Historical redlining practices in the United States date back to the 1930s and have continued to impact cities socially, environmentally, and economically since then. This study explores current social vulnerability inequity among former HOLC (Home Owners' Loan Corporation) neighborhoods with four color-coded grades in 196 U.S. digitized cities. Social vulnerability measurements for each historical HOLC neighborhood were calculated using 2018 census tract-level social vulnerability data through areal apportionment. Kruskal-Wallis tests were employed to compare four grades of former HOLC neighborhoods regarding overall social vulnerability index (SVI), four thematic SVIs (Social economic status, Household composition and disability, Minority status and language, and Housing type and transportation), and SVIs for fifteen social vulnerability factors. After applying multiple comparison corrections, the results indicate significant differences among four HOLC grades concerning overall social vulnerability, four themes, and most of social vulnerability factors. In general, grade A (green) neighborhoods exhibit the lowest vulnerability, while grade D (red) neighborhoods have the highest vulnerability. A similar pattern of social vulnerability inequity persists even after adjusting for inter-city differences in baseline social factors using the city-normalized SVI (CSVI). This finding suggests that urban planners and policymakers should work to reduce social vulnerability inequity across neighborhoods of different grades.

Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with better knowledge. Better understanding through harmonising and analysing data in large numbers of cities is essential to identifying the most effective means of disease prevention and understanding context dependencies important for policy.

Socially disadvantaged groups generally are more likely to reside in areas with less desirable conditions. We examined longitudinal relationships between neighborhood resident characteristics and amenities from 1990 to 2010 in an urban area of Utah, U.S. Four temporal patterns of social inequities are described using mixed-effects models: historical inequities; differential selection into amenity-rich tracts; differential investment in amenities; and simultaneous twenty-year change. Results indicate historical differences by neighborhood socioeconomic status, with lower status tracts having fewer green/natural amenities and higher air pollution in 1990 but also greater walkability and more food stores. Differences in amenities by neighborhood socioeconomic status widened over time as aggregate socioeconomic status disproportionately increased in tracts with more green/natural amenities, less air pollution, and lower walkability in 1990, consistent with differential selection. Tract percentage non-Hispanic White did not predict historical differences, but tracts that were less walkable and had fewer healthy food stores in 1990 experienced larger subsequent increases in racial/ethnic diversity. Tracts with higher relative to lower percentage non-Hispanic White in 1990 had larger decreases in air pollution but declining green/natural amenities. This study shows how social inequities in neighborhood amenities change over time, providing evidence of historical socioeconomic differences increasing from differential resident selection.

The rising prevalence of non-communicable diseases (NCDs) has led to increased attention on understanding how built environments affect NCD risks. However, there's a significant gap in the literature regarding the relationship between housing vacancy duration and NCDs in metropolitan areas with varying development rates. Our research addresses this gap by examining the association between housing vacancy duration and NCDs across all U.S. metropolitan areas, considering growing, shrinking, and fluctuating counties. We used a Multiscale Geographically Weighted Regression (MGWR) model to analyze this relationship, finding that longer-term vacant housing (over 3 years) is more positively associated with NCDs compared to short-term vacancies. We also discovered that this association is non-uniform across metropolitan counties, except for cancer and stroke outcomes. Shrinking counties in the Northeast are particularly affected, emphasizing the need for targeted public health interventions in these areas. This study underscores the importance of revitalizing vacant homes, especially those vacant for over 3 years, in both shrinking and growing regions to improve public health. Policymakers should adopt tailored strategies, engage public health experts, and invest in healthcare infrastructure to effectively address the health risks linked to vacant housing.

