The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping.
census.gov-boundaries-2018-1.2.8. Modified 2020-08-12T04:35:14
Resources | Packages | Documentation| Contacts| References| Data Dictionary
Resources
- state_500k. State boundaries, scale 500K:1
- state_5m. State boundaries, scale 5M:1
- state_20m. State boundaries, scale 20M:1
- county_500k. County boundaries, scale 500K:1
- county_5m. County boundaries, scale 5M:1
- county_20m. County boundaries, scale 20M:1
- nation_5m. National boundary, 5M:1
- nation_20m. National boundary 20M:1
- zcta_500k. ZCTA (Zip Codes), scale 500K:1
Documentation
This data package collects a selection of the US Census cartographic shape files, which are designed to be used in creating maps. For each type of region, they come in a varity of resolutions, so they are more suitable for creating web maps than the much larger TIGER files.
These files have been processed to all have the same schema and use ACS style Geoids. The original files have AFF style geoids, which have extra ’00’ characters in them.
The use of a single schema means that in some files, some columns are
consistently empty. For instance, ZCTA ( which are similar to ZIP code regions)
may not be entirely within a single state or county, so they don’t have a
well-defined state and county. In these cases, the state_fips stusab
and county_fips columns will be empty.
Documentation Links
- Documentation Page
- Homepage Page with links to all of the files
Contacts
- Wrangler
Data Dictionary
boundary_schemaboundary_schema
| Column Name | Data Type | Description |
|---|---|---|
| geoid | string | ACS style geoid |
| name | string | Name of region |
| stusab | string | US state abbreviation |
| state_fips | integer | State FIPS code |
| county_fips | integer | County FIPS code |
| land_area | integer | Land area, in square meters |
| water_area | integer | Water area, in square meters |
| geometry | string | Geometry in WKT format. |
References
Urls used in the creation of this data package.
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_500k.zip. State boundaries, scale 500K:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_5m.zip. State boundaries, scale 5M:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_20m.zip. State boundaries, scale 20M:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip. County boundaries, scale 500K:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_5m.zip. County boundaries, scale 5M:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_20m.zip. County boundaries, scale 20M:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_nation_5m.zip. National boundary, 5M:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_nation_20m.zip. National boundary 20M:1
- shape+https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_zcta510_500k.zip. ZCTA (Zip Codes), scale 500K:1
Packages
- s3 s3://library.metatab.org/census.gov-boundaries-2018-1.2.8.csv
- csv http://library.metatab.org/census.gov-boundaries-2018-1.2.8.csv
- source https://github.com/CivicKnowledge/tablecloth.io.git
Accessing Data in Vanilla Pandas
import pandas as pd
state_500k_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/state_500k.csv')
state_5m_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/state_5m.csv')
state_20m_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/state_20m.csv')
county_500k_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/county_500k.csv')
county_5m_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/county_5m.csv')
county_20m_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/county_20m.csv')
nation_5m_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/nation_5m.csv')
nation_20m_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/nation_20m.csv')
zcta_500k_df = pd.read_csv('http://library.metatab.org/census.gov-boundaries-2018-1.2.8/data/zcta_500k.csv')Accessing Package in Metapack
import metapack as mp
pkg = mp.open_package('http://library.metatab.org/census.gov-boundaries-2018-1.2.8.csv')
# Create Dataframes
state_500k_gdf = pkg.resource('state_500k').geoframe()
state_5m_gdf = pkg.resource('state_5m').geoframe()
state_20m_gdf = pkg.resource('state_20m').geoframe()
county_500k_gdf = pkg.resource('county_500k').geoframe()
county_5m_gdf = pkg.resource('county_5m').geoframe()
county_20m_gdf = pkg.resource('county_20m').geoframe()
nation_5m_gdf = pkg.resource('nation_5m').geoframe()
nation_20m_gdf = pkg.resource('nation_20m').geoframe()
zcta_500k_gdf = pkg.resource('zcta_500k').geoframe()