MVST-SciVis: narrative visualization and analysis of compound events in scientific data
There is a large volume of spatiotemporally correlated multivariate data in multiple layers of the earth’s environmental system. Compound events arise from the interaction of multiple variables. Current approaches employed by earth scientists lack the flexibility to identify the drivers and correspo...
Gespeichert in:
| Veröffentlicht in: | Journal of visualization 2023-06, Vol.26 (3), p.687-703 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 703 |
|---|---|
| container_issue | 3 |
| container_start_page | 687 |
| container_title | Journal of visualization |
| container_volume | 26 |
| creator | Lu, Xuyi Xu, Yuan Li, Guan Chen, Yi Shan, Guihua |
| description | There is a large volume of spatiotemporally correlated multivariate data in multiple layers of the earth’s environmental system. Compound events arise from the interaction of multiple variables. Current approaches employed by earth scientists lack the flexibility to identify the drivers and corresponding impacts of different events. In this paper, we present MVST-SciVis (MultiVariate SpatioTemporal Scientific data Visualization), a new visual analytics prototype to help scientists explore spatiotemporal correlations among multiple variables, and analyze the drivers and influences of different compound events. MVST-SciVis provides coordinated maps, scatterplots, line charts and bar charts to support a three-level multi-granularity complex visual analysis pipeline. MVST-SciVis also provides a storyline visualization tailored for scientific data that abstracts inter-entity relationships and the driving components information of compound events. Our case studies with the data from two ecosystem circles of climate and agriculture illustrate the usefulness and effectiveness of MVST-SciVis.
Graphical abstract |
| doi_str_mv | 10.1007/s12650-022-00893-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2811253900</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2811253900</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-3198c1065b538bb057c7f8ae37108618eb46b78139f5ed67629a7112f365d9aa3</originalsourceid><addsrcrecordid>eNp9UE1LAzEQDaJgrf4BTwHP0UnSfKw3KX5BxUNrPYZsNisp7W5Ndgv115u6gjcPM_NmeO8xPIQuKVxTAHWTKJMCCDBGAHTBCRyhEdVKEF0ocZwxn3Ci8-EUnaW0AmB0ougIvb8s5wsyd2EZ0i1ubIy2CzuPdyH1dh2-8tY22DZVLrvep5BwW2PXbrZtn49-55su4dDg5EKGoQ4OV7az5-iktuvkL37nGL093C-mT2T2-vg8vZsRx2nRkdy0oyBFKbguSxDKqVpbzxUFLan25USWSlNe1MJXUklWWEUpq7kUVWEtH6OrwXcb28_ep86s2j7mV5NhOhMFLwAyiw0sF9uUoq_NNoaNjXtDwRwCNEOAJgdofgI0BxEfRCmTmw8f_6z_UX0DLuNypQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2811253900</pqid></control><display><type>article</type><title>MVST-SciVis: narrative visualization and analysis of compound events in scientific data</title><source>SpringerNature Journals</source><creator>Lu, Xuyi ; Xu, Yuan ; Li, Guan ; Chen, Yi ; Shan, Guihua</creator><creatorcontrib>Lu, Xuyi ; Xu, Yuan ; Li, Guan ; Chen, Yi ; Shan, Guihua</creatorcontrib><description>There is a large volume of spatiotemporally correlated multivariate data in multiple layers of the earth’s environmental system. Compound events arise from the interaction of multiple variables. Current approaches employed by earth scientists lack the flexibility to identify the drivers and corresponding impacts of different events. In this paper, we present MVST-SciVis (MultiVariate SpatioTemporal Scientific data Visualization), a new visual analytics prototype to help scientists explore spatiotemporal correlations among multiple variables, and analyze the drivers and influences of different compound events. MVST-SciVis provides coordinated maps, scatterplots, line charts and bar charts to support a three-level multi-granularity complex visual analysis pipeline. MVST-SciVis also provides a storyline visualization tailored for scientific data that abstracts inter-entity relationships and the driving components information of compound events. Our case studies with the data from two ecosystem circles of climate and agriculture illustrate the usefulness and effectiveness of MVST-SciVis.
