This is an interesting study from a team of AI researchers at Stanford. What they did was use car images taken directly from Google Street View (so images of cars parked on-street) to predict income levels, racial makeup, educational attainment, and voting patterns at the zip code and precinct level.
Admittedly, it’s not a perfect survey, but when they compared their findings to actual or previously collected data (such as from the American Community Survey), it turns out that their study was actually remarkably accurate. Google Street View allowed them to survey 22 million cars, or about 8% of all cars in the US.
Here are some of the things they found:
– Toyota and Honda vehicles are strongly associated with Asian neighborhoods.
– Buick, Oldsmobile, and Chrysler vehicles are strongly associated with black neighborhoods.
– Pickup trucks, Volkswagens and Aston Martins are strongly associated with white neighborhoods.
Interestingly enough, the ratio of pickup trucks to sedans, alone, is a pretty reliable indicator of voting patterns. If a neighborhood has more pickup trucks than sedans, there’s an 82% chance it voted Republican in the last election.
Perhaps this isn’t all that surprising given that car purchases are highly symbolic. But given that the American Community Survey costs $250 million a year to administer, this study is a good preview of what cheaper and more realtime data collection might look like.