scholarly journals Predicting intent behind selections in scatterplot visualizations

2021 ◽  
Vol 20 (4) ◽  
pp. 207-228
Author(s):  
Kiran Gadhave ◽  
Jochen Görtler ◽  
Zach Cutler ◽  
Carolina Nobre ◽  
Oliver Deussen ◽  
...  
Keyword(s):  
Data Visualization ◽  
Prototype System ◽  
Large Set ◽  
Tabular Data ◽  
Successful Prediction ◽  
Data Visualizations ◽  
Interactive Selection

Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.

scholarly journals Data Visualization + Scrollytelling for Election News Stories : Challenges and Perspectives

2019 ◽  
Author(s):  
Jean-Philippe Corbeil ◽  
Florent Daudens ◽  
Thomas Hurtut
Keyword(s):  
Data Visualization ◽  
French Language ◽  
Daily Newspaper ◽  
Canadian French ◽  
News Stories ◽  
The Past ◽  
Narrative Format ◽  
Election News ◽  
Data Visualizations

This visual case study is conducted by Le Devoir, a Canadian french-language independent daily newspaper gathering around 50 journalists and one million readers every week. During the past twelve months, in collaboration with Polytechnique Montreal, we investigated a scrollytelling format strongly relying on combined series of data visualizations. This visual case study will specifically present one of the news stories we published, which communicates electoral results the day after the last Quebec general election. It gathers all the lessons that we learnt from this experience, the challenges that we tackled and the perspectives for the future. Beyond the specific electoral context of this work, these conclusions might be useful for any practitioner willing to communicate data visualization based stories, using a scrollytelling narrative format.

What we talk about when we talk about beautiful data visualizations

2020 ◽  
Author(s):  
Sara Brinch
Keyword(s):  
Data Visualization ◽  
Best Practice ◽  
Visualization Design ◽  
Data Visualizations

‘Beautiful’ is an adjective often used in descriptions of well-designed data visualizations. How the concept is used, however, reveals that it is applied to characterize a variety of qualities. Going beyond mere descriptions, the use of the concept also lays bare a certain ambivalence among scholars and practitioners towards how beauty matters, and which means it serves in data visualization. Interrogating ‘beautiful’ as a characterizing word, combined with a study of cases of ‘best practice’ used as examples of beautiful visualizations in various discourses, this chapter presents an analysis of what is regarded as beautiful within the field of data visualization design. This, in turn, can inform the understanding of what beauty means in visualizing data, in the purpose of facilitating the viewer’s comprehension and engagement.

The political significance of data visualization: Four key perspectives

2020 ◽  
Author(s):  
Torgeir Uberg Nærland
Keyword(s):  
Social Theory ◽  
Data Visualization ◽  
Status Quo ◽  
Public Deliberation ◽  
The Political ◽  
Progressive Change ◽  
The Status ◽  
Data Visualizations

Practitioners and scholars alike assume that data visualization can have political significance—as vehicle for progressive change, manipulation, or maintaining the status quo. There are, however, a variety of ways in which we can think of data visualization as politically significant. These perspectives imply differing notions of both ‘politics’ and ‘significance’. Drawing upon political and social theory, this chapter identifies and outlines four key perspectives: data visualization and 1) public deliberation, 2) ideology, 3) citizenship, and 4) as a political-administrative steering tool. The aim of this chapter is thus to provide a framework that helps clarify the various contexts, processes, and capacities through which data visualizations attain political significance.

scholarly journals Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments

2019 ◽  
Vol 116 (6) ◽  
pp. 1857-1864 ◽  
Author(s):  
Katy Börner ◽  
Andreas Bueckle ◽  
Michael Ginda
Keyword(s):  
Data Visualization ◽  
Procedural Knowledge ◽  
Information Age ◽  
Current Data ◽  
Mathematical Literacy ◽  
Theoretical Frameworks ◽  
Core Concepts ◽  
Assessment Measures ◽  
Data Visualizations ◽  
Write Text

