
Using Deep Learning and Google Street View to Estimate Demographic Makeup of US - phodo
https://arxiv.org/abs/1702.06683
======
d--b
Dammit AI people, wake up! This is SO backwards!

AI-based profiling of anything is bound to filter off outliers. Censuses are
made in the field for a reason. The goal is to _gather_ data so that you can
make statistical reasoning on it. Not the opposite!!!!

Here, they just gather some car data, and _infer_ demographic data from it.
And then what? We've just created a population which matches the car sample.
We can't draw ANY conclusion from that data, beyond the type of cars that are
found in such and such neighborhoods.

~~~
paganel
In my parts of the world (Eastern Europe) you can infer a lot more about the
people inhabiting a certain area by looking at the cars they drive, I mean,
compared to census and income/tax data. That's because there are lots of way
of not telling the authorities how much you earn, but once it comes to
purchasing a car you leave aside such niceties and you get the car that best
fits your real economic status (with few exceptions).

~~~
d--b
Yes, of course there is obviously some correlation between demographics and
car ownership. But this paper makes it seem that their method is good to
replace a in-the-field census.

Basically, replacing all demographic data that the census brings with the
single factor of car ownership. In the region where the actual correlation is
less than 90%, the data generated is completely useless.

~~~
paganel
> But this paper makes it seem that their method is good to replace a in-the-
> field census.

Ah, I missed that, sorry! To be honest I only skimped through the description,
for other lazy people like me here's the relevant line:

> Our results suggest that automated systems for monitoring demographic trends
> may effectively complement labor-intensive approaches, with the potential to
> detect trends with fine spatial resolution, in close to real time.

which I also find as a little bit crazy.

Otherwise I find the project quite interesting. I quite like to lose (too much
time) on GStreetView, and at the same time I developed an interest in
20-30-40-year old cars, so I wrote a small Chrome extension that allows me to
locally save images of old cars that I find on my country's roads while
"taking a walk" on GStreetView, along with the relevant info (lat-lng,
address, and the make and model of the cars which I input by hand using said
extension). I had also noticed that there's a distinct correlation between a
city's economic status and the cars you can find on its streets, and I was
wondering how hard would it be to do the car "recognition" using some AI-thing
and try to draw some conclusions from that. Glad that someone actually did it.

~~~
xapata
> Ah, I missed that, sorry!

I don't think you missed it. If so, I've missed it after reading the paper
twice.

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stillsut
Everyone jumps to the dystopian scifi implication. But how about lets try
thinking like real technologists?

Example use: I know of three small but vibrant towns in Massachusetts where my
wife and I would like to open a Bed 'n Breakfast. The problem is they're too
expensive. So I feed the street views of these downtowns into the AI, and
filter by <$250K home price. And it returns 20 beautifully "quaint" towns that
I never could have found just by traditional census / business database
searching.

~~~
ucaetano
A major example: data in emerging markets.

For most emerging markets, there's nothing comparable to the per-track census
data in the US (and other developed countries). Some fixed-line ISPs do market
assessments by driving through neighborhoods and counting the number of cars
and AC units in each house, as a proxy for income, and it is unlikely that
better data will be available by traditional means any time soon.

I once had to compile a lot of per-city info on Indonesia, it involved a
friend actually going into the national statistics office, spending several
hours drinking tea with them, getting the actual raw survey data recorded into
a CD (this was a couple years ago, not a couple decades) so I could eventually
process it and get some results (despite the abundance of gaps and errors in
the data).

In other words, Street View data is more pervasive, updated and readily
available than data from the national statistics office.

------
nl
This is a great piece of work.

I wonder if it would be possible to use a similar approach but on shop signs.
Intuitively there should be some kind of relationship between the type of
shops in different areas, but I guess it would be much less dense data than
car type.

I guess the next step is to make a completely end-to-end version without the
manual feature engineering.

~~~
colinbartlett
There was a similar discussion[1] here about that a while back using data
about different business types to assert gentrification levels.

1\.
[https://news.ycombinator.com/item?id=10391753](https://news.ycombinator.com/item?id=10391753)

------
Animats
Or you could just order that data from Experian Automotive, which has vehicle
registration data from all US states.

~~~
backpropaganda
But I need to get a job at one of the big tech companies, and so I need to
show them I can do deep learning.

