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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]

Urbanization can challenge sustainable development if it produces unequal outcomes. Infrastructure is an important urbanization dimension, providing services to support diverse urban activities. However, it can lock in unequal outcomes due to its durable nature. This paper studies inequalities in infrastructure distributions to derive insights into the structure and characteristics of unequal outcomes associated with urbanization. We analyzed infrastructure inequalities in two emerging economies in the Global South: India and South Africa. We developed and applied an inequality measure to understand the structure of inequality in infrastructure provisioning (based on census data) and infrastructure availability (based on satellite nighttime lights [NTLs] data). Consistent with differences in economic inequality, results show greater inequalities in South Africa than in India and greater urban inequalities than rural inequalities. Nevertheless, inequalities in urban infrastructure provisioning and infrastructure availability increase from finer to coarser spatial scales. NTL-based inequality measurements additionally show that inequalities are more concentrated at coarse spatial scales in India than in South Africa. Finally, results show that urban inequalities in infrastructure provisioning covary with urbanization levels conceptualized as a multidimensional phenomenon, including demographic, economic, and infrastructural dimensions. Similarly, inequalities in urban infrastructure availability increase monotonically with infrastructure development levels and urban population size. Together, these findings underscore infrastructure inequalities as a feature of urbanization and suggest that understanding urban inequalities requires applying an inequality lens to urbanization. [ABSTRACT FROM AUTHOR]

The article discusses various scientific research findings in different fields. One study focuses on the mechanisms of leaf folding in response to dehydration in bamboo palm leaves, which could have implications for the design of soft machines. Another study explores an illusion related to retinal image magnification and object shape perception, providing evidence for a failure of cue combination in shape perception. A third study proposes a model for the chemistry of Europa's ocean, suggesting a mechanism for neutralizing reactive oxygen species without significant ocean mixing. The article also mentions a preclinical candidate drug for SARS-CoV-2 infection and a study on how gentrification affects mammal diversity in US cities. [Extracted from the article]

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three datasets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting. [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]

Increased exposure to extreme heat from both climate change and the urban heat island effect--total urban warming--threatens the sustainability of rapidly growing urban settlements worldwide. Extreme heat exposure is highly unequal and severely impacts the urban poor. While previous studies have quantified global exposure to extreme heat, the lack of a globally accurate, fine-resolution temporal analysis of urban exposure crucially limits our ability to deploy adaptations. Here, we estimate daily urban population exposure to extreme heat for 13,115 urban settlements from 1983 to 2016. We harmonize global, fine-resolution (0.05°), daily temperature maxima and relative humidity estimates with geolocated and longitudinal global urban population data. We measure the average annual rate of increase in exposure (person-days/year-1) at the global, regional, national, and municipality levels, separating the contribution to exposure trajectories from urban population growth versus total urban warming. Using a daily maximum wet bulb globe temperature threshold of 30 °C, global exposure increased nearly 200% from 1983 to 2016. Total urban warming elevated the annual increase in exposure by 52% compared to urban population growth alone. Exposure trajectories increased for 46% of urban settlements, which together in 2016 comprised 23% of the planet's population (1.7 billion people). However, how total urban warming and population growth drove exposure trajectories is spatially heterogeneous. This study reinforces the importance of employing multiple extreme heat exposure metrics to identify local patterns and compare exposure trends across geographies. Our results suggest that previous research underestimates extreme heat exposure, highlighting the urgency for targeted adaptations and early warning systems to reduce harm from urban extreme heat exposure. [ABSTRACT FROM AUTHOR]