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Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States

  1. Li Fei-Feia
  1. aArtificial Intelligence Laboratory, Computer Science Department, Stanford University, Stanford, CA 94305;
  2. bVision and Learning Laboratory, Computer Science and Engineering Department, University of Michigan, Ann Arbor, MI 48109;
  3. cThe Center for Genome Architecture, Department of Genetics, Baylor College of Medicine, Houston, TX 77030;
  4. dDepartment of Computer Science, Rice University, Houston, TX 77005;
  5. eThe Center for Genome Architecture, Department of Computational and Applied Mathematics, Rice University, Houston, TX 77005
  1. Edited by Kenneth W. Wachter, University of California, Berkeley, CA, and approved October 16, 2017 (received for review January 4, 2017)


We show that socioeconomic attributes such as income, race, education, and voting patterns can be inferred from cars detected in Google Street View images using deep learning. Our model works by discovering associations between cars and people. For example, if the number of sedans in a city is higher than the number of pickup trucks, that city is likely to vote for a Democrat in the next presidential election (88% chance); if not, then the city is likely to vote for a Republican (82% chance).


The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains <mml:math><mml:mo>~</mml:mo></mml:math>1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.


  • ?1To whom correspondence should be addressed. Email: tgebru{at}stanford.edu.
  • Author contributions: T.G., J.K., J.D., E.L.A., and L.F.-F. designed research; T.G., J.K., Y.W., D.C., J.D., E.L.A., and L.F.-F. performed research; T.G. and J.K. contributed new reagents/analytic tools; T.G., J.K., Y.W., D.C., J.D., E.L.A., and L.F.-F. analyzed data; and T.G., J.K., E.L.A., and L.F.-F. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.danielhellerman.com/lookup/suppl/doi:10.1073/pnas.1700035114/-/DCSupplemental.

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