The impact of the COVID-19 pandemic on people's mobility: A longitudinal study of the U.S. from March to September of 2020

Exploring data sources and mathematical approaches for estimating human mobility rates and implications for understanding COVID-19 dynamics: a systematic literature review

Human mobility, which refers to the movement of people from one location to another, is believed to be one of the key factors shaping the dynamics of the COVID-19 pandemic. There are multiple reasons that can change human mobility patterns, such as fear of an infection, control measures restricting movement, economic opportunities, political instability, etc. Human mobility rates are complex to estimate as they can occur on various time scales, depending on the context and factors driving the movement. For example, short-term movements are influenced by the daily work schedule, whereas long-term trends can be due to seasonal employment opportunities. The goal of the study is to perform literature review to: (i) identify relevant data sources that can be used to estimate human mobility rates at different time scales, (ii) understand the utilization of variety of data to measure human movement trends under different contexts of mobility changes, and (iii) unraveling the associations between human mobility rates and social determinants of health affecting COVID-19 disease dynamics. The systematic review of literature was carried out to collect relevant articles on human mobility. Our study highlights the use of three major sources of mobility data: public transit, mobile phones, and social surveys. The results also provides analysis of the data to estimate mobility metrics from the diverse data sources. All major factors which directly and indirectly influenced human mobility during the COVID-19 spread are explored. Our study recommends that (a) a significant balance between primitive and new estimated mobility parameters need to be maintained, (b) the accuracy and applicability of mobility data sources should be improved, (c) encouraging broader interdisciplinary collaboration in movement-based research is crucial for advancing the study of COVID-19 dynamics among scholars from various disciplines.

Have Incidence and Severity of Craniomaxillofacial Fractures Changed Since SARS-CoV-2?

BACKGROUND

An increase in severity and a decrease in incidence of craniomaxillofacial fractures (CMFs) were identified during the first several months of the SARS-CoV-2 pandemic. It is unclear if these changes have persisted in the current timeframe.

PURPOSE

The investigators hypothesize that the incidence and severity of CMF will not return to baseline prepandemic (control) levels as the pandemic stabilizes and becomes endemic.

STUDY DESIGN, SETTING, SAMPLE

This retrospective cohort study enrolled subjects who presented to Harborview Medical Center a Level 1 trauma center for the evaluation and management of CMF. Inclusion criteria were 1) Presentation timeline 2018 through 2022, 2) CMF identified by the 10th International Classification of Disease. Exclusion criteria were: 1) Undocumented etiology of facial fracture and 2) inadequate/unclear documentation otherwise.

PREDICTOR/EXPOSURE/INDEPENDENT VARIABLE

The predictor variable was year of injury relating to the start of the pandemic. The groups were the prepandemic (2018, 2019) and postpandemic (2020, 2021. 2022).

MAIN OUTCOME VARIABLES

The primary outcome variable was the CMF diagnosis identified using the corresponding International Classification of Disease, 10th Edition codes. The secondary outcome variables were mechanism of injury and injury severity.

COVARIATES

The covariates were age, sex, race/ethnicity, admission status, alcohol intoxication, toxicology screen, reimbursement source, abuse reported, and abuse investigated.

ANALYSES

Univariate and bivariate analyses were performed with statistical significance at P < .05.

RESULTS

The sample was composed of 5203 subjects. The annual volumes of subjects presenting with CMF were consistent over the study period (2018, 2019, 2020, 2021, 2022 n = 1018, 963, 1020, 1062, 1140, respectively). The incidence of Hispanics increased (2018, 2019, 2020, 2021, 2022: 11.1, 9.6, 12.2, 13.9, 13.2% (P < .05)) as did firearm CMF injuries (2018, 2019, 2020, 2021, 2022: 4.13, 4.98, 4.71, 7.16, 6.75% (P < .05)). The Injury Severity Score and Abbreviated Injury Scale were both lower postpandemic compared to prepandemic; mean Injury Severity Score post [18.27 ± 12.46] versus pre [19.25 ± 12.89] (P < .05), mean Abbreviated Injury Scale post [2.94 ± 1.15] versus pre [3.04 ± 1.14] (P < .05).

CONCLUSIONS AND RELEVANCE

While the severity of CMF decreased postpandemic, Hispanic and firearm CMF increased. The overall CMF incidence remained the same. The significant rise in firearm injuries warrants further study.

The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi'an, China

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi'an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.

Development of New Stringency Indices for Nonpharmacological Social Distancing Policies Implemented in Korea During the COVID-19 Pandemic: Random Forest Approach

BACKGROUND

In the absence of an effective treatment method or vaccine, the outbreak of the COVID-19 pandemic elicited a wide range of unprecedented restriction policies aimed at mitigating and suppressing the spread of the SARS-CoV-2 virus. These policies and their Stringency Index (SI) of more than 160 countries were systematically recorded in the Oxford COVID-19 Government Response Tracker (OxCGRT) data set. The SI is a summary measure of the overall strictness of these policies. However, the OxCGRT SI may not fully reflect the stringency levels of the restriction policies implemented in Korea. Korea implemented 33 COVID-19 restriction policies targeting 4 areas: public facilities, public events, social gatherings, and religious gatherings.

OBJECTIVE

This study aims to develop new Korea Stringency Indices (KSIs) that reflect the stringency levels of Korea's restriction policies better and to determine which government-implemented policies were most effective in managing the COVID-19 pandemic in Korea.

METHODS

The random forest method was used to calculate the new KSIs using feature importance values and determine their effectiveness in managing daily COVID-19 confirmed cases. Five analysis periods were considered, including November 01, 2020, to January 20, 2021 (Period 1), January 20, 2021, to June 27, 2021 (Period 2), November 01, 2020, to June 27, 2021 (Period 3), June 27, 2021, to November 01, 2021 (Period 4), and November 01, 2021, to April 24, 2022 (Period 5).

RESULTS

Among the KSIs, public facilities in period 4, public events in period 2, religious gatherings in periods 1 and 3, and social gatherings in period 5 had the highest importance. Among the public facilities, policies associated with operation hour restrictions in cinemas, restaurants, PC rooms, indoor sports facilities, karaoke, coffee shops, night entertainment facilities, and baths or saunas had the highest importance across all analysis periods. Strong positive correlations were observed between daily confirmed cases and public facilities, religious gatherings, and public events in period 1 of the pandemic. From then, weaker and negative correlations were observed in the remaining analysis periods. The comparison with the OxCGRT SI showed that the SI had a relatively lower feature importance and correlation with daily confirmed cases than the proposed KSIs, making KSIs more effective than SI.