Urban poverty is not limited to informal settlements, rather it extends throughout cities, with the poor and affluent often living in close proximity. Using a novel dataset derived from the full Ghanaian Census, we investigate how neighbourhood versus household socio-economic status (SES) relates to a set of household development outcomes (related to housing quality, energy, water and sanitation, and information technology) in Accra, Ghana. We then assess "stranger" households' outcomes within neighbourhoods: do poor households fare better in affluent neighbourhoods, and are affluent households negatively impacted by being in poor neighbourhoods? Through a simple generalized linear model we estimate the variance components associated with household and neighbourhood status for our outcome measures. Household SES is more closely associated with 13 of the 16 outcomes assessed compared to the neighbourhood average SES. Second, for 9 outcomes poor households in affluent areas fair better, and the affluent in poor areas are worse off. For two outcomes, poor households have worse outcomes in affluent areas, and the affluent have better outcomes in poor areas, on average. For three outcomes "stranger" households do worse in strange neighbourhoods. We discuss implications for mixed development and how to direct resources through households versus location-based targets.

Studying the impacts of factors that may vary spatially and temporally as infectious disease progresses is critical for the prediction and intervention of COVID-19. This study aimed to quantitatively assess the spatiotemporal impacts of socio-demographic and mobility-related factors to predict the spread of COVID-19. We designed two different schemes that enhanced temporal and spatial features respectively, and both with the geographically and temporally weighted regression (GTWR) model adopted to consider the heterogeneity and non-stationarity problems, to reveal the spatiotemporal associations between the factors and the spread of COVID-19 pandemic. Results indicate that our two schemes are effective in facilitating the accuracy of predicting the spread of COVID-19. In particular, the temporally enhanced scheme quantifies the impacts of the factors on the temporal spreading trend of the epidemic at the city level. Simultaneously, the spatially enhanced scheme figures out how the spatial variances of the factors determine the spatial distribution of the COVID-19 cases among districts, particularly between the urban area and the surrounding suburbs. Findings provide potential policy implications in terms of dynamic and adaptive anti-epidemic.

Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short- and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facilitating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives.

Equity in health care delivery is a longstanding concern of public health policy. Telehealth is considered an important way to level the playing field by broadening health services access and improving quality of care and health outcomes. This study refines the recently developed "2-Step Virtual Catchment Area (2SVCA) method" to assess the telehealth accessibility of primary care in the Baton Rouge Metropolitan Statistical Area, Louisiana. The result is compared to that of spatial accessibility via physical visits to care providers based on the popular 2-Step Floating Catchment Area (2SFCA) method. The study shows that both spatial and telehealth accessibilities decline from urban to low-density and then rural areas. Moreover, disproportionally higher percentages of African Americans are in areas with higher spatial accessibility scores; but such an advantage is not realized in telehealth accessibility. In the study area, absence of broadband availability is mainly a rural problem and leads to a lower average telehealth accessibility than physical accessibility in rural areas. On the other side, lack of broadband affordability is a challenge across the rural-urban continuum and is disproportionally associated with high concentrations of disadvantaged population groups such as households under the poverty level and Blacks.

The recent worldwide SARS-CoV-2 (COVID-19) pandemic has reshaped the way people live, how they access goods and services, and how they perform various activities. For public transit, there have been health concerns over the potential spread to transit users and transit service staff, which prompted transportation agencies to make decisions about the service, e.g., whether to reduce or temporarily shut down services. These decisions had substantial negative consequences, especially for transit-dependent travelers, and prompted transit users to explore alternative transportation modes, e.g., bikeshare. However, local governments and the public in general have limited information about whether and to what extent bikeshare provides adequate accessibility and mobility to those transit-dependent residents. To fill this gap, this study implemented spatial and visual analytics to identify how micro-mobility in the form of bikesharing has addressed travel needs and improved the resilience of transportation systems. The study analyzed the case of San Francisco in California, USA, focusing on three phases of the pandemic, i.e., initial confirmed cases, shelter-in-place, and initial changes in transit service. First, the authors implemented unsupervised machine learning clustering methods to identify different bikesharing trip types. Moreover, through spatiotemporally matching bikeshare ridership data with transit service information (i.e., General Transit Feed Specification, GTFS) using the tool called OpenTripPlanner (OTP), the authors studied the travel behavior changes (e.g., the proportion of bikeshare trips that could be finished by transit) for different bikeshare trip types over the three specified phases. This study revealed that during the pandemic, more casual users joined bikeshare programs; the proportion of recreation-related bikeshare trips increased; and routine trips became more prevalent considering that docking-station-based bikeshare trips increased. More importantly, the analyses also provided insights about mode substitution, because the analyses identified an increase in dockless bikeshare trips in areas with no or limited transit coverage.

