemen | 1994 | | 269 km | 269 | | https://www.teleyemen.com.ye/ | [] |
| adria-1-0 | Adria-1 | adria-1 | #65b545 | ALBtelecom, Hrvatski Telekom | 1996 | | 440 km | 440 | | | [] |
| aec-1-0 | AEC-1 | aec-1 | #923c96 | EXA Infrastructure | 2016 | | 5,521 km | 5521 | | https://exainfra.net/ | ["https://www.submarinenetworks.com/en/systems/trans-atlantic/aec"] |
| africa-1-0 | Africa-1 | africa-1 | #939597 | G42, Mobily, Pakistan Telecommunications Company Ltd., TeleYemen, Telecom Egypt, Zain Omantel International, e& | 2026 | | 10,000 km | 10000 | | | ["https://www.submarinenetworks.com/en/systems/asia-europe-africa/africa-1"] |
| africa-coast-to-europe-ace-0 | Africa Coast to Europe (ACE) | africa-coast-to-europe-ace | #8cc63f | Bayobab, Cable Consortium of Liberia, Canalink, Dolphin Telecom, GUILAB, Gambia Submarine Cable Company, Internationa... | 2012 | | 17,000 km | 17000 | | | ["https://www.submarinenetworks.com/en/systems/euro-africa/ace"] |
| airraq-0 | Airraq | airraq | #bc3e96 | Unicom, Inc. | 2025 | | 680 km | 680 | | https://www.gci.com/ | [] |
## 29. `internet_cable_landing_points`
Estimated rows: 3,361. Columns: 6. Ordering: `feature_id`, `ordinal`.
| feature_id | ordinal | landing_id | name | country | is_tbd |
| --- | --- | --- | --- | --- | --- |
| 2africa-0 | 0 | luanda-angola | Luanda, Angola | Angola | |
| 2africa-0 | 1 | manama-bahrain | Manama, Bahrain | Bahrain | |
| 2africa-0 | 2 | moroni-comoros | Moroni, Comoros | Comoros | |
| 2africa-0 | 3 | muanda-congo-dem-rep- | Muanda, Congo, Dem. Rep. | Congo, Dem. Rep. | |
| 2africa-0 | 4 | pointe-noire-congo-rep- | Pointe-Noire, Congo, Rep. | Congo, Rep. | |
| 2africa-0 | 5 | abidjan-cte-divoire | Abidjan, Côte d'Ivoire | Côte d'Ivoire | |
| 2africa-0 | 6 | djibouti-city-djibouti | Djibouti City, Djibouti | Djibouti | |
| 2africa-0 | 7 | port-said-egypt | Port Said, Egypt | Egypt | |
| 2africa-0 | 8 | ras-ghareb-egypt | Ras Ghareb, Egypt | Egypt | |
| 2africa-0 | 9 | suez-egypt | Suez, Egypt | Egypt | |
## 30. `internet_city_dominance`
Estimated rows: 4,552. Columns: 13. Ordering: `id`.
Showing 12 of 13 columns. Omitted columns include: `geom`.
| id | city | country | latitude | longitude | country_name | region | status | physical_capacity_tbps | added_physical_capacity_tbps | logical_dominance_ips | top_asns |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| aachen-de | Aachen | DE | 50.7756 | 6.0836 | Germany | Europe | existing | 0 | 0 | 97803 | [{"asn": "47610", "ip_count": "65792", "name": "RWTH-AS"}, {"asn": "34953", "ip_count": "8448", "name": "RelAix Netwo... |
| aalborg-dk | Aalborg | DK | 57.05 | 9.9167 | Denmark | Europe | existing | 0 | 0 | 1074688 | [{"asn": "9158", "ip_count": "914432", "name": "Telenor Denmark"}, {"asn": "6834", "ip_count": "90624", "name": "KMD ... |
| aalen-de | Aalen | DE | 48.8333 | 10.1 | Germany | Europe | existing | 0 | 0 | 96512 | [{"asn": "553", "ip_count": "65536", "name": "BelW\u00fc"}, {"asn": "6735", "ip_count": "24576", "name": "TNG Stadtne... |
| aalsmeer-nl | Aalsmeer | NL | 52.2667 | 4.75 | Netherlands | Europe | existing | 0 | 0 | 50633 | [{"asn": "1136", "ip_count": "32768", "name": "KPN NL"}, {"asn": "44074", "ip_count": "16384", "name": "RFH-AS"}, {"a... |
| aalst-be | Aalst | BE | 50.9383 | 4.0392 | Belgium | Europe | existing | 0 | 0 | 263680 | [{"asn": "5432", "ip_count": "262144", "name": "Proximus"}, {"asn": "204959", "ip_count": "1024", "name": "ONTEX"}, {... |
| aarau-ch | Aarau | CH | 47.4 | 8.05 | Switzerland | Europe | existing | 0 | 0 | 54272 | [{"asn": "33965", "ip_count": "17920", "name": "Litecom AG"}, {"asn": "6730", "ip_count": "17152", "name": "Sunrise G... |
| aarhus-dk | Aarhus | DK | 56.1572 | 10.2107 | Denmark | Europe | existing | 0 | 0 | 70144 | [{"asn": "44869", "ip_count": "65536", "name": "Fibia"}, {"asn": "3292", "ip_count": "1024", "name": "TDC A/S"}, {"as... |
| abakan-ru | Abakan | RU | 53.7167 | 91.4667 | Russia | Europe | existing | 0 | 0 | 82432 | [{"asn": "12389", "ip_count": "73984", "name": "Rostelecom"}, {"asn": "51612", "ip_count": "3072", "name": "ALFATEL-A... |
| aba-ng | Aba | NG | 5.1167 | 7.3667 | Nigeria | Africa | existing | 0 | 0 | 33280 | [{"asn": "36873", "ip_count": "32768", "name": "Airtel Networks Nigeria"}, {"asn": "29465", "ip_count": "512", "name"... |
| abbotsford-ca | Abbotsford | CA | 49.05 | -122.3167 | Canada | North America | existing | 0 | 0 | 59648 | [{"asn": "6327", "ip_count": "58880", "name": "Shaw Cablesystems"}, {"asn": "19662", "ip_count": "512", "name": "Unis... |
## 31. `fcc_bdc_location_provider_aggregates`
Sampled actual table: `public.fcc_bdc_location_provider_aggregate`.
