2f:T5ce,## Background In modern society, public debates are driven by statistics and the visual depictions of those statistics. Therefore readers must know how to decode and use visualisations to participate in those discussions as informed citizens ${bib.citep("Cairo2019")}. Politically-charged topics often generate significant examples of incorrect visualisations and data, for example, misrepresentation of the outcome of electoral votes when depicted geographically ${bib.citeeg("Cairo2019","Bump2021")}. Whilst data-driven decision making has become a popular practice in science, industry, and public policy, data alone is an imperfect and partial representation of reality and often insufficient to make good analysis decisions due to the requirement for contextual knowledge in analysis of the data presented ${bib.citep("Lin2026")}. A challenge in visually communicating data stems from the different levels of literacy possessed by an intended audience, including varying levels of knowledge of the subject itself. As such, designing data visualisation for a diverse audience requires a user-centred approach (FIND REF). If data visualisation should, in its optimal form, serve two primary purposes: contextualise an existing problem by illustrating possible answers and/or highlight facets of a problem that may not be readily visible to a nonspecialist audience ${bib.citeeg("Telea2014","BigData")}, then designing for a non-specialist audience is paramount at the design stage.30:T43b,### Data visusalisation for public audiences - Reflections on Visualizing the COVID-19 Pandemic for the Public ${bib.citep("Losev2026")} - From Touch to Change: Understanding Public Engagement in Data Physicalization for Social Good ${bib.citep("Peng2026")} - Practitioners' Perspectives on Designing Data Visualizations for the General Public ${bib.citep("Schuster2026")} - Enthusiastic and Grounded, Avoidant and Cautious: Understanding Public Receptivity to Data and Visualizations ${bib.citep("He2024")} - Make Interaction Situated: Designing User Acceptable Interaction for Situated Visualization in Public Environments ${bib.citep("Zhu2024")} - Reaching Broader Audiences With Data Visualization ${bib.citep("Lee2020")} - Beyond Transparency: Making the Italian Public Administration more Accessible through Data Storytelling ${bib.citep("Moretti2018")} - Communicating local relevance of ocean observations: Integrating real-time ocean sensor data visualizations, online communications, and ocean issues to engage public audiences ${bib.citep("PattersonBickel2016")}31:T4ba,### Communicative Visualisation / Visual Communication - Definition - Existing research and findings - Visual Explanations: Images and Quantities, Evidence and Narrative ${bib.citep("Tufte3")} - ManyEyes: a Site for Visualization at Internet Scale ${bib.citep("Viegas2007")} - ${bib.cite("AdarLee2021")} concluded the taxonomies available at that time were not adequate in describing, or understanding if a visualisation achieved, the _communicative intent_, suggesting a refined and principled language for describing communicative intent,' is required to build and evaluate communicative visualisations. - Visualizing Thought ${bib.citep("Tversky2010")} - Communicating local relevance of ocean observations: Integrating real-time ocean sensor data visualizations, online communications, and ocean issues to engage public audiences ${bib.citep("PattersonBickel2016")} - Visual brokerage: Communicating data and research through visualisation ${bib.citep("Allen2018")} - The Curse of Knowledge in Visual Data Communication ${bib.citep("Xiong2020")} - Using Web Standards to Design Accessible Data Visualizations in Professional Communication ${bib.citep("Strantz2021")} 32:T492,### Storytelling - Definition - Existing research and findings. - Narrative visualization: Telling stories with data. ${bib.citep("SegelHeer2010")} - Scientific Storytelling Using Visualization ${bib.citep("Ma2012")} - The Influence of the Communication Medium on Data Storytelling ${bib.citep("Nagel2026")} - A deeper understanding of sequence in narrative visualization ${bib.citep("Hullman2013")} - The Effect of information visualization delivery on narrative construction and development ${bib.citep("Badawood2014")} - Timelines revisited: A design space and considerations for expressive storytelling ${bib.citep("Brehmer2016")} - Beyond Transparency: Making the Italian Public Administration more Accessible through Data Storytelling ${bib.citep("Moretti2018")} - Storytelling and visualization: An extended survey ${bib.citep("Tong2018")} - Design Exposition with Literate Visualization ${bib.citep("Wood2019")} - Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic ${bib.citep("Errey2024")} - What Do We Mean When We Talk About Data Storytelling? ${bib.citep("Yang2026")}33:T8b7,---------------------------------------------------------------------------------------------------------------- "Graphs and maps which contain rich and deep messages may require time and effort which will pay off if the chart is well designed. Many visualisations cannot be simple because the stories they tell are not simple. Readers can expect that designers do not make visualisations more complicated than they should be for no good reason."${bib.cite("Cairo2019")} "The persuasiveness of charts has consequences. Very often, charts lie to us because we are prone to lying to ourselves. We humans employ numbers and charts to reinforce our opinions and prejudices, a psychological propensity called the confirmation bias (citation)."${bib.cite("Cairo2019")} "No chart can ever capture reality in all its richness. However, a chart can be made worse or better depending on its ability to strike a balance between oversimplifying that reality or obscuring it with too much detail."${bib.cite("Cairo2019")} "In the past, only professionals with access to publishing platforms, journalists and the owners of newspapers, magazines, or TV stations, controlled the information that the public received. Today, every single one of us is a curator of information, a role that implies certain responsibilities. One of them is to make sure, whenever we can, that whatever content we read and help spread looks legitimate, particularly if that content seems to confirm our most strongly rooted idealogical beliefs and prejudices. Sometimes, lives may be at stake."${bib.cite("Cairo2019")}
"Everyone with an online presence today is a publisher, a role that used to be played just by journalists, news organisations, and other media institution. Some of us publish for small groups of people, our friends and family, while others have big followings. No matter the number of followers we have, we call potentially reach thousands, if not millions, of people. This fact begets moral responsibility. we need to stop sharing charts and news stories mindlessly. We all have the civic duty to avoid spreading charts and stories that may be misleadig. We must contribute to a healthier informational environment."${bib.cite("Cairo2019")}34:T5f7,### Mental Models Our prior knowledge and expectations pkay a crucial rose in how we read charts. Cognitive osychologists suggest tjat pir brains store ideal "mental models" to which we compare the graphics we see. Mental models, however, can lead readers astray.