The COVID-19 pandemic has remarkably impacted urban mobility. All non-essential movements were restricted in Valencia (Spain) to contain the virus. Thus, the transport usage patterns of Valencia's bike-sharing system (BSS) users changed during this emergency situation. The primary objective of this study was to analyse the behaviour patterns of BSS users in Valencia before and after the COVID-19 outbreak, specifically those who maintained or changed their transport routines. A within-subjects comparison design was developed using a group of BSS users before and after the onset of the pandemic. Data mining techniques were used on a sample of 4355 regular users and 25 variables were calculated to classify users by self-organising maps analysis. The results show a significant reduction (40 %) in BSS movements after the outbreak during the entire post-outbreak year. There was some recovery during the rest of 2020; however, this has yet to reach the pre-pandemic levels, with variations observed based on the activities performed in different areas of the city. Of the users, 63 % changed their BSS use patterns after the onset of the pandemic (LEAVE group), while 37 % maintained their patterns (REMAIN group). The user profile of the REMAIN group was characterised by a general reduction of approximately 35 % of journeys during 2020, with a slight increase in morning movements compared to those made in the evening. These users also presented an equivalent number of cycling days to those of the previous year, reduced the number of connections and increased the network's density and the travelling speed. These results can be useful in estimating the percentage of people who do not vary their usual behaviour during emergencies. Finally, several policy implications are outlined based on the findings.

Non-commuting travel is essential for people to meet daily demands and regulate mental health, which is greatly disrupted due to the COVID-19 pandemic. To explore non-commuting intentions during COVID-19 across different groups of residents, this paper uses online survey data in Nanjing and constructs a hybrid latent class choice model that combines sociodemographic characteristics and psychological factors. Results showed that the respondents can be divided into two groups: the "cautious" group versus the "fearless" group. The "cautious" group with lower willingness to travel tend to be older, higher-income, higher-educated, female and full-time employees. Furthermore, the "cautious" group with higher perceived susceptibility is more obedient to government policies. In contrast, the "fearless" group is significantly affected by perceived severity and is more inclined to turn to personal protection against the pandemic. These results suggested that non-commuting trips were influenced not only by individual characteristics but also by psychological factors. Finally, the paper provides implications for the government to formulate COVID-19 management measures for the heterogeneity of different groups.

For a city to maintain its vitality during a crisis like the COVID-19 pandemic, social resilience is pivotal. It is a manifestation of adaptive and transformative capacities in a city, through a multitude of interactions between initiatives and organizations, including local government. Resilience can take many forms: coping, adaptive, transformative; community-based, organizational, and institutional. Due to this hybridity and multiplicity, it remains to be seen how all forms of resilience interact and mutually benefit from one another in a city under crisis. Building further in the relational and dynamic dimensions of resilience, we conceptualize these mutual influences as co-evolution and hypothesise that for mutually beneficial co-evolution a city requires boundary organizations, i.e., organizations that facilitate collaboration and information-flow between differently organized societal domains. In our study of the activities of boundary organizations in the Dutch city Rotterdam during the COVID-19 pandemic, we found that boundary organizations were indeed supportive in building social and especially community resilience, but mainly coping and adaptive. Evidence for co-evolutions between various forms of resilience and institutional transformative resilience remained limited. Transformative potential seemed to get lost in procedural translations, was jeopardized by recentralization policies, and seemed only possible on the currents of already ongoing change.

