E-Thesis 781 views 316 downloads
Visualisation of Long in Time Dynamic Networks on Large Touch Displays / ALEXANDRA LEE
Swansea University Author: ALEXANDRA LEE
DOI (Published version): 10.23889/SUthesis.58974
Abstract
Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic netw...
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Swansea
2021
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| Institution: | Swansea University |
| Degree level: | Doctoral |
| Degree name: | Ph.D |
| Supervisor: | Archambault, Daniel ; Tam, Gary |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa58974 |
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<?xml version="1.0"?><rfc1807><datestamp>2021-12-08T14:06:31.2042165</datestamp><bib-version>v2</bib-version><id>58974</id><entry>2021-12-08</entry><title>Visualisation of Long in Time Dynamic Networks on Large Touch Displays</title><swanseaauthors><author><sid>b7bf343012e56336da76867ecef29b6f</sid><firstname>ALEXANDRA</firstname><surname>LEE</surname><name>ALEXANDRA LEE</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-12-08</date><abstract>Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging fields from travel networks to epidemiology.However, it is very difficult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We first introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To confirm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we find that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also find that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Visualisation; graphs; dynamic graphs; touch-screen display; large display; networks; temporal networks</keywords><publishedDay>8</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-12-08</publishedDate><doi>10.23889/SUthesis.58974</doi><url/><notes>ORCiD identifier: https://orcid.org/0000-0001-7916-024X</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Archambault, Daniel ; Tam, Gary</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><degreesponsorsfunders>Engineering and Physical Sciences Research Council (EPSRC); Research grant number: 1819196</degreesponsorsfunders><apcterm/><lastEdited>2021-12-08T14:06:31.2042165</lastEdited><Created>2021-12-08T13:24:52.3171934</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>ALEXANDRA</firstname><surname>LEE</surname><order>1</order></author></authors><documents><document><filename>58974__21839__e965072661214563b745289d5d571d68.pdf</filename><originalFilename>Lee_Alexandra_PhD_Thesis_Final_Redacted_Signature.pdf</originalFilename><uploaded>2021-12-08T13:44:41.7306355</uploaded><type>Output</type><contentLength>12455411</contentLength><contentType>application/pdf</contentType><version>E-Thesis – open access</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: The author, Alexandra Lee, 2021.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807> |
| spelling |
2021-12-08T14:06:31.2042165 v2 58974 2021-12-08 Visualisation of Long in Time Dynamic Networks on Large Touch Displays b7bf343012e56336da76867ecef29b6f ALEXANDRA LEE ALEXANDRA LEE true false 2021-12-08 Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging fields from travel networks to epidemiology.However, it is very difficult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We first introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To confirm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we find that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also find that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types. E-Thesis Swansea Visualisation; graphs; dynamic graphs; touch-screen display; large display; networks; temporal networks 8 12 2021 2021-12-08 10.23889/SUthesis.58974 ORCiD identifier: https://orcid.org/0000-0001-7916-024X COLLEGE NANME COLLEGE CODE Swansea University Archambault, Daniel ; Tam, Gary Doctoral Ph.D Engineering and Physical Sciences Research Council (EPSRC); Research grant number: 1819196 2021-12-08T14:06:31.2042165 2021-12-08T13:24:52.3171934 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science ALEXANDRA LEE 1 58974__21839__e965072661214563b745289d5d571d68.pdf Lee_Alexandra_PhD_Thesis_Final_Redacted_Signature.pdf 2021-12-08T13:44:41.7306355 Output 12455411 application/pdf E-Thesis – open access true Copyright: The author, Alexandra Lee, 2021. true eng |
| title |
Visualisation of Long in Time Dynamic Networks on Large Touch Displays |
| spellingShingle |
Visualisation of Long in Time Dynamic Networks on Large Touch Displays ALEXANDRA LEE |
| title_short |
Visualisation of Long in Time Dynamic Networks on Large Touch Displays |
| title_full |
Visualisation of Long in Time Dynamic Networks on Large Touch Displays |
| title_fullStr |
Visualisation of Long in Time Dynamic Networks on Large Touch Displays |
| title_full_unstemmed |
Visualisation of Long in Time Dynamic Networks on Large Touch Displays |
| title_sort |
Visualisation of Long in Time Dynamic Networks on Large Touch Displays |
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b7bf343012e56336da76867ecef29b6f |
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b7bf343012e56336da76867ecef29b6f_***_ALEXANDRA LEE |
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ALEXANDRA LEE |
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ALEXANDRA LEE |
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E-Thesis |
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2021 |
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Swansea University |
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10.23889/SUthesis.58974 |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
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| description |
Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging fields from travel networks to epidemiology.However, it is very difficult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We first introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To confirm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we find that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also find that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types. |
| published_date |
2021-12-08T04:15:55Z |
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1763754068314947584 |
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11.037603 |