~~~
NTDF9
Doesn't matter! You'll still be asked to write perfect code, with awesome
variable names, on a whiteboard, with eyes closed.

Don't forget to do handstands to stand out from the crowd.

~~~
narrator
Don't forget to practice doing math like 36^5 in your head. No pencil or paper
allowed!

~~~
logfromblammo
Well, let's see, 36 is 00100100 in binary. Binomial coefficients are 1 5 10 10
5 1. 2^25 + 5 * 2^22 + 10 * 2^19 + 10 * 2^16 + 5 * 2^13 + 2^10. 10 is 5 * 2,
so bit shift those middle two. 5 is binary 0101, so add copies bit shifted by
2. 2^25 + 2^24 + 2^22 + 2^22 + 2^20 + 2^19 + 2^17 + 2^15 + 2^13 + 2^10. Comes
out to

    
    
      0011 1001 1010 1010 0100 0000 0000
      0x039AA400
    

Which is a perfectly cromulent numeric response. If the interviewer asks what
that is in decimal, they ought to be glad I don't have a pencil, because it
would end up in someone's eye on my way out the door.

~~~
tropo
Me as interviewer: have you heard of the XOR operator?

~~~
logfromblammo
Yeah, it looks like a plus sign with a circle around it. It's not in the ASCII
character set.

Sometimes, programming languages will use the caret character to represent XOR
for boolean types and bitwise XOR for integral types, perhaps using two
asterisks for exponentiation, or leaving it as a named function call, like
Math.Exp or Math.Pow. But you have to be careful there, because sometimes
Math.Exp is actually the inverse of the natural log function, rather than
exponentiation.

If caret means XOR, then 36 ^ 5 is 0010 0001, or 33.

And that is the point where the interviewer marks me as "do not hire", because
they can't stand it when they can't show off how smart they are to the
candidate.

------
mempko
Let's ignore building racist AI for a sec. This is hugely flawed because most
street view data is NOT updated on a yearly bases. Many images are years old.
Doing yearly door to door will get fresher data.

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Doctor_Fegg
> We focus on the motor vehicles encountered during this journey because over
> 90% of American households own a motor vehicle

This is a clever hack, but I sorely hope no actual policy decisions are made
on the basis of a methodology that wilfully ignores the 10% who choose not to
own, or can't afford, a car.

~~~
blowski
Can there possibly be a methodology that is 100% correct?

If you try to interview 100% of people, then the results will be out of date
by the time you've reached everybody. (Plus the Hawthorne effect suggests
people will start changing their answers as people comment on the questions
they are asked.)

So you create samples, and necessarily some of those samples will be
unrepresentative because they will miss some nuance. Catch 22 - unless you
interview 100% of the people, you can't know what all the nuances are.

I guess the best we can hope for is a result that merges lots of different
results using lots of different methodologies, and base decisions on that.

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anonu
From the paper on collecting Street View images: "This was done via browser
emulation and requires only the latitude and longitude of each point"

My question is does the TOS for Google Maps or Street View allow for this?

I'm not trying to diminish the research - it is SUPER cool. I'm just thinking
it would be good to have access to the dataset they captured if publicly
available.

~~~
tgebru
At least parts of it will be publicly available. Our goal is to make the
entire code/data publicly available. We might have to provide URLs and
bounding boxes/other meta data for each image. But we're trying to figure out
how to do this properly.

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id122015
I want people to use technology to find what those people who drive a car into
pedestrians have in common. There must be something unique about them, the
media says they all have mental health issues. Suddenly since 2016 people with
metal issues got access to cars and trucks.

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platita
Extracting life quality related data from streetview does not seem to be a new
thing: [https://hackernoon.com/machine-learning-our-
cities-617ce005b...](https://hackernoon.com/machine-learning-our-
cities-617ce005ba27#.eg22ymq75)

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misiti3780
does anyone know how they download this data ?

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h4nkoslo
Weev is working on a similar project:
[https://youtu.be/ZMptVkyZWE4](https://youtu.be/ZMptVkyZWE4)