By using three continuous years of national-scale cellphone signaling data from Jan. 2019 to Dec. 2021, this study adds fresh evidence for job-housing balance changes at the during the COVID-19 period in China. The findings show that according to the resident-balance index and worker-balance index, the job-housing balance jumped when the number of COVID-19 confirmed cases reached its peak in February 2020, with an average of 94.4 % which is the highest level during these three years. The study also found that the -level job-housing balance has generally improved steadily in the two years of the pandemic. In addition, the results highlighted the huge gaps between females and males in the job-housing balance, but the gender disparities in job-housing balance were reduced to a minimum during the pandemic lockdown. In addition, by comparison analysis of the changes in resident-balance index and worker-balance index during this unprecedented crisis, this study found that for with high economic vitality, worker-balance index increased greater than resident-balance index, but for with low economic vitality, the reverse happened. Our findings provide a better understanding of the job-housing relationship during public health crises that can support the urban management in the future policymaking.

The global Covid-19 pandemic had a devastating impact on our lives. The pandemic led to sudden and momentous changes in mobility styles and travel habits. Many users started preferring to travel via private vehicles, which is contrary to sustainability policies. Strict measures were implemented against the Covid-19 pandemic in Turkey during this process, as was the case all over the world. Taking into consideration these realities, the aim of the present study was to examine the impacts of the Covid-19 pandemic on the travel modes (public transportation, walking, and bicycle), anxiety and stress perceptions, and working conditions of individuals. A survey was conducted to measure the impacts of the pandemic and the measures taken. The sample of this study consists of people who regularly attended Suleyman Demirel University in Isparta and Akdeniz University in Antalya, Turkey, before the pandemic. An online survey was conducted for the case study during the May-June 2021 semester, and the survey was representative of the study population with a total of 556 participants. The travel time for both different university students decreased during the pandemic period. The percentage of those traveling to the university with their own cars increased to 77 % in AU and 66.8 % in SDU. The use of public transportation dropped to 6.1 % in AU and 11.8 % in SDU. 56.97 % of AU participants and 51.15 % of SDU participants reported that their walking habits decreased. It was reported that 52.73 % of the respondents in AU and 55.75 % of the respondents in SDU did not change their bicycle usage frequency. 64.24 % of AU and 74.42 % of SDU reported that their anxiety levels increased during the pandemic period. And for each of these analyses, there was no statistical difference between the two different university students.

The spread of COVID-19 greatly restricted physical and economic activities in global cities due to growing fears of infection and lockdown policies. Accordingly, the transfer of shopping activities from traditional markets to e-commerce has accelerated. Urban retail has substantially declined by competing with these disrupting factors, but resilience capabilities are known to vary by scale and regional characteristics. This study identifies which types of retail clusters were more resilient to the economic shock caused by the pandemic. This research focuses on the changes in online and in-store sales and how retailers recovered differently at the neighborhood and district levels in 2019 and 2020. This research compares the resilience of two types of retail clusters by calculating the loss of resilience and online adaptivity. The findings suggest that neighborhood-level retail areas were more resilient and bounced back in sales at a faster rate than district-level retail areas during the pandemic. These findings suggest that retail clusters are more resilient if they have steady populations and low-rent loads and can accommodate online shopping. The study contributes theoretical insights into how sales have changed and how e-commerce has increased the resilience of retail clusters throughout the pandemic by examining in-store and online sales.

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19.

