Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach

Boniphace Kutela; Norris Novat; Neema Langa

doi:10.1016/j.scs.2021.102729

Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach

Sustain Cities Soc. 2021 Apr:67:102729. doi: 10.1016/j.scs.2021.102729. Epub 2021 Jan 23.

Authors

Boniphace Kutela¹, Norris Novat², Neema Langa³

Affiliations

¹ Roadway Safety Program, Texas A&M Transportation Institute, 1111 RELLIS Pkwy, Bryan, TX 77807, United States.
² Department of Civil Engineering, Ardhi University, P.O. Box 35176, Dar es Salaam Tanzania.
³ Department of Sociology, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, United States.

Abstract

The COVID-19 outbreak has extremely impacted the globe due to travel restrictions and lockdowns. Geographically, COVID-19 has shown disproportional impacts; however, the research themes' distribution is yet to be explored. Thus, this study explored the geographical distribution of the research themes that relate to COVID-19 and the transportation sector. The study applied a text network approach on the bibliometric data of over 400 articles published between December 2019 and December 2020. It was found that the researches and the associated themes were geographically distributed based on the events that took place in the respective countries. Most of the articles were published by the authors from four countries, the USA, China, Japan, and the UK. The text network results revealed that the USA-based studies mainly focused on international travelers, monitoring, travel impacts of COVID-19, and social-distancing measures. The Japanese-based studies focused on the princess diamond cruise ship incident. On the other hand, Chinese authors published articles related to travel to Wuhan and China, passenger health, and public transportation. The UK-based studies had diverse topics of interest. Lastly, the remaining 62 countries' studies focused on returning travelers from China, public transportation, and the global spread of COVID-19. The findings are crucial to the transportation sector's researchers for various applications.

Keywords: Bibliometric; COVID-19; Machine learning; Text networks; Transportation.