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Articles by Space TimeLab

Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data

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By Space TimeLab, Geomatic Engineering, Tao Cheng
| Frontiers
1. Introduction Since the onset of the COVID-19 pandemic in early 2020, the virus has spread globally, resulting in over 760 million confirmed cases and more than 6.8 million fatalities to date (1). Prior to the introduction of vaccines, the United Kingdom government employed non-pharmaceutical interventions (NPIs), such as national lockdowns and social distancing measures, in an attempt to mitigate viral transmission and prevent the National Health Service (NHS) from reaching capacity (2–4).
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Framework for constructing multimodal transport networks and routing using a graph database: A case study in London

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By Space TimeLab, Tao Cheng, Seula Park
| Wiley Online Library Verified
Conceptual model for multimodal transport in graphs Interconnected metropolitan transport systems include private travel (e.g., cars, bikes and walking), along with public transit modes, such as subways and buses (Bellocchi et al., 2021). The most basic mode of travel is by foot, which can cover short trips (Orozco et al., 2021). Bicycles or buses can cover medium-distance travel; whereas long-distance travel is most reasonably accomplished via rail systems or cars (Varga et al., 2016).
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Impacts of the COVID-19 pandemic on the spatio-temporal characteristics of a bicycle-sharing system: A case study of Pun Pun, Bangkok, Thailand

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By Tawit Sangveraphunsiri, Tatsuya Fukushige, Natchapon Jongwiriyanurak, Space TimeLab
| PLOS
5.1. Descriptive analysis According to our datasets (i.e. trip data between 2018–2020), it is evident that the number of bicycle-sharing trips is largely affected by the first pandemic wave, especially when the number of confirmed cases is rising, as illustrated by Fig 4. To be precise, the number of trips is rather consistent, with a slight decrease during the summer months (March–May), until the end of 2019, when the global pandemic begins.
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Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe

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By Mohamed Ibrahim Verified, Geomatic Engineering, James Haworth, Space TimeLab
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Mohamed R. Ibrahim , James Haworth, Aldo Lipani, Nilufer Aslam, Tao Cheng, Nicola Christie Mohamed R. Ibrahim, James Haworth, Aldo Lipani, Nilufer Aslam, Tao Cheng, Nicola Christie Published: January 28, 2021 https://doi.org/10.1371/journal.pone.0246120 Figures Abstract Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic.
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