Estimated rows: 998. Columns: 17. Ordering: `as_of_date`, `geography_type`, `geoid`.
Showing 12 of 17 columns. Omitted columns include: `copper_provider_count`, `business_fiber_provider_count`, `business_100_20_provider_count`, `provider_file_rows`, `updated_at`.
| as_of_date | geography_type | geoid | provider_count | fiber_provider_count | cable_provider_count | fixed_wireless_provider_count | provider_100_20_count | business_provider_count | max_advertised_download_mbps | max_advertised_upload_mbps | matched_location_rows |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 2025-12-31 | County | 01069 | 11 | 8 | 4 | 4 | 11 | 11 | 100000.0 | 100000.0 | 273014 |
| 2025-12-31 | County | 01071 | 9 | 7 | 2 | 4 | 9 | 9 | 10000.0 | 10000.0 | 88104 |
| 2025-12-31 | County | 01073 | 16 | 11 | 2 | 4 | 15 | 15 | 100000.0 | 100000.0 | 1123613 |
| 2025-12-31 | County | 01089 | 15 | 12 | 4 | 4 | 14 | 15 | 100000.0 | 100000.0 | 810226 |
| 2025-12-31 | County | 04003 | 15 | 5 | 2 | 12 | 12 | 14 | 2300.0 | 2300.0 | 280879 |
| 2025-12-31 | County | 04013 | 42 | 25 | 5 | 21 | 40 | 38 | 1000000.0 | 1000000.0 | 8603178 |
| 2025-12-31 | County | 04019 | 28 | 9 | 3 | 18 | 24 | 23 | 100000.0 | 100000.0 | 2323405 |
| 2025-12-31 | County | 05119 | 20 | 11 | 5 | 6 | 19 | 19 | 100000.0 | 100000.0 | 694836 |
| 2025-12-31 | County | 05145 | 20 | 11 | 5 | 10 | 17 | 19 | 10000.0 | 10000.0 | 124592 |
| 2025-12-31 | County | 06001 | 25 | 11 | 1 | 16 | 24 | 23 | 100000.0 | 100000.0 | 2476379 |
## 32. `fcc_bdc_broadband_connection_table`
Sampled actual table: `public.data_center_broadband_connection`.
Estimated rows: 1,833. Columns: 48. Ordering: `master_id`.
Showing 12 of 48 columns. Omitted columns include: `source`, `operator`, `city`, `country`, `longitude`, `latitude`, `geom`, `fcc_bdc_as_of_date`, `fcc_bdc_geography_type`, `fcc_bdc_geoid`, `fcc_provider_count`, `fcc_fiber_provider_count`, `fcc_cable_provider_count`, `fcc_fixed_wireless_provider_count`, `fcc_max_advertised_download_mbps`, `fcc_max_advertised_upload_mbps`, `fcc_100_20_provider_count`, `fcc_summary_json`, `fetched_at`, `updated_at`....
| master_id | name | state | census_tract_geoid | census_broadband_subscription_pct | fcc_bdc_status | fcc_provider_geography_type | fcc_county_provider_count | fcc_tract_provider_count | fcc_tract_fiber_provider_count | fcc_tract_business_provider_count | fcc_tract_max_advertised_download_mbps |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| curated/0002744301 | Verizon | NJ | 34023000702 | 95.6 | fcc_location_provider_joined | Tract | 13 | 7 | 4 | 7 | 10000.0 |
| curated/0007805491 | Discover Financial Services New Albany | OH | 39049007205 | 98.9 | fcc_location_provider_joined | Tract | 26 | 5 | 2 | 5 | 5000.0 |
| curated/0009474864 | Google Data Center | NC | 37027030300 | 80.8 | fcc_location_provider_joined | Tract | 10 | 8 | 4 | 8 | 5000.0 |
| curated/0013924557 | Project Alluvion | IA | 19153011028 | 94.9 | fcc_location_provider_joined | Tract | 26 | 11 | 7 | 9 | 10000.0 |
| curated/0014271367e0e039e1b1b386315f6581 | Newton (Atlanta) Data Center | GA | 13297110801 | 87.7 | fcc_location_provider_joined | Tract | 9 | 6 | 4 | 6 | 5000.0 |
| curated/0014593270 | Apple - Maiden Data Center | NC | 37035011702 | 79.5 | fcc_location_provider_joined | Tract | 15 | 7 | 2 | 7 | 5000.0 |
| curated/0014930068 | HPC (880) | NM | 35001980000 | 96.0 | fcc_location_provider_joined | Tract | 21 | 9 | 0 | 8 | 2000.0 |
| curated/0014997588 | Meta Henrico Data Center | VA | 51087201404 | 90.3 | fcc_location_provider_joined | Tract | 7 | 5 | 3 | 5 | 16000.0 |
| curated/0015884451 | Barclays Datacenter | NJ | 34039037300 | 93.0 | fcc_location_provider_joined | Tract | 8 | 5 | 3 | 5 | 10000.0 |
| curated/0016282459 | CyberNAP Glen Burnie | MD | 24003730404 | 91.8 | fcc_location_provider_joined | Tract | 15 | 6 | 2 | 6 | 2300.0 |
## 33. `opposition_cases_geocoded`
Estimated rows: 18. Columns: 15. Ordering: database scan order.
