US Census Geographic Boundaries

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US Census Geographic Boundaries

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Visibility: Public
Owner: Carl Boettiger
Created: 16 Jun 2026
Last Updated: 16 Jun 2026

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census-2024

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US Census 2024 Congressional Districts (119th Congress)

2024 congressional district boundaries for the 119th United States Congress (2025-2027) from the US Census Bureau's TIGER/Line shapefiles, processed into cloud-native formats with H3 hexagonal spatial indexing.

Dataset Overview

Source: US Census Bureau TIGER/Line 2024
Geography Level: Congressional District (119th Congress)
Features: ~435 congressional districts
H3 Resolution: 10 (~0.015 km² per hex)
Parent Resolutions: 9, 8, 0
Last Updated: February 2026
Congress Session: 119th (2025-2027)

Available Formats

GeoParquet

s3://public-census/census-2024/cd.parquet

Format: GeoParquet with ZSTD compression
CRS: EPSG:4326 (WGS84)
Row Groups: 100,000 features
Geometry Type: MultiPolygon
Use Case: Analytical queries with DuckDB, Polars, GeoPandas

PMTiles

s3://public-census/census-2024/cd.pmtiles

Format: PMTiles v3
Use Case: Web map visualization with MapLibre GL JS, Leaflet

H3 Hex Index

s3://public-census/census-2024/cd/hex/h0={cell}/

Format: Partitioned Parquet by H3 resolution 0 cells
Columns: h3_cell (resolution 10), feature_id, plus parent resolutions
Use Case: Fast spatial joins, area-weighted aggregation

Attributes

Key attributes from TIGER/Line shapefiles:

STATEFP: State FIPS code (2 digits)
CD119FP: Congressional district code for 119th Congress (2 digits)
GEOID: Complete geographic identifier (STATEFP + CD119FP)
NAMELSAD: Full district name (e.g., "Congressional District 1")
LSAD: Legal/Statistical Area Description code
CDSESSN: Congressional session number ("119")
ALAND: Land area (square meters)
AWATER: Water area (square meters)

Special District Codes

00: At-large district (states with single representative)
98: Nonvoting delegate district (DC, territories)
ZZ: Area with no representation

Usage Examples

DuckDB Query

Python (GeoPandas)

Electoral Analysis Example

H3 Spatial Indexing

Each congressional district is indexed into H3 hexagons at resolution 10 (~0.015 km² per hex):

Urban districts: ~5,000-15,000 hexes (compact, high population density)
Suburban districts: ~15,000-50,000 hexes
Rural districts: Can exceed 100,000 hexes (Alaska at-large district ~113M hexes)

The hex index enables:

Voter/address assignment: Fast point-in-district lookups for electoral analysis
Demographic aggregation: Area-weighted census data to district boundaries
Redistricting analysis: Compare district characteristics across different boundary proposals

Congressional Districts Background

Congressional districts are geographic divisions for electing members of the US House of Representatives:

Total Districts: 435 voting representatives + 6 non-voting delegates
Apportionment: Based on decennial census population
Redistricting: Most states redraw boundaries after each census
Equal Population: Districts within a state must have nearly equal populations (~760,000 people per district)

119th Congress (2025-2027)

This dataset reflects the district boundaries for the 119th US Congress based on 2020 Census apportionment.

Processing Details

Preprocessing

Source Files: 56 per-state zip files
Method: Parallel download + unzip + merge via cng-convert-to-parquet
Duration: 2 minutes 12 seconds
Features: ~435 congressional districts merged into single GeoParquet

Pipeline

Hex Generation: 200 chunks completed (18 minutes)
- Initial run: 199/200 (chunk 0 OOMKilled with 16Gi)
- Chunk 0 retry: Successful with 32Gi memory (77 seconds)
PMTiles: 14 minutes
Repartition: 14 seconds (all 200 chunks)
Total Processing Time: ~35 minutes

Citation

U.S. Census Bureau (2024). TIGER/Line Shapefiles: Congressional Districts - 119th Congress. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html

License

Public domain. No restrictions on use.

