| Field |
Value |
Language |
|
dc.contributor.author |
Dong, Y |
|
|
dc.contributor.author |
Liang, CJ |
|
|
dc.contributor.author |
Chen, Y |
|
|
dc.contributor.author |
Hua, J |
|
|
dc.date.accessioned |
2024-01-04T05:50:26Z |
|
|
dc.date.available |
2024-01-04T05:50:26Z |
|
|
dc.date.issued |
2023-02 |
|
|
dc.identifier.citation |
Computational Visual Media, 2023, 10, (1), pp. 161-186 |
|
|
dc.identifier.issn |
2096-0433 |
|
|
dc.identifier.issn |
2096-0662 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/174008
|
|
|
dc.description.abstract |
<jats:title>Abstract</jats:title><jats:p>The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called <jats:italic>+msRNAer</jats:italic>. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied <jats:italic>+msRNAer</jats:italic> to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the <jats:italic>+msRNAer</jats:italic> workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that <jats:italic>+msRNAer</jats:italic> provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that <jats:italic>+msRNAer</jats:italic> is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.
</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
Springer Nature |
|
|
dc.relation.ispartof |
Computational Visual Media |
|
|
dc.relation.isbasedon |
10.1007/s41095-023-0353-5 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject.classification |
4603 Computer vision and multimedia computation |
|
|
dc.subject.classification |
4607 Graphics, augmented reality and games |
|
|
dc.subject.classification |
4611 Machine learning |
|
|
dc.title |
A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis: Applied COVID-19 aggregated datasets |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
10 |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science |
|
|
pubs.organisational-group |
/University of Technology Sydney/Strength - VI - Visualisation Institute |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2024-01-04T05:50:22Z |
|
|
pubs.issue |
1 |
|
|
pubs.publication-status |
Published |
|
|
pubs.volume |
10 |
|
|
utslib.citation.issue |
1 |
|