Showing 12 of 15 columns. Omitted columns include: `opposition_type`, `data_source`, `geom`.
| id | state | location | state_id | lat | lon | investment_billion | status | developer | commons_type | governance_response | outcome |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | AZ | Goodyear-Buckeye | 4 | 33.45 | -112.39 | 14.0 | Blocked | Tract | Water+Grid | Zoning denial | Withdrawn then relocated |
| 2 | MO | Peculiar | 29 | 38.72 | -94.46 | 1.5 | Blocked | Diode Ventures | Land+Water | Zoning amendment | Prohibited by ordinance |
| 3 | IN | Chesterton | 18 | 41.61 | -87.06 | 1.3 | Blocked | Provident Realty | Water+Air+Wildlife | Council withdrawal | Developer withdrew |
| 4 | VA | Richmond/Henrico | 51 | 37.51 | -77.33 | 0.5 | Blocked | DC Blox | Noise+Aesthetics | Deferral then withdrawal | Developer withdrew; revised smaller |
| 5 | VA | Catlett Station/Fauquier | 51 | 38.65 | -77.64 | 0.4 | Blocked | Headwaters Dev | Noise+Water+Power+Environment | Pre-hearing withdrawal | Developer withdrew before hearing |
| 6 | OR | Cascade Locks | 41 | 45.67 | -121.87 | 0.1 | Blocked | Roundhouse Digital | Grid rates+Credibility | Recall election | Officials recalled; Port Authority stopped |
| 7 | VA | Prince William | 51 | 38.7 | -77.48 | 24.7 | Delayed | QTS+Compass | Environment+Noise+Grid+Historic | Litigation | 3 lawsuits active; one dismissed+appealed |
| 8 | VA | Culpeper | 51 | 38.47 | -77.99 | 12.0 | Delayed | Culpeper Acquisitions | Land preservation+Historic | Planning denial | Unanimous denial; Board deferred |
| 9 | VA | King George | 51 | 38.27 | -77.15 | 6.0 | Delayed | Amazon | Infrastructure+Resources | Political reversal | Renegotiation; rezoning reconsidered |
| 10 | VA | Midlothian/Powhatan | 51 | 37.44 | -77.81 | 3.0 | Delayed | Province Group | Noise+Traffic+Environment | Deferred then approved | Approved 3-2 despite opposition; utility delay |
## 34. `census_tract_huc8_link`
Estimated rows: 806. Columns: 5. Ordering: database scan order.
| geoid | huc8 | overlap_sqm | overlap_sqkm | tract_overlap_pct |
| --- | --- | --- | --- | --- |
| 26163581600 | 04090004 | 4049402.7790175527 | 4.049402779017552 | 0.9999999999919172 |
| 18097352300 | 05120201 | 2405395.5570320487 | 2.405395557032049 | 1.0000000000003517 |
| 36029009200 | 04120104 | 5329322.178747755 | 5.329322178747755 | 1.0000000000042821 |
| 04013113100 | 15060106 | 1304588.2283894438 | 1.3045882283894439 | 1.0000000000020903 |
| 06085505007 | 18050003 | 3029721.5568704605 | 3.0297215568704603 | 1.0000000000049134 |
| 04013422514 | 15050100 | 1122663.8417252041 | 1.1226638417252042 | 0.45491211388156216 |
| 04013422514 | 15060106 | 1345205.8969148104 | 1.3452058969148104 | 0.5450878842156984 |
| 51059460503 | 02070008 | 32892.9460603483 | 0.0328929460603483 | 0.022607669336869043 |
| 51059460503 | 02070010 | 1422053.3794437945 | 1.4220533794437944 | 0.9773923114961641 |
| 12057011611 | 03100206 | 5641554.468745705 | 5.641554468745705 | 1.0000000000018656 |
## 35. `im3_state_projected_moderate_50`
Estimated rows: 328. Columns: 19. Ordering: database scan order.
Showing 12 of 19 columns. Omitted columns include: `water_cooling_frac`, `cooling_energy_demand_mwh`, `cooling_water_demand_mgy`, `cooling_water_consumption_mgy`, `normalized_locational_cost`, `normalized_gravity_score`, `weighted_siting_score`.
| id | state_code | latitude | longitude | growth_scenario | market_gravity_weight | region | state_id | total_cost_million_usd | campus_size_square_ft | data_center_it_power_mw | mechanical_cooling_frac |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1_0 | AL | | | moderate | 50 | alabama | 1 | 522.04 | 1000000 | 36 | 0.0 |
| 1_1 | AL | | | moderate | 50 | alabama | 1 | 501.9 | 1000000 | 36 | 0.0 |
| 1_2 | AL | | | moderate | 50 | alabama | 1 | 526.54 | 1000000 | 36 | 0.0 |
| 4_0 | AZ | | | moderate | 50 | arizona | 4 | 499.47 | 1000000 | 36 | 0.0 |
| 4_1 | AZ | | | moderate | 50 | arizona | 4 | 499.47 | 1000000 | 36 | 0.0 |
| 4_10 | AZ | | | moderate | 50 | arizona | 4 | 517.41 | 1000000 | 36 | 1.0 |
| 4_11 | AZ | | | moderate | 50 | arizona | 4 | 517.41 | 1000000 | 36 | 1.0 |
| 4_12 | AZ | | | moderate | 50 | arizona | 4 | 515.52 | 1000000 | 36 | 0.75 |
| 4_13 | AZ | | | moderate | 50 | arizona | 4 | 515.52 | 1000000 | 36 | 0.75 |
| 4_2 | AZ | | | moderate | 50 | arizona | 4 | 499.47 | 1000000 | 36 | 0.0 |
## 36. `im3_projected_state_demand_summary`
Estimated rows: 31. Columns: 13. Ordering: database scan order.
