What vehicle is most strongly associated with Republican voting districts? Extended-cab pickup trucks. For Democratic districts? Sedans.
Those conclusions may not be particularly surprising. After all, market researchers and political analysts have studied such things for decades.
But what is surprising is how researchers working on an ambitious project based at Stanford University reached those conclusions: by analyzing 50 million images and location data from Google Street View, the street-scene feature of the online giant’s mapping service.
For the first time, helped by recent advances in artificial intelligence, researchers are able to analyze large quantities of images, pulling out data that can be sorted and mined to predict things like income, political leanings and buying habits. In the Stanford study, computers collected details about cars in the millions of images it processed, including makes and models.
Identifying so many car images in such detail was a technical feat. But it was linking that new data set to public collections of socioeconomic and environmental information, and then tweaking the software to spot patterns and correlations, that makes the Stanford project part of what computer scientists see as the broader application of image data.