CONCLUSIONS

Restriction policies targeting public facilities were the most effective among the policies analyzed. In addition, different periods call for the enforcement of different policies given their effectiveness varies during the pandemic.

People's political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data

BACKGROUND

As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people's residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people's concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people's private locations.

METHODS

We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people's political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables.

RESULTS

We observed a tradeoff relationship between people's privacy concerns and the acceptance (and perceived social benefits) of the control measures. People's perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns.

CONCLUSIONS

Our results first suggest that people's privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people's acceptance and perceived social benefits of the control measures but may not reduce people's privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people's privacy concerns tend to increase with their conservatism and individualism.

Changes of Grocery Shopping Frequencies and Associations with Food Deserts during the COVID-19 Pandemic in the United States

The COVID-19 pandemic has dramatically altered people's lives in multiple aspects, including grocery shopping behaviors. Yet, the changing trend of grocery shopping frequencies during the COVID-19 and its associations with food deserts remain unclear. We aimed to (1) examine variations of grocery shopping frequencies at county level in the USA during the COVID-19 pandemic from March 2020 to December 2021; (2) investigate associations between grocery shopping frequencies and food deserts during the COVID-19 pandemic; and (3) explore heterogeneity in grocery shopping frequencies-food desert associations across urban and rural areas. The county-level grocery shopping frequencies were derived from a grocery pattern dataset obtained from SafeGraph. We divided the 22-month period into 5 stages and employed the growth curve modeling to estimate the trajectories of grocery shopping frequencies and the associations between grocery shopping frequencies and food deserts in each stage, separately. Results revealed that grocery shopping frequencies experienced a "W-shaped" pattern from March 2020 to December 2021. Counties with the least percent of food deserts had slower decrease in grocery shopping frequencies at the initial stage and recovered more rapidly at later stages. Counties with the highest percent of food deserts were subject to deprivation amplification as a result of the pandemic. We also found differences existed in the grocery shopping frequencies-food desert associations between metropolitan counties and rural counties. Our findings suggest the impacts of COVID-19 on grocery shopping frequencies varied across different time periods, shedding light on designing different strategies to reduce the risk of contagion while shopping inside of grocery stores. Further, our findings highlight an urgent need to help people living in food deserts (especially in rural counties) to procure healthy foods safely during health emergencies like COVID-19 pandemic which disrupt mobility and social behaviors.

Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia

BACKGROUND

In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak.

METHODS

The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression.

RESULTS

In the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates.

CONCLUSION

This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.

Age-based spatial disparities of COVID-19 incidence rates in the United States counties

COVID-19 incidence disparities have been documented in the literature, but the different driving factors among age groups have yet to be explicitly explained. This study proposes a community-based COVID-19 spatial disparity model, considering different levels of geographic units (individual and community), various contextual variables, multiple COVID-19 outcomes, and different geographic contextual elements. The model assumes the existence of age nonstationarity effects on health determinants, suggesting that health effects of contextual variables vary among place and age groups. Based on this conceptual model and theory, the study selected 62 county-level variables for 1,748 U.S. counties during the pandemic, and created an Adjustable COVID-19 Potential Exposure Index (ACOVIDPEI) using principal component analysis (PCA). The validation was done with 71,521,009 COVID-19 patients in the U.S. from January 2020 through June 2022, with high incidence rates shifting from the Midwest, South Carolina, North Carolina, Arizona, and Tennessee to the West and East coasts. This study corroborates the age nonstationarity effect of health determinants on COVID-19 exposures. These results empirically identify the geographic disparities of COVID-19 incidence rates among age groups and provide the evidentiary guide for targeting pandemic recovery, mitigation, and preparedness in communities.

Estimation of mobility and population in Spain during different phases of the COVID-19 pandemic from mobile phone data

This work aims to find out the effectiveness of sources based on Big Data like mobile phone records to analyze mobility flows and changes in the population of Spain in different scenarios during the period of the pandemic caused by the COVID-19 virus. To this end, we have used mobile phone data provided by the National Institute of Statistics from four days corresponding to different phases of the pandemic. Origin-Destination matrices and population estimation calculations at the spatial level of population cells have been elaborated. The results show different patterns that correspond to the phenomena that have occurred, as the decrease of the population during the periods associated with the confinement measures. The consistency of findings with the reality and the generally good correlation with the population census data indicate that mobile phone records are a useful source of data for the elaboration of demographic and mobility studies during pandemics.

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

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.

Causal effects of mobility intervention policies on intracity flows during the COVID-19 pandemic: The moderating role of zonal locations in the transportation networks

Many studies have investigated the impact of mobility restriction policies on the change of intercity flows during the outbreak of COVID-19, whereas only a few have highlighted intracity flows. By using the mobile phone trajectory data of approximately three months, we develop an interrupted time series quasi-experimental design to estimate the abrupt and gradual effects of mobility intervention policies during the pandemic on intracity flows of 491 neighborhoods in Shenzhen, China, with a focus on the role of urban transport networks. The results show that the highest level of public health emergency response caused an abrupt decline by 4567 trips and a gradually increasing effect by 34 trips per day. The effectiveness of the second return-to-work order (RtW2) was found to be clearly larger than that of the first return-to-work order (RtW1) as a mobility restoration strategy. The causal effects of mobility intervention policies are heterogenous across zonal locations in varying urban transport networks. The declining effect of health emergency response and rebounding effect of RtW2 are considerably large in better-connected neighborhoods with metro transit, as well as in those close to the airport. These findings provide new insights into the identification of pandemic-vulnerable hotspots in the transport network inside the city, as well as of crucial neighborhoods with increased adaptability to mobility interventions during the onset and decline of COVID-19.

