def get_acs_data(years, tuple_val):
'''
Gets ACS data
Input:
years(list): list of years we want to get
tuple_val(tuple): tuple with all the feature names we want to obtain
Output:
returns dataframe with acs data
'''
c = Census("782594777378b4dae32651de41285b8430095ed4")
df_acs = None
for i,yr in enumerate(years):
data = c.acs5.zipcode(tuple_val, Census.ALL, year = yr)
df_data = pd.DataFrame(data)
df_data['year'] = int(yr)
if df_acs is None:
df_acs = df_data
else:
lst = [df_acs, df_data]
df_acs = pd.concat(lst)
return df_acs
def retrieve_us_data(cities_geo=None,
API_key="9320d66d590a2cc9bb46c24b1c4144d1bc7eccfb",
year=None):
'''Retrieve variables of interest data from the US census API.
Args:
cities_geo (list of lists) : state and city FIPS code for each city of interest, for example [[17, 14000], [36, 36061]] for Chicago and NYC,
(use [[]] for single city)
API_key (string) : API_key for US census (default is key of Leonardo Nicoletti)
year (int) : year of interest (default is latest year in acs5 dataset)
Returns:
list : list of census GeoDataFrames
'''
name = ['NAME']
pop_metrics = ['B00001_001E', 'B01002_001E']
us_citizen = [
'B05001_002E', 'B05001_003E', 'B05001_004E', 'B05001_005E',
'B05001PR_002E', 'B05001PR_003E', 'B05001PR_004E', 'B05001PR_005E'
]
immigrant = ['B05001_006E', 'B05001PR_006E']
white = ['B02001_002E']
minority = [
'B02001_003E', 'B02001_004E', 'B02001_005E', 'B02001_006E',
'B02001_007E', 'B02001_008E', 'B02001_009E'
]
tot_households = ['B11001_001E']
average_household_size = ['B25010_001E']
married_households = ['B11001_003E']
single_parent = ['B11001_005E', 'B11001_006E']
nonfamily_households = ['B11001_007E']
median_household_income = ['B19013_001E']
aggregate_household_income = ['B19025_001E']
per_capita_income = ['B19301_001E']
only_english = ['B16001_002E', 'B06007_002E']
other_languages_bad_english = ['B06007_008E']
other_languages_good_english = ['B06007_007E']
tot_pop_in_housing = ['B25008_001E']
owner = ['B25008_002E']
renter = ['B25008_003E']
median_monthly_housing_costs = ['B25105_001E']
median_house_value = ['B25107_001E']
aggregate_house_value = ['B25108_001E']
total_gross_rent = ['B25063_001E']
median_gross_rent = ['B25064_001E']
aggregate_gross_rent = ['B25065_001E']
tot_gross_rent_as_percent_of_income = ['B25070_001E']
less_than_30_of_income = [
'B25070_002E', 'B25070_003E', 'B25070_004E', 'B25070_005E',
'B25070_006E'
]
more_than_30_of_income = [
'B25070_007E', 'B25070_008E', 'B25070_009E', 'B25070_010E'
]
median_gross_rent_as_percent_of_income = ['B25071_001E']
median_n_rooms = ['B25018_001E']
aggregate_n_rooms = ['B25019_001E']
tot_edu_attainment = ['B15003_001E']
less_than_high_school = [
'B15003_002E', 'B15003_003E', 'B15003_004E', 'B15003_005E',
'B15003_006E', 'B15003_007E', 'B15003_008E', 'B15003_009E',
'B15003_010E', 'B15003_011E', 'B15003_012E', 'B15003_013E',
'B15003_014E', 'B15003_015E', 'B15003_016E'
]
high_school = ['B15003_017E']
associates_degree = ['B15003_021E']
bachelors_degree = ['B15003_022E']
masters_degree = ['B15003_023E']
professional_school_degree = ['B15003_024E']
doctorate_degree = ['B15003_025E']
tot_employment_status = ['B23025_001E']
employed = ['B23025_004E']
unemployed = ['B23025_005E']
categories_of_choice = [
name, pop_metrics, us_citizen, immigrant, white, minority,
tot_households, average_household_size, married_households,
single_parent, nonfamily_households, median_household_income,
aggregate_household_income, per_capita_income, only_english,
other_languages_bad_english, other_languages_good_english,
tot_pop_in_housing, owner, renter, median_monthly_housing_costs,
median_house_value, aggregate_house_value, total_gross_rent,
median_gross_rent, aggregate_gross_rent,
tot_gross_rent_as_percent_of_income, less_than_30_of_income,
more_than_30_of_income, median_gross_rent_as_percent_of_income,
median_n_rooms, aggregate_n_rooms, tot_edu_attainment,
less_than_high_school, high_school, associates_degree,
bachelors_degree, masters_degree, professional_school_degree,
doctorate_degree, tot_employment_status, employed, unemployed
]
categories_of_choice = list(
itertools.chain.from_iterable(categories_of_choice))
# API key for US census and year of interest
c = Census(API_key, year=year)
cities_census = []
for city in cities_geo:
city_census = c.acs5.state_place_blockgroup(
tuple(categories_of_choice),
city[0],
city[1],
return_geometry=True)
city_gdf = gpd.GeoDataFrame.from_features(city_census['features'])
cities_census.append(city_gdf)
print("City collected successfully!")