${bib.citep("Cairo2019")} "To read a chart correctly, or to create a good mental model of a type of chart we've never seen before, we must pay attention to it and never take anything for granted. Charts are based on grammar and a vocabulary made of symbols (lines, circles, bars), visual encodings (length, position, area, color, and so on), and text (the annotation layer). This makes charting as flexible as using written language, if not more." ${bib.citep("Cairo2019")} "If a reader skims a visualisation, they won't understand it, although they may believe they do. Well-designed charts aren't just informative but also graceful and, like a good turn of phrase, sometimes even playful and surprising." ${bib.citep("Cairo2019")} "In the same way that we shouldn't automatically believe a chart before reading it carefully, neither should we rush to call a chart a lie before we think about what it was designed for."${bib.citep("Cairo2019")} "The same way that a long, deep, and complex sentence like that above can't be understood with the blink of an eye, charts that display rich and worthwhile information will often demand some work from you. A good chart isn't an illustration but a visual argument or part of an argument." ${bib.citep("Cairo2019")} 35:T40b,### Communicative Visualisation - Within communicative visualisation, the specific intentions of the creator are as unique as the associated visualisation. Examples include a journalist seeking to explain an insight, a scientist or analyst conveying evidence and an instructor teaching the relationship between two interacting chemicals ${bib.citep("AdarLee2021")} - Affective learning objectives for communicative visualizations. ${bib.citep("LeeRobbinsAdar2023")} - Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic ${bib.citep("Errey2024")} - Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension ${bib.citep("Quadri2024")} - Visualization Badges: Communicating Design and Provenance through Graphical Labels Alongside Visualizations ${bib.citep("Edelsbrunner2026")} - Perceptual and Cognitive Foundations of Information Visualization ${bib.citep("Schloss2025")} - An Analysis of Text Functions in Information Visualization ${bib.citep("Stokes2026")}36:Tad2,#### Learning objectives - ${bib.cite("AdarLee2021")} suggest learning objectives would allow a designer to both describe and evaluate intent within visualisation design. Defined learning objectives can be assessed using specific tests created to validate if viewers reached the objective of the visualisation. Objectives and assessments can be used to compare different visualisation choices, or a designer may define how they intend a visualisation to impact a viewer by stating, for example, that after looking at a visualisation a viewer is able to identify a sharp change in a group of values, or categorise variables based on a measurement. Whilst this approach enables a description of objectives without insight into the wants of the viewer, authors clarify that the needs, goals or high-level tasks of the viewer should not be ignored within this process, noting an effective designer will integrate the goals of the viewers into the defined learning objectives to ensure they are included in the original intent. - Position the proposed work within the space of communicative data visualisation, and the possible types of knowledge it seeks to convey. - Motivated by the failure of existing taxonomies in their evaluation of communicative visualisations, as for exploratory visualisations. - Authors argue that, whilst most evaluative techniques will focus on what occurs when a viewer is interacting with a visualisation, an advantage of incorporating objectives into BLANK is that we can ask if our objectives are achieved not only when the viewer is looking at the visualization but, more importantly, when it was taken away. This is different from most evaluative techniques that focus on what happens when the visualization is in front of the viewer. - Noting that visualisation designers often evaluate communicative visualizations from the perspective of cognitive efficiency by considering whether the recipient can accurately decode the message or insight, authors suggest designers are likely to be dissatisfied if the intended message did not resonate. __The consequence of this inconsistency is that it is difficult to design or select between competing options in a principled way.__ - The study led to the identification of a broad range of objectives in communicative visualisations, along with the prevalence of particular objective types. - Argued visualization designers can address the limitation through a learning lens: that the recipient is a student and the designer a teacher. By using learning objectives, designers can better define, assess, and compare communicative visualizations. We illustrate how the learning-based approach provides a framework for understanding a wide array of communicative goals. 37:T916a,referenceList = ` @article{AdarLee2021, author = {Eytan Adar and Elsie Lee}, title = {Communicative Visualizations as a Learning Problem}, journal = {CoRR}, volume = {abs/2009.07095}, year = {2020}, url = {https://arxiv.org/abs/2009.07095}, eprinttype = {arXiv}, eprint = {2009.07095}, timestamp = {Fri, 18 Sep 2020 15:17:35 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2009-07095.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @article{Allen2018, author = {William L. Allen}, title ={Visual brokerage: Communicating data and research through visualisation}, journal = {Public Understanding of Science}, volume = {27}, number = {8}, pages = {906-922}, year = {2018}, doi = {10.1177/0963662518756853}, note ={PMID: 29400134}, URL = { https://doi.org/10.1177/0963662518756853}, eprint = {https://doi.org/10.1177/0963662518756853} } @article{Badawood2014, author = {Badawood, Dania and Wood, Jo}, title = {The Effect of information visualization delivery on narrative construction and development}, journal = {Proceedings of the 2014 Workshop on Data Exploration and Narrative}, year = {2014}, pages = {13--18} } @article{Bako2025, author = {Bako, Hannah K. and Liu, Xinyi and Ko, Grace and Song, Hyemi and Battle, Leilani and Liu, Zhicheng}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {Unveiling How Examples Shape Visualization Design Outcomes}, year = {2025}, volume = {31}, number = {1}, pages = {1137--1147}, doi = {10.1109/TVCG.2024.3456407} } @book{Bertin2011, title = {Semiology of Graphics: Diagrams, Networks, Maps}, author = {Bertin, J. and Berg, W.J.}, isbn = {9781589482616}, year = {2011}, publisher = {ESRI Press} } @inproceedings{Boger2021, author = {Boger, Tal and Most, Steven B. and Franconeri, Steven L.}, title = {Jurassic Mark: Inattentional Blindness for a Datasaurus Reveals that Visualizations are Explored, not Seen}, booktitle = {2021 IEEE Visualization Conference (VIS)}, year = {2021}, pages = {71--75}, doi = {10.1109/VIS49827.2021.9623273} } @article{Boucher2026, author = {Boucher, Magdalena and Kejstova, Magdalena and Stoiber, Christina and Kandlhofer, Martin and Boucher, Alena and Kriglstein, Simone and Buchinger, Shelley and Aigner, Wolfgang}, title = {Enhancing Data Visualization Literacy: A Comparative Study of Learning Materials in Schools}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2026}, volume = {32}, number = {1}, pages = {1416--1426}, doi = {10.1109/TVCG.2025.3634817} } @article{Boy2014, author = {Boy, Jeremy and Rensink, Ronald A. and Bertini, Enrico and Fekete, Jean-Daniel}, title = {A Principled Way of Assessing Visualization Literacy}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2014}, volume = {20}, number = {12}, pages = {1963--1972}, doi = {10.1109/TVCG.2014.2346984} } @article{Brehmer2013, author = {Brehmer, Matthew and Munzner, Tamara}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {A Multi-Level Typology of Abstract Visualization Tasks}, year = {2013}, volume = {19}, number = {12}, pages = {2376--2385}, doi = {10.1109/TVCG.2013.124} } @article{Brehmer2016, author = {Brehmer, Matthew and Lee, Bongshin and Bach, Benjamin and Riche, Nathalie Henry and Munzner, Tamara}, title = {Timelines revisited: A design space and