Showing 12 of 13 columns. Omitted columns include: `market_gravity_weight`.
| state | state_id | projected_facility_count | total_cost_million_usd | total_it_power_mw | total_campus_area_sqft | total_cooling_energy_demand_mwh | total_cooling_water_demand_mgy | total_cooling_water_consumption_mgy | avg_mechanical_cooling_fraction | avg_water_cooling_fraction | scenario |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ALABAMA | 1 | 3 | 1550.48 | 108.0 | 3000000 | 0.0 | 130.56 | 104.45 | 0.0 | 1.0 | moderate |
| ARIZONA | 4 | 14 | 7134.93 | 504.0 | 14000000 | 685908.0 | 293.76 | 235.01 | 0.518 | 0.482 | moderate |
| CALIFORNIA | 6 | 21 | 10882.16 | 756.0 | 21000000 | 0.0 | 913.91 | 731.13 | 0.0 | 1.0 | moderate |
| COLORADO | 8 | 3 | 1502.28 | 108.0 | 3000000 | 141912.0 | 65.28 | 52.22 | 0.5 | 0.5 | moderate |
| FLORIDA | 12 | 3 | 1499.31 | 108.0 | 3000000 | 283824.0 | 0.0 | 0.0 | 1.0 | 0.0 | moderate |
| GEORGIA | 13 | 14 | 7094.73 | 504.0 | 14000000 | 0.0 | 609.28 | 487.42 | 0.0 | 1.0 | moderate |
| ILLINOIS | 17 | 16 | 7940.21 | 576.0 | 16000000 | 0.0 | 696.31 | 557.05 | 0.0 | 1.0 | moderate |
| IOWA | 19 | 14 | 6884.15 | 504.0 | 14000000 | 0.0 | 609.28 | 487.42 | 0.0 | 1.0 | moderate |
| KENTUCKY | 21 | 3 | 1471.14 | 108.0 | 3000000 | 0.0 | 130.56 | 104.45 | 0.0 | 1.0 | moderate |
| MASSACHUSETTS | 25 | 2 | 994.64 | 72.0 | 2000000 | 0.0 | 87.04 | 69.63 | 0.0 | 1.0 | moderate |
## 37. `utility_rate_tracker_2025_2028`
Estimated rows: 374. Columns: 14. Ordering: database scan order.
Showing 12 of 14 columns. Omitted columns include: `status`, `source_file`.
| state_code | utility_provider | state_name | state_id | service_type | customer_count | total_revenue_increase_2025_2028 | time_period | monthly_increase_amount | monthly_pct_increase_ratio | effective_date | effective_date_raw |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| IN | AES Indiana | Indiana | 18 | Electricity | 530802.0 | 201979178.0 | Increase in annual revenue | 10.0 | 0.0335 | 2026-06-01T00:00:00 | 2026-06-01 00:00:00 |
| OH | AES Ohio | Ohio | 39 | Electricity | 540836.0 | 529465023.41 | Increase in annual revenue | 15.28 | 0.09 | 2025-11-06T00:00:00 | 2025-11-06 00:00:00 |
| OH | AES Ohio | Ohio | 39 | Electricity | 540836.0 | 211350000.0 | January 1, 2027 - December 31, 2029 | 11.47 | 0.0613 | 2027-01-01T00:00:00 | 2027-01-01 00:00:00 |
| AL | Alabama Power Company | Alabama | 01 | Electricity | 1546483.0 | | | 3.32 | | 2028-01-01T00:00:00 | 2028 |
| IL | Ameren Illinois | Illinois | 17 | Electricity | 1224108.0 | 673366000.0 | January 1, 2025 - December 31, 2027 | | | 2025-01-01T00:00:00 | 2025-01-01 00:00:00 |
| IL | Ameren Illinois | Illinois | 17 | Natural Gas | 802310.0 | 218844000.0 | Increase in annual revenue | | | 2026-01-01T00:00:00 | 2026-01-01 00:00:00 |
| MO | Ameren Missouri | Missouri | 29 | Electricity | 1264469.0 | 1273136986.0 | Increase in annual revenue | | | 2025-06-01T00:00:00 | 2025-06-01 00:00:00 |
| MO | Ameren Missouri | Missouri | 29 | Natural Gas | 136274.0 | 105028767.0 | Increase in annual revenue | 13.0 | 0.12 | 2025-09-01T00:00:00 | 2025-09-01 00:00:00 |
| TN | Appalachian Power | Tennessee | 47 | Electricity | 49489.0 | 13006129.0 | September 2025 - August 2027 | 2.99 | | 2025-09-01T00:00:00 | 2025-09-01 00:00:00 |
| WV | Appalachian Power | West Virginia | 54 | Electricity | 418138.0 | 247689863.0 | Increase in annual revenue | | | 2025-09-29T00:00:00 | 2025-09-29 00:00:00 |
## 38. `energy_atlas_layers_catalog`
Estimated rows: 0. Columns: 8. Ordering: `table_name`.
| table_name | source_item_id | source_type | source_title | source_owner | source_url | category | imported_at |
| --- | --- | --- | --- | --- | --- | --- | --- |
| energy_eia_electricity_operating_generator_capacity | electricity_operating-generator-capacity | EIA API | operating-generator-capacity | U.S. Energy Information Administration | https://api.eia.gov/v2/electricity/operating-generator-capacity | power_plants | 2026-05-25T10:49:29.577662+00:00 |
| energy_eia_seds | seds | EIA API | seds | U.S. Energy Information Administration | https://api.eia.gov/v2/seds | state_energy | 2026-05-25T11:16:25.100722+00:00 |
## 39. `legiscan_sessions`
Estimated rows: 646. Columns: 14. Ordering: `session_id`.
