fgregg

pums <- read.csv("small_pums.csv")

hist(pums$WAGP)

male_income <- pums$WAGP[pums$SEX=="male"]
female_income <- pums$WAGP[pums$SEX=="female"]

plot(WAGP ~ SEX, data=pums)

summary(lm(WAGP ~ 1,data=pums))

fgregg

     geoid10     | cases | person_years 
-----------------+-------+--------------
 170318390004004 |   470 |         1265
 170318374002019 |   174 |          535
 170318419002089 |   219 |          530
 170318368002005 |   118 |          513
 170313814001005 |    18 |          463
 170310313004006 |   129 |          392
 170314909022041 |    44 |          358
 170312315005016 |    30 |          311

fgregg

pums <- read.csv("small_pums.csv")

pums$ESR <- factor(pums$ESR,
                   labels=c("civilian employed, at work",
                            "civilian employed, with a job but not at work",
                            "unemployed",
                            "armed forces, at work",
                            "not in labor force"))

pums$college <- factor(pums$SCHL > 17, labels=c("no college", "some college"))

fgregg

#FG's list

Things I haven't read, but would like to

• Tools for Conviviality
• Some things about information systems within corporations, maybe something about Toyota and Walmart
• Some things about modern public relations
• Some things about the Rand Corporation
• Some things about Herman Kahn and early Hudson Institute
• http://www.culturemachine.net/index.php/cm/article/view/511/526

fgregg

Buildings,https://data.cityofchicago.org/resource/ucsz-xe6d.json?$select=date_trunc_y(date_received)%20as%20year,COUNT(*)&$group=year&$order=year
Revenue,https://data.cityofchicago.org/resource/zrv6-shhf.json?$select=date_trunc_y(date_received)%20as%20year,COUNT(*)&$group=year&$order=year
311,https://data.cityofchicago.org/resource/j2p9-gdf5.json?$select=date_trunc_y(date_received)%20as%20year,COUNT(*)&$group=year&$order=year
Transportation,https://data.cityofchicago.org/resource/u9qt-tv7d.json?$select=date_trunc_y(date_received)%20as%20year,COUNT(*)&$group=year&$order=year
Law,https://data.cityofchicago.org/resource/44bx-ncpi.json?$select=date_trunc_y(date_received)%20as%20year,COUNT(*)&$group=year&$order=year

fgregg

Query:

select 
  max(pin::bigint) as example, 
  count(*) num_properties, 
  ROUND(current_total/current_market_value::numeric, 2::int) as assessment_level 
from taxes 
where 
 current_market_value !=0 

fgregg

• A = ADD - review of assessment
• E = EXEMPT - review of exempt status of parcel, because owned by tax exempt private entity
• G = GOVERNMENT - review of exempt status of parcel, because owned by a government
• P = PRE-FILE give data - a review of assessment where complainant has pre-filed evidence
• T = TWO TOWNSHIPS - the property straddles two townships
• C = C OF C - certificate of correction, the tax assessor has made an error and is requesting an adjustment to the assessement
• X = PRORATED PIN - a property that the complainant did not include in their filing, but which the BOR believes is relevant to its judgement
• U = UNDERVAL - a third party complains that a property owner is paying too little taxes

fgregg

fgregg

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

import os
import csv
import tempfile
import time
import logging
import optparse
import itertools

fgregg

 O 2017-6854
 Or 2016-673
 O 2017-6485
 R 2017-734
 O 2017-225
 R 2017-726
 O 2016-5666
 O 2017-1018
 R 2016-908
 Or 2017-365