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A Layout Technique for Storyline-based Visualization of Consecutive Numerical Time-varying Data
Abstract
We commonly represent time-varying values as polyline charts or heatmaps; however, both type of techniques are difficult to simultaneously observe short-term features of time-varying values and cluster transitions. This poster proposes storyline-based visualization technique for consecutive numerical time-varying data. Storyline is a visualization technique to show associative feature among elements over time. Our technique measures similarity of each elements and draw similar elements as proximity storyline. The technique also reflects differential values on storyline as a visual variable to emphasize the amount of line changes.
... The traditional Storyline can neither display the fine-grained temporal trends nor provide the spatial context for ST series analyses. Previous studies [17,62] attempted to extend Storyline techniques to spatiotemporal scenarios but cannot accommodate large-scale datasets with many locations and long time ranges. The Storyline occupies the most effective visual channel (i.e., position) and is compact in layout. ...
... By contrast, we need to model implicit relationships between large-scale ST series and construct sessions. Yagi et al. [62] made an initial attempt on a small dataset with a few ST series and short-term observations. Their approach ignored the spatial context and the temporal sensitivity of relationship modeling, and might require tedious processes with trial and error to tune session generation. ...
... ST series are also associated with the spatial context. Yagi et al. [62] used colors to visually link the curves in Storyline with the locations on a geographic map. Such a manner is not scalable. ...
In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of
patterns of evolution
in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a
time period
when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner.
GeoChron
includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis
... StoryFlow [36] formulated layout computation as a three-stage optimization problem: ordering, alignment, and compact, enabling layout generation at interactive speed. While some works optimized their layout generation on quality metrics [15,19,23,29,68] or streaming data [60], other works employed storyline visualization in different domains [5,6,14,28,38,43,74], including dynamic social networks [4,54]. ...
... SVEN [4] formulated ordering as a seriation problem to minimize line crossings and employed arc segments to depict the dynamic relationship among entities (structure). Others explicitly mapped information of the entities (univariate content), onto the y-axis [3,6,52,54,74]. Our framework utilizes the y-axis to encode function and strength information by imposing constraints on the layout generation. ...
Egocentric networks, often visualized as node-link diagrams, portray the complex relationship (link) dynamics between an entity (node) and others. However, common analytics tasks are multifaceted, encompassing interactions among four key aspects: strength, function, structure, and content. Current node-link visualization designs may fall short, focusing narrowly on certain aspects and neglecting the holistic, dynamic nature of egocentric networks. To bridge this gap, we introduce SpreadLine, a novel visualization framework designed to enable the visual exploration of egocentric networks from these four aspects at the microscopic level. Leveraging the intuitive appeal of storyline visualizations, SpreadLine adopts a storyline-based design to represent entities and their evolving relationships. We further encode essential topological information in the layout and condense the contextual information in a metro map metaphor, allowing for a more engaging and effective way to explore temporal and attribute-based information. To guide our work, with a thorough review of pertinent literature, we have distilled a task taxonomy that addresses the analytical needs specific to egocentric network exploration. Acknowledging the diverse analytical requirements of users, SpreadLine offers customizable encodings to enable users to tailor the framework for their tasks. We demonstrate the efficacy and general applicability of SpreadLine through three diverse real-world case studies (disease surveillance, social media trends, and academic career evolution) and a usability study.
... StoryFlow [36] formulated layout computation as a three-stage optimization problem: ordering, alignment, and compact, enabling layout generation at interactive speed. While some works optimized their layout generation on quality metrics [15,19,23,29,68] or streaming data [60], other works employed storyline visualization in different domains [5,6,14,28,38,43,74], including dynamic social networks [4,54]. ...
... SVEN [4] formulated ordering as a seriation problem to minimize line crossings and employed arc segments to depict the dynamic relationship among entities (structure). Others explicitly mapped information of the entities (univariate content), onto the y-axis [3,6,52,54,74]. Our framework utilizes the y-axis to encode function and strength information by imposing constraints on the layout generation. ...