Census 2024 States - 56 state/territory boundaries
Census 2024 Counties - 3,235 county boundaries
Census 2024 Census Tracts - ~85,000 tract boundaries

Access

Public HTTP Access:

S3 Access (requires credentials):

Future Work

Add demographic/electoral data joins
Historical congressional district comparisons (118th, 117th, etc.)
Voter turnout spatial analysis examples

US Census 2024 Congressional Districts (119th Congress)

Dataset Overview

Source: US Census Bureau TIGER/Line 2024
Geography Level: Congressional District (119th Congress)
Features: ~435 congressional districts
H3 Resolution: 10 (~0.015 km² per hex)
Parent Resolutions: 9, 8, 0
Last Updated: February 2026
Congress Session: 119th (2025-2027)

Available Formats

GeoParquet

s3://public-census/census-2024/cd.parquet

Format: GeoParquet with ZSTD compression
CRS: EPSG:4326 (WGS84)
Row Groups: 100,000 features
Geometry Type: MultiPolygon
Use Case: Analytical queries with DuckDB, Polars, GeoPandas

PMTiles

s3://public-census/census-2024/cd.pmtiles

Format: PMTiles v3
Use Case: Web map visualization with MapLibre GL JS, Leaflet

H3 Hex Index

s3://public-census/census-2024/cd/hex/h0={cell}/

Format: Partitioned Parquet by H3 resolution 0 cells
Columns: h3_cell (resolution 10), feature_id, plus parent resolutions
Use Case: Fast spatial joins, area-weighted aggregation

Attributes

Key attributes from TIGER/Line shapefiles:

STATEFP: State FIPS code (2 digits)
CD119FP: Congressional district code for 119th Congress (2 digits)
GEOID: Complete geographic identifier (STATEFP + CD119FP)
NAMELSAD: Full district name (e.g., "Congressional District 1")
LSAD: Legal/Statistical Area Description code
CDSESSN: Congressional session number ("119")
ALAND: Land area (square meters)
AWATER: Water area (square meters)

Special District Codes

00: At-large district (states with single representative)
98: Nonvoting delegate district (DC, territories)
ZZ: Area with no representation

Usage Examples

DuckDB Query

Python (GeoPandas)

Electoral Analysis Example

H3 Spatial Indexing

Each congressional district is indexed into H3 hexagons at resolution 10 (~0.015 km² per hex):

Urban districts: ~5,000-15,000 hexes (compact, high population density)
Suburban districts: ~15,000-50,000 hexes
Rural districts: Can exceed 100,000 hexes (Alaska at-large district ~113M hexes)

The hex index enables:

Voter/address assignment: Fast point-in-district lookups for electoral analysis
Demographic aggregation: Area-weighted census data to district boundaries
Redistricting analysis: Compare district characteristics across different boundary proposals

Congressional Districts Background

Congressional districts are geographic divisions for electing members of the US House of Representatives:

Total Districts: 435 voting representatives + 6 non-voting delegates
Apportionment: Based on decennial census population
Redistricting: Most states redraw boundaries after each census
Equal Population: Districts within a state must have nearly equal populations (~760,000 people per district)

119th Congress (2025-2027)

This dataset reflects the district boundaries for the 119th US Congress based on 2020 Census apportionment.

Processing Details

Preprocessing

Source Files: 56 per-state zip files
Method: Parallel download + unzip + merge via cng-convert-to-parquet
Duration: 2 minutes 12 seconds
Features: ~435 congressional districts merged into single GeoParquet

Pipeline

Hex Generation: 200 chunks completed (18 minutes)
- Initial run: 199/200 (chunk 0 OOMKilled with 16Gi)
- Chunk 0 retry: Successful with 32Gi memory (77 seconds)
PMTiles: 14 minutes
Repartition: 14 seconds (all 200 chunks)
Total Processing Time: ~35 minutes

Citation

U.S. Census Bureau (2024). TIGER/Line Shapefiles: Congressional Districts - 119th Congress. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html

License

Public domain. No restrictions on use.

