An Initial Visual Analysis of the Relationship between COVID-19 and Local Community Features

Hua, J; Huang, ML; Zhao, C; Hua, S; Shih, C

An Initial Visual Analysis of the Relationship between COVID-19 and Local Community Features

Hua, J Huang, ML Zhao, C Hua, S Shih, C

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Publisher:: IEEE CS
Publication Type:: Conference Proceeding
Citation:: Proc. of 24th International Conference Information Visualisation (IV), 2020, pp. 663-667
Issue Date:: 2020-09-11

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Field	Value	Language
dc.contributor.author	Hua, J
dc.contributor.author	Huang, ML
dc.contributor.author	Zhao, C
dc.contributor.author	Hua, S
dc.contributor.author	Shih, C
dc.contributor.editor	Banissi, E
dc.date	2020-09-07
dc.date.accessioned	2020-09-11T06:33:25Z
dc.date.available	2020-09-11T06:33:25Z
dc.date.issued	2020-09-11
dc.identifier.citation	Proc. of 24th International Conference Information Visualisation (IV), 2020, pp. 663-667
dc.identifier.isbn	978-1-7281-9134-8
dc.identifier.issn	2375-0138
dc.identifier.uri	http://hdl.handle.net/10453/142627
dc.description.abstract	Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades. In this work, we conduct a non-medical/clinical approach, generating graphs from five features concluded from the COVID-19 outbreak data and local community data in NSW (New South Wales), Australia, and offering insights from a visual analysis perspective. The results show that household income, population density and ethnicity affect the infection in different areas. Features such as human behaviours need to be imported for further COVID-19 research in the data science sector. This work is an initial step into this area and allows more insights to be brought into future COVID-19 research through a visual analysis perfective.
dc.language	en
dc.publisher	IEEE CS
dc.relation.ispartof	Proc. of 24th International Conference Information Visualisation (IV)
dc.relation.ispartof	2020 24th International Conference Information Visualisation (IV)
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	An Initial Visual Analysis of the Relationship between COVID-19 and Local Community Features
dc.type	Conference Proceeding
utslib.location.activity	Melbourne in Australia
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2020-09-11T06:33:15Z
pubs.finish-date	2020-09-11
pubs.publication-status	Published
pubs.start-date	2020-09-07

Abstract:

Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades. In this work, we conduct a non-medical/clinical approach, generating graphs from five features concluded from the COVID-19 outbreak data and local community data in NSW (New South Wales), Australia, and offering insights from a visual analysis perspective. The results show that household income, population density and ethnicity affect the infection in different areas. Features such as human behaviours need to be imported for further COVID-19 research in the data science sector. This work is an initial step into this area and allows more insights to be brought into future COVID-19 research through a visual analysis perfective.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/142627