ABSTRACT
The limited exchange between human communities is a key factor in preventing the spread of COVID-19. This paper introduces a digital framework that combines an integration of real mobility data at the country scale with a series of modeling techniques and visual capabilities that highlight mobility patterns before and during the pandemic. The findings not only significantly exhibit mobility trends and different degrees of similarities at regional and local levels but also provide potential insight into the emergence of a pandemic on human behavior patterns and their likely socio-economic impacts. © 2022 IEEE.
ABSTRACT
Over the past two years, the COVID-19 pandemic had a major worldwide health, economic and daily life impact. Amongst many dramatic consequences, such as major human mobility disruptions at all scales, the tourism sector has been largely affected. This raises the need for the development of quantitative and qualitative research to favor a better understanding of the impact of the pandemic on human travel behaviors. This study introduces a computational approach that combines inference mechanisms and statistics to quantify tourists’ travel behaviors before and during the pandemic by exploring the evolution of the patterns extracted from a local tourism social network from 2019 to 2020 in the city of Hong Kong. The results show that the COVID-19 pandemic: 1) has a major influence on travel intentions that mainly swift from journeys with generally long sequences of attractions to rather single attractions;2) lead to a decline when considering connections between popular attractions, while the strength of connections within other attractions increase;3) generates novel patterns such as tourists preferring relaxing visits and even minor attractions. © 2022, Springer Nature Switzerland AG.