considerations for expressive storytelling}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2016}, volume = {23}, number = {9}, pages = {2151--2164}, doi = {10.1109/TVCG.2016.2619315} } @inproceedings{Bressa2024, author = {Bressa, Nathalie and Louis, Jordan and Willett, Wesley and Huron, Samuel}, title = {Input Visualization: Collecting and Modifying Data with Visual Representations}, year = {2024}, isbn = {9798400703300}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3613904.3642808}, doi = {10.1145/3613904.3642808}, booktitle = {Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems}, articleno = {499}, numpages = {18}, location = {Honolulu, HI, USA}, series = {CHI '24} } @inproceedings{Bui2015, author={Bui, Lily}, booktitle={2015 IEEE First International Smart Cities Conference (ISC2)}, title={Breathing smarter: A critical look at representations of air quality sensing data across platforms and publics}, year={2015}, volume={}, number={}, pages={1-5}, doi={10.1109/ISC2.2015.7366185} } @article{Bump2021, author = {Bump, Philip}, publisher = {The Washington Post}, title = {Those big, misleading maps of presidential results by land area are getting worse}, year = {2021}, url = {https://www.washingtonpost.com/politics/2021/02/03/recent-polarization-republican-counties-visualized/} } @inproceedings{Cabouat2025, author = {Cabouat, Anne-Flore and Ciccione, Lorenzo and Huron, Samuel and Isenberg, Tobias and Isenberg, Petra}, title = {Bridging Educational Theories of Cognitive Load to Visualization Design and Evaluation}, booktitle = {2025 IEEE VIS Workshop on Visualization Education, Literacy, and Activities (EduVIS)}, year = {2025}, pages = {33--64}, doi = {10.1109/EduVIS69391.2025.00009} } @book{Cairo2012, address = {Berkeley}, title = {The Functional Art: an introduction to information graphics and visualization}, isbn = {9780321834737}, language = {en}, publisher = {Pearson Education (US)}, author = {Cairo, Alberto}, year = {2012} } @book{Cairo2019, title = {How Charts Lie: Getting Smarter about Visual Information}, author = {Cairo, Alberto}, year = {2019}, publisher = {W. W. Norton \& Company}, address = {New York}, isbn = {978-0393358421} } @inproceedings{Cay2020, author={Çay, Damla and Nagel, Till and Yantaç, Asım Evren}, booktitle={2020 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV)}, title={Understanding User Experience of COVID-19 Maps through Remote Elicitation Interviews}, year={2020}, volume={}, number={}, pages={65-73}, doi={10.1109/BELIV51497.2020.00015} } @article{Comba2020, author={Comba, João L. D.}, journal={Computing in Science & Engineering}, title={Data Visualization for the Understanding of COVID-19}, year={2020}, volume={22}, number={6}, pages={81-86}, doi={10.1109/MCSE.2020.3019834} } @article{Dietrich2012, author = {Dietrich, Daniel and Myrttinen, Henri}, title = {Data Visualisation}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2012}, volume = {5}, url = {https://data.europa.eu/sites/default/files/report/2012_data_visualisation.pdf} } @article{Dimara2022, author = {Dimara, Evanthia and Stasko, John}, title = {A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2022}, volume = {28}, pages = {1128--1138}, doi = {10.1109/tvcg.2021.3114813} } @incollection{Dimitrakopoulou2022, author = {Dimitrakopoulou, Dimitra}, editor = {Schintler, Laurie A. and McNeely, Connie L.}, title = {Digital Literacy}, booktitle = {Encyclopedia of Big Data}, year = {2022}, publisher = {Springer International Publishing}, pages = {393--395}, doi = {10.1007/978-3-319-32010-6_72} } @article{Dykes2010, author = {Dykes, Jason and Wood, Jo and Slingsby, Aidan}, title = {Rethinking Map Legends with Visualization}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2010}, volume = {16}, number = {6}, pages = {890--899}, doi = {10.1109/TVCG.2010.191} } @article{Edelsbrunner2026, author = {Edelsbrunner, Valentin and Wang, Jinrui and Pister, Alexis and Vancisin, Tomas and Phillips, Sian and Chen, Min and Bach, Benjamin}, journal = {IEEE Transactions on Visualization \& Computer Graphics}, title = {{Visualization Badges: Communicating Design and Provenance through Graphical Labels Alongside Visualizations}}, year = {2026}, volume = {32}, number = {01}, ISSN = {1941-0506}, pages = {1153-1163}, doi = {10.1109/TVCG.2025.3634782}, url = {https://doi.ieeecomputersociety.org/10.1109/TVCG.2025.3634782}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, month = jan } @inproceedings{Elshehaly2023, author = {Elshehaly, Mai and Eddy, Lucy and Mon-Williams, Mark}, booktitle = {2023 IEEE Visualization and Visual Analytics (VIS)}, title = {CLEVER: A Framework for Connecting Lived Experiences with Visualisation of Electronic Records}, year = {2023}, volume = {}, number = {}, pages = {126-130}, doi = {10.1109/VIS54172.2023.00034} } @inproceedings{Elshehaly2024, doi = {10.1109/BELIV64461.2024.00006}, month = {November}, year = {2024}, title = {Visualising Lived Experience: Learning from a Master Narrative Framing}, booktitle = {BELIV: evaluation and BEyond - methodoLogIcal approaches for Visualization}, publisher = {IEEE}, journal = {2024 IEEE Visualization and Visual Analytics (VIS)}, author = {Elshehaly, M. and Reljan-Delaney, M. and Dykes, J. and Slingsby, A. and Wood, J. and Spiegel, S.}, issn = {2771-9537}, url = {https://openaccess.city.ac.uk/id/eprint/33839/} } @article{Errey2024, author = {Errey, Nina and Liang, Christy Jie and Leong, Tuck Wah and Chen, Yongqing and Vally, Hassan and Bennett, Catherine M.}, title = {Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic}, year = {2024}, issue_date = {November 2024}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {8}, number = {CSCW2}, url = {https://doi.org/10.1145/3686950}, doi = {10.1145/3686950}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = nov, articleno = {411}, numpages = {21} } @inproceedings{Farhat2023, author={Farhat, Mohamad Khalil and Zhang, Ji and Tao, Xiaohui and Li, Tianning and Yu, Ting}, booktitle={2023 10th International Conference on Behavioural and Social Computing (BESC)}, title={EventsVista: Enhancing Event Visualization and Interpretation}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/BESC59560.2023.10386648} } @article{Fratczak2022, author = {FrƒÖtczak, Monika}, title = {Can Data Visualisations Mobilise People to Act?}, journal = {Significance}, volume = {19}, number = {2}, pages = {6-7}, year = {2022}, month = {04}, issn = {1740-9705}, doi = {10.1111/1740-9713.01624}, url = {https://doi.org/10.1111/1740-9713.01624}, eprint = {https://academic.oup.com/jrssig/article-pdf/19/2/6/49187851/sign_19_2_6.pdf}, } @misc{Ge2025, author = {Ge, Lily and Cabouat, Anne-Flore and Bonilla, Karen and Cui, Yuan and Ding, Yiren and Rakotondravony, Noelle and Creamer, Mackenzie and Otto, Jasmine T. and Hedayati, Maryam and Kwon, Bum Chul}, title = {An Autoethnography on Visualization Literacy: A Wicked Measurement Problem}, year = {2025}, month = {Aug}, publisher = {OSF Preprints}, doi = {10.31219/osf.io/dfr4p_v2} } @inbook{Gough2023, author = {Gough, Phillip and Zhao, Jack}, editor = {Rowland, Susan and Kuchel, Louise}, title = {Data Visualisation}, bookTitle = {Teaching Science Students to Communicate: A Practical Guide}, year = {2023}, publisher = {Springer International Publishing}, address = {Cham}, pages = {57--68}, isbn = {978-3-030-91628-2}, doi = {10.1007/978-3-030-91628-2_7}, url = {https://doi.org/10.1007/978-3-030-91628-2_7} } @inproceedings{Graves2013, author = {Graves, Alvaro and Hendler, James}, title = {Visualization tools for open government data}, booktitle = {Proceedings of the 14th Annual International Conference on Digital Government Research}, year = {2013}, pages = {136--145}, doi = {10.1145/2479724.2479746} } @article{Hao2025, author = {Hao, Jianing and Yang, Manling and Shi, Qing and Jiang, Yuzhe and Zhang, Guang and Zeng, Wei}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {FinFlier: Automating Graphical Overlays for Financial Visualizations With Knowledge-Grounding Large Language Model}, year = {2025}, volume = {31}, number = {9}, pages = {6353--6369}, doi = {10.1109/TVCG.2024.3514138} } @article{He2024, author = {He, Helen Ai and Walny, Jagoda and Thoma, Sonja and Carpendale, Sheelagh and Willett, Wesley}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {Enthusiastic and Grounded, Avoidant and Cautious: Understanding Public Receptivity to Data and Visualizations}, year = {2024}, volume = {30}, number = {1}, pages = {1435-1445}, doi = {10.1109/TVCG.2023.3326917} } @misc{HMTreasury2026, title = {Public spending statistics: February 2026}, author = {{HM Treasury}}, year = {2026}, url = {https://www.gov.uk/government/statistics/public-spending-statistics-release-february-2026/public-spending-statistics-february-2026} } @article{Hullman2011, author = {Hullman, Jessica and Diakopoulos, Nicholas}, title = {Visualization Rhetoric: Framing Effects in Narrative Visualization}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2011}, volume = {17}, number = {12}, pages = {2231--2240}, doi = {10.1109/tvcg.2011.255} } @article{Hullman2013, author = {Hullman, Jessica and Drucker, Steven and Riche, Nathalie Henry and Lee, Bongshin and Fisher, Danyel and Adar, Eytan}, title = {A Deeper Understanding of Sequence in Narrative Visualization}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2013}, volume = {19}, number = {12}, pages = {2406--2415}, doi = {10.1109/TVCG.2013.119} } @inproceedings{Jiang2025, author = {Jiang, Qi and Sun, Guodao and Li, Tong and Tang, Jingwei and Xia, Wang and Wang, Yunchao and Jiang, Li and Liang, Ronghua}, booktitle = {2025 IEEE 18th Pacific Visualization Conference (PacificVis)}, title = {AutoMA: Automated Generation of Multi-level Annotations for Time Series Visualization}, year = {2025}, pages = {80--90}, doi = {10.1109/PacificVis64226.2025.00014} } @article{Johansson2021, title = {Making time/breaking time: critical literacy and politics of time in data visualisation}, journal = {Journal of Documentation}, volume = {78}, number = {1}, pages = {60--82}, year = {2021}, author = {Veronica Johansson and Jorgen Stenlund}, doi = {10.1108/JD-12-2020-0210} } @article{Kauer2025, author = {Kauer, Tobias and Dork, Marian and Bach, Benjamin}, journal = {IEEE Computer Graphics and Applications}, title = {Toward Collective Storytelling: Investigating Audience Annotations in Data Visualizations}, year = {2025}, volume = {45}, number = {3}, pages = {17--31}, doi = {10.1109/MCG.2025.3547944} } @inbook{Khan2021, author={Khan, Asif and Din, Heeba}, booktitle={Big Data Analytics for Internet of Things}, title={New Age Journalism and Big Data (Understanding Big Data and Its Influence on Journalism)}, year={2021}, volume={}, number={}, pages={333-349}, doi={10.1002/9781119740780.ch15} } @article{Kim2021, author = {Kim, N. and Joyner, Shakila Cherise and Riegelhuth, Amalia and Kim, Y.}, year = {2021}, month = {06}, pages = {173-188}, title = {Accessible Visualization: Design Space, Opportunities, and Challenges}, volume = {40}, journal = {Computer Graphics Forum}, doi = {10.1111/cgf.14298} } @inproceedings{Kim2025, author = {Kim, Min Hyeong and Song, Yumin and Kim, Yungun and Cho, Aeri and Lee, Soohyun and Jeon, Hyeon and Seo, Jinwook}, booktitle = {2025 IEEE 18th Pacific Visualization Conference (PacificVis)}, title = {Automated Pipeline for Detecting and Analyzing Misleading Visual Elements}, year = {2025}, pages = {346--351}, doi = {10.1109/PacificVis64226.2025.00041} } @article{Koh2018, author = {Koh, Harvey and Earnest, Arul and Davis, Ian D. and Loh, Erwin and Evans, Sue M.}, title = {Evaluating the effects of data visualisation techniques on interpretation and preference of feedback reports}, journal = {bioRxiv}, year = {2018}, doi = {10.1101/415414} } @inproceedings{Kong2019, author = {Kong, Ha-Kyung and Liu, Zhicheng and Karahalios, Karrie}, title = {Trust and Recall of Information across Varying Degrees of Title-Visualization Misalignment}, year = {2019}, isbn = {9781450359702}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3290605.3300576}, doi = {10.1145/3290605.3300576}, booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems}, pages = {1–13}, numpages = {13}, location = {Glasgow, Scotland Uk}, series = {CHI '19} } @inproceedings{Lan2026, author = {Lan, Xingyu and Li, Xi and Zhang, Yixing and Cheng, Mengqin and Wang, Jiazhe and Chen, Siming}, title = {The Evolving Duet of Two Modalities: A Survey on Integrating Text and Visualization for Data Communication}, year = {2026}, isbn = {9798400722783}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3772318.3791962}, doi = {10.1145/3772318.3791962}, booktitle = {Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems}, articleno = {446}, numpages = {19}, series = {CHI '26} } @article{Lee2020, author = {Lee, Bongshin and Choe, Eun Kyoung and Isenberg, Petra and Marriott, Kim and Stasko, John}, journal = {IEEE Computer Graphics and Applications}, title = {Reaching Broader Audiences With Data Visualization}, year = {2020}, volume = {40}, number = {2}, pages = {82-90}, doi = {10.1109/MCG.2020.2968244} } @article{LeeRobbinsAdar2023, author={Lee-Robbins, Elsie and Adar, Eytan}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={Affective Learning Objectives for Communicative Visualizations}, year={2023}, volume={29}, number={1}, pages={1-11}, doi={10.1109/TVCG.2022.3209500}} @article{Li2026, author={Li, Zikai and Zheng, Chuyi and Li, Ziang and Shi, Yang}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={Data Speaks, But who Gives It a Voice? Understanding Persuasive Strategies in Data-Driven News articles}, year={2026}, volume={32}, number={1}, pages={1339-1349}, doi={10.1109/TVCG.2025.3642509} } @article{Lin2026, author = {Lin, Haihan and Lisnic, Maxim and Akbaba, Derya and Meyer, Miriah and Lex, Alexander}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {Here's what you need to know about my data: Exploring Expert Knowledge's Role in Data Analysis}, year = {2026}, volume = {32}, number = {1}, pages = {1186--1196}, doi = {10.1109/TVCG.2025.3634821} } @article{Losev2026, author = {Losev, Tatiana and Médoc, Nicolas and Mueller, Klaus and Schiller, Nicolás Reinoso and Sondag, Max and Wang, Yong and Wybrow, Michael and Zeeb, Hajo}, journal = {IEEE Computer Graphics and Applications}, title = {Reflections on Visualizing the COVID-19 Pandemic for the Public}, year = {2026}, volume = {46}, number = {3}, pages = {126-132}, doi = {10.1109/MCG.2026.3663946} } @article{Ma2012, author={Ma, Kwan-Liu and Liao, Isaac and Frazier, Jennifer and Hauser, Helwig and Kostis, Helen-Nicole}, journal={IEEE Computer Graphics and Applications}, title={Scientific Storytelling Using Visualization}, year={2012}, volume={32}, number={1}, pages={12-19}, doi={10.1109/MCG.2012.24} } @misc{Mehta2025, title = {Designing for Disclosure in Data Visualizations}, author = {Krisha Mehta and Gordon Kindlmann and Alex Kale}, year = {2025}, eprint = {2508.08383}, url = {https://arxiv.org/abs/2508.08383} } @inproceedings{Mei2025, author = {Mei, Catherine and Pollock⁎, Josh and Hajas, Daniel and Zong, Jonathan and Satyanarayan, Arvind}, title = {Benthic: Perceptually Congruent Structures for Accessible Charts and Diagrams}, year = {2025}, isbn = {9798400706769}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3663547.3746342}, doi = {10.1145/3663547.3746342}, booktitle = {Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility}, articleno = {27}, numpages = {17} } @inproceedings{Moretti2018, author = {Moretti, Matteo and