JoeGermuska

After some discussion about how a map showing that half of US income can be attributed to the major metro areas, I wanted to see if I could work out how public assistance is distributed.

The American Community Survey has a table, B19067, Aggregate Public Assistance Income for Households, which tells the total amount of dollars in public assistance income that go to each geography. As you might guess, the biggest metro areas have the most dollars.

So I downloaded some CSVs from Census Reporter for all CBSA (metro/micropolitan) areas in the US:

• B19067 Aggregate Public Assistance Income for Households
• B19057 Public Assistance Income in the Past 12 Months for Households
• B01003 [Total Population](http://cens

JoeGermuska

select c.geoid, c.name, d2.B19013001, count(*)
  from acs2012_5yr.geoheader c,
       acs2012_5yr.geoheader g,
       acs2012_5yr.B19001 d,
       acs2012_5yr.B19013 d2
 where B19001011 = 0
   and B19001012 = 0
   and B19001013 = 0
   and B19001014 = 0
   and B19001015 = 0

JoeGermuska

<table border="1">
  <tr>
    <th align="center">geoid</th>
    <th align="center">rate</th>
    <th align="center">moe</th>
    <th align="center">b17001001</th>
    <th align="center">b17001002</th>
    <th align="center">b17001001_moe</th>
    <th align="center">b17001002_moe</th>
  </tr>

JoeGermuska

 state | county | tract  | count 
-------+--------+--------+-------
 17    | 031    | 010400 |     1
 17    | 031    | 020702 |     1
 17    | 031    | 030101 |     1
 17    | 031    | 030102 |     1
 17    | 031    | 030103 |     1
 17    | 031    | 030104 |     1
 17    | 031    | 030601 |     1
 17    | 031    | 030604 |     1

JoeGermuska

Demographics
    age
    race
    seniors
    sex (not gender: http://factfinder2.census.gov/help/en/glossary/g/gender.htm)
Social
    ancestry
    citizenship
    disability
    education

JoeGermuska

Table ID,Line Number Decimal M Lines,Long Table Title
B01002,,MEDIAN AGE BY SEX
B01002,0.5,Median age --
B01002A,,MEDIAN AGE BY SEX (WHITE ALONE)
B01002A,0.5,Median age --
B01002B,,MEDIAN AGE BY SEX (BLACK OR AFRICAN AMERICAN ALONE)
B01002B,0.5,Median age --
B01002C,,MEDIAN AGE BY SEX (AMERICAN INDIAN AND ALASKA NATIVE)
B01002C,0.5,Median age --
B01002D,,MEDIAN AGE BY SEX (ASIAN ALONE)

JoeGermuska

   1 "Aggregate interest, dividends, or net rental income in the past 12 months (in 2011 inflation-adjusted dollars)"
   1 "Aggregate number of vehicles (car, truck, or van) used in commuting:"
   1 Aggregate Social Security income in the past 12 months (in 2011 inflation-adjusted dollars)
   1 Aggregate Supplemental Security Income (SSI) in the past 12 months (in 2011 inflation-adjusted dollars)
   1 Aggregate contract rent
   1 Aggregate earnings in the past 12 months (in 2011 inflation-adjusted dollars)
   1 Aggregate earnings in the past 12 months (in 2011 inflation-adjusted dollars):
   1 Aggregate family income in the past 12 months (in 2011 inflation-adjusted dollars)
   3 Aggregate family income in the past 12 months (in 2011 inflation-adjusted dollars):
   1 Aggregate gross rent

JoeGermuska

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from icalendar import Calendar, Event
from datetime import timedelta
from dateutil.parser import parse
from dateutil.tz import tzlocal
from pyquery import PyQuery as pq
from urlparse import urljoin
urls = {

JoeGermuska

The Chicago Tribune is seeking a CAR journalist who can serve a 2-year
residency for the newsroom. The candidate for this Computer Assisted
Reporting position should have proven watchdog skills and experience
with every facet of story development, from filing FOIA requests to
finishing a polished draft of a newspaper story and its digital
package. Clips should show proven results of this kind of work.

A key part of the job is to work with suburban reporters assigned to
specific towns to tell stories that can be told only by those with
command of hard data. This journalist also will be looking

JoeGermuska

#!/usr/bin/env python
import csv, json
from collections import defaultdict
r = csv.reader(open("data.csv"))
headers = r.next()
countries = headers[1:]
out_rows = []
for row in r:
    area = row[0]
    out_row = [area]