Graphical abstract</description><identifier>ISSN: 1343-8875</identifier><identifier>EISSN: 1875-8975</identifier><identifier>DOI: 10.1007/s12650-022-00893-0</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Charts ; Classical and Continuum Physics ; Computer Imaging ; Engineering ; Engineering Fluid Dynamics ; Engineering Thermodynamics ; Heat and Mass Transfer ; Mathematical analysis ; Multivariate analysis ; Pattern Recognition and Graphics ; Regular Paper ; Scientific visualization ; Scientists ; Vision ; Visualization</subject><ispartof>Journal of visualization, 2023-06, Vol.26 (3), p.687-703</ispartof><rights>The Visualization Society of Japan 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-3198c1065b538bb057c7f8ae37108618eb46b78139f5ed67629a7112f365d9aa3</citedby><cites>FETCH-LOGICAL-c319t-3198c1065b538bb057c7f8ae37108618eb46b78139f5ed67629a7112f365d9aa3</cites><orcidid>0000-0002-8283-2278</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12650-022-00893-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12650-022-00893-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Lu, Xuyi</creatorcontrib><creatorcontrib>Xu, Yuan</creatorcontrib><creatorcontrib>Li, Guan</creatorcontrib><creatorcontrib>Chen, Yi</creatorcontrib><creatorcontrib>Shan, Guihua</creatorcontrib><title>MVST-SciVis: narrative visualization and analysis of compound events in scientific data</title><title>Journal of visualization</title><addtitle>J Vis</addtitle><description>There is a large volume of spatiotemporally correlated multivariate data in multiple layers of the earth’s environmental system. Compound events arise from the interaction of multiple variables. Current approaches employed by earth scientists lack the flexibility to identify the drivers and corresponding impacts of different events. In this paper, we present MVST-SciVis (MultiVariate SpatioTemporal Scientific data Visualization), a new visual analytics prototype to help scientists explore spatiotemporal correlations among multiple variables, and analyze the drivers and influences of different compound events. MVST-SciVis provides coordinated maps, scatterplots, line charts and bar charts to support a three-level multi-granularity complex visual analysis pipeline. MVST-SciVis also provides a storyline visualization tailored for scientific data that abstracts inter-entity relationships and the driving components information of compound events. Our case studies with the data from two ecosystem circles of climate and agriculture illustrate the usefulness and effectiveness of MVST-SciVis.
Graphical abstract</description><subject>Charts</subject><subject>Classical and Continuum Physics</subject><subject>Computer Imaging</subject><subject>Engineering</subject><subject>Engineering Fluid Dynamics</subject><subject>Engineering Thermodynamics</subject><subject>Heat and Mass Transfer</subject><subject>Mathematical analysis</subject><subject>Multivariate analysis</subject><subject>Pattern Recognition and Graphics</subject><subject>Regular Paper</subject><subject>Scientific visualization</subject><subject>Scientists</subject><subject>Vision</subject><subject>Visualization</subject><issn>1343-8875</issn><issn>1875-8975</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9UE1LAzEQDaJgrf4BTwHP0UnSfKw3KX5BxUNrPYZsNisp7W5Ndgv115u6gjcPM_NmeO8xPIQuKVxTAHWTKJMCCDBGAHTBCRyhEdVKEF0ocZwxn3Ci8-EUnaW0AmB0ougIvb8s5wsyd2EZ0i1ubIy2CzuPdyH1dh2-8tY22DZVLrvep5BwW2PXbrZtn49-55su4dDg5EKGoQ4OV7az5-iktuvkL37nGL093C-mT2T2-vg8vZsRx2nRkdy0oyBFKbguSxDKqVpbzxUFLan25USWSlNe1MJXUklWWEUpq7kUVWEtH6OrwXcb28_ep86s2j7mV5NhOhMFLwAyiw0sF9uUoq_NNoaNjXtDwRwCNEOAJgdofgI0BxEfRCmTmw8f_6z_UX0DLuNypQ</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Lu, Xuyi</creator><creator>Xu, Yuan</creator><creator>Li, Guan</creator><creator>Chen, Yi</creator><creator>Shan, Guihua</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-8283-2278</orcidid></search><sort><creationdate>20230601</creationdate><title>MVST-SciVis: narrative visualization and analysis of compound events in scientific