Census Cartographic Boundary Files, 2018 vintage

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

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.

Contacts

Data Dictionary

boundary_schema

boundary_schema

Column NameData TypeDescription
geoidstringACS style geoid
namestringName of region
stusabstringUS state abbreviation
state_fipsintegerState FIPS code
county_fipsintegerCounty FIPS code
land_areaintegerLand area, in square meters
water_areaintegerWater area, in square meters
geometrystringGeometry 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

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()