Twitter mobility dynamics during the COVID-19 pandemic: A case study of London

PLOS ONE RESEARCH ARTICLE Twitter mobility dynamics during the COVID19 pandemic: A case study of London Chen Zhong ID1*, Robin Morphet ID1, Mitsuo Yoshida ID2 1 Center for Advanced Spatial Analysis, University College London, London, United Kingdom, 2 Institute of Business Sciences, University of Tsukuba, Tsukuba, Japan * a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Zhong C, Morphet R, Yoshida M (2023) Twitter mobility dynamics during the COVID-19 pandemic: A case study of London. PLoS ONE 18(4): e0284902. https://doi.org/10.1371/journal. pone.0284902 Editor: Francesco Branda, University of Calabria: Universita della Calabria, ITALY Received: June 7, 2021 Accepted: April 11, 2023 Published: April 26, 2023 Copyright: © 2023 Zhong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Twitter data was collected using Twitter’s public Streaming API from the public domain following Twitter’s Developer Agreement. Access to the data is restricted due to possible ethical/privacy considerations that enable identification of Geo-tagged Twitter data. However, statistical summary tables and python codes are made available on figshare (10.6084/m9.figshare. 16864567) for researchers to reproduce the analysed results. Funding: The research has received funding from the European Research Council (ERC) under the Abstract The current COVID-19 pandemic has profoundly impacted people’s lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people’s travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 –Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale. 1 Introduction The World Health Organization (WHO) officially declared the SARS-CoV-2 outbreak a Public Health Emergency of International Concern on the 30th of January 2020 and a Global Pandemic on the 11th of March 2020. WHO urged countries to adopt strict social distancing and quarantine measures to prevent virus spread and protect the public health [1]. Despite international efforts to contain the transmission, coronavirus has spread worldwide, resulting in more than 130 million cases and 2.8 million deaths as of March 2021, with more than 700,000 cases PLOS ONE | https://doi.org/10.1371/journal.pone.0284902 April 26, 2023 1 / 20 PLOS ONE European Union’s Horizon 2020 research and innovation programme (grant agreement No 949670). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: NO authors have competing interests. Twitter mobility during Covid tested positive, and 18,000 Coronavirus (Covid-19) related deaths occurred in London alone [2], and this is likely to continue increasing, although at a reduced rate. To mitigate these effects, measures like social distancing and lockdown have been imposed worldwide. In the UK, the initial lockdown began on the 23rd of March 2020. To promote economic recovery, the government encouraged people to go back to work on the 9th of July 2020. Subsequent lockdowns were enforced from the 5th of November to the 2nd of December, given the second wave and again from the 5th of January 2021. Each lockdown has prompted a significant change in human behaviour. Substantial evidence has shown profound changes in people’s lifestyles and travel behaviours, which may persist post-pandemic [3]. How the movement of people has accelerated the virus transmission, and in reverse, how much Covid measures (i.e., travel quarantine, border control) have impacted people’s movement has been broadly investigated by incorporating human movement data into various analytical models [4–7], with a good number of studies conducted to project the impact of travel limitations at national and international scales [5, 6, 8–10]. For instance, Chinazzi, Davis [6] developed a global metapopulation disease transmission model and found the travel quarantine of Wuhan delayed the overall epidemic progression significantly on the international scale compared to that at the level of the Wuhan local area. Most of the relevant studies at a global scale used aviation networks, which are relatively coarse datasets. The nationwide movements were tracked using finer human movement data. Thus travel between Wuhan and the other cities in China was tracked using mobile phone data and revealed a strong correlation between total population flow and the number of infections at the city level [10]. The impact of COVID-19 on mobility also varies over time. The disease spreading process consisted of a significant inter-city diffusion before the Chinese New Year and a subsequent intra-city diffusion after the Chinese New Year [7]. In such a context, cost-effective and easily accessible approaches for monitoring continuous changes in disease transmission and human mobility become essential for addressing people’s social needs and travel demands in the short term and for securing economic recovery in the longer term. Other than frequently updated surveys, efforts have been made to develop alternative, less time-consuming data sources and approaches. The mobile app—NHS COVID-19 is designed for contact tracing and sending alerts to people who have visited (...truncated)