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TrajectoryVis: a visual approach to explore movement trajectories
Social networks are a dominant data source for sharing, participation, and exchanging information. For example, Twitter is a microblogging site that enables users to express opinions by transmitting brief messages (i.e., Tweets). Tweets can be used to extract information on users’ movements or traje...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113926/ https://www.ncbi.nlm.nih.gov/pubmed/35600999 http://dx.doi.org/10.1007/s13278-022-00879-8 |
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author | Fadloun, Samiha Morakeb, Yacine Cuenca, Erick Choutri, Kheireddine |
author_facet | Fadloun, Samiha Morakeb, Yacine Cuenca, Erick Choutri, Kheireddine |
author_sort | Fadloun, Samiha |
collection | PubMed |
description | Social networks are a dominant data source for sharing, participation, and exchanging information. For example, Twitter is a microblogging site that enables users to express opinions by transmitting brief messages (i.e., Tweets). Tweets can be used to extract information on users’ movements or trajectories over time. Information visualization (InfoVis) is helpful to understand, analyze, and make decisions about these trajectories. To better understand and compare existing visual encoding methods in InfoVis, we propose TrajectoryVis, a generic trajectory visualization tool to represent social network datasets (e.g., Twitter). Individual and aggregated trajectories can be visualized using different visual coding approaches. Our approach is assessed using a user and a COVID-19 case study to prove its effectiveness. |
format | Online Article Text |
id | pubmed-9113926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-91139262022-05-18 TrajectoryVis: a visual approach to explore movement trajectories Fadloun, Samiha Morakeb, Yacine Cuenca, Erick Choutri, Kheireddine Soc Netw Anal Min Original Article Social networks are a dominant data source for sharing, participation, and exchanging information. For example, Twitter is a microblogging site that enables users to express opinions by transmitting brief messages (i.e., Tweets). Tweets can be used to extract information on users’ movements or trajectories over time. Information visualization (InfoVis) is helpful to understand, analyze, and make decisions about these trajectories. To better understand and compare existing visual encoding methods in InfoVis, we propose TrajectoryVis, a generic trajectory visualization tool to represent social network datasets (e.g., Twitter). Individual and aggregated trajectories can be visualized using different visual coding approaches. Our approach is assessed using a user and a COVID-19 case study to prove its effectiveness. Springer Vienna 2022-05-18 2022 /pmc/articles/PMC9113926/ /pubmed/35600999 http://dx.doi.org/10.1007/s13278-022-00879-8 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Fadloun, Samiha Morakeb, Yacine Cuenca, Erick Choutri, Kheireddine TrajectoryVis: a visual approach to explore movement trajectories |
title | TrajectoryVis: a visual approach to explore movement trajectories |
title_full | TrajectoryVis: a visual approach to explore movement trajectories |
title_fullStr | TrajectoryVis: a visual approach to explore movement trajectories |
title_full_unstemmed | TrajectoryVis: a visual approach to explore movement trajectories |
title_short | TrajectoryVis: a visual approach to explore movement trajectories |
title_sort | trajectoryvis: a visual approach to explore movement trajectories |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113926/ https://www.ncbi.nlm.nih.gov/pubmed/35600999 http://dx.doi.org/10.1007/s13278-022-00879-8 |
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