De Chiara, Francesca and Napolitano, Maurizio}, booktitle = {2018 22nd International Conference Information Visualisation (IV)}, title = {Beyond Transparency: Making the Italian Public Administration more Accessible through Data Storytelling}, year = {2018}, volume = {}, number = {}, pages = {247-250}, doi = {10.1109/iV.2018.00050} } @inproceedings{Nagel2026, author = {Nagel, Tamara and Bogers, Toine}, title = {The Influence of the Communication Medium on Data Storytelling}, year = {2026}, booktitle = {Proceedings of the 2026 Conference on Human Information Interaction and Retrieval}, pages = {204--214}, doi = {10.1145/3786304.3787869} } @inproceedings{Or2013, author = {Or, Olivia}, title = {Data visualisation and its application in official statistics}, booktitle = {Proceedings 59th ISI World Statistics Congress}, year = {2013}, pages = {4839--4844} } @article{Oral2024, author = {Oral, Emre and Chawla, Ria and Wijkstra, Michel and Mahyar, Narges and Dimara, Evanthia}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making}, year = {2024}, volume = {30}, number = {1}, pages = {359-369}, doi = {10.1109/TVCG.2023.3326593} } @inproceedings{Owda2023, author={Owda, Majdi and Owda, Amani Yousef and Fasli, Maria}, booktitle={2023 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)}, title={An Exploratory Data Analysis and Visualizations of Underprivileged Communities Diabetes Dataset for Public Good}, year={2023}, volume={}, number={}, pages={581-585}, doi={10.1109/WI-IAT59888.2023.00096} } @article{Pandey2014, author = {Pandey, Anshul Vikram and Manivannan, Anjali and Nov, Oded and Satterthwaite, Margaret and Bertini, Enrico}, title = {The Persuasive Power of Data Visualization}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2014}, volume = {20}, number = {12}, pages = {2211--2220}, doi = {10.1109/TVCG.2014.2346419} } @inproceedings{Pandey2015, author = {Pandey, Anshul Vikram and Rall, Katharina and Satterthwaite, Margaret L. and Nov, Oded and Bertini, Enrico}, title = {How Deceptive are Deceptive Visualizations? An Empirical Analysis of Common Distortion Techniques}, booktitle = {Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems}, year = {2015}, pages = {1469--1478}, doi = {10.1145/2702123.2702608} } @inproceedings{PattersonBickel2016, author = {Patterson, Jennifer and Bickel, Aric}, booktitle = {OCEANS 2016 MTS/IEEE Monterey}, title = {Communicating local relevance of ocean observations: Integrating real-time ocean sensor data visualizations, online communications, and ocean issues to engage public audiences}, year = {2016}, volume = {}, number = {}, pages = {1-4}, doi = {10.1109/OCEANS.2016.7761325} } @inproceedings{Peng2026, author = {Peng, Yechun and Wu, Runxi and Cao, Nan and Shi, Yang}, title = {From Touch to Change: Understanding Public Engagement in Data Physicalization for Social Good}, year = {2026}, isbn = {9798400722783}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3772318.3790877}, doi = {10.1145/3772318.3790877}, booktitle = {Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems}, articleno = {1181}, numpages = {18}, series = {CHI '26} } @article{Prantl2026, author={Prantl, Verena and Möller, Torsten and Koesten, Laura}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={Untangling Rhetoric, Pathos, and Aesthetics in Data Visualization}, year={2026}, volume={32}, number={2}, pages={2435-2453}, doi={10.1109/TVCG.2025.3628181} } @inproceedings{Quadri2024, author = {Quadri, Ghulam Jilani and Wang, Arran Zeyu and Wang, Zhehao and Adorno, Jennifer and Rosen, Paul and Szafir, Danielle Albers}, title = {Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension}, year = {2024}, isbn = {9798400703300}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3613904.3642813}, doi = {10.1145/3613904.3642813}, booktitle = {Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems}, articleno = {204}, numpages = {26}, location = {Honolulu, HI, USA}, series = {CHI '24} } @article{Rahman2025a, author = {Rahman, Md Dilshadur and Quadri, Ghulam Jilani and Doppalapudi, Bhavana and Szafir, Danielle Albers and Rosen, Paul}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space}, year = {2025}, volume = {31}, number = {1}, pages = {360--370}, doi = {10.1109/TVCG.2024.3456359} } @article{Ragan2016, author = {Ragan, Eric D. and Endert, Alex and Sanyal, Jibonananda and Chen, Jian}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes}, year = {2016}, volume = {22}, number = {1}, pages = {31-40}, doi = {10.1109/TVCG.2015.2467551} } @inproceedings{Rahman2025b, title = {AnnoGram: An Annotative Grammar of Graphics Extension}, booktitle = {2025 IEEE Visualization and Visual Analytics (VIS)}, author = {Rahman, Md Dilshadur and Rahat-Uz-Zaman, Md and McNutt, Andrew and Rosen, Paul}, year = {2025}, pages = {236--240}, doi = {10.1109/VIS60296.2025.00053} } @article{Rall2016, author = {Rall, Katharina and Satterthwaite, Margaret L. and Pandey, Anshul Vikram and Emerson, John and Boy, Jeremy and Nov, Oded and Bertini, Enrico}, title = {Data Visualization for Human Rights Advocacy}, journal = {Journal of Human Rights Practice}, volume = {8}, number = {2}, pages = {171--197}, year = {2016}, doi = {10.1093/jhuman/huw011} } @incollection{Ridgway2019, author = {Ridgway, Jim and Nicholson, James and Sutherland, Sinclair and Hedger, Spencer}, title = {27: Critical statistical literacy and interactive data visualisations}, booktitle = {Data in Society}, year = {2019}, publisher = {Policy Press}, pages = {349--358}, doi = {10.51952/9781447348245.ch027} } @incollection{Roth2017, author = {Roth, Robert}, year = {2017}, title = {Visual Variables}, booktitle = {The International Encyclopedia of Geography}, doi = {10.1002/9781118786352.wbieg0761} } @inproceedings{Saharan2026, author = {Saharan, Shehryar and Al-Hazwani, Ibrahim and Meyer, Miriah and Garrison, Laura}, title = {A Critical Reflection on the Values and Assumptions in Data Visualization}, year = {2026}, isbn = {9798400722783}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3772318.3791501}, doi = {10.1145/3772318.3791501}, booktitle = {Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems}, articleno = {1146}, numpages = {8}, series = {CHI '26} } @article{Sander2020, author = {Sander, Ina}, title = {What is critical big data literacy and how can it be implemented?}, journal = {Internet Policy Review}, year = {2020}, volume = {9}, number = {2}, doi = {10.14763/2020.2.1479} } @book{BigData, title = {Encyclopedia of Big Data}, editor = {Schintler, Laurie A. and McNeely, Connie L.}, year = {2022}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-319-32010-6}, isbn = {978-3-319-32009-0} } @inproceedings{Schuster2026, author = {Schuster, Regina and Gregory, Kathleen and Möller, Torsten and Koesten, Laura}, title = {Practitioners' Perspectives on Designing Data Visualizations for the General Public}, year = {2026}, isbn = {9798400722783}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3772318.3790627}, doi = {10.1145/3772318.3790627}, booktitle = {Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems}, articleno = {1185}, numpages = {19}, series = {CHI '26} } @article{Schloss2025, author = {Schloss, Karen}, year = {2025}, month = {09}, pages = {303-330}, title = {Perceptual and Cognitive Foundations of Information Visualization}, volume = {11}, journal = {Annual Review of Vision Science}, doi = {10.1146/annurev-vision-110323-110009} } @article{Schuster2024, author={Schuster, Regina and Gregory, Kathleen and Möller, Torsten and Koesten, Laura}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={“Being Simple on Complex Issues” – Accounts on Visual Data Communication About Climate Change}, year={2024}, volume={30}, number={9}, pages={6598-6611}, doi={10.1109/TVCG.2024.3352282} } @inproceedings{Schuster2026, author = {Schuster, Regina and Gregory, Kathleen and M{\"o}ller, Torsten and Koesten, Laura}, title = {Practitioners' Perspectives on Designing Data Visualizations for the General Public}, year = {2026}, isbn = {9798400722783}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3772318.3790627}, doi = {10.1145/3772318.3790627}, booktitle = {Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems}, articleno = {1185}, numpages = {19}, series = {CHI '26} } @article{SegelHeer2010, author = {Segel, Edward and Heer, Jeffrey}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {Narrative Visualization: Telling Stories with Data}, year = {2010}, volume = {16}, number = {6}, pages = {1139-1148}, doi = {10.1109/TVCG.2010.179} } @article{Slingsby2014, author = {Slingsby, A. and Dykes, J. and Wood, J. and Radburn, R.