In the information age, the ability to read and construct data visualizations becomes as important as the ability to read and write text. However, while standard definitions and theoretical frameworks to teach and assess textual, mathematical, and visual literacy exist, current data visualization literacy (DVL) definitions and frameworks are not comprehensive enough to guide the design of DVL teaching and assessment. This paper introduces a data visualization literacy framework (DVL-FW) that was specifically developed to define, teach, and assess DVL. The holistic DVL-FW promotes both the reading and construction of data visualizations, a pairing analogous to that of both reading and writing in textual literacy and understanding and applying in mathematical literacy. Specifically, the DVL-FW defines a hierarchical typology of core concepts and details the process steps that are required to extract insights from data. Advancing the state of the art, the DVL-FW interlinks theoretical and procedural knowledge and showcases how both can be combined to design curricula and assessment measures for DVL. Earlier versions of the DVL-FW have been used to teach DVL to more than 8,500 residential and online students, and results from this effort have helped revise and validate the DVL-FW presented here.

scholarly journals Aesthetics and Clarity in Information Visualization: The Designer’s Perspective

Arts ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 72
Author(s):  
Annemarie Quispel ◽  
Alfons Maes ◽  
Joost Schilperoord
Keyword(s):  
Mass Media ◽  
Data Visualization ◽  
Information Visualization ◽  
Information Design ◽  
Future Research ◽  
Good Design ◽  
The Public ◽  
Objective Information ◽  
Data Visualizations ◽  
Important Design

Designers are increasingly involved in creating ‘popular’ data visualizations in mass media. Scientists in the field of information visualization propose collaborations between designers and scientists in popular data visualization. They assume that designers put more emphasis on aesthetics than on clarity in their representation of data, and that they aim to convey subjective, rather than objective, information. We investigated designers’ criteria for good design for a broad audience by interviewing professional designers and by reviewing information design handbooks. Additionally, we investigated what might make a visualization aesthetically pleasing (attractive) in the view of the designers. Results show that, according to the information designers, clarity and aesthetics are the main criteria, with clarity being the most important. They aim to objectively inform the public, rather than conveying personal opinions. Furthermore, although aesthetics is considered important, design literature hardly addresses the characteristics of aesthetics, and designers find it hard to define what makes a visualization attractive. The few statements found point at interesting directions for future research.

scholarly journals Dynamic Data Visualizations to Enhance Insight and Communication Across the Lifecycle of a Scientific Project

2021 ◽  
Author(s):  
Kristina Wiebels ◽  
David Moreau
Keyword(s):  
Data Visualization ◽  
Web Applications ◽  
Scientific Communication ◽  
R Language ◽  
Challenges And Opportunities ◽  
Recent Developments ◽  
Data Points ◽  
Scientific Project ◽  
Active Exploration ◽  
Data Visualizations

In scientific communication, figures are typically rendered as static displays. This often prevents active exploration of the underlying data, for example to gauge the influence of particular data points or of particular analytic choices. Yet modern data visualization tools, from animated plots to interactive notebooks and reactive web applications, allow psychologists to share and present their findings in dynamic and transparent ways. In this tutorial, we present a number of recent developments to build interactivity and animations into scientific communication and publications, using examples and illustrations in the R language. In particular, we discuss when and how to build dynamic figures, with step-by-step reproducible code that can easily be extended to the reader’s own projects. We illustrate how interactivity and animations can facilitate insight and communication across a project lifecycle—from initial exchanges and discussions within a team to peer-review and final publication—and provide a number of recommendations to use dynamic visualizations effectively. We close with a reflection on how the scientific publishing model is currently evolving, and consider the challenges and opportunities this shift might bring along for data visualization.

