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A longstanding line of research in urban studies explores how cities can be understood through their appearance. However, what remains unclear is to what extent urban dwellers' everyday life can be explained by the visual clues of the urban environment. In this paper, we address this question by applying a computer vision model to 27 million street view images across 80 counties in the United States. Then, we use the spatial distribution of notable urban features identified through the street view images, such as street furniture, sidewalks, building façades, and vegetation, to predict the socioeconomic profiles of their immediate neighborhood. Our results show that these urban features alone can account for up to 83% of the variance in people's travel behavior, 62% in poverty status, 64% in crime, and 68% in health behaviors. The results outperform models based on points of interest (POI), population, and other demographic data alone. Moreover, incorporating urban features captured from street view images can improve the explanatory power of these other methods by 5% to 25%. We propose "urban visual intelligence" as a process to uncover hidden city profiles, infer, and synthesize urban information with computer vision and street view images. This study serves as a foundation for future urban research interested in this process and understanding the role of visual aspects of the city. [ABSTRACT FROM AUTHOR]

Diversification of police forces is widely promoted as a reform for reducing racial disparities in police-civilian interactions and increasing police legitimacy. Despite these potential benefits, nearly every municipal police department in the United States remains predominately White and male. Here, we investigate whether the scale and persistence of minority underrepresentation in policing might partly be explained by a lack of support for diversification among voters and current police officers. Across two studies (N = 2, 661) sampling the US adult population and residents from a city with one of the least representative police forces in the country, individuals significantly overestimate officer diversity at both the local and national levels. We find that correcting these biased beliefs with accurate information reduces trust in police and increases support for hiring new officers from underrepresented groups. In the municipal sample, these corrections also cause an increase in residents' willingness to vote for reforms to diversify their majority White police department. Additional paired decision-making experiments (N = 1, 663) conducted on these residents and current police officers demonstrate that both prefer hiring new officers from currently underrepresented groups, independent of civil service exam performance and other hiring criteria. Overall, these results suggest that attitudes among voters and police officers are unlikely to pose a major barrier to diversity reforms. [ABSTRACT FROM AUTHOR]