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create_json.py
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create_json.py
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# -*- coding: utf-8 -*-
import copy, csv, json, math, os, re
from scipy.ndimage import filters
import shapefile
import matplotlib.pyplot as plt
import numpy as np
import os
pjoin = os.path.join
from shapely import geometry
from shapely.geometry import Point
import pandas as pd
def flatten_json(j, r = None, prefix = [], delimiter = '/'):
if r is None:
r = {}
for k in j:
assert delimiter not in k, k
if type(j[k]) is dict:
flatten_json(j[k], r, prefix + [k])
else:
r[delimiter.join(prefix + [k])] = j[k]
return r
def pad(t, n, c=' '):
t = str(t)
return max(n - len(t), 0) * c + t
state_abbreviation_to_name = {
"AL": "Alabama",
"AK": "Alaska",
"AZ": "Arizona",
"AR": "Arkansas",
"CA": "California",
"CO": "Colorado",
"CT": "Connecticut",
"DE": "Delaware",
"DC": "District of Columbia",
"FL": "Florida",
"GA": "Georgia",
"GU": "Guam",
"HI": "Hawaii",
"ID": "Idaho",
"IL": "Illinois",
"IN": "Indiana",
"IA": "Iowa",
"KS": "Kansas",
"KY": "Kentucky",
"LA": "Louisiana",
"ME": "Maine",
"MD": "Maryland",
"MA": "Massachusetts",
"MI": "Michigan",
"MN": "Minnesota",
"MS": "Mississippi",
"MO": "Missouri",
"MT": "Montana",
"NE": "Nebraska",
"NV": "Nevada",
"NH": "New Hampshire",
"NJ": "New Jersey",
"NM": "New Mexico",
"NY": "New York",
"NC": "North Carolina",
"ND": "North Dakota",
"OH": "Ohio",
"OK": "Oklahoma",
"OR": "Oregon",
"PA": "Pennsylvania",
"RI": "Rhode Island",
"SC": "South Carolina",
"SD": "South Dakota",
"TN": "Tennessee",
"TX": "Texas",
"UT": "Utah",
"VT": "Vermont",
"VI": "Virgin Islands",
"VA": "Virginia",
"WA": "Washington",
"WV": "West Virginia",
"WI": "Wisconsin",
"WY": "Wyoming",
}
state_name_to_abbreviation = {}
for k in state_abbreviation_to_name:
state_name_to_abbreviation[state_abbreviation_to_name[k]] = k
fips_code_to_name = {
"01": "Alabama",
"02": "Alaska",
"04": "Arizona",
"05": "Arkansas",
"06": "California",
"08": "Colorado",
"09": "Connecticut",
"10": "Delaware",
"11": "District of Columbia",
"12": "Florida",
"13": "Georgia",
"15": "Hawaii",
"16": "Idaho",
"17": "Illinois",
"18": "Indiana",
"19": "Iowa",
"20": "Kansas",
"21": "Kentucky",
"22": "Louisiana",
"23": "Maine",
"24": "Maryland",
"25": "Massachusetts",
"26": "Michigan",
"27": "Minnesota",
"28": "Mississippi",
"29": "Missouri",
"30": "Montana",
"31": "Nebraska",
"32": "Nevada",
"33": "New Hampshire",
"34": "New Jersey",
"35": "New Mexico",
"36": "New York",
"37": "North Carolina",
"38": "North Dakota",
"39": "Ohio",
"40": "Oklahoma",
"41": "Oregon",
"42": "Pennsylvania",
"44": "Rhode Island",
"45": "South Carolina",
"46": "South Dakota",
"47": "Tennessee",
"48": "Texas",
"49": "Utah",
"50": "Vermont",
"51": "Virginia",
"53": "Washington",
"54": "West Virginia",
"55": "Wisconsin",
"56": "Wyoming",
"60": "American Samoa",
"66": "Guam",
"69": "Northern Mariana Islands",
"72": "Puerto Rico",
"78": "Virgin Islands",
}
state_name_to_fips_code = {
"Alabama": "01",
"Alaska": "02",
"Arizona": "04",
"Arkansas": "05",
"California": "06",
"Colorado": "08",
"Connecticut": "09",
"Delaware": "10",
"District of Columbia": "11",
"Florida": "12",
"Georgia": "13",
"Hawaii": "15",
"Idaho": "16",
"Illinois": "17",
"Indiana": "18",
"Iowa": "19",
"Kansas": "20",
"Kentucky": "21",
"Louisiana": "22",
"Maine": "23",
"Maryland": "24",
"Massachusetts": "25",
"Michigan": "26",
"Minnesota": "27",
"Mississippi": "28",
"Missouri": "29",
"Montana": "30",