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

scholarly journals Exploring narrativity in data visualization in journalism

2020 ◽  
Author(s):  
Wibke Weber
Keyword(s):  
Data Visualization ◽  
Corpus Analysis ◽  
Temporal Dimension ◽  
News Stories ◽  
Visual Elements ◽  
Key Terms ◽  
Data Visualizations

Many news stories are based on data visualization, and storytelling with data has become a buzzword in journalism. But what exactly does storytelling with data mean? When does a data visualization tell a story? And what are narrative constituents in data visualization? This chapter first defines the key terms in this context: story, narrative, narrativity, showing and telling. Then, it sheds light on the various forms of narrativity in data visualization and, based on a corpus analysis of 73 data visualizations, describes the basic visual elements that constitute narrativity: the instance of a narrator, sequentiality, temporal dimension, and tellability. The paper concludes that understanding how data are transformed into visual stories is key to understanding how facts are shaped and communicated in society.

scholarly journals What Geoscience Experts And Novices Look At, And What They See, When Viewing Data Visualizations

2016 ◽  
Vol 3 (1) ◽  
pp. 27 ◽  
Author(s):  
Kim A. Kastens ◽  
Thomas F. Shipley ◽  
Alexander P. Boone ◽  
Frances Straccia
Keyword(s):  
High Resolution ◽  
Eye Tracking ◽  
Data Visualization ◽  
Gaze Direction ◽  
Exploration Time ◽  
Wide Range ◽  
Tracking Behavior ◽  
Data Visualizations ◽  
Shaded Relief ◽  
The Continuum

This study examines how geoscience experts and novices make meaning from an iconic type of data visualization: shaded relief images of bathymetry and topography.  Participants examined, described, and interpreted a global image, two high-resolution seafloor images, and 2 high-resolution continental images, while having their gaze direction eye-tracked and their utterances and gestures videoed. In addition, experts were asked about how they would coach an undergraduate intern on how to interpret this data.  Not unexpectedly, all experts were more skillful than any of the novices at describing and explaining what they were seeing.  However, the novices showed a wide range of performance.  Along the continuum from weakest novice to strongest expert, proficiency developed in the following order: making qualitative observations of salient features, making simple interpretations, making quantitative observations.  The eye-tracking analysis examined how the experts and novices invested 20 seconds of unguided exploration, after the image came into view but before the researcher began to ask questions.  On the cartographic elements of the images, experts and novices allocated their exploration time differently:  experts invested proportionately more fixations on the latitude and longitude axes, while students paid more attention to the color bar.  In contrast, within the parts of the image showing the actual geomorphological data, experts and novices on average allocated their attention similarly, attending preferentially to the geologically significant landforms.   Combining their spoken responses with their eye-tracking behavior, we conclude that the experts and novices are looking in the same places but “seeing” different things.

scholarly journals Introduction: “Speaking to the Eyes”—Reassessing the Enlightenment in the Digital Age

2021 ◽  
pp. 1-27
Author(s):  
Ileana Baird
Keyword(s):  
Eighteenth Century ◽  
Public Sphere ◽  
Intellectual History ◽  
Data Visualization ◽  
Digital Humanities ◽  
Political Scientist ◽  
Use Of Data ◽  
The Enlightenment ◽  
New Findings ◽  
Data Visualizations

AbstractThis introduction provides a brief survey of the evolution of data visualization from its eighteenth-century beginnings, when the Scottish engineer and political scientist William Playfair created the first statistical graphs, to its present-day developments and use in period-related digital humanities projects. The author highlights the growing use of data visualization in major institutional projects, provides a literature review of representative works that employ data visualizations as a methodological tool, and highlights the contribution that this collection makes to digital humanities and the Enlightenment studies. Addressing essential period-related themes—from issues of canonicity, intellectual history, and book trade practices to canonical authors and texts, gender roles, and public sphere dynamics—, this collection also makes a broader argument about the necessity of expanding the very notion of “Enlightenment” not only spatially but also conceptually, by revisiting its tenets in light of new data. When translating the new findings afforded by the digital in suggestive visualizations, we can unveil unforeseen patterns, trends, connections, or networks of influence that could potentially revise existing master narratives about the period and the ideological structures at the core of the Enlightenment.

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