Showing 12 of 14 columns. Omitted columns include: `bill_count`, `imported_at`.
| session_id | state_id | state_abbr | year_start | year_end | session_title | session_tag | is_special | is_prior | dataset_hash | dataset_date | dataset_size_mb |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1161 | 17 | KY | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | b4e38f344e60236669dbdd690d13af16 | 2021-12-31 | 2.83556 |
| 1164 | 41 | SD | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | 0a8b763b54e5dd488be5a1b3fb734baa | 2021-12-31 | 1.935084 |
| 1168 | 48 | WV | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | 6f937790ca345729d2b96dbd0b7b56d8 | 2021-12-31 | 4.768601 |
| 1169 | 9 | FL | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | 6459a5c03d80799db88fc81f57fcc7e2 | 2021-12-31 | 4.00847 |
| 1170 | 34 | ND | 2017 | 2018 | 2017-2018 Regular Session | Regular Session | False | True | 93c768b8ca89afcc365010abd0ceeb2c | 2021-12-31 | 2.815085 |
| 1171 | 36 | OK | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | b93a03306038b9fbebe2ae3db73a6254 | 2021-12-31 | 9.760202 |
| 1172 | 1 | AL | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | e800c0b0cdea4446e6a9ef36040ac9ba | 2021-12-31 | 3.003159 |
| 1180 | 44 | UT | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | 7b12b1a2effde90d73d81bd62e2d36c6 | 2021-12-31 | 3.234579 |
| 1187 | 46 | VA | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | 48a324f85c12c45e250a3ac0ed4d1959 | 2021-12-31 | 10.107357 |
| 1188 | 3 | AZ | 2016 | 2016 | 2016 Regular Session | Regular Session | False | True | 748c6048c5e630c61254b2823e3007b4 | 2021-12-31 | 4.386376 |
## 40. `legiscan_bills`
Estimated rows: 1,263,868. Columns: 21. Ordering: `bill_id`.
Showing 12 of 21 columns. Omitted columns include: `session_id`, `description`, `state_link`, `change_hash`, `subjects`, `sponsor_count`, `vote_count`, `text_count`, `imported_at`.
| bill_id | state | bill_number | bill_type | title | status | status_date | completed | body | is_relevant | relevance_tags | url |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 777770 | KY | HB11 | B | AN ACT relating to service animals. | 1 | 2016-01-05 | 0 | H | False | | https://legiscan.com/KY/bill/HB11/2016 |
| 780574 | KY | HB70 | B | AN ACT proposing an amendment to Section 145 of the Constitution of Kentucky relating to persons entitled to vote. | 2 | 2016-02-05 | 0 | H | False | | https://legiscan.com/KY/bill/HB70/2016 |
| 780667 | KY | HB40 | B | AN ACT relating to criminal records. | 4 | 2016-04-12 | 1 | H | False | | https://legiscan.com/KY/bill/HB40/2016 |
| 783555 | KY | HB12 | B | AN ACT relating to problem, compulsive, or pathological gambling and making an appropriation therefor. | 1 | 2016-01-05 | 0 | H | False | | https://legiscan.com/KY/bill/HB12/2016 |
| 783597 | KY | SB11 | B | AN ACT relating to alcoholic beverages. | 4 | 2016-04-09 | 1 | S | False | | https://legiscan.com/KY/bill/SB11/2016 |
| 784891 | KY | SB12 | B | AN ACT relating to the extended hours supplemental license. | 2 | 2016-01-15 | 0 | S | False | | https://legiscan.com/KY/bill/SB12/2016 |
| 784982 | KY | HB13 | B | AN ACT relating to driving under the influence and declaring an emergency. | 1 | 2016-01-05 | 0 | H | False | | https://legiscan.com/KY/bill/HB13/2016 |
| 786510 | KY | SR13 | R | Adjourn the Senate in honor and loving memory of Colonel Walker Russell "Russ" Reynolds. | 4 | 2016-04-15 | 0 | S | False | | https://legiscan.com/KY/bill/SR13/2016 |
| 786515 | KY | SR12 | R | Adjourn in honor and loving memory of Mary Alice Willett Higdon. | 4 | 2016-04-15 | 0 | S | False | | https://legiscan.com/KY/bill/SR12/2016 |
| 786520 | KY | SR11 | R | A RESOLUTION honoring Wilbur M. Zevely upon his recognition by the Northern Kentucky Bar Foundation with its Lifetime... | 0 | | 0 | S | False | | https://legiscan.com/KY/bill/SR11/2016 |

233
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@@ -13,12 +13,13 @@
## Table Organization ## Table Organization
Tables are organized into five categories: Tables are organized into six categories:
1. **Core Data Center Tables** - Master inventories and source data 1. **Core Data Center Tables** - Master inventories and source data
2. **Enrichment Tables** - Data centers joined with contextual data 2. **Enrichment Tables** - Data centers joined with contextual data
3. **Base Layer Tables** - Geographic and demographic reference layers 3. **Environmental and Election Source Tables** - Long-form climate, drought, fire/smoke, and precinct-election source layers
4. **Infrastructure Tables** - Energy and connectivity infrastructure 4. **Base Layer Tables** - Geographic and demographic reference layers
5. **Legislation Tables** - LegiScan state and federal bill data (2016-2026) 5. **Infrastructure Tables** - Energy and connectivity infrastructure
6. **Legislation Tables** - LegiScan state and federal bill data (2016-2026)
--- ---
@@ -147,20 +148,224 @@ Tables are organized into five categories:
**Source**: FEMA National Risk Index (December 2025 release) **Source**: FEMA National Risk Index (December 2025 release)
### `data_center_rdh_precinct_vote_matches` ### `data_center_historical_climate`
**Rows**: Varies **Rows**: 1,833
**Purpose**: Per-facility precinct-level election results **Purpose**: One-row-per-facility historical climate summary for data center locations
**Key Columns**: **Key Columns**:
- Data center identifiers - `master_id` (TEXT) - FK to `master_data_centers`