"Nebraska": "31",
"Nevada": "32",
"New Hampshire": "33",
"New Jersey": "34",
"New Mexico": "35",
"New York": "36",
"North Carolina": "37",
"North Dakota": "38",
"Ohio": "39",
"Oklahoma": "40",
"Oregon": "41",
"Pennsylvania": "42",
"Rhode Island": "44",
"South Carolina": "45",
"South Dakota": "46",
"Tennessee": "47",
"Texas": "48",
"Utah": "49",
"Vermont": "50",
"Virginia": "51",
"Washington": "53",
"West Virginia": "54",
"Wisconsin": "55",
"Wyoming": "56",
"American Samoa": "60",
"Guam": "66",
"Northern Mariana Islands": "69",
"Puerto Rico": "72",
"Virgin Islands": "78",
}
not_states = set([
"American Samoa",
"Guam",
"Northern Mariana Islands",
"Puerto Rico",
"Virgin Islands",
])
# Maps formly independent cities to the counties they
# now belong to. This way we can add the deaths from
# these cities (which the CDC keeps separate, since its
# data goes back to 1999) to the counties the cities
# now belong to.
former_independent_cities_to_counties = {
"Virginia": {
"clifton forge city": "alleghany county",
"bedford city": "bedford county",
}
}
class CountyNameMerger:
kHardCoded = {
"Alabama": {
"de kalb": "dekalb county",
},
"Alaska": {
"anchorage borough": "anchorage municipality",
"juneau borough": "juneau city and borough",
"petersburg borough/census area": "petersburg borough",
"sitka borough": "sitka city and borough",
"skagway-hoonah-angoon census area" : "skagway municipality",
"wrangell-petersburg census area": "wrangell city and borough",
"yakutat borough": "yakutat city and borough",
# Formerly known as Wade Hampton Census Area
"wade hampton census area": "kusilvak census area",
# Renamed in 2008
"prince of wales-outer ketchikan census area": "prince of wales-hyder census area",
"anchorage borough/municipality": "anchorage municipality",
"juneau borough/city": "juneau city and borough",
"sitka borough/city": "sitka city and borough",
"wrangell borough/city": "wrangell city and borough",
"yakutat borough/city": "yakutat city and borough",
"municipality of anchorage": "anchorage municipality",
"city and borough of juneau": "juneau city and borough",
"petersburg census area": "petersburg borough",
},
"California": {
"san francisco county/city": "san francisco county",
},
"Colorado": {
"broomfield county/city": "broomfield county",
},
"District of Columbia": {
"washington": "district of columbia",
"district of columbia county": "district of columbia",
},
"Florida": {
"de soto": "desoto county",
},
"Georgia": {
"de kalb": "dekalb county",
},
"Idaho": {
"fremont (includes yellowstone park)": "fremont county"
},
"Illinois": {
"la salle": "lasalle county",
"du page": "dupage county",
"de kalb": "dekalb county",
},
"Indiana": {
"de kalb": "dekalb county",
"de kalb county": "dekalb county",
"la porte county": "laporte county",
"la porte": "laporte county",
"de kalb": "dekalb county",
"la grange": "lagrange county",
},
"Iowa": {
"o brien": "o'brien county",
},
"Louisiana": {
"la salle parish": "lasalle parish",
"la salle": "lasalle parish",
},
"Maryland": {
"baltimore (independent city)": "baltimore city",
"baltimore city county": "baltimore city",
"prince georges": "prince george's county",
"queen annes": "queen anne's county",
"st. marys": "st. mary's county",
},
"Mississippi": {
"de soto": "desoto county",
},
"Missouri": {
"jackson county (including other portions of kansas city)": "jackson county",
"city of st. louis": "st. louis city",
"st. louis city county": "st. louis city",
"Jackson County (including other portions of Kansas City)": "Jackson County",