Estimation of indoor soil/dust-skin adherence factors and health risks for adults and children in two typical cities in southern China

Soil/dust (SD) skin adherence is key dermal exposure parameter used for calculating the health risk of dermal exposure to contaminants. However, few studies of this parameter have been conducted in Chinese populations. In this study, forearm SD samples were randomly collected using the wipe method from population in two typical cities in southern China as well as office staff in a fixed indoor environment. SD samples from the corresponding areas were also sampled. The wipes and SD were analyzed for tracer elements (aluminum, barium, manganese, titanium, and vanadium). The SD-skin adherence factors were 14.31 μg/cm² for adults in Changzhou, 7.25 μg/cm² for adults in Shantou, and 9.37 μg/cm² for children in Shantou, respectively. Further, the recommended values for indoor SD-skin adherence factors for adults and children in Southern China were calculated to be 11.50 μg/cm² and 9.37 μg/cm², respectively, which were lower than the U.S. Environmental Protection Agency (USEPA) recommended values. And the SD-skin adherence factor value for the office staff was small (1.79 μg/cm²), but the data were more stable. In addition, PBDEs and PCBs in dust samples from industrial and residential area in Shantou were also determined, and health risks were assessed using the dermal exposure parameters measured in this study. None of the organic pollutants posed a health risk to adults and children via dermal contact. These studies emphasized the importance of localized dermal exposure parameters, and further studies should be conducted in the future.

Assessing the spatial distribution of and inequality in 15-minute PCR test site accessibility in Beijing and Guangzhou, China

China has been planning to construct SARS-CoV-2 antigen testing sites within a 15-min walk in most major cities to timely identify asymptomatic cases and stop the transmission of COVID-19. However, little is known about the spatial distribution of 15-min accessibility to PCR test sites. In this study, we analyze the spatial distribution of and inequality in 15-min accessibility to PCR test sites in two major Chinese cities (Beijing and Guangzhou) based on the cumulative-opportunity model. The results indicate that the current distribution of 15-min accessibility to PCR test sites is satisfactory when normal commuting is not disrupted. However, disruptions of normal commuting (e.g., due to work-from-home restrictions) can negatively influence 15-min accessibility to PCR test sites and increase its inequality. Our study provides policymakers with up-to-date knowledge about the spatial distribution of 15-min accessibility to PCR test sites, identifies the disadvantaged neighborhoods in terms of test site accessibility, and highlights the changes in accessibility and inequality because of travel disruptions.

Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Making Space in Geographical Analysis

In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for all. We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high-level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets.

Impacts of the COVID-19 pandemic on the profile and preferences of urban mobility in Brazil: Challenges and opportunities

Daily commuting characteristics were highly affected by the COVID-19 pandemic, since restriction of the movement of people was one of the main preventive measures adopted. Understanding of the effects that the pandemic had on mobility is essential to help in mitigating the problems arising from this crisis, while also providing an opportunity for the implementation of sustainable policies in the post-pandemic period. Therefore, the aim of this study was to identify the impacts of the pandemic on the profile of travel behavior and mobility preferences in Brazil, using a case study of cities located in the state of Rio Grande do Sul. The data obtained from an online survey were modeled using exploratory factor analysis, resulting in the extraction of 15 main factors that explain behavioral changes in mobility due to the effects of the pandemic, as well as future perspectives. In the pandemic period, the use of private vehicles grew as the main mode of transport to the principal activity. Conversely, the use of public transport decreased drastically, due to compulsory measures taken by the health authorities to prevent the spread of the new virus. There was also greater receptivity to the adoption of active mobility, especially the bicycle, although it is necessary to provide better conditions for use of this transport mode. The findings support the development of public policies to reduce urban mobility problems and to provide guidelines for sustainable planning in the post-pandemic period.

COVID-19, traffic demand, and activity restriction in China: A national assessment

The global COVID pandemic of 2020, affected travel patterns across the world. The level of impact was influenced not only by the virus itself, but also by the nature, extent, and duration of governmental restriction on commerce and personal activity to limit its spread. This paper focuses on the interaction between COVID-19 transmission and traffic volume and further explores the impact of traffic control policies on the interaction. Roadway traffic volume was used to quantify and assess the Chinese response to the pandemic; specifically, the relationship between government restrictions, travel activity, and COVID-19 progression across 29 provinces. Space and time distributions of traffic volume across China during the first half of 2020, were used to quantity the response and recovery of travel during the critical initial onset period of the virus. Most revealing of these trends were the impact of the Chinese restriction policies on both travel and the virus as well as the relationship of traffic trends during the closure period with the speed and extent of the recovery "bounce" across individual provinces based on location, economic activity, and restriction policy. These suggest that the most significant and rapid declines in traffic volume during the restriction period resulted in the most pronounced returns to normal (or more) demand levels. Based on these trends a Susceptible Infection Recovery model was created to simulate a range of outbreak and restriction policies to examine the relationship between COVID-19 spread and traffic volume in China.

Longitudinal Analysis of COVID-19 Impacts on Mobility: An Early Snapshot of the Emerging Changes in Travel Behavior

The COVID-19 pandemic has caused a huge disruption worldwide with direct and indirect effects on travel behavior. In response to extensive community spread and potential risk of infection, during the early stage of the pandemic many state and local governments implemented non-pharmaceutical interventions that restricted non-essential travel for residents. This study evaluates the impacts of the pandemic on mobility by analyzing micro panel data (N = 1,274) collected in the United States via online surveys in two periods, before and during the early phase of the pandemic. The panel makes it possible to observe initial trends in travel behavior change, adoption of online shopping, active travel, and use of shared mobility services. This analysis intends to document a high-level overview of the initial impacts to spur future research to dive deeper into these topics. With the analysis of the panel data, substantial shifts are found from physical commutes to teleworking, more adoption of e-shopping and home delivery services, more frequent trips by walking and biking for leisure purposes, and changes in ridehailing use with substantial variations across socioeconomic groups. The social and environmental implications of these findings are discussed and suggestions for effective policy and directions for future research are made in the conclusion.

Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: A study using individual-level GPS-based real-time sensing data

Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using individual-level data collected with GPS-tracking smartphones, mobile air-pollutant and noise sensors, an activity-travel diary, and a questionnaire from two typical neighborhoods in a dense and well-developed city (i.e., Hong Kong), this study seeks to examine 1) the associations between multiple environmental exposures (i.e., different types of greenspace, PM_2.5, and noise) and housing conditions (i.e., housing types, ownership, and overcrowding) with individuals' COVID-19 risk both in residential neighborhoods and along daily mobility trajectories; 2) which social groups are disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple linear regression and logistical regression models, we found a significant negative association between COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along people's daily mobility trajectories. Meanwhile, we also found that high open space and recreational land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise exposure was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with work places in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk.

Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens' smart cards in the UK's second largest city region

Harnessing a unique data source - longitudinal travel smartcard data linked to passenger demographics from 2019 to 2022 - we use methods of survival analysis to model the recovery of public transport patronage among 183,891 senior citizens resident in the West Midlands metropolitan region in the United Kingdom. Comparing pre and peri-pandemic patronage, we identify pronounced social and spatial inequalities in the speed of return to public transport. We find that male, younger and non-White passengers are more likely to return to public transport as soon as movement restrictions were lifted, whereas passengers from White ethnic background and affluent areas do not return to public transport within the first year after the outbreak. Pronounced social inequalities persist into the middle of 2021, and only thence they began to attenuate as part of a wider return to public transport among passengers post retirement age. In 2022, 80% of these passengers have returned to public transport but the frequency of use has remained lower than prior to the pandemic. We discuss implications for transport policy and planning.

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

By the end of 2021, the Omicron variant of coronavirus disease 2019 had become the dominant cause of a worldwide pandemic crisis. This demands a deeper analysis to support policy makers in creating interventions that not only protect people from the pandemic but also remedy its negative effects on the economy. Thus, this study investigated people's mobility changes through the relationship between spatiotemporal population density and urban facilities. Results showed that places related to daily services, restaurants, commercial areas, and offices experienced decreased visits, with the highest decline belonging to commercial facilities. Visits to health care and production facilities were stable on weekdays but increased on holidays. Educational institutions' visits decreased on weekdays but increased on holidays. People's visits to residential housing and open spaces increased, with the rise in residential housing visits being more substantial. The results also confirmed that policy interventions (e.g., declaration of emergency and upgrade of restriction level) have a great impact on people's mobility in the short term. The findings would seem to indicate that visit patterns at service and restaurant places decreased least during the pandemic. The analysis outcomes suggest that policy makers should pay more attention to risk perception enhancement as a long-term measure. Furthermore, the study clarified the population density of each facility type in a time series. Improving model performance would be promising for tracking and predicting the spread of future pandemics.

Resilience Strategies of Ports against Covid-19 in Terms of Chaos Theory

During the Covid-19 pandemic, all sectors experienced chaotic dynamics worldwide. For example, maritime transport, particularly ports as one of its main elements, had to continue operating in this chaotic environment. Ports developed their own strategies to provide resilience against these challenges. However, any study in the related literature has not been reached that reveals resilience strategies of ports by combining literature review and interviews with port practitioners. As a novelty of the study, it was tried to evaluate resilience strategies of ports by grounding chaos theory. Therefore, this study had two aims: (1) identifying the Covid-19 strategies of Turkish container ports; (2) prioritizing these strategies in terms of impact level. First, interviews were conducted with Turkish container port representatives to find out their resilience strategies. These strategies were then validated with a literature review and new ones were detected. Second, separate relation analyses of the strategies were conducted for the interviews and literature. Finally, ports' resilience strategies against Covid-19 disruptions were prioritized using Fuzzy Analytic Hierarchy Process (AHP) based on the port managers' evaluations. Fuzzy AHP is widely used and accepted in the maritime business literature. This method also diminishes inconsistencies and subjective evaluations by employing fuzzy logic. The results showed that 'Control Mechanism', 'Hygienic Measures', and 'Information Exchange' were the most effective resilience strategies. By using chaos theory, this study helped to theoretically clarify the role of port management approaches to the challenges of the Covid-19 pandemic. These findings can therefore guide container port practitioners in overcoming pandemic conditions.

Evaluating the accessibility benefits of the new BRT system during the COVID-19 pandemic in Winnipeg, Canada

Recently, in Winnipeg, the implementation of new bus rapid transit (BRT) system in the middle of the COVID-19 pandemic has raised many concerns, challenging the rationale behind the untimely release. However, the new BRT service can benefit low-income, socio-economically vulnerable, and transit captive passengers who must travel to essential services and work opportunities during the pandemic. This study evaluates whether the new BRT system has positive impacts on accessibility to such essential services during the pandemic. Isochrones with different time budgets as well as times of a day are generated based on high-resolution public transit network via the General Transit Feed Specification (GTFS) data and used for evaluating accessibility benefits before and after the BRT construction. The new BRT service in Winnipeg demonstrates varying accessibility impacts across different parts of the BRT corridor. Areas near dedicated lane-section show a significant increase, whereas areas near non-dedicated lane sections show a decrease in accessibility. Nevertheless, across the whole BRT corridor, the new BRT service presents an overall increase in accessibility to essential services. This demonstrates the positive accessibility benefits of the new BRT service to residents seeking essential services during the COVID-19 pandemic. A decrease in accessibility along some parts suggests the necessity of using local transit improvement strategies (e.g., dedicated lanes) to improve service speed when planning BRT services within urban areas.

The role of the socio-economic context in the spread of the first wave of COVID-19 in the Marche Region (central Italy)

The first wave of COVID-19 arrived in Italy in February 2020 severely hitting the northern regions and delineating sharp differences across the country, from North to South. The Marche Region (central Italy) is a good example of such uneven distribution of contagion and casualties. This paper discusses the spatial diffusion of COVID-19 during the spring of 2020 in the five provinces of Marche and discusses it by means of descriptive and quantitative analysis of local socio-economic variables. Results show that the high impact of COVID-19 in Pesaro and Urbino, the northernmost province of Marche, might be reasonably attributable to higher mobility of local residents, especially northbound. Similarly, the larger contagion among the elderly in the center and norther provinces, is possibly due to a high number of hospices and seniors' residential facilities. Finally, the North-to-South diffusion of the virus can be explained by the Region's transportation infrastructures and urban layout along the coastal area.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

Inequality and support for government responses to COVID-19

Despite a deep literature studying the impact of inequality on policy outcomes, there has been limited effort to bring these insights into the debates about comparative support for government responses to the COVID-19 pandemic. We fill this gap by analyzing rich survey data at the beginning of the pandemic in April 2020 from six countries spanning different income levels and geographical locations—China, Italy, Japan, South Korea, the United Kingdom, and the United States. We find that poorer individuals are less supportive of government responses. Furthermore, poorer individuals residing in more economically unequal countries offer even less government support. We also find that both economic and non-economic factors could affect the poor’s decisions to support stringent government policies. These findings suggest that greater transfers to the poor may offer an option to help increase support for strict policies and may reduce the potential deepening of social inequalities caused by the pandemic.

