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With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and in a safe location. In this paper, we unveil how transit service to employment hubs, not just housing policies, can prevent individuals from improving their housing conditions. We approach this question in three steps, applying the workflow to 20 cities in the United States of America. First, we propose a comprehensive framework to quantify housing insecurity and assign a housing demographic to each neighbourhood. Second, we use transit-pedestrian networks and public transit timetables (GTFS feeds) to estimate the time it takes to travel between two neighbourhoods using public transportation. Third, we apply geospatial autocorrelation to identify employment hotspots for each housing demographic. Finally, we use stochastic modelling to highlight how commuting to areas associated with better housing conditions results in transit commute times of over an hour in 15 cities. Ultimately, we consider the compounded burdens that come with housing insecurity, by having poor transit access to employment areas. In doing so, we highlight the importance of understanding how negative outcomes of housing insecurity coincide with various urban mechanisms, particularly emphasising the role that public transportation plays in locking vulnerable demographics into a cycle of poverty. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Transportation practitioners and scholars increasingly view car-free mobility as an essential component of a sustainable, healthy, and safe transportation future. In the United States, however, strikingly few households—only around 9%—do not own cars. Moreover, most zero-vehicle individuals—around 80%—are without vehicles due of constraint (e.g., because they cannot afford one), while a much smaller fraction—around 20%—are car-free because they can meet their mobility needs without one. This latter group is of interest to transportation researchers and practitioners, given the imperative to reduce driving and its associated negative externalities. In this paper, I examine factors that contribute to individuals' decisions to live car-free. This research is based on 430 comments posted on the social content-sharing website Reddit, in which commenters shared their first-hand experiences related to the prospect of going car-free. These comments were obtained from 46 threads situated in seven different US cities, focusing specifically on car ownership decisions. Commenters described accessible neighborhoods as foundational to decisions to forego a vehicle. While such conditions were typically necessary, however, they sometimes were not sufficient to foster car-free choices. Costs of car ownership, especially parking costs, were often the deciding factor for commenters who opted not to have a car. Finally, inconveniences related to car ownership and travel, like traffic-related stress, were discussed as benefits of living car-free. In sum, the observed individual decisions about car ownership tend to consist of people balancing supportive conditions for non-car travel against costs of and constraints on car-travel. [ABSTRACT FROM AUTHOR]

This paper examines the effects of dollar store entry on independent grocery retailers in the United States between 2000 and 2019. We utilized an establishment‐level dataset comprising all grocery retailers and dollar stores in the country. Our findings indicate that dollar store entry is associated with a 5.7% decrease in sales, a 3.7% reduction in employment, and a 2.3% rise in the likelihood of independent grocery retailers quitting the business. These adverse entry effects are three times larger in rural than urban areas. Event studies indicate that the negative impact on sales and employment disappears gradually in urban areas but persists in rural communities. We document differences in treatment effects of dollar store entry across regions, retail formats, and time. [ABSTRACT FROM AUTHOR]

Although there is substantial agreement how microeconomic forces—income, risk aversion—shape public health outcomes, there is substantial disagreement about the relationship between macroeconomic forces—market liberalization and economic freedom—on public health. In this paper, we investigate the relationship between public health, economic freedom, and wealth using a large sample of metropolitan‐level data from the United States. We find that economic freedom does have a statistically significant and positive impact on general, physical, and mental health, but the overall results are small in magnitude. When we disaggregate the three areas of economic freedom, we find that areas with lower government spending and freer labor markets have the strongest positive effect on physical and mental health. However, our results are strongest for the richest group of respondents, suggesting that the economic freedom‐health relationship is perhaps indirect, and shown through income. [ABSTRACT FROM AUTHOR]