return [city_census for city_census in cities_census]
import csv
import json
import sys
from census_area import Census
from config import CENSUS_API_KEY
c = Census(CENSUS_API_KEY)
writer = csv.writer(sys.stdout)
with open('raw/cpd_district_boundaries.geojson', 'r') as f:
police_districts = json.load(f)
# ACS5 SEX BY AGE (B01001) SUBTABLE DEFINITIONS
#
# RACE/ETHNICITY
# B - Black only
# H - White, not Hispanic
# I - Hispanic
#
# SEX/AGE
# 005 - Boys, ages 10-14
# 006 - Boys, ages 15-17
# 020 - Girls, ages 10-14
# 021 - Girls, ages 15-17
table_map = {
'black_male': ['B01001B_005E', 'B01001B_006E'],
'black_female': ['B01001B_020E', 'B01001B_021E'],
import json
import sys
import scrapelib
import tqdm
from scrapelib.cache import FileCache
from census_area import Census
s = scrapelib.Scraper(raise_errors=False, requests_per_minute=0)
API_KEY = 'ac94ba69718a7e1da4f89c6d218b8f6b5ae9ac49'
geographies = json.load(sys.stdin)
c = Census(API_KEY, session=s, year=int(sys.argv[1]))
VARS = {'B03002_001E': 0, # total population,
'B03002_003E': 0, # Not Hispanic or Latino white
'B03002_004E': 0, # Not Hispanic or Latino black
'B03002_006E': 0, # Not Hispanic or Latino asian
'B03002_012E': 0, # Hispanic or Latino
'B25120_001E': 0, # Aggregate household income in the past 12 months
'B19001_001E': 0, # Households,
'B05003_009E': 0, # Native Born, Male, Over 18
'B05003_011E': 0, # Naturalized U.S. Citizen, Male, Over 18
'B05003_020E': 0, # Native Born, female, Over 18
'B05003_022E': 0, # Naturalized U.S. Citizen, Female, Over 18
}
READABLE_VARS = {'geography number': None,
'B03002_001E': 'Total Population',
'B03002_003E': 'Not Hispanic or Latino Origin, Whites',
import sys
import os
from census_area import Census
from secrets import CENSUS_API_KEY
# Default to total population if no environment variable set.
# See https://api.census.gov/data/2018/acs/acs5/variables.html for variable definitions.
tables = os.environ.get('ACS_TABLES', 'B01003_001E').split(',')
writer = csv.writer(sys.stdout)
writer.writerow(['community_area', 'ward', *tables])
c = Census(CENSUS_API_KEY, year=2017)
for feature in json.load(sys.stdin):
community_area = feature['properties']['community_area'].title()
ward = feature['properties']['ward']
table_values = []
for table in tables:
data_by_tract = c.acs5.geo_tract(('NAME', table), feature)
# data_by_tract contains a three-tuple of the tract feature, properties
# of the tract, and the proportion of the tract that overlaps with the
# ward. Multiply the tract figure by the overlap in order to estimate
# the portion of the value that belongs to the ward.
count = sum(tract_data[table] * percent_overlap for _, tract_data, percent_overlap in data_by_tract)
import scrapelib
import tqdm
from scrapelib.cache import FileCache
from census_area import Census
s = scrapelib.Scraper(raise_errors=False, requests_per_minute=0)
cache = FileCache('cache')
s.cache_storage = cache
s.cache_write_only = False
API_KEY = 'ac94ba69718a7e1da4f89c6d218b8f6b5ae9ac49'
geographies = json.load(sys.stdin)
c = Census(API_KEY, session=s)
VARS = {
'B03002_001E': 0, # total population,
'B03002_003E': 0, # Not Hispanic or Latino white
'B03002_004E': 0, # Not Hispanic or Latino black
'B03002_006E': 0, # Not Hispanic or Latino asian
'B03002_012E': 0, # Hispanic or Latino
'B25120_001E': 0, # Aggregate household income in the past 12 months
'B19001_001E': 0, # Households
}
TRACT_LEVEL_VARS = {'B19001_001E', 'B25120_001E'}
READABLE_VARS = {
'geography number': None,
import sys
import json
import csv
from census_area import Census
from secrets import API_KEY
writer = csv.writer(sys.stdout)
writer.writerow(('community', 'population'))
geojson = json.load(sys.stdin)
features = geojson["features"]
c = Census(API_KEY)
fields = {'total_pop': 'B01003_001E'}
for feature in features:
community = feature["properties"]["community"]
tracts = c.acs5.geo_tract(('NAME', fields['total_pop']),
feature["geometry"])
total_pop = sum(int(tract[1]['B01003_001E']) for tract in tracts)
# Remove population of Cook County Jail
# (source: https://performance.cookcountyil.gov/reports/Sheriff-DOC)
if community == "SOUTH LAWNDALE":
total_pop -= 9000
writer.writerow((community, total_pop))
# # PART I<br>DATA WRANGLING & EXPLANATORY ANALYSIS
# # 1 Demographic Variables of Census Tracts
# ## 1.1 Data Collection Through API
# In[1]:
from census_area import Census
# In[2]:
# Initialize API key
api_key = "56ee867dc43e9c68de842ea51d8b52130c9ea382"
c = Census(key=api_key)
# In[3]:
# Set FIPS codes of PA and Philly
PA_code = 42
Philly_code = 60000
# In[8]:
# Codes for demographics of interest
codes = [
'NAME', 'B01001_002E', 'B01001_026E', 'B02001_002E', 'B01002_001E',
'B06001_002E', 'B19001_001E', 'B06009_005E'
]
# Request ACS data