Socio-economic disparities in activity-travel behavior adaptation during the COVID-19 pandemic in North Carolina

The COVID-19 pandemic significantly affected human mobility. This study examines the changes in people's activity-travel behavior over 23 months (from Jan 2020 to Nov 2021) and how these changes are associated with the socio-economic status (SES) at the block group level in North Carolina. We identified 5 pandemic stages with different restriction regimes: the pre-pandemic, lockdown, reopening stage, restriction, and complete opening stage. Using the block-group mobility data from SafeGraph, we quantify visits to 8 types of destinations during the 5 stages. We construct regression models with interaction terms between SES and stages and find that visit patterns during the pandemic vary for different types of destinations and SES areas. Specifically, we show that visits to retail stores have a slight decrease for low and medium SES areas, and visits to retail stores and restaurants and bars bounced back immediately after the lockdown for all SES areas. The results suggest that people in low SES areas continued traveling during the pandemic. Transportation planners and policymakers should carefully design the transportation system to satisfy travel needs of those residents. Furthermore, the results also highlight the importance of designing mitigation policies that recognize the immediate recovery of visits to retail locations, restaurants, and bars.

Structural changes in intercity mobility networks of China during the COVID-19 outbreak: A weighted stochastic block modeling analysis

This study focuses on a mesoscale perspective to examine the structural and spatial changes in the intercity mobility networks of China from three phases of before, during and after the Wuhan lockdown due to the outbreak of COVID-19. Taking advantages of mobility big data from Baidu Maps, we introduce the weighted stochastic block model (WSBM) to measure and compare mesoscale structures in the three mobility networks. The results reveal significant changes to volume and structure of the intercity mobility networks. Particularly, WSBM results show that the intercity network transformed from a typical core-periphery structure in the normal phase, to a hybrid and asymmetric structure with mixing core-peripheries and local communities in the lockdown phase, and to a multi-community structure with nested core-peripheries during the post-lockdown phase. These changes suggest that the outbreak of COVID-19 and the travel restrictions deconstructed the original hierarchy of the intercity mobility network in China, making the network more locally or regionally fragmented, even at the recovery stage. This study provides new empirical and methodological insights into understanding mobility network dynamics under the impact of COVID-19, helping assess the emergency-induced impact as well as the recovery process of the mobility network.

The disturbance of urban mobility in the context of COVID-19 pandemic

Since the COVID-19 outbreaks, extensive studies have focused on mobility changes to demonstrate the pandemic effect; some studies identified remarkable mobility declines and revealed a negative relationship between mobility and the number of COVID-19 cases. However, counter-arguments have been raised, exemplifying insignificant variations, recuperated travel frequency, and transitory decline effect. This paper copes with this contentious issue, analyzing time series mobility data in comprehensive timelines. The assessment of the pandemic effect builds on significant change rate (SCR) ceilings and the density of the semantic outliers derived from the kernel-based approach. The comparison between pre- and post-pandemic periods indicated that mobility decline pervaded Australia, Europe, New York, New Zealand, and Seoul. However, the degree of the effect was alleviated over time, showing decreased/increased SCR ceilings of negative/positive outliers. The changes in resulting outlier density and SCR ceilings corroborated that the pandemic outbreaks did not lead to persistent mobility decline. The findings provide useful insights for predicting epidemics and setting appropriate restrictions and transportation systems in urban areas.

Mobility in pandemic times: Exploring changes and long-term effects of COVID-19 on urban mobility behavior

The COVID-19 pandemic marked a global disruption of unprecedented scale which was closely associated with human mobility. Since mobility acts as a facilitator for spreading the virus, individuals were forced to reconsider their respective behaviors. Despite numerous studies having detected behavioral changes during the first lockdown period (spring 2020), there is a lack of longitudinal perspectives that can provide insights into the intra-pandemic dynamics and potential long-term effects. This article investigates COVID-19-induced mobility-behavioral transformations by analyzing travel patterns of Berlin residents during a 20-month pandemic period and comparing them to the pre-pandemic situation. Based on quantitative analysis of almost 800,000 recorded trips, our longitudinal examination revealed individuals having reduced average monthly travel distances by ∼20%, trip frequencies by ∼11%, and having switched to individual modes. Public transportation has suffered a continual regression, with trip frequencies experiencing a relative long-term reduction of ∼50%, and a respective decrease of traveled distances by ∼43%. In contrast, the bicycle (rather than the car) was the central beneficiary, indicated by bicycle-related trip frequencies experiencing a relative long-term increase of ∼53%, and travel distances increasing by ∼117%. Comparing behavioral responses to three pandemic waves, our analysis revealed each wave to have created unique response patterns, which show a gradual softening of individuals' mobility related self-restrictions. Our findings contribute to retracing and quantifying individuals' changing mobility behaviors induced by the pandemic, and to detecting possible long-term effects that may constitute a "new normal" of an entirely altered urban mobility landscape.

Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

The global spread of the coronavirus disease 2019 (COVID-19) pandemic has affected the world in many ways. Due to the communicable nature of the disease, it is difficult to investigate the causal reason for the epidemic's spread sufficiently. This study comprehensively investigates the causal relationship between the spread of COVID-19 and mobility level on a multi time-scale and its influencing factors, by using ensemble empirical mode decomposition (EEMD) and the causal decomposition approach. Linear regression analysis investigates the significance and importance of the influential factors on the intrastate and interstate causal strength. The results of an EEMD analysis indicate that the mid-term and long-term domain portrays the macroscopic component of the states' mobility level and COVID-19 cases, which represents overall intrinsic characteristics. In particular, the mobility level is highly associated with the long-term variations of COVID-19 cases rather than short-term variations. Intrastate causality analysis identifies the significant effects of median age and political orientation on the causal strength at a specific time-scale, and some of them cannot be identified from the existing method. Interstate causality results show a negative association with the interstate distance and the positive one with the airline traffic in the long-term domain. Clustering analysis confirms that the states with the higher the gross domestic product and the more politically democratic tend to more adhere to social distancing. The findings of this study can provide practical implications to the policymakers that whether the social distancing policies are effectively working or not should be monitored by long-term trends of COVID-19 cases rather than short-term.

