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A resilient urban system should cope with the dynamic human activities in its subsystems. Since travel allows people to have their needs satisfied in scattered locales, the dynamics of travel and its effects on people's well-being can vary across different spatial scales (including different subsystems) and over time. However, little empirical research has been done regarding these spatiotemporal variations. In this article, we introduced a trip resilience (TR) index to measure the time-varying travel characteristics, especially trip attraction changes, across different spatial scales. Using empirical data from Hong Kong, we quantified the TR indices for metro trips at a station across three spatial scales: local, neighborhood, and citywide. Then, we examined the spatial distribution of the TR and investigated which station (area) characteristics could explain the stations' TR indices for trips in different scales. We found that the TR indices and their predictors varied across the scales. The diversity of points of interest significantly predicted the TR indices across all the scales. Yet, other characteristics, such as the median age of residents, street density, working population, and provisions of parking spaces, only predicted the TR indices for trips at one or two of the scales. These findings shed light on more refined urban and transport planning strategies and policies concerning travel demand management across spatial scales in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

The impacts of coronavirus disease 2019 (COVID-19) on society and economy are wide-ranging, long-lasting, and global. The experience of multiple countries or regions in fighting the pandemic indicates that there could be multiple COVID-19 surges, where a growing number of cases can be observed in the more recent surge(s). Were COVID-19 cases and clusters of cases (across surges) randomly distributed in spaces? Did population density and activity centres influence clusters of cases and associated venues? Based on information on the associated venues of the four surges of COVID-19 cases between January 2020 and February 2021 as well as population density, visuals were made to distinguish the relationships between population density, activity centres, and clusters of cases in Hong Kong. Different spatial patterns were observed across the four surges: fewer cases were observed in the first surge with a more evenly distributed pattern of clusters; the second surge as compared to the first surge saw a wider distribution and an increase in the number/layer of clusters; compared to the second surge, the third surge suffered from many more cases but saw a decrease in the general number of clusters; and compared to the previous three surges, the fourth surge had the largest number of cases, yet even fewer clusters were observed, where several clusters are again concentrated in specific areas similar to the previous surge. Across the four surges, a few locales could see recurrent clusters of cases and a few communities were without cases. [ABSTRACT FROM AUTHOR]

The beginning of 2020 has seen coronavirus spreading to many countries and regions. To contain the virus, China adopted, arguably, the most stringent quarantine countermeasures in the country's history concerning restricting people flows, limiting outdoor activities, and extending the Spring Festival break. On the one hand, thousands of doctors and nurses directly fought and are still fighting coronavirus in various medical facilities; on the other hand, millions and even billions of residents and tourists self-quarantined and are still self-quarantining themselves in their homes (or temporary homes), fighting the virus in another manner. Across cities and regions, which have the highest percentage of "fighters" at home? To answer this, we downloaded and processed the publicly available Baidu Qianxi (migration) data for 11 consecutive days in 2019 and 2020. Then we geovisualized the answer. The visual indicates that several cities in Hubei, as expected, had the highest percentage, followed by several cities in Zhejiang Province and several cities in Guangxi Zhuang Autonomous Region and Hainan Province. In terms of percentage ranking across regions, East China is no.1. [ABSTRACT FROM AUTHOR]