Census 2024 States - 56 state/territory boundaries
Census 2024 Counties - 3,235 county boundaries
Census 2024 Census Tracts - ~85,000 tract boundaries

Access

Public HTTP Access:

S3 Access (requires credentials):

Future Work

Add demographic/electoral data joins
Historical congressional district comparisons (118th, 117th, etc.)
Voter turnout spatial analysis examples

1https://s3-west.nrp-nautilus.io/public-census/census-2024/cd.parquet
2https://s3-west.nrp-nautilus.io/public-census/census-2024/cd.pmtiles

1s3://public-census/census-2024/cd.parquet
2s3://public-census/census-2024/cd.pmtiles
3s3://public-census/census-2024/cd/hex/h0=*/  # All 200 chunks

1https://s3-west.nrp-nautilus.io/public-census/census-2024/cd.parquet
2https://s3-west.nrp-nautilus.io/public-census/census-2024/cd.pmtiles

1s3://public-census/census-2024/cd.parquet
2s3://public-census/census-2024/cd.pmtiles
3s3://public-census/census-2024/cd/hex/h0=*/  # All 200 chunks

1-- Load congressional districts
2LOAD spatial;
3CREATE TABLE districts AS 
4  SELECT * FROM read_parquet('s3://public-census/census-2024/cd.parquet');
5
6-- Query districts in a specific state (California's 52 districts)
7SELECT CD119FP, NAMELSAD, ALAND/1000000 as land_sq_km
8FROM districts
9WHERE STATEFP = '06'
10ORDER BY CD119FP;
11
12-- Find at-large districts (single-rep states)
13SELECT STATEFP, NAMELSAD
14FROM districts
15WHERE CD119FP = '00'
16ORDER BY STATEFP;
17
18-- Non-voting delegates
19SELECT STATEFP, NAMELSAD
20FROM districts
21WHERE CD119FP = '98';
22
23-- Join with H3 hex index for spatial queries
24CREATE TABLE cd_hexes AS
25  SELECT * FROM read_parquet('s3://public-census/census-2024/cd/hex/h0=*/');
26  
27-- District sizes by hex count (proxy for area at res 10)
28SELECT d.GEOID, d.NAMELSAD, COUNT(h.h3_cell) as hex_count
29FROM cd_hexes h
30JOIN districts d ON h.feature_id = d._cng_fid
31GROUP BY d.GEOID, d.NAMELSAD
32ORDER BY hex_count DESC
33LIMIT 20;

1-- Load congressional districts
2LOAD spatial;
3CREATE TABLE districts AS 
4  SELECT * FROM read_parquet('s3://public-census/census-2024/cd.parquet');
5
6-- Query districts in a specific state (California's 52 districts)
7SELECT CD119FP, NAMELSAD, ALAND/1000000 as land_sq_km
8FROM districts
9WHERE STATEFP = '06'
10ORDER BY CD119FP;
11
12-- Find at-large districts (single-rep states)
13SELECT STATEFP, NAMELSAD
14FROM districts
15WHERE CD119FP = '00'
16ORDER BY STATEFP;
17
18-- Non-voting delegates
19SELECT STATEFP, NAMELSAD
20FROM districts
21WHERE CD119FP = '98';
22
23-- Join with H3 hex index for spatial queries
24CREATE TABLE cd_hexes AS
25  SELECT * FROM read_parquet('s3://public-census/census-2024/cd/hex/h0=*/');
26  
27-- District sizes by hex count (proxy for area at res 10)
28SELECT d.GEOID, d.NAMELSAD, COUNT(h.h3_cell) as hex_count
29FROM cd_hexes h
30JOIN districts d ON h.feature_id = d._cng_fid
31GROUP BY d.GEOID, d.NAMELSAD
32ORDER BY hex_count DESC
33LIMIT 20;

1import geopandas as gpd
2
3# Read congressional districts
4districts = gpd.read_parquet('s3://public-census/census-2024/cd.parquet')
5
6print(f"Total districts: {len(districts)}")
7
8# Count districts per state
9by_state = districts.groupby('STATEFP').size().sort_values(ascending=False)
10print("States with most districts:")
11print(by_state.head(10))
12
13# At-large districts
14at_large = districts[districts['CD119FP'] == '00']
15print(f"At-large districts: {len(at_large)}")
16
17# Calculate land area statistics
18districts['area_km2'] = districts['ALAND'] / 1e6
19print(f"Mean district area: {districts['area_km2'].mean():.0f} sq km")
20print(f"Largest district: {districts.loc[districts['area_km2'].idxmax()]['NAMELSAD']}")
21print(f"Smallest district: {districts.loc[districts['area_km2'].idxmin()]['NAMELSAD']}")

