Census Geography

Analyzing US Census Data in Python

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

Request All Geographies

import requests

HOST = "https://api.census.gov/data"
year = "2010"
dataset = "dec/sf1"
base_url = "/".join([HOST, year, dataset])

predicates = {}
predicates["get"] = "NAME,P001001"

predicates["for"] = "state:*"

r = requests.get(base_url, params=predicates)

Request Specific Geographies

import requests

HOST = "https://api.census.gov/data"
year = "2010"
dataset = "dec/sf1"
base_url = "/".join([HOST, year, dataset])

predicates = {}
predicates["get"] = "NAME,P001001"

predicates["for"] = "state:42"

r = requests.get(base_url, params=predicates)

A screenshot of the Missouri Census Data Center webpage showing states and their Geographic Identifiers

¹ https://census.missouri.edu/geocodes/

Geographic Entities

Legal/Administrative

State
County
Congressional Districts
School Districts
etc.

Statistical

Block
(Census) Tract
Metropolitan/Micropolitan Statistical Area
ZIP Code Tabulation Area
etc.

¹ https://www.census.gov/geo/education/legstat_geo.html

A hierarchy diagram showing Census geographies connected by lines indicating which geographies are contained by other, larger geographies.

The "in" Predicate

Request all counties in specific states:

predicates["for"] = "county:*"
predicates["in"] = "state:33,50"

Request specific counties in one state:

predicates["for"] = "county:001,003"
predicates["in"] = "state:33"

r = requests.get(base_url, params=predicates)

Places

"An incorporated place is established to provide governmental functions for a concentration of people…. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions."
"Census Designated Places (CDPs) are the statistical counterparts of incorporated places, and are delineated to provide data for settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located."

Source: https://www.census.gov/geo/reference/gtc/gtc_place.html

Geography Level	Geography Hierarchy
40	state
50	state› county
60	state› county› county subdivision
101	state› county› tract› block
140	state› county› tract
150	state› county› tract› block group
160	state› place

https://api.census.gov/data/2010/dec/sf1/geography.html

Part Geographies

state› congressional district› county (or part)

predicates = {}
predicates["get"] = "NAME,P001001"

predicates["for"] = "county (or part):*"

predicates["in"] = "state:42;congressional district:02"

r = requests.get(base_url, params=predicates)
print(r.text)

[["NAME","P001001","state","congressional district","county"],
["Montgomery County (part)","36793","42","02","091"],
["Philadelphia County (part)","593484","42","02","101"]]

Let's practice!

Analyzing US Census Data in Python