}, title = {Designing an Exploratory Visual Interface to the Results of Citizen Surveys}, journal = {International Journal of Geographical Information Science}, year = {2014}, volume = {28}, number = {10}, pages = {2090--2125}, doi = {10.1080/13658816.2014.920845} } @article{Stokes2026, author = {Stokes, Chase and Arunkumar, Anjana and Hearst, Marti A. and Padilla, Lace}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {An Analysis of Text Functions in Information Visualization}, year = {2026}, volume = {32}, number = {1}, pages = {769--779}, doi = {10.1109/TVCG.2025.3634632} } @article{Strantz2021, author={Strantz, Adam}, journal={IEEE Transactions on Professional Communication}, title={Using Web Standards to Design Accessible Data Visualizations in Professional Communication}, year={2021}, volume={64}, number={3}, pages={288-301}, doi={10.1109/TPC.2021.3091784} } @book{Telea2014, title = {Data Visualization: Principles and Practice}, author = {Telea, Alexandru C.}, year = {2014}, publisher = {A K Peters/CRC Press}, address = {New York}, isbn = {9780429074080} } @article{Tong2018, author = {Tong, Chao and Roberts, Richard and Borgo, Rita and Walton, Sean and Laramee, Robert S. and Wegba, Kodzo and Lu, Aidong and Wang, Yun and Qu, Huamin and Luo, Qiong and Ma, Xiaojuan}, title = {Storytelling and Visualization: An Extended Survey}, journal = {Information}, volume = {9}, year = {2018}, number = {3}, articleno = {65}, url = {https://www.mdpi.com/2078-2489/9/3/65}, issn = {2078-2489} } @book{Tufte3, author = {Tufte, Edward R.}, year = {1997}, title = {Visual explanations: images and quantities, evidence and narrative}, publisher = {Graphics Press}, address = {Cheshire, Conn}, isbn = {0961392126}, language = {English} } @article{Tversky2010, author = {Tversky, Barbara}, year = {2010}, month = {08}, pages = {499-535}, title = {Visualizing Thought}, volume = {3}, isbn = {978-1-4614-7484-5}, journal = {Topics in Cognitive Science}, doi = {10.1111/j.1756-8765.2010.01113.x} } @article{Viegas2007, author={Viegas, Fernanda B. and Wattenberg, Martin and van Ham, Frank and Kriss, Jesse and McKeon, Matt}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={ManyEyes: a Site for Visualization at Internet Scale}, year={2007}, volume={13}, number={6}, pages={1121-1128}, doi={10.1109/TVCG.2007.70577} } @article{Walny2020, author={Walny, Jagoda and Storteboom, Sarah and Pusch, Richard and Hwang, Steven Munsu and Knudsen, Søren and Carpendale, Sheelagh and Willett, Wesley}, journal={IEEE Computer Graphics and Applications}, title={PixelClipper: Supporting Public Engagement and Conversation About Visualizations}, year={2020}, volume={40}, number={2}, pages={57-70}, doi={10.1109/MCG.2020.2968906} } @article{Wood2008, author = {Wood, Denis and Fels, John}, title = {The Natures of Maps: Cartographic Constructions of the Natural World}, journal = {Cartographica}, year = {2008}, volume = {43}, number = {3}, pages = {189--202}, doi = {10.3138/carto.43.3.189} } @article{Wood2014, author = {Wood, Jo and Beecham, Roger and Dykes, Jason}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization}, year = {2014}, volume = {20}, number = {12}, pages = {2171-2180}, doi = {10.1109/TVCG.2014.2346323} } @article{Wood2019, author = {Wood, Jo and Kachkaev, Alexander and Dykes, Jason}, title = {Design Exposition with Literate Visualization}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2019}, volume = {25}, number = {1}, pages = {759--768}, doi = {10.1109/TVCG.2018.2864836} } @misc{Wu2024, title = {ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering}, author = {Wu, Yifan and Yan, Lutao and Shen, Leixian and Wang, Yunhai and Tang, Nan and Luo, Yuyu}, year = {2024}, doi = {10.48550/arXiv.2405.07001} } @article{Xiong2020, author={Xiong, Cindy and Van Weelden, Lisanne and Franconeri, Steven}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={The Curse of Knowledge in Visual Data Communication}, year={2020}, volume={26}, number={10}, pages={3051-3062}, doi={10.1109/TVCG.2019.2917689} } @article{Yang2023, author = {Yang, Leni and Xiong, Cindy and Wong, Jason K. and Wu, Aoyu and Qu, Huamin}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {Explaining With Examples: Lessons Learned From Crowdsourced Introductory Description of Information Visualizations}, year = {2023}, volume = {29}, number = {3}, pages = {1638-1650}, doi = {10.1109/TVCG.2021.3128157} } @misc{Yang2026, title={What Do We Mean When We Talk About Data Storytelling?}, author={Leni Yang and Zezhong Wang and Xingyu Lan}, year={2026}, eprint={2510.04761}, archivePrefix={arXiv}, primaryClass={cs.HC}, url={https://arxiv.org/abs/2510.04761}, } @article{Zhang2026, author = {Boning Zhang and Haipeng Dong and Jiayi Xu and Yuxin Zhang}, title = {Emerging Trends in Storytelling: Genre-Based Analysis of Narrative Visualization Through Interactivity and Animation Effects}, journal = {International Journal of Human–Computer Interaction}, volume = {0}, number = {0}, pages = {1--25}, year = {2026}, publisher = {Taylor \& Francis}, doi = {10.1080/10447318.2026.2651342}, URL = {https://doi.org/10.1080/10447318.2026.2651342}, eprint = {https://doi.org/10.1080/10447318.2026.2651342} } @inproceedings{Zhu2024, author = {Zhu, Qian and Wang, Zhuo and Zeng, Wei and Tong, Wai and Lin, Weiyue and Ma, Xiaojuan}, title = {Make Interaction Situated: Designing User Acceptable Interaction for Situated Visualization in Public Environments}, year = {2024}, isbn = {9798400703300}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3613904.3642049}, doi = {10.1145/3613904.3642049}, booktitle = {Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems}, articleno = {196}, numpages = {21}, location = {Honolulu, HI, USA}, series = {CHI '24} } `38:[] 2d:{"title":"Current version Lens Thinking Cont.","cells":[{"id":0,"value":"# Current version Lens Thinking Cont.\nLink to old lens thinking: https://old.observablehq.com/d/95a66a0d93ed1cad","pinned":false,"hidden":false,"mode":"md"},{"id":151,"value":"## Beth Taylor","pinned":false,"hidden":false,"mode":"md"},{"id":951,"value":"Establishing the effectiveness of data visualisation designed to communicate complex narrative to public audiences.","pinned":false,"hidden":false,"mode":"md"},{"id":1125,"value":"Why do I care about this? Why does this matter?","pinned":false,"hidden":false,"mode":"md"},{"id":1029,"value":"**Working keywords:** data visualisation, narrative visualization, storytelling, design methods, user testing, transparency, graph comprehension, visual communication, public infrastructure","pinned":false,"hidden":false,"mode":"md"},{"id":953,"value":"## Introduction\n\nData visualisation designed to communicate information to public audiences is widely employed across the private and public sectors. When visualised, data can be a powerful tool for communicating complex narratives and persuading readers.