人口普查地理

使用 Python 分析美國 Census 資料

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

請求所有地理層級

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)

請求特定地理層級

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)

密蘇里人口普查資料中心網頁截圖，顯示各州及其地理識別碼

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

地理實體

法律／行政

State（州）
County（郡）
Congressional Districts（國會選區）
School Districts（學區）
等等

統計

Block（街區）
（Census）Tract（普查分區）
Metropolitan/Micropolitan Statistical Area（都會／次都會統計區）
ZIP Code Tabulation Area（ZIP 編碼彙編區）
等等

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

一張階層圖，顯示人口普查地理層級，以連線表示較小地理包含於較大地理之中。

「in」述詞

在特定州請求所有郡：

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

在單一州請求特定郡：

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

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

Places（聚落）

「incorporated place（建制聚落）是為一群人口提供政府功能而設立的……建制聚落通常是 city、town、village 或 borough，也可能有其他法律稱謂。」
「Census Designated Places (CDPs)（人口普查指定地區）是建制聚落的統計對應，用於提供對具名、已聚居但未依其所在州法律正式建制之地區的人口資料。」

來源：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

部分地理區

state› congressional district› county（或部分）

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"]]

一起來練習吧！

使用 Python 分析美國 Census 資料