Segregasi Metropolitan

Menganalisis Data Sensus AS dengan Python

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

Peta Amerika Serikat yang menampilkan batas wilayah statistik metropolitan dan mikropolitan.

Sumber: United States Census Bureau

Permintaan API Sensus: Data Metro/Mikro

import requests

# Bangun URL dasar
HOST = "https://api.census.gov/data"
year = "2012"
dataset = "acs/acs5"
base_url = "/".join([HOST, year, dataset])

# Tentukan variabel yang diminta
# B01001_001E = Perkiraan total populasi
# B03002_003E = Perkiraan populasi kulit putih non-Hispanik
# B03002_004E = Perkiraan populasi kulit hitam non-Hispanik
get_vars = ["NAME", "B01001_001E", "B03002_003E", "B03002_004E"]

Permintaan API Sensus: Data Metro/Mikro

# Tentukan variabel yang diminta
get_vars = ["NAME", "B01001_001E", "B03002_003E", "B03002_004E"]

# Buat kamus predikat
predicates = {}
predicates["get"] = ",".join(get_vars)

# Geografi yang diminta
predicates["for"] = \
    "metropolitan statistical area/micropolitan statistical area:*"

Permintaan API Sensus: Data Metro/Mikro

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

print(r.json()[:5])

[['NAME', 'B01001_001E', 'B03002_003E', 'B03002_004E', 'metropolitan statistical area/micropolitan statistical area'],
 ['Adjuntas, PR Micro Area', '19458', '140', '0', '10260'],
 ['Aguadilla-Isabela-San Sebastián, PR Metro Area', '305538', '5602', '231', '10380'],
 ['Coamo, PR Micro Area', '71596', '228', '53', '17620'],
 ['Fajardo, PR Metro Area', '70633', '543', '195', '21940']]

Permintaan API Sensus: Data Metro/Mikro

# Buat nama kolom yang mudah dibaca
col_names = ["name", "pop", "white", "black", "msa"]

# Muat respons JSON ke DataFrame
msa = pd.DataFrame(columns=col_names, data=r.json()[1:])

# Ubah tipe data kolom jumlah menjadi int
msa[["pop", "white", "black"]] = msa["pop", "white", "black"]].astype(int)

Definisi Wilayah Metropolitan

  state county   tract  white  black
0    01    001  020100   1601    217
1    01    001  020200    844   1214
2    01    001  020300   2538    647
3    01    001  020400   4030    191
4    01    001  020500   8438   1418

     msa      msa_name          county_name    state_name state county
0  10100  Aberdeen, SD         Brown County  South Dakota    46    013
1  10100  Aberdeen, SD       Edmunds County  South Dakota    46    045
2  10140  Aberdeen, WA  Grays Harbor County    Washington    53    027
3  10180   Abilene, TX      Callahan County         Texas    48    059
4  10180   Abilene, TX         Jones County         Texas    48    253

Metode Merge di Pandas

import pandas as pd

# Menggabungkan DataFrame pada kolom yang cocok
tracts_with_msa_id = pd.merge(...)

Metode Merge di Pandas

import pandas as pd

# Menggabungkan DataFrame pada kolom yang cocok
tracts_with_msa_id = pd.merge(tracts, msa_def, ...)

Metode Merge di Pandas

import pandas as pd

# Menggabungkan DataFrame pada kolom yang cocok
tracts_with_msa_id = pd.merge(tracts, msa_def, 
    left_on = ["state", "county"], right_on = ["state", "county"])

# Alternatif saat nama kolom sama
tracts_with_msa_id = pd.merge(tracts, msa_def, on = ["state", "county"])

Metode Merge di Pandas

# DataFrame dengan nama negara bagian
st.head()

       state_name
state            
01        Alabama
02         Alaska
04        Arizona
05       Arkansas
06     California

Metode Merge di Pandas

# Gabungkan DataFrame tracts dan st
tracts_st = pd.merge(tracts, st, left_on = "state", right_index = True)

tracts_st.head()

  state county   tract  white  black state_name
0    01    001  020100   1601    217    Alabama
1    01    001  020200    844   1214    Alabama
2    01    001  020300   2538    647    Alabama
3    01    001  020400   4030    191    Alabama

Ayo berlatih!

Menganalisis Data Sensus AS dengan Python