In March 2020, the United States government began a series of measures designed to dramatically restrict immigration as part of its response to the global health crisis caused by the coronavirus pandemic. This included Title 42, which deported asylum seekers immediately and prevented them from applying for asylum. These measures worsened an already precarious situation at the US–Mexico border for an estimated 60,000 asylum seekers who were prevented, by the Trump administration’s ‘Remain in Mexico’ (aka MPP) policy enacted in January 2019, from remaining in the United States while they awaited their asylum hearings. In-depth interviews, participant observation, and social media analysis with humanitarian and legal advocates for asylum seekers living in a camp at the border in Matamoros, Mexico reveal that COVID-19’s impacts are not limited to public health concerns. Rather, COVID-19’s impacts center on how the Trump administration weaponized the virus to indefinitely suspend the asylum system. We argue that the Matamoros refugee camp provides a strategic vantage point to understand the repercussions of state policies of exclusion on im/mobility and survival strategies for asylum seekers. Specifically, we use the analytical lenses of the politics of im/mobility, geographies of exclusion, and asylum seeker resilience to identify how COVID-19 has shaped the im/mobility and security of the camp and its residents in unexpected ways. At the same time, our research illustrates that camp residents exercise im/mobility as a form of political visibility to contest and ameliorate their precarity as they find themselves in conditions not of their choosing.

This research investigates how travel restrictions affect the spatiotemporal pattern of human mobility and COVID-19 confirmed cases. Based on recorded movement and Baidu mobility index, in- and out-migration were estimated to examine the geographic pattern of human mobility across many Chinese cities from Jan 1 – Feb 11 of 2020. In addition to the baseline model of city lockdown , this study also explored the time lag effect of COVID-19 incubation period before/after Jan 28 (i.e. 5 days) and Feb 6 (i.e. 2 weeks) as well. Full factorial Analysis of Variance (ANOVA) tests reviewed significant differences of migration pattern by lockdown and origin/destination, which are also significantly associated with the confirmed cases of COVID-19 as well. Specifically, human mobility dropped proportionally after the lockdown regardless of origin location, but Hubei destination was significantly lower than non-Hubei destination. The model assuming an incubation period of 5 days differentiated the differences of COVID-19 cases better than the baseline and 14 days model. Spatiotemporal cluster analysis identified multiple space-time windows that were related to migration trajectory assuming a 5–14 days incubation period. The pre-lockdown clusters due to traveler’s outflow from Wuhan to those megacities were the pathways for international transmission of COVID-19, whereas the post-lockdown clusters were partially related to the migration pattern especially within the eastern part of Hubei around Wuhan. The geographic pattern revealed from this study confirmed the presence of super spreaders that were responsible for regional spreading at the early stage and caused local outbreaks in the latter stage.

As urban populations age, creating cities that cater to the needs of elderly residents becomes increasingly vital. This paper addresses the imperative to develop urban environments supporting the well-being of the elderly against a backdrop of changing demographics. With the proportion of elderly individuals steadily rising, the demand for age-inclusive urban design is more pressing than ever. The research question driving this study is: How can sustainable retrofit strategies contribute to age-inclusive urban design, considering the physiological and psychological characteristics of the elderly? Begins with examining the theoretical foundations of sustainable retrofit practices, shedding light on their significance and common objectives. Shortcomings in current urban retrofit practices are scrutinized, highlighting the gaps that need addressing. Integrating research on the physiological and psychological characteristics of the elderly, along with their distinct needs in outdoor environments, the review contributes to a nuanced understanding of age-specific urban requirements. A bespoke sustainable urban retrofit strategy for the elderly is conceptualized, rooted in the principles of urban design. Finally, acknowledging the challenges faced in achieving age-inclusive urban spaces and provides forward-looking recommendations.

Transport data are important for understanding human mobility and urban interactions within a city. As China’s transportation infrastructure continues to grow, more research is needed to analyse the spatial patterns of travel flows and to understand how these patterns change over time. With the development of online car-hailing and ride sharing services, floating car data have become a new resource to facilitate the analysis of human mobility patterns and the interactions of urban mobility within a city. The detection of urban communities based on urban networks is a helpful way to represent urban interactions. However, understanding community changes using online car-hailing data remains an underexplored topic. To this end, this study applies a community detection method to explore community changes over time based on the newly available floating car data (DiDi Chuxing (‘DiDi’)) in Chengdu, China. We applied undirected graphs to examine the spatial distribution of DiDi usage and the spatial patterns of travel distance. In addition, we explored the spatial-temporal variations of the communities at the taxi zone level using Blondel’s iterative algorithm, a modularity optimization approach. Results suggest that: 1) taxi zones on the south and west sides of Chengdu have more average daily trips compared to those in other areas; 2) residential taxi zones in the northeast area have a long median travel distance, indicating people living in those areas travel longer distances; and 3) the detected community structures change at different times. These findings provide valuable information for urban planning and location-based services in Chengdu.

Cardiometabolic diseases (CMDs) affect significant numbers of adults in the United States, with 11% diagnosed with diabetes and 10% with cardiovascular diseases. Walking plays a crucial role in reducing health risks, particularly obesity and diabetes. We aim to explore the association between neighborhood walkability and CMD measures in Texas, while controlling for age, sex, racial/ethnic background, and family history of diabetes. We collected 1994 observations for the year 2019, merging data from the Texas Behavioral Risk Factor Surveillance System and the Environmental Protection Agency. We employed multilevel linear regression and multilevel logistic regression analyses to assess the association between CMD measures and neighborhood walkability. Our findings revealed that higher neighborhood walkability is significantly associated with a lower body mass index (BMI) (β = − 0.28, CI − 0.45 to − 0.10) and a reduced risk of diabetes (OR 0.93, CI 0.86–0.99), indicating that when walkability increases by one unit, an individual’s BMI decreases by 0.28 kg/m² and the odds of having diabetes decrease by 7%. We also found that African Americans living in communities with lower walkability scores compared to other racial/ethnic groups. Our findings highlight the need for urban planning policies promoting walkable neighborhoods, suggesting community-based approaches to health promotion.

Asthma is one of the most prevalent chronic conditions in the United States and is particularly sensitive to environmental changes in urban areas. While it is known that traffic congestion contributes to increased vehicle emissions and poorer air quality, its direct association with asthma incidence has not been thoroughly explored. This study aimed to address this void by analyzing 148 city-level observations from 2016 to 2020 in Texas, using data from the Texas A&M Transportation Institute and Definitive Healthcare. We investigated the association between traffic congestion, measured by the travel time index, and annual city-level asthma hospital discharges, while adjusting for refinery productivity, minority groups, and education levels through multivariate regression. Our findings revealed a significant positive correlation between the travel time index and asthma visits, indicating that higher traffic congestion is associated with increased hospital visits for asthma. This finding remains consistent across different models, regardless of whether control variables are included. For the control variables, we found that higher refinery productivity was linked to elevated risks of asthma-related hospitalizations, aligning with previous research findings. Although correlations with Black or African American and Hispanic or Latino populations, as well as those with less than a high school education, were not statistically significant, a positive trend was observed. These results emphasize the impact of traffic congestion on asthma prevalence and the necessity for targeted public health interventions and urban planning strategies.