Amid the COVID-19 pandemic, face-to-face contacts decreased but still existed despite people's fear of virus infection and governments' social gathering restrictions. These interactions influenced virus transmission routes, if any and reflected people's essential social interactive demands in the city. In this article, we identified people who intentionally travel as groups (ITGs) to characterize social interactions before and amid COVID-19. To systematically understand ITGs' mobility patterns, an ITG structure was defined and measured in multiple dimensions, including composition, function, size, intensity, quality, and spatiotemporal distribution. Based on a longitudinal smartcard dataset in Hong Kong spanning the year of 2020, we operationalized the ITG structure in the local metro system and examined whether and to what degree the structure changed during the pandemic. We found that ITGs' activities fluctuated as the pandemic progressed and their changes differed across different ITG groups. The long-distance ITGs saw the most significant change. The spatial distribution of persistent ITG trips before and amid the pandemic became spatiotemporally more concentrated. Stations with similar ITG indices clustered in proximity, and features of station areas like residents' education level and quantity of commercial facilities could well predict stations' ITG indices. In other words, inequal distribution of essential facilities and opportunities could notably influence ITGs, social contacts, and socioeconomic benefits brought about by them amid COVID-19. The findings provide insights concerning both resilience management amid the crisis and the long-term planning of essential facilities and services that facilitate group-based outgoings and activities. • A concept of ITG structure was proposed to measure intentional group travel activities. • Both proprietorial and publicly available data can be used to operationalize the ITG structure and its correlates. • The ITG structure varied across space and time during the pandemic. • Spatial pattern of the ITG distribution across metro station could be well predicted by station-area features. [ABSTRACT FROM AUTHOR]

Abrupt socioeconomic changes have become increasingly commonplace. In face of these, both institutions and individuals must adapt. Against the backdrop of the COVID-19 pandemic, suddenness, scale, and impacts of which are unprecedented as compared to its counterparts in history, we first propose transferable measures and methods that can be used to quantify and geovisualize COVID-19 and subsequent events' impacts on metro riders' travel behaviors. Then we operationalize and implement those measures and methods with empirical data from Hong Kong, a metropolis heavily reliant on transit/metro services. We map out where those impacts were the largest and explores its correlates. We exploit the best publicly available data to assemble probable explanatory variables and to examine quantitatively whether those variables are correlated to the impacts and if so, to what degree. We find that both macro- and meso-level external/internal events following the COVID-19 outbreak significantly influenced of metro riders' behaviors. The numbers of public rental housing residents, public and medical facilities, students' school locations, residents' occupation, and household income significantly predict the impacts. Also, the impacts differ across social groups and locales with different built-environment attributes. This means that to effectively manage those impacts, locale- and group-sensitive interventions are warranted. • Propose measures and methods that can be used to quantify COVID-19 and subsequent events' impacts. • Operationalize and implement those measures and methods with empirical data from Hong Kong. • Examine quantitatively which variables are correlated to those impacts and if so, to what degree. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 and subsequent pandemic containment measures have significantly affected our daily life, which has been extensively examined in the existing scholarship. However, the existing scholarship has done little on the jobs/housing relationship impacts of COVID-19. We attempted to fill this gap by using an excess-commuting approach. The approach allows us to analyse a series of jobs-housing matrices based on the location-based service big data of around fifty million individuals in the Pearl River Delta (PRD), China before and amid COVID-19. In the PRD, a zero-COVID policy was implemented, which presents a distinct and interesting context for our study. We found that after the COVID-19 outbreak: (1) residences and employment became more centrally located in downtowns, which is opposite to the suburbanization trend elsewhere; (2) in the whole PRD, the minimum and maximum commutes became smaller while the actual commute became larger, indicating the simultaneous presences of some paradoxical phenomena: a better spatial juxtaposition of jobs and housing, more compressed distribution of jobs and housing, and longer average actual commutes; (3) inter-city commutes between large cities were significantly refrained and decreased, while new inter-city commuters between smaller cities emerged; (4) it was more likely for the less-educated and female workers to see smaller minimum commutes amid COVID-19. This paper illustrates the potential of big data in the longitudinal study on jobs-housing relationships and excess commuting. It also produces new insights into such relationships in a unique context where stringent anti-COVID-19 policies have been continuously in place. [ABSTRACT FROM AUTHOR]

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. • How travel changed relative to perceived infection risks across space and time remains understudied. • A station's elasticity is measured as the ratio of changes in trip length to the case footprints around the station. • Stations varied in elasticity values across space and different surges of the local pandemic. • Stations' physical attributes and socioeconomic characteristics could predict their elasticities. [ABSTRACT FROM AUTHOR]