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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)

  1. Fig. 2.

    We use all of the cities in counties starting with A, B, and C (shown in purple on the map) to train a model estimating socioeconomic data from car attributes. Using this model, we estimate demographic variables at the zip code level for all of the cities shown in green. We show actual vs. predicted maps for the percentage of Black, Asian, and White people in Seattle, WA (i–iii); the percentage of people with less than a high school degree in Milwaukee, WI (iv); and the percentage of people with graduate degrees in Milwaukee, WI (v). (vi) Maps the median household income in Tampa, FL. The ground truth values are mapped on Left, and our estimated results are on Right. We accurately localize zip codes with the highest and lowest concentrations of each demographic variable such as the three zip codes in Eastern Seattle with high concentrations of Caucasians, one Northern zip code in Milwaukee with highly educated inhabitants, and the least wealthy zip code in Southern Tampa.

  2. Fig. 3.

    Actual and inferred voting patterns. A, i and ii map the actual and predicted percentage of people who voted for Barack Obama in the 2008 presidential election (r = 0.74). iii maps the ratio of detected pickup trucks to sedans in the 165 cities in our test set. As can be seen from the map, the ratio is very low in Democratic cities such as those in the East Coast and high in Republican cities such as those in Texas and Wyoming. (B) Shows actual vs. predicted voter affiliations for various cities in our test set at the precinct level using our full model. Democratic precincts are shown in blue, and Republican precincts are shown in red. Our model correctly classifies Casper, WY as a Republican city and Los Angeles, CA as a Democratic city. We accurately predict that Milwaukee, WI is a Democratic city except for a few Republican precincts in the southern, western, and northeastern borders of the city.

Online Impact

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