Background: Obesity is associated with locality and alcohol use; however, less is known about how the interaction of these two factors may compound the risk of obesity among adolescents. Objectives: This study examines the relationship between alcohol use and obesity among adolescents from rural and urban areas in the United States. Methods: Data came from a sample of American adolescents aged 12–17 years from the National Survey on Drug Use and Health (2015–2019; n = 39,489). Obesity was regressed on age, sex, race/ethnicity, income, cigarette smoking, locality, and alcohol use, with an interaction term to examine locality x alcohol use. Predicted probabilities were plotted to assess the interaction. Results: Compared to adolescents from urban areas, those from rural areas had 1.35 times higher odds of being obese (95% CI 1.25, 1.47). Predicted probabilities indicated that the probability of being obese was higher for rural adolescents at lower levels of drinking, up to about 40 drinks in the past 12 months. Conclusions: Findings suggest rural-urban differences at the intersection of alcohol use and obesity could depend on the frequency of use, but overall adolescents from rural areas may be more at risk. [ABSTRACT FROM AUTHOR]

In this study, we analyze the extent of user reactions based on user's tweets to news articles, demonstrating the potential for home location prediction. To achieve this, we quantify users' reactions to specific news articles based on the textual similarity between tweets and news articles, showcasing that users' reactions to news articles about their cities are significantly higher than those about other cities. To maximize the difference in reactions, we introduce the concept of News Distinctness, which highlights the news articles that affect a specific location. By incorporating News Distinctness with users' reactions to the news, we magnify its effects. Through experiments conducted with tweets collected from users whose home locations are in five representative cities within the United States and news articles describing events occurring in those cities, we observed a 6.75% to 40% improvement in the reaction score when compared to the average reactions towards news for outside of home location, clearly predicting the home location. Furthermore, News Distinctness increases the difference in reaction score between news in the home location and the average of the news outside of the home location by 12% to 194%. These results demonstrate that our proposed idea can be utilized to predict the users' location, potentially recommending meaningful information based on the users' areas of interest. [ABSTRACT FROM AUTHOR]

Civil infrastructure underpins urban receipts of food, energy, and water (FEW) produced in distant watersheds. In this study, we map flows of FEW goods from watersheds of the contiguous United States to major population centers and highlight the critical infrastructure that supports FEW flows. To do this, we draw upon detailed records of agriculture, electricity, and public water supply production and couple them with commodity flow and infrastructure information. We also compare the flows of virtual water embedded in food and energy commodity flows with physical water flows in inter‐basin water transfer projects around the country. We found that the virtual blue water transfers through crops and electricity to major US cities was 53 billion and 8 billion m3 in 2017, respectively, while physical interbasin water transfers for crops, electricity, and public supply water averaged 20.8 billion m3. Highways are the primary infrastructure used to import virtual water associated with food and fuel into cities, although waterways and railways are most utilized for long‐distance transport. All of the 204 watersheds in the contiguous US support the food, energy, and/or water supplies of major US cities, with dependencies stretching far beyond each city's borders. Still, most cities source the majority of their FEW and embedded water resources from nearby watersheds. Infrastructure such as water supply dams and inland ports serve as important buffers for both local and supply‐chain sourced water stress. These findings can inform efforts to reduce water resources and infrastructure risks in domestic supply chains. Key Points: Civil infrastructure enables US cities to access FEW resources from distant watershedsMost cities depend on nearby watersheds for FEW and embedded waterVirtual water transfers through food and energy exceed physical water transfers [ABSTRACT FROM AUTHOR]

Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

City-scale traffic data, such as traffic flow, speed, and density on every road segment, are the foundation of modern urban research. However, accessing such data on a city scale is challenging due to the limited number of sensors and privacy concerns. Consequently, most of the existing traffic datasets are typically limited to small, specific urban areas with incomplete data types, hindering the research in urban studies, such as transportation, environment, and energy fields. It still lacks a city-scale traffic dataset with comprehensive data types and satisfactory quality that can be publicly available across cities. To address this issue, we propose a unified approach for producing city-scale traffic data using the classic traffic assignment model in transportation studies. Specifically, the inputs of our approach are sourced from open public databases, including road networks, traffic demand, and travel time. Then the approach outputs comprehensive and validated citywide traffic data on the entire road network. In this study, we apply the proposed approach to 20 cities in the United States, achieving an average correlation coefficient of 0.79 in average travel time and an average relative error of 5.16% and 10.47% in average travel speed when compared with the real-world data. [ABSTRACT FROM AUTHOR]

