About: Spatial-Temporal Potential Exposure Risk Analytics and Urban Sustainability Impacts related to COVID-19 Mitigation: A Perspective from Car Mobility Behaviour

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Spatial-Temporal Potential Exposure Risk Analytics and Urban Sustainability Impacts related to COVID-19 Mitigation: A Perspective from Car Mobility Behaviour
Creator	Ma, Stefan Jiang, Peng Fan, Yee Klemeš, Jiří Van Fan, Yee Chen, Piao Fu, Xiuju Zhang, Wanbing
Source	Elsevier; Medline; PMC
abstract	Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0 %. It contributes to a 44.3 % to 55.4 % reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6 % by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning.
has issue date	2020-08-19(xsd:dateTime)
bibo:doi	10.1016/j.jclepro.2020.123673
bibo:pmid	32836914
has license	no-cc
sha1sum (hex)	909731784f3bf23704c998a6678a55e130d5c1ab
schema:url	https://doi.org/10.1016/j.jclepro.2020.123673
resource representing a document's title	Spatial-Temporal Potential Exposure Risk Analytics and Urban Sustainability Impacts related to COVID-19 Mitigation: A Perspective from Car Mobility Behaviour
has PubMed Central identifier	PMC7435297
has PubMed identifier	32836914
schema:publication	J Clean Prod
resource representing a document's body	covid:909731784f3bf23704c998a6678a55e130d5c1ab#body_text
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