"de kalb": "dekalb county",
},
"Nevada": {
"carson city county": "carson city"
},
"New Mexico": {
"debaca county": "de baca county",
"dona ana county": "doña ana county",
"dona ana": "doña ana county",
},
"North Dakota": {
"la moure": "lamoure county",
},
"Pennsylvania": {
"mc kean county": "mckean county",
},
"South Dakota": {
"shannon county": "oglala lakota county",
"shannon": "oglala lakota county",
},
"Tennessee": {
"de kalb": "dekalb county",
},
"Texas": {
"de witt": "dewitt county",
},
"Virginia": {
"colonial heights cit": "colonial heights city"
}
}
# if county not in states[state] and county + ' county' in states[state]:
# county = county + ' county'
# if county not in states[state] and county + ' parish' in states[state]:
# county = county + ' parish'
# assert county in states[state], f'{county}, {state}'
def __init__(self):
with open('base.json', 'r') as f:
self.states = json.load(f)
def merge_state(self, state, list1, list2, allow_missing, missing):
if (not allow_missing) and len(missing.get(state, {})) == 0:
assert len(list1) == len(list2), f"{state}\n\n{sorted(list1)}\n\n{sorted(list2)}"
assert len(set(list1)) == len(list1)
assert len(set(list2)) == len(list2)
hardCoded = CountyNameMerger.kHardCoded.get(state, {})
M = {}
i = 0
while i < len(list1):
county = list1[i].lower()
if county[:3] == 'st ':
county = 'st. ' + county[3:]
if county[-19:] == ' (independent city)':
if county[-23:-18] != 'city ':
county = county[:-19] + ' city'
else:
county = county[:-19]
if county[-12:] == ' county/city':
county = county[:-5]
if county[-12:] == ' county/town':
county = county[:-5]
if county in hardCoded:
j = list2.index(hardCoded[county])
M[list1[i]] = list2[j]
del list1[i]
del list2[j]
elif county in list2:
j = list2.index(county)
M[list1[i]] = list2[j]
del list1[i]
del list2[j]
elif county + ' county' in list2:
j = list2.index(county + ' county')
M[list1[i]] = list2[j]
del list1[i]
del list2[j]
elif state == 'Louisiana' and county + ' parish' in list2:
j = list2.index(county + ' parish')
M[list1[i]] = list2[j]
del list1[i]
del list2[j]
else:
i += 1
list1.sort()
list2.sort()
if state not in missing:
assert len(list1) == 0, f"{state}\n\n{list1}\n\n{list2}"
else:
assert len([x for x in list1 if x not in missing[state]]) == 0
if not allow_missing:
assert len(list2) - len(missing.get(state, {})) == 0, list2
# Assert mapping is not many-to-1
assert len(M.values()) == len(set(M.values()))
return M
def merge_with_fips(self, counties, missing=set()):
kFipsConversions = {
# Shannon County renamed to Oglala Lakota County
"46113": "46102",
# kusilvak census area renamed to wade hampton census area
"02158": "02270",
}
for state_name in self.states:
for county_name in self.states[state_name]:
county = self.states[state_name][county_name]
fips = county["fips"]
if kFipsConversions.get(fips, None) in counties:
fips = kFipsConversions[fips]
if fips in missing:
continue
self.add_to_json(county, counties[fips])
def merge(self, states, allow_missing=False, missing={}):
if not allow_missing:
assert len(states) == 51
for state in states:
M = self.merge_state(
state,
list(states[state].keys()),
list(self.states[state].keys()),
allow_missing=allow_missing,
missing=missing
)
for county in M:
self.add_to_json(
self.states[state][M[county]],
states[state][county]
)
def add_to_json(self, base, addition):
for k in addition:
assert k not in base, f'base already has "{k}"'
base[k] = addition[k]
def get_geometry():
counties = {}
sf = shapefile.Reader(pjoin('data', 'tl_2017_us_county/tl_2017_us_county.shp'))
