Exploring the Sensitivity of Choropleths under Attribute Uncertainty
The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a c...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics 2020-08, Vol.26 (8), p.2576-2590 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2590 |
|---|---|
| container_issue | 8 |
| container_start_page | 2576 |
| container_title | IEEE transactions on visualization and computer graphics |
| container_volume | 26 |
| creator | Huang, Zhaosong Lu, Yafeng Mack, Elizabeth A. Chen, Wei Maciejewski, Ross |
| description | The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis. |
| doi_str_mv | 10.1109/TVCG.2019.2892483 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_2179347442</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8611178</ieee_id><sourcerecordid>2420300221</sourcerecordid><originalsourceid>FETCH-LOGICAL-c392t-a52612dcf88023426a59d920cdf11b544dbecacc0fadbc7a3f41a9edd11d53ec3</originalsourceid><addsrcrecordid>eNpdkE2LFDEQhoMo7of-ABGkwYuXHqsq6Y8cl9l1FRY8uOs1pJNqJ0tP95ikF-ff28OMe_BUBfW8L8UjxDuEFSLoz_c_17crAtQrajWpVr4Q56gVllBB_XLZoWlKqqk-ExcpPQKgUq1-Lc4k1ApqbM7F9c2f3TDFMP4q8oaLHzymkMNTyPti6ov1ZorTbuC8ScU8eo7FVc4xdHPm4mF0HLMNY96_Ea96OyR-e5qX4uHLzf36a3n3_fbb-uqudFJTLm1FNZJ3fdsCSUW1rbTXBM73iF2llO_YWeegt75zjZW9QqvZe0RfSXbyUnw69u7i9HvmlM02JMfDYEee5mQIGy1VoxQt6Mf_0MdpjuPynSFFIAGIcKHwSLk4pRS5N7sYtjbuDYI5KDYHxeag2JwUL5kPp-a527J_TvxzugDvj0Bg5udzWyNi08q_dVyAMw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2420300221</pqid></control><display><type>article</type><title>Exploring the Sensitivity of Choropleths under Attribute Uncertainty</title><source>IEEE Electronic Library (IEL)</source><creator>Huang, Zhaosong ; Lu, Yafeng ; Mack, Elizabeth A. ; Chen, Wei ; Maciejewski, Ross</creator><creatorcontrib>Huang, Zhaosong ; Lu, Yafeng ; Mack, Elizabeth A. ; Chen, Wei ; Maciejewski, Ross</creatorcontrib><description>The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.</description><identifier>ISSN: 1077-2626</identifier><identifier>EISSN: 1941-0506</identifier><identifier>DOI: 10.1109/TVCG.2019.2892483</identifier><identifier>PMID: 30640617</identifier><identifier>CODEN: ITVGEA</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>choropleth ; Classification ; Clustering algorithms ; Correlation ; Data analysis ; Data visualization ; Geospatial analysis ; Scientific visualization ; Spatial data ; Spatial databases ; Statistical analysis ; Uncertainty ; Uncertainty analysis ; Visual analytics ; Visualization</subject><ispartof>IEEE transactions on visualization and computer graphics, 2020-08, Vol.26 (8), p.2576-2590</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-a52612dcf88023426a59d920cdf11b544dbecacc0fadbc7a3f41a9edd11d53ec3</citedby><cites>FETCH-LOGICAL-c392t-a52612dcf88023426a59d920cdf11b544dbecacc0fadbc7a3f41a9edd11d53ec3</cites><orcidid>0000-0001-8803-6355 ; 0000-0003-0354-8365 ; 0000-0002-3978-9550 ; 0000-0002-8365-4741</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8611178$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8611178$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30640617$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Huang, Zhaosong</creatorcontrib><creatorcontrib>Lu, Yafeng</creatorcontrib><creatorcontrib>Mack, Elizabeth A.</creatorcontrib><creatorcontrib>Chen, Wei</creatorcontrib><creatorcontrib>Maciejewski, Ross</creatorcontrib><title>Exploring the Sensitivity of Choropleths under Attribute Uncertainty</title><title>IEEE transactions on visualization and computer graphics</title><addtitle>TVCG</addtitle><addtitle>IEEE Trans Vis Comput Graph</addtitle><description>The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.</description><subject>choropleth</subject><subject>Classification</subject><subject>Clustering algorithms</subject><subject>Correlation</subject><subject>Data analysis</subject><subject>Data visualization</subject><subject>Geospatial analysis</subject><subject>Scientific visualization</subject><subject>Spatial data</subject><subject>Spatial databases</subject><subject>Statistical analysis</subject><subject>Uncertainty</subject><subject>Uncertainty analysis</subject><subject>Visual analytics</subject><subject>Visualization</subject><issn>1077-2626</issn><issn>1941-0506</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE2LFDEQhoMo7of-ABGkwYuXHqsq6Y8cl9l1FRY8uOs1pJNqJ0tP95ikF-ff28OMe_BUBfW8L8UjxDuEFSLoz_c_17crAtQrajWpVr4Q56gVllBB_XLZoWlKqqk-ExcpPQKgUq1-Lc4k1ApqbM7F9c2f3TDFMP4q8oaLHzymkMNTyPti6ov1ZorTbuC8ScU8eo7FVc4xdHPm4mF0HLMNY96_Ea96OyR-e5qX4uHLzf36a3n3_fbb-uqudFJTLm1FNZJ3fdsCSUW1rbTXBM73iF2llO_YWeegt75zjZW9QqvZe0RfSXbyUnw69u7i9HvmlM02JMfDYEee5mQIGy1VoxQt6Mf_0MdpjuPynSFFIAGIcKHwSLk4pRS5N7sYtjbuDYI5KDYHxeag2JwUL5kPp-a527J_TvxzugDvj0Bg5udzWyNi08q_dVyAMw</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Huang, Zhaosong</creator><creator>Lu, Yafeng</creator><creator>Mack, Elizabeth A.