data</title><author>Lu, Xuyi ; Xu, Yuan ; Li, Guan ; Chen, Yi ; Shan, Guihua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-3198c1065b538bb057c7f8ae37108618eb46b78139f5ed67629a7112f365d9aa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Charts</topic><topic>Classical and Continuum Physics</topic><topic>Computer Imaging</topic><topic>Engineering</topic><topic>Engineering Fluid Dynamics</topic><topic>Engineering Thermodynamics</topic><topic>Heat and Mass Transfer</topic><topic>Mathematical analysis</topic><topic>Multivariate analysis</topic><topic>Pattern Recognition and Graphics</topic><topic>Regular Paper</topic><topic>Scientific visualization</topic><topic>Scientists</topic><topic>Vision</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lu, Xuyi</creatorcontrib><creatorcontrib>Xu, Yuan</creatorcontrib><creatorcontrib>Li, Guan</creatorcontrib><creatorcontrib>Chen, Yi</creatorcontrib><creatorcontrib>Shan, Guihua</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of visualization</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lu, Xuyi</au><au>Xu, Yuan</au><au>Li, Guan</au><au>Chen, Yi</au><au>Shan, Guihua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MVST-SciVis: narrative visualization and analysis of compound events in scientific data</atitle><jtitle>Journal of visualization</jtitle><stitle>J Vis</stitle><date>2023-06-01</date><risdate>2023</risdate><volume>26</volume><issue>3</issue><spage>687</spage><epage>703</epage><pages>687-703</pages><issn>1343-8875</issn><eissn>1875-8975</eissn><abstract>There is a large volume of spatiotemporally correlated multivariate data in multiple layers of the earth’s environmental system. Compound events arise from the interaction of multiple variables. Current approaches employed by earth scientists lack the flexibility to identify the drivers and corresponding impacts of different events. In this paper, we present MVST-SciVis (MultiVariate SpatioTemporal Scientific data Visualization), a new visual analytics prototype to help scientists explore spatiotemporal correlations among multiple variables, and analyze the drivers and influences of different compound events. MVST-SciVis provides coordinated maps, scatterplots, line charts and bar charts to support a three-level multi-granularity complex visual analysis pipeline. MVST-SciVis also provides a storyline visualization tailored for scientific data that abstracts inter-entity relationships and the driving components information of compound events. Our case studies with the data from two ecosystem circles of climate and agriculture illustrate the usefulness and effectiveness of MVST-SciVis.
Graphical abstract</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12650-022-00893-0</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-8283-2278</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1343-8875 |
| ispartof | Journal of visualization, 2023-06, Vol.26 (3), p.687-703 |
| issn | 1343-8875 1875-8975 |
| language | eng |
| recordid | cdi_proquest_journals_2811253900 |
| source | SpringerNature Journals |
| subjects | Charts Classical and Continuum Physics Computer Imaging Engineering Engineering Fluid Dynamics Engineering Thermodynamics Heat and Mass Transfer Mathematical analysis Multivariate analysis Pattern Recognition and Graphics Regular Paper Scientific visualization Scientists Vision Visualization |
| title | MVST-SciVis: narrative visualization and analysis of compound events in scientific data |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T03%3A51%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=MVST-SciVis:%20narrative%20visualization%20and%20analysis%20of%20compound%20events%20in%20scientific%20data&rft.jtitle=Journal%20of%20visualization&rft.au=Lu,%20Xuyi&rft.date=2023-06-01&rft.volume=26&rft.issue=3&rft.spage=687&rft.epage=703&rft.pages=687-703&rft.issn=1343-8875&rft.eissn=1875-8975&rft_id=info:doi/10.1007/s12650-022-00893-0&rft_dat=%3Cproquest_cross%3E2811253900%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2811253900&rft_id=info:pmid/&rfr_iscdi=true |