Mr. Swensson:
Good catch - if I had checked my notes first I would have realized my comment was based on
BEA data, not BLS. Their tables are itemized, so even with employer-provided benefits
taken out, the total income still runs well ahead of what's reported to the Census.
Sent from my iPhone
On Dec 5, 2017, at 5:21 PM, Pete Swensson
<pete(a)swensson.us> wrote:
There are many Federal sources of income data, and they do not all measure the same
thing, or even the same workers. The most widely used BLS data includes all forms of
employee compensation for employees covered by unemployment insurance (about 80-85% of the
workforce). This data tallies both wages/salaries and benefits, such as health insurance,
retirement contributions, unemployment insurance, etc. This data set does not include
proprietors (owners of businesses), people earning their income from commissions (e.g.,
real estate agents, car sales people, etc.), student interns, or elected officials.
BEA data includes both employees and proprietors and their income, by summing the BLS
data for employees, with IRS data for proprietors. People earning their income only from
commissions are lumped in with proprietors. Student interns and elected officials are
classed as employees rather than proprietors. The BEA data will have different numbers
for total employment for the same geographic area, compared to BLS.
ACS asks the respondent how much cash income they earned in the last twelve months, so it
does not include the value of benefits (which most respondents would likely not know and
report very reliably anyway). Up until the 2000 Census, this question was asked for the
previous calendar year, and since the survey was done mostly in April, most respondents
probably had just filed their income tax return, and had a pretty good idea of their cash
income for the previous year. Starting with the 2005 ACS, the surveys have been conducted
every month. The question has been what was your income for the previous 12 month period,
which for most included parts of two tax years, the more recent of which was still
on-going. For this reason, many users of the ACS income data are suspicious of comparing
the ACS data with the old decennial Census data (to look for trends) because of these
differences in methodology.
Bottom line, comparing income data from the various sources is comparing apples and
oranges. Since ACS asks households only about cash income, while BLS asks employers about
wages plus benefits, you can count on there being a substantial difference, with no
underreporting implied. Make comparisons across geographies using the same dataset, and
you should have reasonably useful results.
Pete Swensson
1201 Terrace Lane SW
Olympia, WA 98502
(360) 352-7909 home
(360) 628-6621 cell
pete(a)swensson.us
From: ctpp-news [mailto:ctpp-news-bounces@chrispy.net] On Behalf Of Sam Granato
Sent: Monday, December 04, 2017 3:39 AM
To: ctpp-news(a)chrispy.net
Subject: Re: [CTPP] The Washington Post: Scientists can now figure out detailed, accurate
neighborhood demographics using Google Street View photos
Cars parked on the street don't always reflect who lives there, and the same can be
said for what gets reported to the Census. (Take a look at what the BLS publishes to get a
sense of how much, in aggregate, the populace underreports income in the ACS.). Maybe the
best lesson from this study is that aggregating as high as zip code level can cancel out a
lot of errors.
Sent from my iPhone
On Dec 1, 2017, at 8:26 PM, Pete Swensson <pete(a)swensson.us> wrote:
Just an anecdotal observation: Vehicle make and model preferences vary a lot by geography
for reasons other than income or demographics. Lots more pick-ups in some parts of the
country than others. Subarus ALL have 4-wheel drive, and they are consequently a very
popular make in my county. Japanese makes of cars (e.g., Honda) have long been more
popular on the West Coast (regardless of ethnicity) than in the Midwest, where there is
stronger brand loyalty to American makes manufactured in the Midwest (in theory – many are
now made in Mexico).
Even if one accepts the underlying premise of correlating vehicle types to demographics
and voting patterns, I think Ed’s suggestion of processing vehicle registration files
instead of Google photos is right on target. Much easier. And it wouldn’t surprise me if
users of Big Data for political analysis are already doing this.
Pete Swensson
1201 Terrace Lane SW
Olympia, WA 98502
(360) 352-7909 home
(360) 628-6621 cell
pete(a)swensson.us
From: ctpp-news [mailto:ctpp-news-bounces@chrispy.net] On Behalf Of Steve Wilson
Sent: Friday, December 01, 2017 9:04 AM
To: ctpp-news(a)chrispy.net
Subject: Re: [CTPP] The Washington Post: Scientists can now figure out detailed, accurate
neighborhood demographics using Google Street View photos
Streetview + ACS can also be used to develop a model estimating likelihood of
encountering man on porch yelling “hey you kids – get off my lawn”. :)
From: Polzin, Steven [mailto:polzin@cutr.usf.edu]
Sent: Friday, December 01, 2017 10:58 AM
To: ctpp-news(a)chrispy.net
Subject: Re: [CTPP] The Washington Post: Scientists can now figure out detailed, accurate
neighborhood demographics using Google Street View photos
Be careful who you let parked in front of your house. It might change your income.