- `precinct_name`, `precinct_id` - `source`, `name`, `operator`, `city`, `state`, `country`
- `latitude`, `longitude`, `geom`
- `daymet_dataset_version`, `gridmet_dataset_version`
- `climate_period_start`, `climate_period_end` - Current period: 1991-01-01 to 2020-12-31
- **Temperature**: `mean_annual_temperature_c`, `mean_summer_temperature_c`, `max_daily_temperature_c`, `min_daily_temperature_c`
- **Humidity / wet bulb**: `mean_relative_humidity_pct`, `mean_wet_bulb_temperature_c`, `max_wet_bulb_temperature_c`, `extreme_wet_bulb_days`
- **Cooling / heat**: `cooling_degree_days_c`, `annual_cooling_degree_days_c_mean`, `extreme_heat_days`, `annual_extreme_heat_days_mean`
- **Precipitation**: `precipitation_total_mm`, `annual_precipitation_mm_mean`, `annual_precipitation_cv`, `wet_day_precipitation_p95_mm`
- **Wind**: `mean_wind_speed_ms`, `max_daily_mean_wind_speed_ms`, `sustained_wind_days`, `annual_sustained_wind_days_mean`
**Source**: Daymet + gridMET historical climate data
**Notes**: Built by `historical_climate_data_centers.ipynb` / `open_meteo_historical_data_centers.ipynb`
### `data_center_usdm_drought_exposure`
**Rows**: 1,833
**Purpose**: Per-facility drought exposure summary from weekly U.S. Drought Monitor polygons
**Key Columns**:
- `master_id` (TEXT) - FK to `master_data_centers`
- `source`, `name`, `operator`, `city`, `state`, `country`
- `latitude`, `longitude`, `geom`
- `usdm_status` - `covered` or `no_coverage`
- `drought_period_start`, `drought_period_end` - Current period: 2000-01-04 to 2025-12-30
- `weeks_observed`
- `weeks_in_d0_or_worse`, `weeks_in_d1_or_worse`, `weeks_in_d2_or_worse`, `weeks_in_d3_or_worse`, `weeks_in_d4`
- `pct_weeks_in_d0_or_worse`, `pct_weeks_in_d1_or_worse`, `pct_weeks_in_d2_or_worse`, `pct_weeks_in_d3_or_worse`, `pct_weeks_in_d4`
- `worst_dm_category`, `mean_dm_category`
- `longest_d0_streak_weeks`, `longest_d2_streak_weeks`, `longest_d3_streak_weeks`
**Source**: U.S. Drought Monitor weekly spatial data
**Notes**:
- Summary table is rolled up from `data_center_usdm_drought_dc_week`
- `dm_category` scale: D0-D4, stored as 0-4
- 1,830 facilities have covered status; 3 have no coverage
### `data_center_hms_smoke_exposure`
**Rows**: 1,833
**Purpose**: Per-facility wildfire-smoke exposure summary from NOAA HMS smoke polygons
**Key Columns**:
- `master_id` (TEXT) - FK to `master_data_centers`
- `source`, `name`, `operator`, `city`, `state`, `country`
- `latitude`, `longitude`, `geom`
- `hms_status`
- `smoke_period_start`, `smoke_period_end` - Current period: 2005-08-05 to 2026-05-22
- `days_observed`
- `days_with_any_smoke`, `days_with_light_or_worse`, `days_with_medium_or_worse`, `days_with_heavy_smoke`
- `pct_days_with_any_smoke`, `pct_days_with_light_or_worse`, `pct_days_with_medium_or_worse`, `pct_days_with_heavy_smoke`
- `worst_density_rank`, `worst_density`, `mean_density_rank`
- `longest_any_smoke_streak_days`, `longest_medium_or_heavy_streak_days`, `longest_heavy_smoke_streak_days`
**Source**: NOAA Hazard Mapping System (HMS) smoke polygons
**Notes**:
- Summary table is rolled up from `data_center_hms_smoke_dc_day`
- Density rank: 0 = observed no smoke, 1 = Light, 2 = Medium, 3 = Heavy
- HMS product path uses NOAA's `/FIRE/web/HMS/Smoke_Polygons/` archive
### `data_center_election_context`
**Rows**: 1,833
**Purpose**: Standardized one-row-per-facility election context derived from RDH precinct matches
**Key Columns**:
- `master_id` (TEXT) - FK to `master_data_centers`
- `name`, `city`, `state`
- `rdh_layer_title`
- `precinct_identifier_name`
- `election_year`, `office` - `election_year`, `office`
- `candidate`, `party`, `votes` - `democratic_votes`, `republican_votes`, `total_votes`
- `vote_share_pct` - `turnout_or_vote_share`
- `updated_at`
**Source**: Redistricting Data Hub precinct election shapefiles
**Notes**:
- Built from `data_center_rdh_precinct_vote_matches` plus RDH feature properties
- Current rows cover 2020-2024 election layers; 1,829 facilities have non-null election year context
### `data_center_rdh_precinct_vote_matches`
**Rows**: 3,330
**Purpose**: Spatial join bridge between data centers and RDH precinct vote features
**Key Columns**:
- `master_id` (TEXT) - FK to `master_data_centers`
- `feature_id` (TEXT) - FK to `rdh_precinct_vote_features`
- `layer_id` (TEXT) - FK to `rdh_precinct_vote_layers`
- `state_code`
- `join_method`
- `match_distance_m`
- `matched_at`
**Source**: Redistricting Data Hub precinct shapefiles **Source**: Redistricting Data Hub precinct shapefiles
**Notes**: Spatial join to voting precincts (point-in-polygon) **Notes**: Spatial join to voting precincts (point-in-polygon, with nearest/fallback logic where needed)
---
## Environmental and Election Source Tables
### `usdm_drought_weekly`
**Rows**: 12,080
**Purpose**: Raw weekly U.S. Drought Monitor polygons by drought category
**Key Columns**:
- `id` (BIGINT) - Primary key
- `week_date` (DATE)
- `dm_category` (SMALLINT) - Drought Monitor category D0-D4 stored as 0-4
- `objectid`, `shape_leng`, `shape_area`
- `geom` (GEOMETRY) - Drought polygon geometry
**Source**: U.S. Drought Monitor spatial archive
**Notes**: Source table for `data_center_usdm_drought_dc_week`
### `data_center_usdm_drought_dc_week`
**Rows**: ~2.48 million
**Purpose**: Long-form weekly drought exposure for each covered data center
**Key Columns**:
- `master_id` (TEXT) - FK to `master_data_centers`
- `week_date` (DATE)
- `worst_dm` (SMALLINT) - Worst drought category covering the facility that week
