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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]

Mobility is a critical element of one's quality of life regardless of one's age. Although the challenges for women are more significant than those for men as they age, far less is known about the gender differences in mobility patterns of older adults, especially in the United States (US) context. This paper reports on a study that examined potential gender gaps in mobility patterns of older adults (aged 65 years and over) in the US by analyzing data from the 2017 National Household Travel Survey. Elderly respondents were first classified into one of six clusters based on socio-demographic variables. A Structural Equation Model (SEM) was then estimated and showed that gender gaps existed in the mobility patterns of the elderly, and the differences were diverse across the different clusters. The most substantial gender gap was found in the Senior Elder with Medical Condition(s) cluster, followed by the High-income Workers cluster and the Middle-income Urban Residents cluster. In contrast, females in the Low-Income Single Elder cluster enjoyed statistically significant positive mobility differences with their male counterparts. Our results also found that female elderly in the Senior Elder with Medical Condition(s) and the Low-income Family Elder clusters suffered most after the cessation of driving, with the largest mobility gender gap in the Middle-income Urban Resident cluster. This study will help transportation planners and policymakers understand gender and other socio-demographic differences in elderly mobility. Thus, it will facilitate the development of measures to improve elderly mobility and reduce gender gaps by recognizing and addressing specific target groups' mobility characteristics and needs rather than treating the elderly as a single potential user group. [ABSTRACT FROM AUTHOR]

Urban–rural differences in partisan political loyalty are as familiar in the United States as they are in other countries. In this paper, we examine Gallup survey data from the early-2000s through 2018 to understand the urban–rural fissure that has been so noticeable in recent elections. We consider the potential mechanisms of an urban–rural political divide. We suggest that urban and rural dwellers oppose each other because they reside in far apart locations without much interaction and support different political parties because population size structures opinion quite differently in small towns compared with large cities. In particular, we consider the extent to which the compositional characteristics (i.e., race, income, education, etc.) of the individuals living in these locales drives the divide. We find that sizable urban–rural differences persist even after accounting for an array of individual-level characteristics that typically distinguish them. [ABSTRACT FROM AUTHOR]

Social mix policies have emerged as a prominent mechanism to legitimate neighbourhood redevelopment efforts across the US. Despite integrationist rhetoric, results often disabuse marginalised communities of their claims to the city. This paper employs a hybrid spatio‐temporal analysis at the intersection of political‐economic theories of gentrification and post‐colonial and Black geographies literatures to examine underlying cultural logics and affective experiences animating such processes of neighbourhood transformation, contestation, and succession. Reflecting on 15 years of experience researching Over‐the‐Rhine (OTR), Cincinnati, we contribute a stylised distinction between the foundational, mature, and ongoing legacies of urban settler colonial relations. Our account discloses the power geometries shaping neighbourhood space by illustrating the impact of the discourses, tactics, and strategies employed by pro‐development actors and neighbourhood activists as OTR's socio‐political landscape shifted over time. In conclusion, we engage the thorny questions these dynamics raise surrounding how inner‐city neighbourhoods are theorised and struggled over after gentrification. [ABSTRACT FROM AUTHOR]

Many factors influence yard care in urban and suburban areas, but the explicit difference between front versus back yards is one factor that has not been fully examined. This paper introduces the Landscape Mullet concept. The two key components of this concept are: (1) social norms are an important driver of yard management; and (2) the influence of those norms varies spatially between front (public) to back (private). Thirty-six semi-structured interviews were conducted in six neighborhoods of Baltimore, MD, USA in the summer of 2016. We found that social pressures for yard care practices were not evenly expressed across front/back yards. Moreover, this front/back unevenness holds true in five out of the six neighborhoods we studied, indicating that the front/back, public/private distinction does not appear to be neighborhood-specific. This research extends prior work on the role of social pressures in yard care. Future research should consider this spatial scale of heterogeneity for social and environmental reasons. [ABSTRACT FROM AUTHOR]

