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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]

Urban centres in the US, Britain and Canada have responded to identified visible 'social problems' such addiction, mental health and homelessness by providing some supportive housing for the urban poor and marginalized. While some critics have questioned what supportive housing specifically entails in terms of the built environment, what remains under explored, though a growing area of concern, is the relationship between surveillance and supportive housing for urban residents identified as having addiction and mental health problems - a gap addressed in this paper. Drawing upon qualitative ethnographic observational data we examine some of the measures of control and coercion that are encroaching into social housing primarily established for poor and marginalized people with addiction and mental health problems in the urban centre of Vancouver, Canada. We witnessed three modes of regulation and control, that vary widely, among the residencies observed: physical surveillance technologies; site-specific modes of coercion; police presence and staff surveillance, which all together impact the everyday lives of residents living in low-income and supportive housing. We argue that supportive housing has the potential to provide its intended commitment - safe and secure affordable housing. However, owing to an (over)emphasis on 'security', the supportive housing we observed were also sites of social control. [ABSTRACT FROM AUTHOR]

The spatial distribution of income shapes the structure and organisation of cities and its understanding has broad societal implications. Despite an abundant literature, many issues remain unclear. In particular, all definitions of segregation are implicitely tied to a single indicator, usually rely on an ambiguous definition of income classes, without any consensus on how to define neighbourhoods and to deal with the polycentric organization of large cities. In this paper, we address all these questions within a unique conceptual framework. We avoid the challenge of providing a direct definition of segregation and instead start from a definition of what segregation is not. This naturally leads to the measure of representation that is able to identify locations where categories are over- or underrepresented. From there, we provide a new measure of exposure that discriminates between situations where categories co-locate or repel one another. We then use this feature to provide an unambiguous, parameter-free method to find meaningful breaks in the income distribution, thus defining classes. Applied to the 2014 American Community Survey, we find 3 emerging classes—low, middle and higher income—out of the original 16 income categories. The higher-income households are proportionally more present in larger cities, while lower-income households are not, invalidating the idea of an increased social polarisation. Finally, using the density—and not the distance to a center which is meaningless in polycentric cities—we find that the richer class is overrepresented in high density zones, especially for larger cities. This suggests that density is a relevant factor for understanding the income structure of cities and might explain some of the differences observed between US and European cities. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide, for the first time, comparative estimates of racial residential segregation of blacks, Hispanics, and Native Americans in nonmetropolitan and metropolitan places in 1990 and 2000. Analyses are based on block data from the 1990 and 2000 U.S. decennial censuses. The results reveal a singularly important and perhaps surprising central conclusion: levels and trends in recent patterns of racial segregation in America's small towns are remarkably similar to patterns observed in larger metropolitan cities. Like their big-city counterparts, nonmetropolitan blacks are America's most highly segregated racial minority--roughly 30% to 40% higher than the indices observed for Hispanics and Native Americans. Finally, baseline ecological models of spatial patterns of rural segregation reveal estimates that largely support the conclusions reached in previous metropolitan studies. Racial residential segregation in rural places increases with growing minority percentage shares and is typically lower in "new" places (as measured by growth in the housing stock), while racially selective annexation and the implied "racial threat" at the periphery exacerbate racial segregation in rural places. Our study reinforces the need to broaden the spatial scale of segregation beyond its traditional focus on metropolitan cities or suburban places, especially as America's population shifts down the urban hierarchy into exurban places and small towns. [ABSTRACT FROM AUTHOR]