**Source**: Spatial join of `master_data_centers` to `usdm_drought_weekly`
**Notes**:
- Primary key: (`master_id`, `week_date`)
- `worst_dm = -1` indicates an observed week with no drought polygon covering the facility
### `hms_smoke_days`
**Rows**: 7,075
**Purpose**: One row per observed NOAA HMS smoke product day, including zero-polygon days
**Key Columns**:
- `smoke_date` (DATE) - Primary key
- `source`, `source_file`, `source_url`
- `feature_count` (INTEGER) - Number of smoke polygons for the day
- `fetched_at`, `updated_at`
**Source**: NOAA HMS smoke polygon archive
**Notes**: Denominator table for daily smoke-exposure percentages
### `hms_smoke_daily`
**Rows**: 536,286
**Purpose**: Raw daily NOAA HMS smoke polygons with density categories
**Key Columns**:
- `id` (BIGINT) - Primary key
- `smoke_date` (DATE) - FK to `hms_smoke_days`
- `satellite`
- `start_raw`, `end_raw`, `start_utc`, `end_utc`
- `density`, `density_rank`
- `source`, `source_file`, `source_url`
- `geom` (GEOMETRY) - Smoke polygon geometry
**Source**: NOAA Hazard Mapping System (HMS) smoke polygons
**Notes**: Density rank 1-3 corresponds to Light, Medium, Heavy
### `data_center_hms_smoke_dc_day`
**Rows**: ~13.9 million
**Purpose**: Long-form daily smoke exposure for each data center and observed HMS product day
**Key Columns**:
- `master_id` (TEXT) - FK to `master_data_centers`
- `smoke_date` (DATE) - FK to `hms_smoke_days`
- `max_density_rank` (SMALLINT) - Maximum smoke density covering the facility on that date
- `polygon_hits` (INTEGER)
**Source**: Spatial join of `master_data_centers` to `hms_smoke_daily`
**Notes**:
- Primary key: (`master_id`, `smoke_date`)
- `max_density_rank = 0` indicates an observed HMS day with no smoke polygon covering the facility
### `rdh_precinct_vote_layers`
**Rows**: 69
**Purpose**: Metadata for downloaded RDH precinct election layers
**Key Columns**:
- `layer_id` (TEXT) - Primary key
- `state_code`
- `title`
- `format`
- `datasetid`
- `source_url`
- `filename`, `local_path`, `spatial_path`
- `metadata` (JSONB)
- `loaded_at`
**Source**: Redistricting Data Hub precinct election datasets
**Notes**: Current loaded layers cover 45 distinct state codes
### `rdh_precinct_vote_features`
**Rows**: 260,953
**Purpose**: Staged RDH precinct polygons and source attributes
**Key Columns**:
- `feature_id` (TEXT) - Primary key
- `layer_id` (TEXT) - FK to `rdh_precinct_vote_layers`
- `state_code`
- `source_row`
- `properties` (JSONB) - Raw RDH election attributes
- `geom` (GEOMETRY) - Precinct polygon geometry
**Source**: Redistricting Data Hub precinct election shapefiles
**Notes**: Source feature table for `data_center_rdh_precinct_vote_matches`
--- ---
@@ -293,7 +498,7 @@ Tables are organized into five categories:
- Use for proximity analysis (e.g., "all generators within 50 km of data center") - Use for proximity analysis (e.g., "all generators within 50 km of data center")
#### `energy_eia_facility_fuel_flat` #### `energy_eia_facility_fuel_flat`
**Rows**: Varies **Rows**: Not loaded yet
**Purpose**: Monthly generation by plant/fuel **Purpose**: Monthly generation by plant/fuel
**Key Columns**: **Key Columns**:
@@ -305,6 +510,8 @@ Tables are organized into five categories:
**Source**: EIA Form 923 via API **Source**: EIA Form 923 via API
**Notes**: Target table defined in `ingest_eia_energy_layers.py`; current database does not yet have `public.energy_eia_facility_fuel_flat`.
#### `energy_eia_seds_flat` #### `energy_eia_seds_flat`
**Rows**: 2.57 million **Rows**: 2.57 million
**Purpose**: Annual state energy consumption/production (1960-2024) **Purpose**: Annual state energy consumption/production (1960-2024)

162
docs/research-ideas.md
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@@ -167,6 +167,163 @@ ORDER BY current.pct_grid_saturated DESC;
--- ---
## Ready-to-Run Analyses Enabled by New Context Tables
**Status**: Four one-row-per-facility context tables are now loaded and documented:
- `data_center_historical_climate`
- `data_center_usdm_drought_exposure`
- `data_center_hms_smoke_exposure`
- `data_center_election_context`
These make several publishable descriptive analyses possible without another major ingestion step.
### Climate Exposure and Cooling Burden
**Core idea**: Data centers are energy-intensive cooling loads. The historical climate table lets us ask whether facilities are already sited in hotter, wetter-bulb, or more cooling-intensive climates.
**Research Questions**:
- Are clustered facilities in hotter or more humid climate regimes than isolated facilities?
- Do hyperscalers choose cooler/non-metro climates more often than colocation providers?
- Are facilities with high `cooling_degree_days_c` or `extreme_wet_bulb_days` also near constrained grids?
- Do hotter sites overlap with lower-income or politically less powerful communities?
**Suggested Output**: `output/data_center_climate_exposure_summary.md`
**Starter Query**:
```sql
SELECT
dc.state,
COUNT(*) AS facilities,
AVG(c.mean_annual_temperature_c) AS mean_temp_c,
AVG(c.annual_cooling_degree_days_c_mean) AS annual_cdd_c,
AVG(c.extreme_wet_bulb_days) AS extreme_wet_bulb_days
FROM master_data_centers dc
JOIN data_center_historical_climate c USING (master_id)
GROUP BY dc.state
HAVING COUNT(*) >= 10
ORDER BY annual_cdd_c DESC;
```
### Drought Exposure and Water-Use Politics
**Core idea**: The USDM summary table makes drought exposure measurable at each facility, and it can be joined to HUC8 watersheds, opposition cases, and climate metrics.