Examining the Influence of Housing Conditions and Daily Greenspace Exposure on People’s Perceived COVID-19 Risk and Distress

Many people have worried about COVID-19 infection, job loss, income reduction, and family conflict during the COVID-19 pandemic. Some social groups may be particularly vulnerable due to their residential neighborhoods and daily activities. On the other hand, people’s daily exposure to greenspace offers promising pathways for reducing these worries associated with COVID-19. Using data collected with a questionnaire and a two-day activity diary from two typical neighborhoods in Hong Kong, this study examines how people’s housing conditions and daily greenspace exposure affect their perceived COVID-19 risk and distress (i.e., worries about job loss, income reduction, and family conflict) during the pandemic. First, the study compares people’s perceived COVID-19 risk and distress based on their residential neighborhoods. Further, it examines the associations between people’s perceived COVID-19 risk and distress with their housing conditions and daily greenspace exposure using ordinal logistic regression models. The results indicate that living in a high-risk neighborhood, being married, renting a residential unit, and living in a large household are significantly associated with a higher neighborhood-based perceived COVID-19 risk and distress during the pandemic. In addition, people also reported lower mobility-based perceived COVID-19 risk when compared to their neighborhood-based perceived COVID-19 risk, while they still have a high perceived COVID-19 risk in their occupational venues if they have to work in a high-risk district (e.g., Kowloon). Lastly, daily greenspace exposure (i.e., woodland) could reduce people’s perceived COVID-19 risk and distress. These results have important implications for the public health authority when formulating the measures during the COVID-19 pandemic.

An examination of water, sanitation, and hygiene (WASH) accessibility and opportunity in urban informal settlements during the COVID-19 pandemic: Evidence from Nairobi, Kenya

This research examines water, sanitation, and hygiene (WASH) accessibility and opportunity in Kibera and Mathare during the COVID-19 pandemic in 2021. Kibera and Mathare are two of the largest urban informal settlements in Nairobi (the capital city of Kenya) as well as Sub-Saharan Africa. Accessibility indicates how easily a person can reach WASH facilities from their home by walking. Opportunity represents how many WASH options a person has near their home. We utilize the data on water and toilet facilities collected by GroundTruth Initiative in partnership with Map Kibera Trust (local community partners) between February and April 2021 - amid the COVID-19 pandemic. By conducting quantitative geospatial analysis, we illustrate WASH accessibility and related issues that were not evident in previous studies: (1) 77.4% of people living in Kibera have limited WASH facility accessibility or opportunity; (2) 60.6% of people living in Mathare have limited WASH facility accessibility or opportunity; (3) there is a clear geographic pattern in WASH facility accessibility and opportunity; and (4) overall accessibility and opportunity is better in Mathare than in Kibera. This study is one of the first studies to examine WASH accessibility and opportunity in urban informal settlements during the COVID-19 pandemic by utilizing the current data and quantitative geospatial methods. Based on the results, we discuss important public health policy implications for people living in urban informal settlements to improve their WASH facility accessibility and opportunity during the COVID-19 pandemic.

Exploring Associations between Multimodality and Built Environment Characteristics in the U.S

This study demonstrated associations between multimodality and built environment characteristics, and proposed policy implications for fostering multimodal travel behaviors. It conducted a U.S. nationwide analysis using ordinary least square regression and gradient boosting decision tree regressor models with American Community Survey 2015–2019 5-year estimates and the United States Environmental Protection Agency Smart Location Database version 3.0. Notable findings were as follows: First, built environment characteristics were found to be statistically significant predictors of multimodality across the U.S. Second, certain features were identified as having considerable importance, specifically including population density, regional accessibility, walkability index, and network density, all of which should be given particular attention by transportation and land-use planners. Third, the non-linear effects of built environment characteristics on multimodality suggested an effective range to encourage multimodal transportation choice behaviors in various situations. The findings can guide the development of effective strategies to transform the built environment, which may subsequently be used to minimize reliance on automobiles and promote people to travel more sustainably.

Accessibility and Essential Travel: Public Transport Reliance Among Senior Citizens During the COVID-19 Pandemic

Using smart card travel data, we compare demand for bus services by passengers of age 65 or older prior to and during the COVID-19 pandemic to identify public transport-reliant users residing in more car-dependent environments—i.e., people who rely on public transport services to carry out essential activities, such as daily shopping and live in areas with low public transport accessibility. Viewing lockdowns as natural experiments, we use spatial analysis combined with multilevel logistic regressions to characterize the demographic and geographic context of those passengers who continued to use public transport services in these areas during lockdown periods, or quickly returned to public transport when restrictions were eased. We find that this particular type of public transport reliance is significantly associated with socio-demographic characteristics alongside urban residential conditions. Specifically, we identify suburban geographies of public transport reliance, which are at risk of being overlooked in approaches that view public transport dependence mainly as an outcome of deprivation. Our research demonstrates once again that inclusive, healthy and sustainable mobility can only be achieved if all areas of metropolitan regions are well and reliably served by public transport.

Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan

COVID-19 is one of the worst global health crises in a century. Japan confirmed its first case of COVID-19 in mid-January and declared a state of emergency in April and May 2020, urging people to stay at home and reduce travel. Using Mobile Spatial Statistics (i.e., population statistics created from operational data of mobile terminal networks), we estimated daily intra- and inter-prefectural population mobility in the Tokyo Megalopolis Region, Japan in 2020. Then, we developed a compartmental model with population mobility to explore the role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19. This model describes the COVID-19 pandemic through a susceptible-exposed-presymptomatic infectious-undocumented and documented infectious-removed (SEPIR) process and incorporates intra- and inter-prefectural population mobility into the transmission process. We found that people significantly reduced travel during the state of emergency, although stay-at-home requests and travel restrictions were recommended rather than mandatory. The reduction in population mobility, combined with other control measures, resulted in a substantial reduction in effective reproduction numbers to below 1, thus controlling the first wave of the pandemic. Moreover, the relationship between population mobility and COVID-19 transmission changed over time. The dampening of the second wave of the pandemic indicated that smaller reductions in population mobility could result in pandemic control, probably because of other social distancing behaviors. Our proposed model can be used to analyze the impact of different public health interventions, and our findings shed light on the effectiveness of soft containments in curbing the spread of COVID-19.

