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.

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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@chrispy.net; 'Ed Christopher' <edc@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@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@berwyned.com>
Cc: ctpp-news@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@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
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Krishnan Viswanathan
5628 Burnside Circle
Tallahassee FL 32312