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Census Bureau Shapefiles

Geographic boundary data from the U.S. Census Bureau TIGER/Line program.

Overview​

The Census Bureau provides comprehensive geographic boundary files (shapefiles) for all U.S. administrative divisions. These are essential for mapping, spatial analysis, and geographic visualization.

Data Source: U.S. Census Bureau TIGER/Line Files

Update Frequency: Annual (typically released in summer)

Latest Vintage: 2023

Available Boundary Types​

1. States​

  • File: cb_{year}_us_state_500k.zip
  • Features: 50 states + DC + territories (56 total)
  • Use Cases: State-level aggregation, choropleth maps, jurisdiction lookup
  • Size: ~3 MB

2. Counties​

  • File: cb_{year}_us_county_500k.zip
  • Features: 3,143 counties and county equivalents
  • Use Cases: County-level analysis, regional mapping, jurisdiction boundaries
  • Size: ~15 MB

3. ZIP Code Tabulation Areas (ZCTAs)​

  • File: cb_{year}_us_zcta520_500k.zip
  • Features: ~33,000 ZIP Code Tabulation Areas
  • Use Cases: Postal code mapping, demographic analysis, service area definition
  • Size: ~350 MB
  • Note: ZCTAs are statistical approximations of ZIP codes, not exact postal routes

Cartographic vs. Full TIGER Files​

We use Cartographic Boundary Files (cb_* prefix) instead of full TIGER/Line files because:

FeatureCartographic (cb_)Full TIGER (tl_)
DetailSimplified 1:500kHigh detail 1:100k
File SizeSmaller (faster downloads)Larger (slower)
RenderingFaster (optimized for maps)Slower
Water BoundariesClipped at shorelineInclude water features
Use CaseWeb mapping, visualizationDetailed GIS analysis

Recommendation: Use cartographic files for Open Navigator's web-based mapping.

Installation & Setup​

Prerequisites​

# Shapefile processing requires geopandas
pip install geopandas pyogrio

Download Script​

# Download all shapefiles for 2023
python scripts/datasources/census/download_shapefiles.py --year 2023

# Download only states and counties
python scripts/datasources/census/download_shapefiles.py --year 2023 --types states counties

# Download and extract automatically
python scripts/datasources/census/download_shapefiles.py --year 2023 --extract

# Download only ZIP codes
python scripts/datasources/census/download_shapefiles.py --year 2023 --types zcta

Data Storage​

Downloaded shapefiles are cached in:

data/cache/census/shapefiles/{year}/
├── cb_2023_us_state_500k.zip
├── cb_2023_us_county_500k.zip
└── cb_2023_us_zcta520_500k.zip

Extracted files (if --extract used):

data/cache/census/shapefiles/{year}/
├── cb_2023_us_state_500k/
│   ├── cb_2023_us_state_500k.shp       # Geometry
│   ├── cb_2023_us_state_500k.shx       # Shape index
│   ├── cb_2023_us_state_500k.dbf       # Attributes
│   ├── cb_2023_us_state_500k.prj       # Projection
│   └── cb_2023_us_state_500k.xml       # Metadata
├── cb_2023_us_county_500k/
└── cb_2023_us_zcta520_500k/

Loading Shapefiles​

import geopandas as gpd
from pathlib import Path

# Load from ZIP (no extraction needed!)
states = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_state_500k.zip")

# Or from extracted directory
states = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_state_500k/cb_2023_us_state_500k.shp")

# View data
print(states.head())
print(f"Total states: {len(states)}")
print(f"CRS: {states.crs}")  # Should be EPSG:4269 (NAD83)

Key Attributes​

States​

  • STATEFP - State FIPS code (2 digits)
  • STUSPS - State postal abbreviation (2 letters)
  • NAME - State name
  • ALAND - Land area (square meters)
  • AWATER - Water area (square meters)

Counties​

  • STATEFP - State FIPS code
  • COUNTYFP - County FIPS code (3 digits)
  • GEOID - Combined state+county FIPS (5 digits)
  • NAME - County name
  • NAMELSAD - Full name with legal/statistical designation

ZCTAs​

  • ZCTA5CE20 - 5-digit ZCTA code
  • ALAND - Land area (square meters)
  • AWATER - Water area (square meters)

Conversion to Other Formats​

GeoJSON (for web mapping)​

import geopandas as gpd

# Load shapefile
states = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_state_500k.zip")

# Convert to Web Mercator for web maps
states_web = states.to_crs("EPSG:3857")

# Save as GeoJSON
states_web.to_file("data/gold/boundaries/states.geojson", driver="GeoJSON")

GeoParquet (for efficient storage)​

import geopandas as gpd

# Load shapefile
counties = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_county_500k.zip")

# Save as GeoParquet (smaller, faster than GeoJSON)
counties.to_parquet("data/gold/boundaries/counties.parquet")

# Load back
counties = gpd.read_parquet("data/gold/boundaries/counties.parquet")

Simplified Geometries (for faster rendering)​

import geopandas as gpd

# Load and simplify
zcta = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_zcta520_500k.zip")

# Simplify to 100m tolerance (reduces file size)
zcta_simple = zcta.copy()
zcta_simple['geometry'] = zcta_simple.geometry.simplify(tolerance=100)