\n\nVisualised data offers a quick and effective way to identify abnormal patterns, including outliers and missing values ${bib.citep(\"Or2013\")}. A good graphic may been defined as one that allows readers to visualise trends and patterns without having to read all the numbers. If designed and interpreted properly, data visualisations can make us smarter and inform conversations ${bib.citep(\"Cairo2019\")}.","pinned":false,"hidden":false,"mode":"md"},{"id":601,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":757,"value":"STOP READING PLEASE :) :) :) the rest is in note form","pinned":false,"hidden":false,"mode":"md"},{"id":754,"value":"-----------","pinned":false,"hidden":false,"mode":"md"},{"id":1074,"value":"## Relevant(?) research","pinned":false,"hidden":false,"mode":"md"},{"id":1023,"value":"$30","pinned":false,"hidden":false,"mode":"md"},{"id":1082,"value":"### Annotation\n- An Analysis of Text Functions in Information Visualization ${bib.citep(\"Stokes2026\")}\n- ${bib.cite(\"Cairo2019\")} considers the short, textual notes which supplement a chart and highlight or clarify important points, the 'annotation layer,' as part of the content of a visualisation content.${bib.cite(\"Cairo2019\")}\n\n- Annotations embedded into visualisation have been found helpful when used as a device for sharing personal perspectives, as an aid for readers to contextualise and relate their own experiences to the data subject ${bib.citep(\"Kauer2025\")}.\n\n- ${bib.cite(\"Lin2026\")} explored the incorporation of additional information sources by analysts working with data.","pinned":false,"hidden":false,"mode":"md"},{"id":1088,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":1140,"value":"### Narrative Visualisation\nNarrative information visualisations typically employ a combination of persuasive, rhetorical techniques to communicate an intentional story to users ${bib.citep(\"Hullman2011\")}.\n\nThey also use as exploratory, dialectic strategies aimed at providing the user with control over the insights she gains from interaction.\n\n- Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic ${bib.citep(\"Errey2024\")} \n\n- Narrative visualization: Telling stories with data. ${bib.citep(\"SegelHeer2010\")}\n\n- ${bib.citep(\"Tong2018\")}\n\n- ${bib.citep(\"Yang2023\")}\n\n- ${bib.citep(\"Elshehaly2024\")}\n\n- ${bib.citep(\"Lan2026\")}\n\n- ${bib.citep(\"Zhang2026\")}\n","pinned":false,"hidden":false,"mode":"md"},{"id":1078,"value":"### Deception\n - Definition, existing research and findings.\n - Exploring the development of frameworks and taxonomies in data visualisation design in disclosing information that is revealed, distorted, or obscured ${bib.citep(\"Mehta2025\")}.\n - Data visualisation misrepresenting underlying data ${bib.citep(\"Kim2025\")}, through data transformation ${bib.citep(\"Mehta2025\")}; by displaying either the wrong or too little data, or through poor design or labelling. Users without knowledge of how to read the visualisation may also be misinformed, even through correct design ${bib.citep(\"Cairo2019\")}.\n\n - ${bib.cite(\"AdarLee2021\")} found many task and evaluation taxonomies detailed in ‘analytical’ tasks that classify all communicative tasks as one. Viewers may be found able to decode a point in a visualisation or determine whether a point meets specific criteria but neither measure an understanding of the designers intent.","pinned":false,"hidden":false,"mode":"md"},{"id":1080,"value":"\n### Mental models\n - Definition, existing research and findings.\n\n\n#### Seven foundational genres\n - Definition, existing research and findings\n - ${bib.citep(\"Zhang2026\")} \n\n","pinned":false,"hidden":false,"mode":"md"},{"id":1094,"value":"### Rhetoric visualisation\n - Definition\n - Existing research and findings\n - Visualization rhetoric: Framing effects in narrative visualization ${bib.citep(\"Hullman2011\")} \n - A Critical Reflection on the Values and Assumptions in Data Visualization ${bib.citep(\"Saharan2026\")}\n - Untangling Rhetoric, Pathos, and Aesthetics in Data Visualization ${bib.citep(\"Prantl2026\")}\n - ","pinned":false,"hidden":false,"mode":"md"},{"id":1096,"value":"### Accessible visualisation\n - Definition\n - Existing research and findings\n - Visualization tools for open government data ${bib.citep(\"Graves2013\")} \n - Beyond Transparency: Making the Italian Public Administration more Accessible through Data Storytelling ${bib.citep(\"Moretti2018\")}\n - Using Web Standards to Design Accessible Data Visualizations in Professional Communication ${bib.citep(\"Strantz2021\")}\n - Accessible visualization: Design space, opportunities, and challenges ${bib.citep(\"Kim2021\")}\n - Benthic: Perceptually Congruent Structures for Accessible Charts and Diagrams ${bib.citep(\"Mei2025\")}","pinned":false,"hidden":false,"mode":"md"},{"id":1098,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":1100,"value":"### Decision-making\n- Definition\n- Existing research and findings\n - The area of decision-making with data visualisation can be grounded in work by Eventhia Dimara, e.g., ${bib.cite(\"Dimara2022\")}, ${bib.cite(\"Oral2024\")}), and the incorporation of ‘soft intelligence’ (${bib.cite(\"Elshehaly2023\")}), or lived experience (${bib.cite(\"Elshehaly2024\")}) in policy domains.\n","pinned":false,"hidden":false,"mode":"md"},{"id":1102,"value":"## Provenance\n - Definition\n - Existing research and findings.\n - ${bib.cite(\"Ragan2016\")})\n - Visualization Badges: Communicating Design and Provenance through Graphical Labels Alongside Visualizations ${bib.citep(\"Edelsbrunner2026\")}\n\n### Within public communication settings\nReflection on the idea of provenance within public communication settings is also relevant.","pinned":false,"hidden":false,"mode":"md"},{"id":1105,"value":"------------","pinned":false,"hidden":false,"mode":"md"},{"id":59,"value":"\n\n\n### Data sources \n\nWhilst the quality of a visualisation may depends on how well it encodes the underlying data, accurately and proportionally, a separate and prior consideration is the reliability of said data. The source of the information represented is one of the first features to look at in a data visualisation. However, it is unrealistic to expect all readers to verify the data of all charts they see every day — many often will not have time and may lack the knowledge to do so ${bib.cite(\"Cairo2019\")}.","pinned":false,"hidden":false,"mode":"md"},{"id":634,"value":"### Visualisation Deception\n\nCharts may lie in multiple ways: by displaying the wrong data, by including an inappropriate amount of data, by being badly designed-or, even if they are professionally done, they end up lying to us because we read too much into them or see in them what we want to believe. At the same time, charts -good and bad- are everywhere, and they can be very persuasive.
This combination of factors may lead to a perfect storm of misinformation and disinformation. We all need to turn into attentive and informared chart reasires. We must become more graphicate.\n\nYou can't decode a chart if you don't grasp its vocabulary or its syntax, or if you can't make the right inferences based on what you're looking at.${bib.cite(\"Cairo2019\")}","pinned":false,"hidden":false,"mode":"md"},{"id":681,"value":"#### Deconstructing the Visualisation\n\n${bib.cite(\"Cairo2019\")} deconstructs the chart, or visualisation, by isolating the content, i.e., how the data is represented and encoded, and the features which surround the content, i.e., titles, legends, scales, bylines of authorship, sources, which he names the scaffolding. ${bib.cite(\"Cairo2019\")} states it is critical to read the scaffolding carefully to grasp what the chart is about, what is being measured, and how it's being measured.

