Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States
| Autor: | Jia Deng, Jonathan Krause, Yilun Wang, Li Fei-Fei, Erez Lieberman Aiden, Duyun Chen, Timnit Gebru |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Truck
Satellite Imagery demography 010504 meteorology & atmospheric sciences Presidential election media_common.quotation_subject Precinct Population 0211 other engineering and technologies 02 engineering and technology 01 natural sciences computer vision American Community Survey Machine Learning social analysis Voting Humans Socioeconomic status 021101 geological & geomatics engineering 0105 earth and related environmental sciences media_common Multidisciplinary Computer Sciences deep learning Advertising Census United States Geography Socioeconomic Factors 8. Economic growth Unemployment Physical Sciences Demographic economics Automobiles |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America |
| ISSN: | 1091-6490 0027-8424 |
| Popis: | Significance 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 ∼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. |
| Databáze: | OpenAIRE |
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