# Save simplified version
zcta_simple.to_file("data/gold/boundaries/zcta_simplified.geojson", driver="GeoJSON")

Integration with Open Navigator​

1. Jurisdiction Boundary Lookup​

Match jurisdiction names to their geographic boundaries:

import geopandas as gpd
import pandas as pd

# Load jurisdictions from Open Navigator
jurisdictions = pd.read_parquet("data/gold/jurisdictions_deduplicated.parquet")

# Load county boundaries
counties = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_county_500k.zip")

# Merge on name + state
merged = jurisdictions.merge(
    counties[['GEOID', 'NAME', 'STATEFP', 'geometry']],
    left_on=['jurisdiction_name', 'state_code'],
    right_on=['NAME', 'STATEFP'],
    how='left'
)

# Save with geometries
merged_gdf = gpd.GeoDataFrame(merged, geometry='geometry')
merged_gdf.to_parquet("data/gold/jurisdictions_with_boundaries.parquet")

2. Choropleth Mapping​

Create interactive maps colored by data values:

import geopandas as gpd
import folium

# Load state boundaries
states = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_state_500k.zip")

# Example: Count of jurisdictions per state
jurisdiction_counts = jurisdictions.groupby('state_code').size().reset_index(name='count')

# Merge with geometries
states_data = states.merge(
    jurisdiction_counts,
    left_on='STUSPS',
    right_on='state_code',
    how='left'
)

# Create choropleth map
m = folium.Map(location=[39.8, -98.5], zoom_start=4)
folium.Choropleth(
    geo_data=states_data,
    data=states_data,
    columns=['STUSPS', 'count'],
    key_on='feature.properties.STUSPS',
    fill_color='YlOrRd',
    legend_name='Jurisdiction Count'
).add_to(m)

m.save('jurisdiction_heatmap.html')

3. Spatial Joins​

Find which jurisdictions overlap with which ZIP codes:

import geopandas as gpd

# Load data
counties = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_county_500k.zip")
zcta = gpd.read_file("data/cache/census/shapefiles/2023/cb_2023_us_zcta520_500k.zip")

# Spatial join (find which county each ZCTA is in)
zcta_counties = gpd.sjoin(
    zcta[['ZCTA5CE20', 'geometry']],
    counties[['GEOID', 'NAME', 'geometry']],
    how='left',
    predicate='intersects'
)

# Save mapping
zcta_counties.to_parquet("data/gold/zcta_to_county_mapping.parquet")

Use Cases in Open Navigator​

1. Policy Heatmap Enhancement​

  • Add actual geographic boundaries to policy heatmap
  • Show exact county/state shapes instead of markers
  • Enable click-to-zoom on boundary
  • "Find all nonprofits in ZIP code 35401"
  • "Show meetings within 50 miles of this location"
  • Point-in-polygon lookups for jurisdiction detection

3. Service Area Visualization​

  • Display coverage areas for advocacy organizations
  • Show legislative district boundaries
  • Map nonprofit service regions

4. Data Validation​

  • Verify jurisdiction names against official boundaries
  • Detect geocoding errors (city in wrong state)
  • Fill missing location data using boundaries

File Size Considerations​

Full Downloads:

  • States: ~3 MB
  • Counties: ~15 MB
  • ZCTAs: ~350 MB
  • Total: ~368 MB

Recommendations:

  • Store in data/cache/ (excluded from git)
  • Convert to GeoParquet for 50-70% size reduction
  • Simplify geometries for web display
  • Consider only downloading needed regions (e.g., priority states)

License & Attribution​

License: Public Domain (U.S. Government Work)

Attribution (recommended):

Boundary data from U.S. Census Bureau TIGER/Line Shapefiles
https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html

Troubleshooting​

Installation Issues​

If GeoPandas install fails:

# Use conda (easier for spatial packages)
conda install geopandas

# Or use pyogrio instead of fiona
pip install geopandas pyogrio

CRS/Projection Issues​

TIGER files use NAD83 (EPSG:4269). For web maps, convert to Web Mercator (EPSG:3857):

gdf_web = gdf.to_crs("EPSG:3857")

Large File Handling​

For ZCTAs (350 MB), consider:

# Load only needed columns
zcta = gpd.read_file("cb_2023_us_zcta520_500k.zip", columns=['ZCTA5CE20', 'geometry'])

# Filter to specific states
zcta_al = zcta[zcta['ZCTA5CE20'].str.startswith('35')]  # Alabama ZCTAs

Next Steps​

After downloading shapefiles:

  1. Convert to GeoParquet for efficient storage
  2. Join with Open Navigator jurisdiction data
  3. Integrate into PolicyMap component for boundary display
  4. Add spatial search capabilities to API