\n\"Focusing on the scaffolding of a chart, its scales and legends, before decoding its content will help identify distortions.\"${bib.cite(\"Cairo2019\")}\n\n\n#### Understanding literacy\n\nBefore learning how visualisations can misinform, ${bib.cite(\"Cairo2019\")} claims readers must learn how they are supposed to work when they are built appropriately, according to grammar, a vocabulary of symbols, and plenty of conventions. To properly engage with a visualisation, the reader must know _how_ to read it ${bib.citep(\"Cairo2019\")}.\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":752,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":701,"value":"$34","pinned":false,"hidden":false,"mode":"md"},{"id":684,"value":"$35","pinned":false,"hidden":false,"mode":"md"},{"id":686,"value":"$36","pinned":false,"hidden":false,"mode":"md"},{"id":395,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"## Approach\n- To enhance data visualisation with additional data encoding and/or include qualitative annotation to visualised data which provides relevant knowledge and aids better understanding of the quantitative data visualised.\n - The annotation would enrich the graphic and provide insight into relevant contextual factors. The qualitative information could include:\n - Events relating to the subject the principle dataset represents. For example, classification or taxonomy of data.\n - Methodology underpinning the data making up the principle dataset. For example, how data were obtained; the metrics, measurements and focus.","pinned":false,"hidden":false,"mode":"md"},{"id":761,"value":"------------------","pinned":false,"hidden":false,"mode":"md"},{"id":399,"value":"### Newer texts","pinned":false,"hidden":false,"mode":"md"},{"id":375,"value":"Data profiling is the systematic analysis of a data source, typically prior to any specific use, to determine how useful it is and how best to work with it. This analysis will typically address matters such as what information the source contains, the metadata about the information, the quality of data, whether or not there are issues such as missing or erroneous elements, and patterns present within the data that may influence its use. Data profiling helps identify and improve data quality, which in turn improves the information systems built upon them (Azeroual et al. 2018):
\n- Missing data\n- Incorrect information\n- Duplicate data\n- Inconsistently represented data\n\n${bib.cite(\"BigData\")} page 316.","pinned":false,"hidden":false,"mode":"md"},{"id":368,"value":"In addition to actual errors, data can be profiled for bias and representativeness. Depending upon how the data is collected, not all elements of the relevant universe will be equally sampled, which in turn will bias inferences drawn from the database and reduce their accuracy. ${bib.cite(\"BigData\")} page 317.\n\nData provenance refers to the description of the origin, creation, and propagation process of data. Provenance is the lineage and the derivation of the data, documented history of an object, in other words, how the object was created, modified, propagated, and disseminated to its currenty location/status. ${bib.cite(\"BigData\")} page 317.","pinned":false,"hidden":false,"mode":"md"},{"id":364,"value":"Recently, the domain for provenance has gained signficant attention in digital world and e-science. The provenance of data is crucial for validating, debugging, auditing, evaluating the quality of data and determining reliability in data. ${bib.cite(\"BigData\")} page 318.","pinned":false,"hidden":false,"mode":"md"},{"id":346,"value":"Broadly degined, data discovery is hte process of dinding patterns and trends in processed, analyzed, or visualised data. The reason data discovery must de feined \"broadly\" is because this process is ppoular across domains. These patterns and trends can be \"discoveRed\" from the data using different methods depending on the context and domain of the work. ${bib.cite(\"BigData\")} page 279.

\n(Use in different contexts)
\nDepending on context and domain of application, the process of \"discovery\" changes, though the end goal is identifying patterns and trends and to gain knowledge from them. ${bib.cite(\"BigData\")} page 279.","pinned":false,"hidden":false,"mode":"md"},{"id":321,"value":"Data engineering is a challenging process. Different stages of the process affect the outcome ina variety of ways. Manpower, system design, data formatting, variety of data sources, size of the software, and project budget are among the variables that could alter the outcome of an engineering project. Nevertheless, software and data anomalies pose one of the most challenging obstacles in the success of any project. ${bib.cite(\"BigData\")} page 25.","pinned":false,"hidden":false,"mode":"md"},{"id":378,"value":"- Clustering is a method to find distinct groups of data based on their characteristics. Social media companies usually use it to identify people with similar interests. It is a type of unsupervised learning and works with both qualitative and quantitative data.\n- Association Rule Mining is a method to find relationships between variables such as \"which things or events occur together frequently in the dataset?\". This method was initially developed to do market basket analysis. Researchers in mineralogy apply association rule mining to use one mineral as the indicator of other minerals.\n- Outlier Analysis, also called anomaly detection, in a method to find data items that are different from the majority of the data.\n- Time Series Analysis is a set of methods to detect trends and patterns from the time series data. Time series data are a set of data indexed with time, and this type of data are used in many different domains. ${bib.cite(\"BigData\")} page 314.","pinned":false,"hidden":false,"mode":"md"},{"id":385,"value":"Data visualisation encompasses the planning, production, and circulation of interactive, \"graphical representations\" that emerge from big data analyses (Ward et al. 2010, 1). Given the volume and complexity of big data, data visualisation is employed as a tool to artfully demonstrate underlying patterns and trends - especially for lay audiences who may lack expertise to directly engage with large-scale datasets. Visually depicting such insights thereby broadens the use of big data beyond computational experts, making big data analyses more approachable for a wider segment of society. ${bib.cite(\"BigData\")} page 349.\n\nHumans have long been naturally inclined toward processing information visually, which helps anchor individual-level decision-making (Cairo 2012). In this context, visualising complex phenomena predates our current digital era of data visualisation (Friendly 2008). ${bib.cite(\"BigData\")} page 349.\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":330,"value":"Humans have the ability to perform the processes of reasoning, thinking, and planning effectively; ideas could be managed, organized, and even conveyed in a comprehensible and quick manner. Context awareness is a \"trivial\" skill for humans. That is because humans receive years of training while observing the world around them, use agreed-upon syntax (language), comprehend the context in which they are in, and accommodate their understanding of events accordingly. ${bib.cite(\"BigData\")} page 215.","pinned":false,"hidden":false,"mode":"md"},{"id":338,"value":"Data aggregation refers to the process by which raw data are gathered, reformatted, and presented in a summary form for subsequent data sharing and further analyses. In general, raw data can be aggregated in several ways, such as by time (e.g., monthly and quarterly), by location (e.g., city), or by data source. Aggregated data have ongoing been ysed to delineate new and unusual data patterns (e.g., Wen et al. 2018). ${bib.cite(\"BigData\")} page 260.","pinned":false,"hidden":false,"mode":"md"},{"id":401,"value":"While manipulation of the facts and/or deception of the reader will usually not be intentional in the human rights realm, accidentally misleading visualizations can have a similar effect. The research team conducted a series of user studies to propose, test and establish the hypothesis that visual distortions can have an effect on the audience’s interpretation of the visualization message and to determine how severe the different distortion techniques are in terms of deceiving the user. ${bib.cite(\"Rall2016\")}","pinned":false,"hidden":false,"mode":"md"},{"id":397,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":108,"value":"### Reference List","pinned":false,"hidden":false,"mode":"md"},{"id":98,"value":"bib.bibliography()","pinned":false,"hidden":false,"mode":"ojs"},{"id":102,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":100,"value":"## Bibliography","pinned":false,"hidden":false,"mode":"md"},{"id":104,"value":"bib.refTable()","pinned":false,"hidden":false,"mode":"ojs"},{"id":86,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":95,"value":"$37","pinned":true,"hidden":false,"mode":"ojs"},{"id":219,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":230,"value":"### JavaScript support","pinned":false,"hidden":false,"mode":"md"},{"id":88,"value":"import {Refs} from \"@gicentre/references-class\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":93,"value":"bib = Refs.create(referenceList)","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"2","titling":"auto"}