# Add geometric data for countries.
for s in sf:
state = fips_code_to_name[s.record.STATEFP]
if state in not_states:
continue
fips = s.record.GEOID
county_name = s.record.NAMELSAD.lower()
# There is one county that crosses from negative
# to positive longitudes, which screws up center-point
# computations. To fix this we subtact 360 from
# positive longitudes.
poly = geometry.Polygon([(x - 360 if x > 0 else x, y) for x, y in s.shape.points])
# We explicitly compute centroids rather than use
# s.record.INTPTLAT and s.record.INTPTLON, since
# I can't find any documentation of how these
# points are actually picked. We use the convex
# hull since some 'polygons' are weird, due to some
# islands containing islands of territory (figuratively
# and literally).
center = poly.convex_hull.centroid
counties[fips] = {
"land_area (km^2)": s.record.ALAND / 1e6,
"area (km^2)": (s.record.ALAND + s.record.AWATER) / 1e6,
# NOTE: we don't undo the "- 360" transformation
# above, since most use cases probably *prefer*
# not having to deal with the wrapping behavior.
"longitude (deg)": center.x,
"latitude (deg)": center.y,
}
return counties
def get_zips():
with open(pjoin('data', 'zip_county_fips_2018_03.csv'), 'r') as f:
reader = csv.reader(f, delimiter=',')
header = next(reader)
rows = [row for row in reader]
counties = {}
for zipcode, fips, city, state, county, _ in rows:
if state in ["PR", "GU", "VI"]:
continue
if fips not in counties:
counties[fips] = {
'zip-codes': []
}
counties[fips]['zip-codes'].append(zipcode)
# kusilvak census area
counties["02158"] = {
"zip-codes": [
"99554", "99563", "99581", "99585", "99604", "99620", "99632", "99650", "99657", "99658", "99662", "99666"
]
}
# oglala lakota county
counties["46113"] = {
"zip-codes": [
"57716", "57752", "57756", "57764", "57770", "57772", "57794",
]
}
return counties
def get_demographics():
age_code_to_group = {
0: "all",
1: "0-4",
2: "5-9",
3: "10-14",
4: "15-19",
5: "20-24",
6: "25-29",
7: "30-34",
8: "35-39",
9: "40-44",
10: "45-49",
11: "50-54",
12: "55-59",
13: "60-64",
14: "65-69",
15: "70-74",
16: "75-79",
17: "80-84",
18: "85+"
}
# https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-alldata.pdf
year_code_to_year = {
"1": "4/1/2010",
"2": "4/1/2010", # sic
"3": "7/1/2010",
"4": "7/1/2011",
"5": "7/1/2012",
"6": "7/1/2013",
"7": "7/1/2014",
"8": "7/1/2015",
"9": "7/1/2016",
"10": "7/1/2017",
"11": "7/1/2018",
"12": "7/1/2019",
}
# After downloading this file you should open it with a text editor (
# I use Sublime) and re-encode it as utf8.
counties = {}
populations = {}
with open(pjoin('data', 'cc-est2019-alldata.csv'), 'r') as f:
reader = csv.reader(f, delimiter=',')
header = next(reader)
rows = [row for row in reader]
assert header[:7] == ['SUMLEV', 'STATE', 'COUNTY', 'STNAME', 'CTYNAME', 'YEAR', 'AGEGRP']
for row in rows:
fips = row[1] + row[2]
county = row[4].lower()
year = year_code_to_year[row[5]].split('/')[-1]
if fips not in populations:
populations[fips] = {}
age_group = int(row[6])
total = int(row[7])
if age_group == 0:
populations[fips][year] = total
# We only grab the latest year available and ignore the
# other rows.
if year_code_to_year[row[5]] != '7/1/2019':
continue
if age_group == 0:
# age group "0" is everyone. We grab racial data from
# this row. The racial break down done by the Census
# Bureau is... intense, with 73 different columns. To
# keep the file size reasonable I don't track them all.
# Fortunately the code and data is freely available so
# it is trivial for you to add more columns if you like!
# We assume this is the first row we see.
assert fips not in counties
counties[fips] = {}
counties[fips]['race'] = {}
counties[fips]['age'] = {}
counties[fips]['male'] = int(row[8])
counties[fips]['female'] = int(row[9])
counties[fips]['population'] = total
counties[fips]['race']['non_hispanic_white_alone_male'] = int(row[34]) / total
counties[fips]['race']['non_hispanic_white_alone_female'] = int(row[35]) / total
counties[fips]['race']['black_alone_male'] = int(row[12]) / total
counties[fips]['race']['black_alone_female'] = int(row[13]) / total
counties[fips]['race']['asian_alone_male'] = int(row[16]) / total
counties[fips]['race']['asian_alone_female'] = int(row[17]) / total
counties[fips]['race']['hispanic_male'] = int(row[56]) / total
counties[fips]['race']['hispanic_female'] = int(row[57]) / total
else:
counties[fips]['age'][age_code_to_group[int(row[6])]] = int(row[7]) / counties[fips]['population']
for fip in counties:
counties[fip]['population'] = populations[fip]
return counties
def get_cdc_deaths():
counties = {}
fns = [
"Compressed Mortality, 1999-2019 (all suicides).txt",
"Compressed Mortality, 1999-2019 (firearm suicides).txt",
"Compressed Mortality (assaults), 1999-2019.txt",
"Compressed Mortality (land vehicle deaths; ICD-10 codes V01-V89), 1999-2019.txt"
]
for varname, fn in zip(['suicides', 'firearm suicides', 'homicides', 'vehicle'], fns):
with open(pjoin('data', 'CDC', fn), 'r') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
rows = [row for row in reader]
header = rows[0]
rows = rows[1:]
rows = rows[:rows.index(['---']) - 1]
former_independent_cities = {}
for row in rows:
_, county, fips, deaths, _, _ = row
county = county.lower()
state = state_abbreviation_to_name[county.split(', ')[-1].upper()]
county = ', '.join(county.split(', ')[:-1])
# These counties changed their names recently, and rows with
# both the old names and new names are found in the CDC
# dataset, so we simply ignore these names.
if county in ['prince of wales-outer ketchikan census area', 'skagway-hoonah-angoon census area', "wrangell-petersburg census area"]:
continue
if deaths == 'Suppressed':
deaths = None
elif deaths == 'Missing':
deaths = -1
else:
deaths = int(deaths) / 21.0
if state in former_independent_cities_to_counties and county in former_independent_cities_to_counties[state]:
county = former_independent_cities_to_counties[state][county]
if state not in former_independent_cities:
former_independent_cities[state] = {}
former_independent_cities[state][county] = deaths
continue
if fips not in counties:
counties[fips] = {
"deaths": {}
}
assert varname not in counties[fips]["deaths"]
counties[fips]["deaths"][varname] = deaths
# Add formly independent cities to their respective counties.
for state in former_independent_cities:
for county in former_independent_cities[state]:
# If either value was suppressed, we keep the concatenated
# value as None.
if counties[fips]["deaths"][varname] is None:
continue
if former_independent_cities[state][county] is None:
continue
counties[fips]["deaths"][varname] += former_independent_cities[state][county]
return counties
# Labor force data
# https://www.bls.gov/lau/#cntyaa
def get_labor_force():
counties = {}
years = ['2004', '2008', '2012', '2016', '2020']
for year in years:
with open(pjoin('data', 'bls', year + '.txt'), 'r') as f:
lines = f.readlines()
for line in lines[6:]:
line = line.strip()
if len(line) == 0:
break
laus_code, state_fips_code, county_fips_code, county_name, year, labor_force, employed, unemployed, unemployment_rate = re.sub(r" +", " ", line).split(" ")
if state_fips_code == '72': # PR
continue
fips = state_fips_code + county_fips_code
if fips not in counties:
counties[fips] = {'bls': {}}
c = {}
c['labor_force'] = float(labor_force.replace(",",""))
c['employed'] = float(employed.replace(",",""))
c['unemployed'] = float(unemployed.replace(",",""))
# NOTE: we exclude unemployment_rate since it is trivially computable as
# "unemployed / labor_force"
# c['unemployment_rate'] = float(unemployment_rate)
counties[fips]['bls'][year] = c
return counties
def get_fatal_police_shootings():
states = {}
for year in ['2017', '2018', '2019', '2020']:
for varname in [f'total-{year}', f'unarmed-{year}', f'firearmed-{year}']:
with open(pjoin('generated', 'police_shootings', varname + '.json'), 'r') as f:
shootings = json.load(f)
for k in shootings:
state_name = state_abbreviation_to_name[k[-2:].upper()]
if state_name not in states:
states[state_name] = {}
state = states[state_name]
county_name = k[:-4]
if county_name not in state:
state[county_name] = {
"fatal_police_shootings": {}
}
state[county_name]["fatal_police_shootings"][varname] = shootings[k]
return states
def get_police_deaths():
states = {}
with open(pjoin('data', 'police-deaths-2019.txt'), 'r') as f:
lines = f.readlines()[8:]
lines = [line.strip() for line in lines]
F = {}
for i in range(0, len(lines), 5):
name = lines[i + 0]
cause = lines[i + 2]
F[cause] = F.get(cause, 0) + 1
location = lines[i + 3]
state = state_abbreviation_to_name[location[-2:]]
county = location[:-4].lower()
if state not in states:
states[state] = {}
if county not in states[state]:
states[state][county] = {}
states[state][county]['police_deaths'] = states[state][county].get('police_deaths', 0) + 1
return states
def get_avg_income():
states = {}
for fn in os.listdir(pjoin('data', 'CAINC1')):
with open(pjoin('data', 'CAINC1', fn), 'r', encoding='Latin-1') as f:
reader = csv.reader(f, delimiter=',')
header = next(reader)
rows = [row for row in reader]
for row in rows[3:-1]:
if len(row) < 7:
continue
if row[6] != 'Per capita personal income (dollars) 2/':
continue
# This is an effective way to ensure the row is
# sensible (and not a footer / footnote).
try:
avg_income = int(row[-1])
except ValueError:
continue
loc = row[1]
# ignore fotenotes...
while loc[-1] == '*':
loc = loc[:-1]
assert loc[-4:-2] == ', '
county = loc[:-4].lower()
state = state_abbreviation_to_name[loc[-2:]]
if state not in states:
states[state] = {}
# These counties are combined...
if loc == 'Maui + Kalawao, HI':
assert "maui county" not in states[state]
assert "kalawao county" not in states[state]
states[state]['maui county'] = {
"avg_income": avg_income
}
states[state]['kalawao county'] = {
"avg_income": avg_income
}
continue
# Independent cities are merged with their surrounding counties.
# We un-merge them here.
if '+' in county:
parts = [x.strip() for x in re.findall(r"[^,\+]+", county)]
assert parts[0] + ' county' not in states[state]
states[state][parts[0] + ' county'] = {
"avg_income": avg_income
}
for part in parts[1:]:
if part[-5:] != ' city':
part += ' city'
assert part not in states[state]
states[state][part] = {}
states[state][part]['avg_income'] = avg_income
continue
assert county not in states[state]
states[state][county] = {
"avg_income": avg_income
}
for state in states:
for county in states[state]:
assert 'avg_income' in states[state][county]