**Research Questions**:
- Which major clusters have the highest share of weeks in D2+ drought?
- Are water-sensitive regions still attracting new or projected facilities?
- Are opposition cases more common where `pct_weeks_in_d2_or_worse` or `longest_d2_streak_weeks` is high?
- Do non-metro hyperscaler sites trade cheaper land/power for higher drought exposure?
**Suggested Output**: `output/data_center_drought_water_risk_summary.md`
**Starter Query**:
```sql
SELECT
w.huc8,
w.huc8_name,
COUNT(*) AS facilities,
AVG(d.pct_weeks_in_d2_or_worse) AS avg_pct_d2_or_worse,
MAX(d.longest_d2_streak_weeks) AS max_d2_streak_weeks
FROM data_center_watershed_huc8 w
JOIN data_center_usdm_drought_exposure d USING (master_id)
GROUP BY w.huc8, w.huc8_name
HAVING COUNT(*) >= 5
ORDER BY avg_pct_d2_or_worse DESC;
```
### Wildfire Smoke, Operational Resilience, and Worker Exposure
**Core idea**: Smoke exposure is a climate-adaptation issue for facility operations and for workers who build, maintain, and secure these sites.
**Research Questions**:
- Are facilities in the West and Mountain West systematically more smoke-exposed?
- Do major clusters create regional redundancy risk because many facilities share the same smoke exposure profile?
- Are smoke-exposed data centers in communities already facing higher FEMA NRI risk or lower resilience scores?
- Do smoke exposure patterns differ by operator strategy?
**Suggested Output**: `output/data_center_smoke_resilience_summary.md`
**Starter Query**:
```sql
SELECT
dc.state,
COUNT(*) AS facilities,
AVG(s.pct_days_with_any_smoke) AS avg_any_smoke_days,
AVG(s.pct_days_with_heavy_smoke) AS avg_heavy_smoke_days,
MAX(s.longest_heavy_smoke_streak_days) AS max_heavy_smoke_streak
FROM master_data_centers dc
JOIN data_center_hms_smoke_exposure s USING (master_id)
GROUP BY dc.state
HAVING COUNT(*) >= 10
ORDER BY avg_heavy_smoke_days DESC;
```
### Political Geography of Host Communities
**Core idea**: `data_center_election_context` provides a rough but reusable local political context for each facility. It is not a causal measure of support/opposition, but it can help frame siting politics and legislative outcomes.
**Research Questions**:
- Are data centers more common in precincts with stronger Democratic or Republican vote shares?
- Do clustered and isolated facilities sit in different local political environments?
- Are opposition cases associated with precinct partisanship, turnout, or close elections?
- Do state-level data center bills emerge from states where host precincts differ from statewide political averages?
**Suggested Output**: `output/data_center_political_geography_summary.md`
**Starter Query**:
```sql
SELECT
dc.state,
COUNT(*) AS facilities,
AVG(ec.democratic_votes / NULLIF(ec.total_votes, 0)) AS avg_dem_vote_share,
AVG(ec.republican_votes / NULLIF(ec.total_votes, 0)) AS avg_rep_vote_share
FROM master_data_centers dc
JOIN data_center_election_context ec USING (master_id)
WHERE ec.total_votes > 0
GROUP BY dc.state
HAVING COUNT(*) >= 10
ORDER BY facilities DESC;
```
### Compound Exposure Index
**Core idea**: Combine NRI, historical climate, drought, smoke, watershed concentration, and demographics into a transparent screening index for cumulative exposure.
**Research Questions**:
- Which facilities or clusters have high climate, drought, smoke, and FEMA risk simultaneously?
- Are compound-exposure sites demographically different from lower-exposure sites?
- Do projected IM3 facilities fall into lower- or higher-risk exposure profiles than current facilities?
**Implementation Notes**:
- Standardize each indicator as a percentile rank before combining.
- Keep the index descriptive and auditable; avoid black-box weighting.
- Report sensitivity using equal weights, environment-only weights, and infrastructure-weighted variants.
**Suggested Output**: `output/data_center_compound_exposure_index.csv`
**Starter Query**:
```sql
WITH joined AS (
SELECT
dc.master_id,
dc.name,
dc.state,
c.annual_cooling_degree_days_c_mean,
d.pct_weeks_in_d2_or_worse,
s.pct_days_with_heavy_smoke,
n."RISK_SCORE"
FROM master_data_centers dc
LEFT JOIN data_center_historical_climate c USING (master_id)
LEFT JOIN data_center_usdm_drought_exposure d USING (master_id)
LEFT JOIN data_center_hms_smoke_exposure s USING (master_id)
LEFT JOIN data_center_nri_exposure n USING (master_id)
)
SELECT *
FROM joined
ORDER BY
annual_cooling_degree_days_c_mean DESC NULLS LAST,
pct_weeks_in_d2_or_worse DESC NULLS LAST,
pct_days_with_heavy_smoke DESC NULLS LAST
LIMIT 50;
```
---
## Methodological Extensions ## Methodological Extensions
### 6. Time-Series Analysis of Cluster Growth ### 6. Time-Series Analysis of Cluster Growth
@@ -538,9 +695,10 @@ If you're interested in collaborating on any of these research directions, pleas
**Priorities for external collaboration**: **Priorities for external collaboration**:
1. Power capacity data acquisition 1. Power capacity data acquisition
2. Water stress/drought overlay 2. Climate, drought, smoke, and compound-exposure analysis
3. Opposition cases database compilation 3. Opposition cases database compilation
4. International comparative analysis 4. Water stress/drought overlay
5. International comparative analysis
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