The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

The ongoing COVID-19 pandemic has posed a global threat to human health. In order to prevent the spread of this virus, many countries have imposed travel restrictions. This difficult situation has dramatically affected the airline industry by reducing the passenger volume, number of flights, airline flow patterns, and even has changed the entire airport network, especially in Northeast Asia (because it includes the original disease seed). However, although most scholars have used conventional statistical analysis to describe the changes in passenger volume before and during the COVID-19 outbreak, very few of them have applied statistical assessment or time series analysis, and have not even examined how the impact may be different from place to place. Therefore, the purpose of this study was to identify the impact of COVID-19 on the airline industry and affected areas (including the origin-destination flow and the airport network). First, a Clustering Large Applications (CLARA) algorithm was used to group numerous origin-destination (O-D) flow patterns based on their characteristics and to determine if these characteristics have changed the severity of the impact of each cluster during the COVID-19 outbreak. Second, two statistical tests (the paired t-test and the Wilcoxon signed-rank test) were utilized to determine if the entire airport network and the top 30 hub airports changed during COVID-19. Four centrality measurement indices (degree, closeness, eigenvector, and betweenness centrality) of the airports were used to assess the entire network and ranking of individual hub airports. The study data, provided by The Official Aviation Guide (OAG) from December 2019 to April 2020, indicated that during the COVID-19 outbreak, there was a decrease in passenger volume (60%-98.4%) as well as the number of flights (1.5%-82.6%). However, there were no such significant changes regarding the popularity ranking of most airports during the outbreak. Before this occurred (December 2019), most hub airports were in China (April 2020), and this trend remain similar during the COVID-19 outbreak. However, the values of the centrality measurement decreased significantly for most hub airports due to travel restrictions issued by the government.

Exploring Food Deserts in Seoul, South Korea during the COVID-19 Pandemic (from 2019 to 2021)

Since the coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization, our lifestyle (e.g., food culture) has changed. In particular, the food insecurity issue has exacerbated. To address this issue, this study aims to measure spatial accessibility to food outlets and identify food deserts in Seoul, South Korea during the COVID-19 pandemic (i.e., 2019–2021). To assess spatial access to food outlets, we used the enhanced two-step floating catchment area (E2SFCA) method. The results from the E2SFCA methods showed that spatial accessibility to restaurants increased, but access to grocery stores decreased. A noticeable change occurred in Gangnam and Seocho. The Gini coefficients indicated that equality in spatial accessibility to restaurants fluctuated (i.e., worsened from 2019 to 2020 and improved from 2020 to 2021), whereas equality in spatial accessibility to grocery stores improved. The results help to identify prioritized regions where additional food resources can be placed, especially for marginalized people who have limited access to food due to their socio-economic status.

Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China

The purpose of this paper is to gain an insight into commuting and travel mode choices in the post-COVID-19 era. The surveys are divided into two waves in Qingdao, China: the first-wave questionnaires were collected under the background of a three-month zero growth of cases; the second wave was implemented after the new confirmed cases of COVID-19. The latent class nested logit (LCNL) model is applied to capture heterogeneous characteristics among the various classes. The results indicate that age, income, household composition, and the frequency of use of travel modes are latent factors that impact users' attitudes toward mass transit and the private car nests when undergoing the shock of the COVID-19 pandemic. Individuals' trepidation regarding health risks began to fade, but this is still a vital consideration in terms of mode choice and the purchase of vehicles. Moreover, economic reinvigoration, the increase in car ownership, and an increase in the desire to purchase a car may result in great challenges for urban traffic networks.

Analysis of the Relationship between the Characteristics of the Areas of Influence of Bus Stops and the Decrease in Ridership during COVID-19 Lockdowns

This study aimed to investigate the relationship between the characteristics of the areas of influence of bus stops and the decrease in ridership during COVID-19 lockdowns and subsequent initial reopening processes. A novel GIS methodology was developed to determine these characteristics from a large amount of data with high spatial detail and accurately assign them to individual bus stops. After processing the data, several multiple linear regression models were developed to determine the variables related to different activities and changes in mobility during lockdown that may explain the variation in demand owing to the COVID-19 pandemic. The characteristics related to population and land use were also studied. The proposed methodology can be used to improve transit planning during exceptional situations, by strengthening public transport in areas with a predictably higher transit demand, instead of uniformly decreasing the availability of public transport services, promoting sustainable mobility. The efficiency of the proposed methodology was shown by performing a case study that analysed the variation in bus demand in A Coruña, Spain. The areas with the highest sustained demand were those with low inhabitant incomes, a high population density, and significant proportions of land use dedicated to hospitals, offices, or supermarkets.

Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA

Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R² (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R² values show that local regression models (mean adj. R² = 0.80) outperformed the global regression models (mean adj. R² = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.

Impacts of COVID-19 Pandemic on the Global Flows of People and Goods: Implications on the Dynamics of Urban Systems

The emergence of the COVID-19 pandemic has significantly disrupted the flows or spatial mobility of people, goods, and services globally. The present study explored the impact of the pandemic on the global flows of people and goods, and the implications on the dynamics of urban systems. The study utilized desktop research methodology to collect relevant literature and secondary data, which were analyzed using content analysis and descriptive statistics. The study found that the restrictive measures imposed during the pandemic severely disrupted the global flows of people and goods. As a result, global movements of people declined by over 40% in 2020 from the 2019 levels. Similarly, the global flows of goods shrunk by at least 10% within the same period. These lockdown-related disruptions have significant implications on how socioeconomic activities are organized and conducted within and between cities, with civil aviation and hospitality sectors the hardest hit. The study concludes that there is a need for resilient urban systems in which cities, people, institutions, and firms can effectively adapt to the impact of the pandemic.

Estimating CO2 emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport

The environmental cost of disaster-related emergency supplies is significant. However, little research has been conducted on the estimation of emergency-supply transportation-related carbon emissions. This study created an "emergency supply emission estimation methodology" (ESEEM). The CO₂ emissions from the global air dispatch of COVID-19 vaccines were estimated using two hypothetical scenarios of one dose per capita and additional doses secured. The robustness of the model was tested with the Monte Carlo Simulation method (MCM) based one-sample t-test. The model was validated using the "Expression of Uncertainty in Measurement (GUM)" and GUM's MCM approaches. The results showed that to dispatch at least one dose of the COVID-19 vaccine to 7.8 billion people, nearly 8000 Boeing 747 flights will be needed, releasing approximately 8.1 ± 0.30 metric kilotons (kt) of CO₂. As countries secure additional doses, these figures will increase to 14,912 flights and about 15 ± 0.48 kt of CO₂. According to the variance-based sensitivity analysis, the total number of doses (population), technology, and wealth play a significant role in determining CO₂ emissions across nations. Thus, wealthy nations' long-term population reduction efforts, technological advancements, and mitigation efforts can benefit the environment as a whole and the CO₂ burdens associated with current COVID-19 and any future disasters' emergency-supply transportation.