return states
def get_poverty():
with open('data/poverty.tsv', 'r') as f:
reader = csv.reader(f, delimiter='\t')
header = next(reader)
rows = [row for row in reader]
fipsIdx = header.index('FIPStxt')
povertyIdx = header.index('PCTPOVALL_2019')
fips2poverty = {}
for row in rows:
p = row[povertyIdx]
p = p.replace(',', '')
fips2poverty[row[fipsIdx]] = {
'poverty-rate': float(p)
}
return fips2poverty
def get_education():
with open('data/education.tsv', 'r') as f:
reader = csv.reader(f, delimiter='\t')
header = next(reader)
rows = [row for row in reader]
fipsIdx = header.index('FIPS Code')
lessThanHighSchoolIdx = header.index('Percent of adults with less than a high school diploma, 2015-19')
highSchoolIdx = header.index('Percent of adults with a high school diploma only, 2015-19')
someCollegeIdx = header.index("Percent of adults completing some college or associate's degree, 2015-19")
bachelorsIdx = header.index("Percent of adults with a bachelor's degree or higher, 2015-19")
fips2edu = {}
for row in rows:
A = {
'less-than-high-school': row[lessThanHighSchoolIdx],
'high-school': row[highSchoolIdx],
'some-college': row[someCollegeIdx],
'bachelors+': row[bachelorsIdx]
}
for k in A:
if A[k] == '':
A[k] = None
else:
A[k] = float(A[k])
fips2edu[row[fipsIdx]] = { 'edu': A }
return fips2edu
def get_covid():
fips2covid = {}
for varname, fn in zip(['deaths', 'confirmed'], ['covid_deaths_usafacts.csv', 'covid_confirmed_usafacts.csv']):
with open(pjoin('data', fn), 'r') as f:
reader = csv.reader(f, delimiter=',')
header = next(reader)
countyColumn = header.index('countyFIPS')
stateColumn = header.index('State')
rows = [row for row in reader]
for row in rows:
for date in [date for date in header if re.match(r"\d{4}-\d\d-01", date)]:
column = header.index(date)
fips = row[countyColumn]
if fips == '0':
# Unallocated cases make up a vanishingly small proportion of data.
continue
if len(fips) == 4:
fips = '0' + fips
if fips not in fips2covid:
fips2covid[fips] = {}
if f"covid-{varname}" not in fips2covid[fips]:
fips2covid[fips][f"covid-{varname}"] = {}
assert date not in fips2covid[fips][f"covid-{varname}"], (date, fips)
fips2covid[fips][f"covid-{varname}"][date] = int(row[column])
with open(pjoin('data', 'COVID-19_Vaccinations_in_the_United_States_County.csv'), 'r') as f:
reader = csv.reader(f, delimiter=',')
header = next(reader)
rows = [row for row in reader]
def fix_vac_date(date):
month, day, year = date.split('/')
return '-'.join([year, month, day])
for row in rows[::-1]:
fips = row[1]
if not re.match(r"^\d\d/01/2021$", row[0]):
continue
if fips not in fips2covid:
continue
if 'covid-vaccination' not in fips2covid[fips]:
fips2covid[fips]['covid-vaccination'] = {}
fips2covid[fips]["covid-vaccination"][fix_vac_date(row[0])] = float(row[5])
return fips2covid
def get_elections():
states = {}
fips_to_county = {
'08014': ('broomfield county', 'Colorado')
}
with open(pjoin('data', 'fips_to_county.txt'), 'r') as f:
for line in f.readlines():
if len(line.strip()) == 0:
continue
code, county, state = line.strip().split('\t')
# American Samoa, Northern Mariana Islands, Puerto Rico
if state in ['AS', 'MP', 'PR']:
continue
fips_to_county[code] = (county.lower(), state_abbreviation_to_name[state])
with open(pjoin('data', 'US_County_Level_Presidential_Results_08-16.csv'), 'r') as f:
reader = csv.reader(f, delimiter=',')
header = next(reader)
assert header == ['fips_code', 'county', 'total_2008', 'dem_2008', 'gop_2008', 'oth_2008', 'total_2012', 'dem_2012', 'gop_2012', 'oth_2012', 'total_2016', 'dem_2016', 'gop_2016', 'oth_2016']
rows = [row for row in reader]
for row in rows:
county, state = fips_to_county[row[0]]
if state not in states:
states[state] = {}
all2008 = int(row[2])
dem2008 = int(row[3])
gop2008 = int(row[4])
all2012 = int(row[6])
dem2012 = int(row[7])
gop2012 = int(row[8])
all2016 = int(row[10])
dem2016 = int(row[11])
gop2016 = int(row[12])
states[state][county] = {
"elections": {
"2008": {
"total": all2008,
"dem": dem2008,
"gop": gop2008,
},
"2012": {
"total": all2012,
"dem": dem2012,
"gop": gop2012,