Background: The paper examines the role of community-based participatory research (CBPR) within the context of social justice literature and practice. Methods: Two CBPR case studies addressing health inequities related to Type 2 Diabetes and Cardiovascular disease were selected from a national cross-site study assessing effective academic-community research partnerships. One CBPR partnership works with African Americans in rural Pemiscot County, Missouri and the other CBPR partnership works with African American and Latinos in urban South Bronx, New York City. Data collection included semi-structured key informant interviews and focus groups. Analysis focused on partnerships' context/ history and their use of multiple justice-oriented strategies to achieve systemic and policy changes in order to address social determinants of health in their communities. Results: Community context and history shaped each partnership's strategies to address social determinants. Four social justice approaches (identity/recognition, procedural, distributive, and structural justice) used by both partnerships were identified. These social justice approaches were employed to address underlying causes of inequitable distribution of resources and power structures, while remaining within a scientific research framework. Conclusion: CBPR can bridge the role of science with civic engagement and political participation, empowering community members to become political agents who integrate evidence into their social justice organizing strategies. [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]

• Epilepsy can be burdensome for patients, families, health systems, and society. • Health disparities of people with epilepsy can be worsened by living in rural areas. • Demographics and socioeconomic status drive the poor outcomes, not rurality per se. • Prospective studies with standard definitions of rural are needed. Epilepsy is a leading cause of global disease burden, with people with epilepsy (PWE) experiencing adverse health outcomes related to the psychiatric comorbidities and socioeconomic consequences of the disorder. Rural populations are more likely to be impoverished or uninsured, which could impact health outcomes for rural-dwelling PWE (RPWE). This systematic literature review identified original research studying health disparities and outcomes among RPWE in the United States and Canada to (1) characterize the disparities faced by RPWE and (2) elucidate the effects of these disparities upon clinical outcomes. We performed a systematic search of six electronic databases: Pubmed, Cochrane, PsychInfo, Web of Science, Scopus, and Ovid. Articles considered were original research reports conducted in Canada or the United States before August 2020. A modified Newcastle Ottawa Scale was used to assess the quality of the included studies. Our search returned 2093 articles that examined the health disparities of RPWE, of which six met criteria for this review. Outcome measures of health disparity included in these papers were mortality (2; 33%), use of health resources (2; 33%), and epilepsy prevalence (2; 33%). Only one paper (16%) concluded that RPWE experienced worse health outcomes relative to urban-dwelling PWE, while 5 (84%) found no difference. Our study did not find sufficient evidence that RPWE in the US and Canada experience significant health disparities compared to similar urban populations of PWE. More research using prospective studies and datasets allowing better characterization of rurality is required. [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]

This paper reviews and then discusses selected findings from a seventeen year study about the population prevalence of people who inject drugs (PWID) and of HIV prevalence and mortality among PWID in 96 large US metropolitan areas. Unlike most research, this study was conducted with the metropolitan area as the level of analysis. It found that metropolitan area measures of income inequality and of structural racism predicted all of these outcomes, and that rates of arrest for heroin and/or cocaine predicted HIV prevalence and mortality but did not predict changes in PWID population prevalence. Income inequality and measures of structural racism were associated with hard drug arrests or other properties of policing. These findings, whose limitations and implications for further research are discussed, suggest that efforts to respond to HIV and to drug injection should include supra-individual efforts to reduce both income inequality and racism. At a time when major social movements in many countries are trying to reduce inequality, racism and oppression (including reforming drug laws), these macro-social issues in public health should be both addressable and a priority in both research and action. [ABSTRACT FROM AUTHOR]

• Proposing an interpretable utility-based framework for dynamic activity pattern estimation. • Mapping from individual trip chains to aggregated activity location types. • Estimating traveler behavior with the support of multiple data sources. • Integrating machine learning tools, nested logit model and transportation domain knowledge. • Testing the proposed framework using 2017 national household survey and Google mobility dataset. During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases. [ABSTRACT FROM AUTHOR]

This paper details an intergenerational photovoice project exploring spatial and perceptual differences of "place" and health among adult and youth public housing residents in a small urban rustbelt city in the Midwestern United States. It specifically highlights the value of fully participatory photovoice processes-participatory narrative-coding and digital web-based photo-mapping-in furthering conceptual and analytical understanding of "place" and health. Results indicate the critical import of accounting for non-residential locations (i.e. activity spaces), and the significance of engaging the generationally- and spatially-specific social and physical landscapes of residents' lived "place" to improve health opportunities within place-based strategies involving public housing. [ABSTRACT FROM AUTHOR]