Information Visualization最新文献

{"title":"Exploring annotation taxonomy in grouped bar charts: A qualitative classroom study","authors":"Md Dilshadur Rahman, Ghulam Jilani Quadri, Danielle Albers Szafir, Paul Rosen","doi":"10.1177/14738716241270247","DOIUrl":"https://doi.org/10.1177/14738716241270247","url":null,"abstract":"Annotations are an essential part of data analysis and communication in visualizations, which focus a readers attention on critical visual elements (e.g. an arrow that emphasizes a downward trend in a bar chart). Annotations enhance comprehension, mental organization, memorability, user engagement, and interaction and are crucial for data externalization and exploration, collaborative data analysis, and narrative storytelling in visualizations. However, we have identified a general lack of understanding of how people annotate visualizations to support effective communication. In this study, we evaluate how visualization students annotate grouped bar charts when answering high-level questions about the data. The resulting annotations were qualitatively coded to generate a taxonomy of how they leverage different visual elements to communicate critical information. We found that the annotations used significantly varied by the task they were supporting and that whereas several annotation types supported many tasks, others were usable only in special cases. We also found that some tasks were so challenging that ensembles of annotations were necessary to support the tasks sufficiently. The resulting taxonomy of approaches provides a foundation for understanding the usage of annotations in broader contexts to help visualizations achieve their desired message.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing complex network visualisations using the wayfinding map metaphor","authors":"Nicola Cerioli, Rupesh Vyas, Mary Pat Reeve, Masood Masoodian","doi":"10.1177/14738716241270341","DOIUrl":"https://doi.org/10.1177/14738716241270341","url":null,"abstract":"Despite their widespread use, network visualisations can be rather challenging to design and use. This is due to the fact that such visualisations are generally used to represent highly complex underlying data sets. As such, the resulting charts often include a very large number of visual elements and many non-linear relations between them that must be displayed. More effective design-oriented approaches are therefore needed to better support designers in creating network visualisations for complex data sets that are more understandable and usable for their users. The use of visual metaphors seems to offer such an approach to designing visualisations of complex data. In this article, we propose the use of wayfinding map metaphor in network diagrams to support both the designers and users of this type of data visualisation. We also provide a mapping of the three common map wayfinding tasks – orientation, exploration, and navigation – to three categories of network diagram user interactions. To demonstrate the potential of our proposed approach, we provide an example case study using a prototype network diagram visualisation tool – Colocalisation Network Explorer – which we have developed to support the exploration of relationships between various diseases and the portion of the human genome involved in their onset.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph & Network Visualization and Beyond","authors":"Quang Vinh Nguyen, Mabule Samuel Mabakane, Adrian Rusu, Ebad Banissi","doi":"10.1177/14738716241270224","DOIUrl":"https://doi.org/10.1177/14738716241270224","url":null,"abstract":"","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STEP: Sequence of time-aligned edge plots","authors":"Moataz Abdelaal, Fabian Kannenberg, Antoine Lhuillier, Marcel Hlawatsch, Achim Menges, Daniel Weiskopf","doi":"10.1177/14738716241265111","DOIUrl":"https://doi.org/10.1177/14738716241265111","url":null,"abstract":"We present sequence of time-aligned edge plots (STEP): a sequence- and edge-scalable visualization of dynamic networks and, more broadly, graph ensembles. We construct the graph sequence by ordering the individual graphs based on specific criteria, such as time for dynamic networks. To achieve scalability with respect to long sequences, we partition the sequence into equal-sized subsequences. Each subsequence is represented by a horizontal axis placed between two vertical axes. The horizontal axis depicts the order within the subsequence, while the two vertical axes depict the source and destination vertices. Edges within each subsequence are depicted as segmented lines extending from the source vertices on the left to the destination vertices on the right throughout the entire subsequence, and only the segments corresponding to the sequence members where the edges occur are drawn. By partitioning the sequence, STEP provides an overview of the graphs’ structural changes and avoids aspect ratio distortion. We showcase the utility of STEP for two realistic datasets. Additionally, we evaluate our approach by qualitatively comparing it against three state-of-the-art techniques using synthetic graphs with varying complexities. Furthermore, we evaluate the generalizability of STEP by applying it to a graph ensemble dataset from the architecture domain.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A framework to improve causal inferences from visualizations using counterfactual operators","authors":"Arran Zeyu Wang, David Borland, David Gotz","doi":"10.1177/14738716241265120","DOIUrl":"https://doi.org/10.1177/14738716241265120","url":null,"abstract":"Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-layer visual analytics of truckers’ risk-coping social network","authors":"Qi Huang, Mao Lin Huang, Yi-Na Li","doi":"10.1177/14738716241265110","DOIUrl":"https://doi.org/10.1177/14738716241265110","url":null,"abstract":"Within organizations, managers’ specific responsibilities and domain expertise shape their interests in the output of social network analysis. Our proposed visualization approach is tailored to meet the operation-directed needs and preferences for visual analysis of specific tasks. This method prioritizes an overall geographical map with focal-contextual dynamics within the network. To enable a comprehensive and in-depth understanding of pinpointed focal areas, we customize an analytical framework for analyzing inter-community networks. We extract focal sub-networks from specific nodes to create graph visualization for detailed analysis, represent rich types of domain-specific graphic properties, and provide direct zoom+filtering interactions to allow easy pattern recognition and knowledge discovery. We applied our approach to visualizing the data from interactions among 300 city-based truck communities on the largest occupational platform for truckers in China. We also conduct a case study to demonstrate that our approach is effective in supporting managers’ network analysis and knowledge discovery.