1import geopandas as gpd
2
3# Read congressional districts
4districts = gpd.read_parquet('s3://public-census/census-2024/cd.parquet')
5
6print(f"Total districts: {len(districts)}")
7
8# Count districts per state
9by_state = districts.groupby('STATEFP').size().sort_values(ascending=False)
10print("States with most districts:")
11print(by_state.head(10))
12
13# At-large districts
14at_large = districts[districts['CD119FP'] == '00']
15print(f"At-large districts: {len(at_large)}")
16
17# Calculate land area statistics
18districts['area_km2'] = districts['ALAND'] / 1e6
19print(f"Mean district area: {districts['area_km2'].mean():.0f} sq km")
20print(f"Largest district: {districts.loc[districts['area_km2'].idxmax()]['NAMELSAD']}")
21print(f"Smallest district: {districts.loc[districts['area_km2'].idxmin()]['NAMELSAD']}")

1import duckdb
2
3# Assign voter addresses to congressional districts using H3
4con = duckdb.connect()
5con.execute("LOAD spatial; INSTALL h3 FROM community; LOAD h3;")
6
7# Your voter data with lat/lon
8voters_df = pd.DataFrame({
9    'voter_id': [...],
10    'lat': [...],
11    'lon': [...]
12})
13
14con.execute("""
15    -- Convert voter locations to H3 cells
16    CREATE TABLE voters AS
17    SELECT voter_id, h3_latlng_to_cell(lat, lon, 10) as h3_cell
18    FROM voters_df;
19    
20    -- Join with district hex index to assign districts
21    CREATE TABLE voters_by_district AS
22    SELECT v.voter_id, d.GEOID, d.NAMELSAD
23    FROM voters v
24    JOIN read_parquet('s3://public-census/census-2024/cd/hex/h0=*/') h 
25      ON v.h3_cell = h.h3_cell
26    JOIN read_parquet('s3://public-census/census-2024/cd.parquet') d 
27      ON h.feature_id = d._cng_fid;
28    
29    -- Count voters per district
30    SELECT GEOID, NAMELSAD, COUNT(*) as voter_count
31    FROM voters_by_district
32    GROUP BY GEOID, NAMELSAD
33    ORDER BY voter_count DESC;
34""")

1import duckdb
2
3# Assign voter addresses to congressional districts using H3
4con = duckdb.connect()
5con.execute("LOAD spatial; INSTALL h3 FROM community; LOAD h3;")
6
7# Your voter data with lat/lon
8voters_df = pd.DataFrame({
9    'voter_id': [...],
10    'lat': [...],
11    'lon': [...]
12})
13
14con.execute("""
15    -- Convert voter locations to H3 cells
16    CREATE TABLE voters AS
17    SELECT voter_id, h3_latlng_to_cell(lat, lon, 10) as h3_cell
18    FROM voters_df;
19    
20    -- Join with district hex index to assign districts
21    CREATE TABLE voters_by_district AS
22    SELECT v.voter_id, d.GEOID, d.NAMELSAD
23    FROM voters v
24    JOIN read_parquet('s3://public-census/census-2024/cd/hex/h0=*/') h 
25      ON v.h3_cell = h.h3_cell
26    JOIN read_parquet('s3://public-census/census-2024/cd.parquet') d 
27      ON h.feature_id = d._cng_fid;
28    
29    -- Count voters per district
30    SELECT GEOID, NAMELSAD, COUNT(*) as voter_count
31    FROM voters_by_district
32    GROUP BY GEOID, NAMELSAD
33    ORDER BY voter_count DESC;
34""")

US Census Geographic Boundaries

US Census Geographic Boundaries

US Census 2024 Congressional Districts (119th Congress)

Dataset Overview

Available Formats

GeoParquet

PMTiles

H3 Hex Index

Attributes

Special District Codes

Usage Examples

DuckDB Query

Python (GeoPandas)

Electoral Analysis Example

H3 Spatial Indexing

Congressional Districts Background

119th Congress (2025-2027)

Processing Details

Preprocessing

Pipeline

Citation

License

Related Datasets

Access

Future Work

US Census 2024 Congressional Districts (119th Congress)

Dataset Overview

Available Formats

GeoParquet

PMTiles

H3 Hex Index

Attributes

Special District Codes

Usage Examples

DuckDB Query

Python (GeoPandas)

Electoral Analysis Example

H3 Spatial Indexing

Congressional Districts Background

119th Congress (2025-2027)

Processing Details

Preprocessing

Pipeline

Citation

License

Related Datasets

Access

Future Work