From: ctpp-news [mailto:ctpp-news-bounces@chrispy.net] On Behalf Of Scott Ramming
Sent: Friday, December 01, 2017 11:55 AM
To: ctpp-news(a)chrispy.net
Subject: Re: [CTPP] The Washington Post: Scientists can now figure out detailed, accurate
neighborhood demographics using Google Street View photos
As I understand it, their models aren’t based on auto ownership levels, but the type and
age of the autos parked in view.
There’s a big difference in neighborhoods where you find concentrations of say 2018 Range
Rovers versus 1964 Ford Mavericks.
Plus one to Thomas’s point about high density areas where people either don’t own
vehicles or park the vehicles they do in garages. The same lack of sample would also apply
to where HOAs dictate that all vehicles must be parked in personal garages with the door
lowered. Perhaps lack of variation in the paint color scheme could be used as an
explanatory variable for such neighborhoods.
Scott Ramming, PhD PE | Senior Travel Modeler | Transportation Planning & Operations
Direct: 303-480-6711 | Fax: 303-480-6790 | Email: sramming(a)drcog.org
<image001.png>
1290 Broadway • Suite 100 • Denver, Colorado 80203-5606
main: 303-455-1000 • email: drcog(a)drcog.org • web:
www.drcog.org
<image002.gif>
<image003.png> <image004.png> <image005.png>
From: ctpp-news [mailto:ctpp-news-bounces@chrispy.net] On Behalf Of Alan E. Pisarski
Sent: Friday, December 01, 2017 8:59 AM
To: ctpp-news(a)chrispy.net; 'Ed Christopher' <edc(a)berwyned.com>
Subject: Re: [CTPP] The Washington Post: Scientists can now figure out detailed, accurate
neighborhood demographics using Google Street View photos
Re auto ownership: it was at one time a pretty good proxie for wealth etc. but vehicles
are so ubiquitous now that it is far less reliable. Not far from here in a neighborhood
that is largely low/mid income housing the streets are lined at night with white vans that
the immigrant painters, home construction guys use. Great area to steal a ladder. Alan
Alan E. Pisarski
alanpisarski(a)alanpisarski.com
703-941-4257 landline
703 650-8925 cell
From: ctpp-news [mailto:ctpp-news-bounces@chrispy.net] On Behalf Of Krishnan Viswanathan
Sent: Friday, December 01, 2017 10:15 AM
To: Ed Christopher <edc(a)berwyned.com>
Cc: ctpp-news(a)chrispy.net
Subject: Re: [CTPP] The Washington Post: Scientists can now figure out detailed, accurate
neighborhood demographics using Google Street View photos
Totally agree with you Ed. I think the Washington Post headline is a dose of hyperbole. I
have a question about spurious correlations and as far as I know the ACS does not ask what
type of vehicle is there in the household so this paragraph in their paper gave me pause:
Using ACS and presidential election voting data for regions in our training set, we train
a logistic regression model to estimate race and education levels and a ridge regression
model to estimate income and voter preferences on the basis of the collection of vehicles
seen in a region. This simple linear model is sufficient to identify positive and negative
associations between the presence of specific vehicles (such as Hondas) and particular
demographics (i.e., the percentage of Asians) or voter preferences (i.e., Democrat).
On Fri, Dec 1, 2017 at 9:38 AM, Ed Christopher <edc(a)berwyned.com> wrote:
Interesting stuff Krishnan--
If their basic assumption were true, that vehicle ownership somehow translates into
demographics (ie voter behavior), then why not just cut Google out and process vehicle
registration files. It seems to me that would be a lot easier and cheaper. Then again you
have to buy the basic assumption. Also, if you look at precinct by precinct voter behavior
two things will surprise you. First, precincts are not all one color (red or blue) in most
places and the number of people who do vote are very small when considering the total
population. While I found this work interesting I would not be out their trying to
oversell what its capabilities are without a whole lot more work and research. As I see it
we have a very long way to go before we have something that is a kin to the ACS and all
its by-products.
On 11/30/2017 8:39 PM, Krishnan Viswanathan wrote:
This will interest people in this group and also foster discussion about the methods
used. The article itself has a link to the paper.
Scientists can now figure out detailed, accurate neighborhood demographics using Google
Street View photos
http://wapo.st/2AnuP9L
Krishnan Viswanathan
5628 Burnside Circle
Tallahassee FL 32312
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Ed Christopher
Transportation Planning Consultant
708-269-5237
--
Krishnan Viswanathan
5628 Burnside Circle
Tallahassee FL 32312
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