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are pie charts evil? An assessment of the value of pie and donut charts compared to bar charts","authors":"Andrew Hill","doi":"10.1177/14738716241259432","DOIUrl":"https://doi.org/10.1177/14738716241259432","url":null,"abstract":"Many data visualization experts recommend the use of bar charts over pie charts because they consider comparing the area or angle of segments to be less accurate than comparing bars on a bar chart. However, many studies show that when the pie chart is used to estimate proportions (arguably its main function) it is as accurate as the bar chart. A major issue is that most previous studies have only looked at one method of extracting information from pie charts, for example either by comparing the segment to the circle (the part-whole relationship) or one segment to another (relative magnitude estimation). Therefore, in this study I test multiple metrics to provide a more holistic assessment of the pie and donut chart against the bar chart. I also measured cognitive load through pupillometry. In summary, bar charts were more precise than pie and donut charts for ranking elements, but all charts were equally accurate for extracting the part-whole relationship. There was little difference in cognitive load between chart types, although bar charts were consistently faster to use on average. Overall, the bar chart was more flexible, but where there were statistically significant differences between charts, the effect sizes were often small, and unlikely to prevent effective extraction of quantitative information. That is, as long as they were used appropriately, all chart types were arguably acceptable for displaying simple, categorical data.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visually encoding orthogonal planar graph drawings as graph mazes: An eye tracking study","authors":"Michael Burch, Marco Lemmenmeier, Robert Ospelt, Zilka Bajraktarevic","doi":"10.1177/14738716241255598","DOIUrl":"https://doi.org/10.1177/14738716241255598","url":null,"abstract":"There are various styles to visually represent relational data in the form of node-link diagrams. In particular, for planar graphs we can find orthogonal node-link diagrams consisting of links bending only at ninety degrees a successful and prominent variant. One of the benefits of such drawings is the tracking of longer paths through a network with the eyes due to their limited number of link orientations, changes, and variations, but on the negative side the links can have arbitrary bending shapes. In this article we developed a novel way to visualize such orthogonal planar drawings by making use of mazes that look more natural to the human eye due to the street-like visual metaphor that many people are familiar with. Tracking paths is one of the major tasks in such graph visualizations, similar to orthogonal node-link diagrams, however, we argue that mazes are a more natural way to find paths. To get insights in the visual scanning behavior when reading graph mazes we conducted a comparative eye tracking study with 26 male versus female participants of different experience levels while also alternating between orthogonal node-link drawings and graph mazes as well as different graph size levels. The major result of this comparative study is that the participants can track paths in both representation styles, including a geodesic path tendency in their visual search behavior, but typically have a longer fixation duration at branching nodes and locations in the mazes that lead to opposite directions to the geodesic path tendency, maybe the viewers had to start a reorientation phase in their visual scanning behavior. We also found out that the size, that is the number of graph vertices has an impact on the visual scanning behavior for both orthogonal node-link diagrams as well as street-like maze representations, but for the mazes we found this impact to be less strong (in terms of the eye movement data metrics fixation durations and saccade lengths) compared to the node-link diagrams. To conclude the article, we discuss limitations and scalability issues of our approach. Moreover, we give an outlook and future work for possible extensions.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualisation of Random Forest classification","authors":"Catarina Maçãs, João R Campos, Nuno Lourenço, Penousal Machado","doi":"10.1177/14738716241260745","DOIUrl":"https://doi.org/10.1177/14738716241260745","url":null,"abstract":"Decision Trees (DTs) stand out as a prevalent choice among supervised Machine Learning algorithms. These algorithms form binary structures, effectively dividing data into smaller segments based on distinct rules. Consequently, DTs serve as a learning mechanism to identify optimal rules for the separation and classification of all elements within a dataset. Due to their resemblance to rule-based decisions, DTs are easy to interpret. Additionally, their minimal need for data pre-processing and versatility in handling various data types make DTs highly practical and user-friendly across diverse domains. Nevertheless, when confronted with extensive datasets or ensembles involving multiple trees, such as Random Forests, its analysis can become challenging. To facilitate the examination and validation of these models, we have developed a visual tool that incorporates a range of visualisations providing both an overview and detailed insights into a set of DTs. Our tool is designed to offer diverse perspectives on the same data, enabling a deeper understanding of the decision-making process. This article outlines our design approach, introduces various visualisation models, and details the iterative validation process. We validate our methodology through a telecommunications use case, specifically employing the visual tool to decipher how a DT-based model determines the optimal communication channel (i.e. phone call, email, SMS) for a telecommunication operator to use when contacting a client.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualizing congestion at large-scale events with an interactive-view system incorporating proximity-based networks","authors":"Sayaka Morikoshi, Masaki Onishi, Takayuki Itoh","doi":"10.1177/14738716241256380","DOIUrl":"https://doi.org/10.1177/14738716241256380","url":null,"abstract":"Contact with infected individuals can lead to the spread of infectious diseases. During the COVID-19 pandemic, people were strongly urged to avoid the three Cs: closed spaces, crowded places, and close-contact settings. To hold large-scale events under such circumstances, reducing crowd congestion is key to preventing the further spread of infection. Therefore, identifying the pedestrian behaviors and walking patterns that pose a high risk of infection and utilizing them for effective crowd control is necessary. In this study, we propose an approach for visualizing walking paths while maintaining visibility from large-scale human flow data and representing both spatial and temporal features. The proposed method enables the visualization of the pedestrian proximity status as a network containing three components: a proximity network, proximity path, and pedestrian statistics that interact with each other. By operating the three components of this system interactively, we can observe the spatial and temporal features of situations with a high risk of infection during crowd congestion. An example of the operation of this system is presented by visualizing real-world human flow data measured at an event venue and identifying the proximity of the pedestrians.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}