The COVID-19 pandemic has had significant effects on urban and regional food systems. Local administrations worldwide have been challenged to design and implement policies to mitigate immediate food system disruptions while planning for longer-term equity and resilience. The fast pace and high degree of uncertainty of the pandemic have made systematic tracking and assessment of food system change and related policy responses arduous. To address this gap, this paper applies the multilevel perspective on sociotechnical transitions and the multiple streams framework on policy change to 16 months of food policy (March 2020 through June 2021) during the New York State-issued COVID-19 state of emergency, comprising more than 300 food policies advanced by New York City and State legislators and administrators. Content analysis of these policies revealed the most salient policy areas during this period, the status of legislation, and key programs and budget allocations, as well as local food governance and the organizational spaces within which food policy operates. The paper shows that food policy domains that gained prominence focused on support for food businesses and food workers and on ensuring and expanding food access through food security and nutrition policies. Most COVID-19 food policies were incremental and were limited to the duration of the emergency, yet the crisis allowed for enactment of novel policies that deviated from the common policy issues or the typical scale of changes proposed pre-pandemic. Taken together, and viewed through a multilevel and policy streams framework, the findings provide insight into the trajectory of food policymaking in New York during the pandemic and the areas that food justice activists, researchers, and policy makers should focus on as the COVID-19 pandemic is abated.

To examine the association between urban neighborhood disorder and the recurrence risk of patients with acute myocardial infarction (AMI) in central Beijing, China.

COVID-19 measures implied many changes to travel behaviour and transport mode choice during the pandemic. This study seeks to understand what individual characteristics and travel attributes are related to transport mode choice before, during, and after the first lockdown in Italy. Based on an online survey (carried out in May 2020 in Milan), three multinomial regression models are presented. The results show that and in which measure parameters regarding distance and duration of daily travel are markedly related to transport mode before the lockdown. However, these factors are less significantly associated with the transport mode during and after the lockdown. Meanwhile, factors such as and have more significant relationship with the modal choice during the pandemic. Regarding individual characteristics, women are more likely to use active mode during and after the lockdown. Additionally, two personality traits of , and are related to transport mode during and after the lockdown, respectively. Overall, this study reveals that in addition to socio-demographic factors, other variables such as worry about using public transport, preference, and personality are associated with the choice of transport mode during the lockdown.

The COVID-19 pandemic, which lasted for three years, has had a great impact on the public health system, society and economy of cities, revealing the insufficiency of urban resilience under large-scale public health events (PHEs). Given that a city is a networked and multidimensional system with complex interactions, it is helpful to improve urban resilience under PHEs based on system thinking. Therefore, this paper proposes a dynamic and systematic urban resilience framework that incorporates four subsystems (governance, infrastructures, socioeconomy and energy-material flows). The composite index, system dynamics and epidemic simulation model are integrated into the framework to show the nonlinear relationships in the urban system and reflect the changing trend of urban resilience under PHEs. Then, urban resilience under different epidemic scenarios and response policy scenarios is calculated and discussed to provide some suggestions for decision-makers when faced with the trade-off between the control of PHEs and the maintenance of city operation. The paper concludes that control policies could be adjusted according to the characteristics of PHEs; strict control policies under a severe epidemic could lead to a significant decrease in urban resilience, while a more flexible control strategy can be adopted under a mild epidemic scenario to ensure the normal operation of urban functions. Moreover, the critical functions and impact factors of each subsystem are identified.

Housing is a critical social determinant of health. Research on the impact of housing on health among migrants is more complex than that of the general population because of migrants' health decline over time: while migrants exhibit a health advantage upon arrival, they gradually lose it as they stay longer in the host city. Existing studies on migrants' housing and health have paid little attention to the confounding effect of residence duration and are thus prone to misleading results. Using data from the 2017 China Migrants Dynamic Survey (CMDS), this study fills in the gap by examining how the incorporation of residence duration alters the relationship of housing cost burden and homeownership with migrant self-rated health (SRH). The study shows that migrant workers with higher housing cost burden and longer residence duration tend to have worse SRH. Incorporating residence duration attenuates the crude association between homeownership and worse SRH. The results imply that the health decline among migrants can be attributed to the discriminatory system-a system that limits migrants' access to social welfare and puts them in a socioeconomically disadvantaged position. The study thus emphasizes the removal of structural and socio-economic barriers faced by the migrant population.