Using Deep Learning and Google Street View to Estimate Demographic Makeup of US
arxiv.org
arxiv.org
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.
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.
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.
I don't think you missed it. If so, I've missed it after reading the paper twice.
That is not what I read. They wrote, "complement," and never suggested replacing.
Determining "stuff" on car ownership may be misleading.
Examples. Mercedes acquired by co worker. He said family was so poor he had to wear his big brothers clothes.
Another person I am working with now used to live out of his car. Within his 2nd paycheck he leased a new BMW.
Cars were used to "feel good" and perhaps elevate class self perception.
I always answer: that's why I have a lot of money.
My point is that there is a hype around AI that makes it look that it can do things that it can't. What they do in this paper is equivalent to using a correlation matrix, and a bunch of observations to generate a completely random population that matches what we input in there (ok, the neural net may find some non-linear relationships, but you get the idea). Yes, there is a significant correlation between car ownership and demographics, so the results do look good from far away, but the reality is that they will only map the car ownership factors on to other traits, and in terms of information, it is way poorer than actually doing a census.
I am curious, because at first glance this seems really useful. But trying to see where the limits actually are.
So, you don't trust surveys? You don't A/B test your marketing?
Estimates are useful. Hell, your brain is just estimating what's in front of you based on a sample of photons coming in through your eyes. If you trust your vision, you're trusting statistical sampling. I suppose it's possible the USS Enterprise is cloaked and somehow your eyes are deceiving you, but the photons are certainly correlated with what's in front of you.
Some of the best statistical innovations are simply discoveries of good proxies for something that's difficult to measure. Google found that what you search for is a good proxy for what you want to buy. Facebook found that what your friends click on is a good proxy for what you'll click on. Of course you won't like everything Netflix suggests, but its suggestions will (hopefully) be better than just picking movies randomly.
You may use census data to train a neural net to draw some conclusions, not to produce brand new census data.
Why not appreciate the proxy for what it is?
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.
After all, doesn't the kind of of digital truth (or digital behaviourism) epitomized by this study threaten to undermine the foundation of ideas like individual emancipation or of the common?
Like let's say you move to a quaint town that the AI found for you. Who moved in and is now a member of that community, you and your wife or your digital doppelgangers? And where did you move? The town or it's statistical analog warehoused in a data-center somewhere.
When a new shop opens up in the town and you find your favorite brand of wine from your previous town on the shelves ... what's going on?
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.
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.
Don't forget to do handstands to stand out from the crowd.
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0x039AA400
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.
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.
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.
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.