Egocentric networks, often visualized as node-link diagrams, portray the complex relationship (link) dynamics between an entity (node) and others. However, common analytics tasks are multifaceted, encompassing interactions among four key aspects: strength, function, structure, and content. Current node-link visualization designs may fall short, focusing narrowly on certain aspects and neglecting the holistic, dynamic nature of egocentric networks. To bridge this gap, we introduce SpreadLine, a novel visualization framework designed to enable the visual exploration of egocentric networks from these four aspects at the microscopic level. Leveraging the intuitive appeal of storyline visualizations, SpreadLine adopts a storyline-based design to represent entities and their evolving relationships. We further encode essential topological information in the layout and condense the contextual information in a metro map metaphor, allowing for a more engaging and effective way to explore temporal and attribute-based information. To guide our work, with a thorough review of pertinent literature, we have distilled a task taxonomy that addresses the analytical needs specific to egocentric network exploration. Acknowledging the diverse analytical requirements of users, SpreadLine offers customizable encodings to enable users to tailor the framework for their tasks. We demonstrate the efficacy and general applicability of SpreadLine through three diverse real-world case studies (disease surveillance, social media trends, and academic career evolution) and a usability study.
... For disease spreading, this encoding can be used to show contacts in the same ward and forms a key part of our approach. Research in storyline visualization has focused on optimizing the compactness of storyline visualizations (either automatic or users-assisted) [4,23,37,47,48,51,61,62,67,68], reducing crossings [25,32,70,79], plotting approaches [60], combining storylines with event-based methods [3], genealogical data [31], streaming and dynamic data [66,81], and contacts between living things or actors exhibiting similar behavior [52]. Reda et al. [52] is the closest approach to ours, but it needs to consider all contacts in the storyline. ...
Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak -- the patient zero or index patient -- requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.
Storyline visualizations are a powerful way to compactly visualize how the relationships between people evolve over time. Real-world relationships often also involve space, for example the cities that two political rivals visited together or alone over the years. By default, Storyline visualizations only show implicitly geospatial co-occurrence between people (drawn as lines), by bringing their lines together. Even the few designs that do explicitly show geographic locations only do so in abstract ways (
e.g.,
annotations) and do not communicate geospatial information, such as the direction or extent of their political campains. We introduce Geo-Storylines, a collection of visualisation designs that integrate geospatial context into Storyline visualizations, using different strategies for compositing time and space. Our contribution is twofold. First, we present the results of a sketching workshop with 11 participants, that we used to derive a design space for integrating maps into Storylines. Second, by analyzing the strengths and weaknesses of the potential designs of the design space in terms of legibility and ability to scale to multiple relationships, we extract the three most promising: Time Glyphs, Coordinated Views, and Map Glyphs. We compare these three techniques first in a controlled study with 18 participants, under five different geospatial tasks and two maps of different complexity. We additionally collected informal feedback about their usefulness from domain experts in data journalism. Our results indicate that, as expected, detailed performance depends on the task. Nevertheless, Coordinated Views remain a highly effective and preferred technique across the board.
Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak — the patient zero or index patient — requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.
We have various interesting time-varying data in our daily life, such as weather data (e.g., temperature and air pressure) and stock prices. Such time-varying data is often associated with other information: for example, temperatures can be associated with weather, and stock prices can be associated with social or economic incidents. Meanwhile, we often draw large-scale time-varying data by multiple polylines in one space to compare the time variation of multiple values. We think it should be interesting if such time-varying data is effectively visualized with their associated information. This paper presents a technique for polyline-based visualization and level-of-detail control of tagged time-varying data. Supposing the associated information is attached as tags of the time-varying values, the technique generates clusters of the time-varying values grouped by the tags, and selects representative values for each cluster, as a preprocessing. The technique then draws the representative values as polylines. It also provides a user interface so that users can interactively select interesting representatives, and explore the values which belong to the clusters of the representatives.
Storyline visualization is a technique used to depict the temporal dynamics of social interactions. This visualization technique was first introduced as a hand-drawn illustration in XKCD's “Movie Narrative Charts” [21]. If properly constructed, the visualization can convey both global trends and local interactions in the data. However, previous methods for automating storyline visualizations are overly simple, failing to achieve some of the essential principles practiced by professional illustrators. This paper presents a set of design considerations for generating aesthetically pleasing and legible storyline visualizations. Our layout algorithm is based on evolutionary computation, allowing us to effectively incorporate multiple objective functions. We show that the resulting visualizations have significantly improved aesthetics and legibility compared to existing techniques.
Movie narrative charts
- R Munroe