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How people pay is critically important to consumers and businesses alike. Many consumers are choosing to pay for goods and services from an increasing number of options. Tech‐savvy urbanites buy coffee by tapping their phone on a reader. Parents returning from a night out use peer‐to‐peer payment apps, such as Venmo, to pay the sitter. The recent explosion of financial innovations promises faster, more efficient, and cheaper transactions. These increasing digital payment options coincide with decreased number and volume of cash transactions. However, vulnerable populations face more constrained payment choices and often rely exclusively on cash. Without experience, devices, or acceptable credit histories, vulnerable populations may have little access to digital transactions. The advantages of falling digital costs and other benefits thus accrue to the digitally connected. At the same time, those who must transact in coins and paper money are likely to find their relative costs increasing. This paper highlights payment trends and the potential impact on vulnerable populations in the United States as we move toward a cashless society. This paper should stimulate discussions of public policy initiatives to mitigate digital payment issues for vulnerable people. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of energy consumption's Green Quality Index (GQI) on carbon dioxide (CO 2) emissions in the United States from 1970 to 2021. At this point, the estimated models control for the effects of gross domestic product (GDP) per capita, urban population, and globalization on CO 2 emissions. The Autoregressive Distributed Lag estimations demonstrate that GQI and globalization reduce CO 2 emissions in the short- and long run. However, GDP per capita and urban population are positively related to CO 2 emissions in the short- and long-run. Different estimation techniques confirm the long-run baseline findings. Potential policy implications for reducing CO 2 emissions in the United States are also provided. [ABSTRACT FROM AUTHOR]

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]

This paper empirically investigates the conventional wisdom that urban residents have reacted to the Covid-19 pandemic by fleeing city centres for the suburbs. A conventional panel model of US ZIP code-level data provides mixed evidence in support of a shifting housing preference for more space or neighbourhoods farther from the urban core. Regressions accounting for spatial dependence and spatial heterogeneity show strong support of an urban flight within metro areas, but this local phenomenon is uneven across broad regions of the United States. The finding of geographical disparity underscores both the local as well as the regional nature of housing market conditions. [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]

• 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]

• 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]

The article offers information on the segregation and the initial provision of water in the U.S. It mentions that water and sewer infrastructure have historically played a crucial role in eliminating waterborne threats. It discusses that higher rates of residential segregation built their waterworks earlier but were slower to eliminate typhoid fever.