Structural modeling of COVID-19 spread in relation to human mobility

Human mobility is considered as one of the prominent non-pharmaceutical interventions to control the spread of the pandemic (positive effect from mobility to infection). Conversely, the spread of the pandemic triggered massive changes to people's daily schedules by limiting their movement (negative effect from infection to mobility). The purpose of this study is to investigate this bi-directional relationship between human mobility and COVID-19 spread across U.S. counties during the early phase of the pandemic when infection rates were stabilizing and activity-travel behavior reflected a fairly steady return to normal following the drastic changes observed during the pandemic's initial shock. In particular, we applied Structural Regression (SR) model to investigate a bi-directional relationship between COVID-19 infection rate and the degree of human mobility in a county in association with socio-demographic and location characteristics of that county, and state-wide COVID-19 policies. Combining U.S. county-level cross-sectional data from multiple sources, our model results suggested that during the study period, human mobility and infection rate in a county both influenced each other, but in an opposite direction. Metropolitan counties experienced higher infection and lower mobility than non-metropolitan counties in the early stage of the pandemic. Counties with highly infected neighboring counties and more external trips had a higher infection rate. During the study period, community mitigation strategies, such as stay at home order, emergency declaration, and non-essential business closure significantly reduced mobility whereas public mask mandate significantly reduced infection rates. The findings of this study will provide important insights to policy makers in understanding the two-way relationship between human mobility and COVID-19 spread and to derive mobility-driven policy actions accordingly.

The COVID-19 Crisis and the Case for Online GeoParticipation in Spatial Planning

This Special Issue, titled “GIS for Spatial/Political Participation in the Decision-Making Processes of Local Administrations”, in the ISPRS International Journal of Geo-Information is aimed at analysing state-of-the-art geoparticipatory tools for citizen participation in community decision-making processes, and suggesting the effective implementation of the geoparticipatory tools available for local administrations [...]

Regional poverty and infection disease: early exploratory evidence from the COVID-19 pandemic

This paper examines the role of regional poverty on the COVID-19 pandemic in the USA. It also explores how the effects differ with the concentration of ethnic minorities. We find that poverty is a significant and consistent determinant of higher COVID-19 infections and fatalities. Prevalent poverty areas experienced higher infections due to economic structure that require hypermobility (high mobility and interpersonal interaction)-more physical human to human contact resulting in higher deaths from limited access to health services. These are also regions where minority groups are concentrated. Disproportionate infections and fatalities occurred within the black, Hispanic, and Asian population. Our evidence is robust to state fixed effects that capture local COVID-19 mitigation policies, multi-level hierarchical modeling, spatial autoregressive assessment, and large sets of county-level health, social, and economic factors. This paper contributes to the literature on health and economic disparities and their resulting consequences for infectious diseases.

Estimating the Economic Effects of the Early Covid-19 Emergency Response in Cities Using Intracity Travel Intensity Data

In the early days of the Covid-19 pandemic, China implemented the most stringent and serious emergency response. To understand the effect of such an emergency response strategy on the economic system, this study proposed a simultaneous overall estimation method using intracity travel intensity data. The overall effect is represented by the difference between intracity travel intensity with and without the emergency response. Using historical data and time series analysis, we compared intracity travel intensity post China’s implementation of the emergency response with predicted intracity travel intensity without such a response. The loss rates, defined by the proportion of intracity travel intensity loss, were calculated for 360 cities within 33 provincial-level regions in China based on data availability. We found that 30 days after the emergency response, 21% of the cities saw over 80% recovery and 10% of the cities showed more than 90% recovery; 45 days after the emergency response, more than 83% of the 360 cities witnessed 80% recovery. The correlation between gross domestic production loss rate and travel intensity loss rate was studied quantitatively to demonstrate the representativeness of the intracity travel intensity loss rate. This indicator was also used to analyze the spatial and temporal patterns of the effects on the economy. The results of this study can help us understand the economic effects caused by the early Covid-19 emergency response and the method can be a reference for fast and real-time economic loss estimation to support emergency response decision making under pandemic conditions.

Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective

The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.

Using twitter to investigate responses to street reallocation during COVID-19: Findings from the U.S. and Canada

The COVID-19 pandemic has challenged and encouraged local governments to reallocate street space. The chief purpose of new regimes of street management is to expand spaces for walking and bicycling, and to ease business interactions such as curbside pickup and dining while maintaining social distancing guidelines. We investigated how North Americans on Twitter viewed alternative uses and forms of street reallocation, specifically during the early months of the pandemic from April 1, 2020 to July 1, 2020. Relying on a crowdsourced dataset of government actions (Combs and Pardo 2021), we identified five areas of policy initiative that were broadly representative of government actions: cycling, walking, driving, business, and curbside. First, we identified a corpus of 292,108 geolocated tweets from the U.S. and Canada. Next, we used word vectors, built on this Twitter corpus, to generate similarity scores across the five areas of policy initiative for each tweet. Finally, we selected the top tweets that closely matched ideas contained in the areas of policy initiative, thus creating a finer corpus of 1,537 tweets. Using the five categories as guideposts, we conducted an inductive content analysis to understand opinions expressed on Twitter. Our analysis suggests that renewed use of the curb has opened up possibilities for reimaging this space. Particularly, business uses of the curb for dining and pick up zones have expanded widely, and there is more use of sidewalks; yet both spaces have limited capacity. Planners need to think of expanding these assets while reducing cost burdens for their alternative uses.

Transport disrupted - Substituting public transport by bike or car under Covid 19

The Covid 19 pandemic has caused dramatic disruptions in the public transport sector that has seen a stark downturn in many cities across the globe, calling into question previous efforts to reduce air pollution and CO2 emissions by expanding this sector. Especially, the current surge of individual car use is worrying and the question remains which users might be able and willing to substitute public transport by cycling. This effect is interesting to study for the case of Hanover Region, because of the well-developed biking infrastructure that makes biking a viable alternative to individual car use. In this paper, we analyze survey data from June 2020 on the use of transportation modes before and during the pandemic in the Hanover Region. We ask if and how the over 4.000 participants substitute public transport and what characterizes those who chose biking over individual car use. We use multivariate regression models and find evidence that Stadtbahn (local light rail) and bus are substituted by bike, car and working from home, while train use is not significantly replaced by car and seems to be positively related to bike use. The data also shows that women have a higher level of fear of infection than men have during public transport use and therefore reduce public transport use more. Moreover, income displays a positive effect on increased car use while cycling is independent of socio-economic indicators but instead driven by the eco-consciousness of users. Surprisingly, we find that car use was increased in particular by residents of Hanover city, while it was decreased by residents of less densely populated urban areas in the region.