</creator><creator>Chen, Wei</creator><creator>Maciejewski, Ross</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8803-6355</orcidid><orcidid>https://orcid.org/0000-0003-0354-8365</orcidid><orcidid>https://orcid.org/0000-0002-3978-9550</orcidid><orcidid>https://orcid.org/0000-0002-8365-4741</orcidid></search><sort><creationdate>20200801</creationdate><title>Exploring the Sensitivity of Choropleths under Attribute Uncertainty</title><author>Huang, Zhaosong ; Lu, Yafeng ; Mack, Elizabeth A. ; Chen, Wei ; Maciejewski, Ross</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c392t-a52612dcf88023426a59d920cdf11b544dbecacc0fadbc7a3f41a9edd11d53ec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>choropleth</topic><topic>Classification</topic><topic>Clustering algorithms</topic><topic>Correlation</topic><topic>Data analysis</topic><topic>Data visualization</topic><topic>Geospatial analysis</topic><topic>Scientific visualization</topic><topic>Spatial data</topic><topic>Spatial databases</topic><topic>Statistical analysis</topic><topic>Uncertainty</topic><topic>Uncertainty analysis</topic><topic>Visual analytics</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Zhaosong</creatorcontrib><creatorcontrib>Lu, Yafeng</creatorcontrib><creatorcontrib>Mack, Elizabeth A.</creatorcontrib><creatorcontrib>Chen, Wei</creatorcontrib><creatorcontrib>Maciejewski, Ross</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on visualization and computer graphics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Huang, Zhaosong</au><au>Lu, Yafeng</au><au>Mack, Elizabeth A.</au><au>Chen, Wei</au><au>Maciejewski, Ross</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploring the Sensitivity of Choropleths under Attribute Uncertainty</atitle><jtitle>IEEE transactions on visualization and computer graphics</jtitle><stitle>TVCG</stitle><addtitle>IEEE Trans Vis Comput Graph</addtitle><date>2020-08-01</date><risdate>2020</risdate><volume>26</volume><issue>8</issue><spage>2576</spage><epage>2590</epage><pages>2576-2590</pages><issn>1077-2626</issn><eissn>1941-0506</eissn><coden>ITVGEA</coden><abstract>The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>30640617</pmid><doi>10.1109/TVCG.2019.2892483</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-8803-6355</orcidid><orcidid>https://orcid.org/0000-0003-0354-8365</orcidid><orcidid>https://orcid.org/0000-0002-3978-9550</orcidid><orcidid>https://orcid.org/0000-0002-8365-4741</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1077-2626 |
| ispartof | IEEE transactions on visualization and computer graphics, 2020-08, Vol.26 (8), p.2576-2590 |
| issn | 1077-2626 1941-0506 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2179347442 |
| source | IEEE Electronic Library (IEL) |
| subjects | choropleth Classification Clustering algorithms Correlation Data analysis Data visualization Geospatial analysis Scientific visualization Spatial data Spatial databases Statistical analysis Uncertainty Uncertainty analysis Visual analytics Visualization |
| title | Exploring the Sensitivity of Choropleths under Attribute Uncertainty |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T08%3A24%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Exploring%20the%20Sensitivity%20of%20Choropleths%20under%20Attribute%20Uncertainty&rft.jtitle=IEEE%20transactions%20on%20visualization%20and%20computer%20graphics&rft.au=Huang,%20Zhaosong&rft.date=2020-08-01&rft.volume=26&rft.issue=8&rft.spage=2576&rft.epage=2590&rft.pages=2576-2590&rft.issn=1077-2626&rft.eissn=1941-0506&rft.coden=ITVGEA&rft_id=info:doi/10.1109/TVCG.2019.2892483&rft_dat=%3Cproquest_RIE%3E2420300221%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2420300221&rft_id=info:pmid/30640617&rft_ieee_id=8611178&rfr_iscdi=true |