Over decades, many cities have been expanded and functionally diversified by population activities, socio-demographics and attributes of the built environment. Urban expansion and development have led to the emergence of spatial structures of cities. Uncovering cities' spatial structures is critical to understanding various urban phenomena such as segregation, equity of access, and sustainability. In this study, we propose using a neural embedding model—graph neural network (GNN)—that leverages the heterogeneous features of urban areas and their interactions captured by human mobility networks to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components (such as distribution of facilities) and population attributes and activities. The clustered representations of urban areas show the shared characteristics among urban areas in the same cluster. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the spatial structure in another county, allowing us to make cross-county comparisons and inferences. The findings reveal complex relationships among urban components in cities. Since the identified multifaceted spatial structures capture the combined effects of various mechanisms, such as segregation, disparate facility distribution, and human mobility, the findings could help identify the limitations of the current city structure to inform planning decisions and policies. Also, the model and findings set the stage for a variety of research in urban planning, engineering and social science through an integrated understanding of how the complex interactions between urban components and population activities and attributes shape the spatial structures in cities. [ABSTRACT FROM AUTHOR]

In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring. [ABSTRACT FROM AUTHOR]

Developing interventions to prevent firearm-related violence and to address its consequences requires an improved understanding of when these violent events are most likely to occur. We explored gunshot events in 6 of the most populated cities in the United States by time of day, day of week, holiday/non-holiday, and month using publicly available datasets. In some of these cities, gunshot events occurred most often at nighttime, on holidays and weekends, and during summer months, with significant interaction effects. There were also time-related changes in characteristics of the victims. Primary prevention efforts aimed at curbing firearm-related violence should consider these differential risks. [ABSTRACT FROM AUTHOR]

This study investigates the impact of an active workstation on heating and cooling energy consumption in commercial buildings. The improved performance and health of office workers are well-known and supported by several surveys and studies. However, the preferred and revised indoor air temperature for different activities of the employees, such as standing and walking, directly impacts the energy consumption of the building. Therefore, this study systematically quantifies the impact of indoor environments on building energy consumption using a simulation tool. Based on literature reviews with multidisciplinary fields, revised setpoint temperature, metabolic rate, and required outdoor air rate due to the level of active workstation (standing and walking) are applied to EnergyPlus reference building models. The impact on heating and cooling energy consumption is analyzed by considering several representative cities in the United States. Their average sensitivity was found to be 7.28% lower and 14.28% higher for heating and cooling energy, respectively. The total increment of $13,887 represents approximately 0.16% of the total profit of the corporation. This increment can be justified given the benefits to workers' health, which include lower insurance costs and improved performance. This suggests that it is economically desirable to deploy active workstations widely. [ABSTRACT FROM AUTHOR]

As housing unaffordability and climate change impose increasingly greater costs on American cities and towns, there is a growing sense that single-family residential zoning ordinances are partly to blame for these challenges. Many Americans remain unwilling to address these difficulties by welcoming large apartment buildings into their neighborhoods. Fortunately, policies designed to promote "middle housing" development--visually attractive duplexes and townhome projects--tend to be more politically feasible than policies that drive apartment development. Further, such policies do much to improve the affordability and environmental sustainability of residential neighborhoods. This Article describes how promoting greater middle housing development in the United States would help the nation to accelerate its transition to more affordable and sustainable housing and identifies some specific policy strategies for driving middle housing development across the country. [ABSTRACT FROM AUTHOR]