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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 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]

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]