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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]

There is increasing evidence that interactions between microbes and their hosts not only play a role in determining health and disease but also in emotions, thought, and behavior. Built environments greatly influence microbiome exposures because of their built-in highly specific microbiomes coproduced with myriad metaorganisms including humans, pets, plants, rodents, and insects. Seemingly static built structures host complex ecologies of microorganisms that are only starting to be mapped. These microbial ecologies of built environments are directly and interdependently affected by social, spatial, and technological norms. Advances in technology have made these organisms visible and forced the scientific community and architects to rethink gene-environment and microbe interactions respectively. Thus, built environment design must consider the microbiome, and research involving host-microbiome interaction must consider the built-environment. This paradigm shift becomes increasingly important as evidence grows that contemporary built environments are steadily reducing the microbial diversity essential for human health, well-being, and resilience while accelerating the symptoms of human chronic diseases including environmental allergies, and other more life-altering diseases. New models of design are required to balance maximizing exposure to microbial diversity while minimizing exposure to human-associated diseases. Sustained trans-disciplinary research across time (evolutionary, historical, and generational) and space (cultural and geographical) is needed to develop experimental design protocols that address multigenerational multispecies health and health equity in built environments. [ABSTRACT FROM AUTHOR]

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual's accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies. [ABSTRACT FROM AUTHOR]