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FLOWVIZMENU, P-SPLOM AND AD-LAYOUT: HYBRID VISUALIZATIONS FOR NETWORK EXPLORATION
CONNECTEDCHARTS: A HYBRID VISUALIZATION BY EXPLICIT LINKING Christophe Viau1, Michael J. McGuffin1,
Abstract
Multidimensional multivariate data can be visualized using many different well-known charts, such as bar charts, stacked bar charts, grouped bar charts, scatterplots, or pivot tables, or also using more advanced high-dimensional techniques such as scatterplot matrices or parallel coordinate plots. These many techniques have different advantages, and users may wish to use several charts or data graphics to understand a dataset from different perspectives. We present ConnectedCharts, a technique for displaying relationships between multiple charts. ConnectedCharts allow for hybrid combinations of bar charts, scatterplots, and parallel coordinates, with curves drawn to show the conceptual links between charts. The charts can be thought of as coordinated views, where linking is achieved not only through interactive brushing, but also with explicitly drawn curves that connect corresponding data tuples or axes. We present a formal description of a design space of many simple charts, and also identify different kinds of connections that can be displayed between related charts. Our prototype implementation demonstrates how the connections between multiple charts can make relationships clearer and can serve to document the history of a user’s analytical process, leading to potential applications in visual analytics and dashboard design.
Introduction
The use of multiple views onto the same data, or related data, is a common approach in visualization. Users are given multiple perspectives of their data, allowing them to benefit from the advantages of each view, and to compare across views. To help the user understand the relationships between views, some form of linking is required. Two common approaches are to use color or drawing line segments or curves to link corresponding data elements. However, if there are many data elements and all links are displayed at once, we either quickly run out of distinguishable colors, or suffer from line clutter. For this reason, linking is often only displayed in response to mouse motion: hovering over an element causes the links between that element and related ones to be displayed (either in the form of a color highlight or line or curve segments). This approach is elegant and can be applied to even large data sets, but it constrains the user to exploring relationships one element at a time, requiring the user to interact with the data, possibly for an extended period of time, to extract useful visual feedback. We propose a new technique, called ConnectedCharts (Figures 3.1, 3.2), situated in the middle ground between the extremes of linking all elements, and only linking the element under the mouse cursor. In our work, curves are drawn between charts (data graphics), showing the correspondence between data elements (tuples) when possible, or otherwise between axes. Occlusion is avoided within each chart by “anchoring” the curves to the edges or axes of the chart. Also, rather than drawing all possible links, only those that have been created by the user (in the process of creating the charts) are displayed. We have applied our technique to visualizing multidimensional multivariate data, such as data from a relational database table. Some very flexible visualizations of such data already exist, for example, FLINA (Claessen and van Wijk, 2011), which supports combinations of scatterplots and parallel coordinate plots, and Polaris (Stolte et al., 2002) / Tableau (Mackinlay et al., 2007), which can display multiple charts of the same type within a tabular grid. ConnectedCharts supports scatterplots and PCPs, but unlike FLINA, it also supports bar charts and other 2D charts, and also allows data tuples to be aggregated differently in each chart. Compared to Polaris / Tableau, ConnectedCharts offers more flexibility in that different kinds of charts can be instantiated at once, and can be positioned freely within a 2D space instead of being limited to a grid.
A user may use ConnectedCharts to explore a data set, creating new charts to answer new questions, with connections displayed to the previous charts. In such a scenario, the connections may serve to record and retrace the history of a user’s analytical steps. A set of charts and their connections may also be designed and presented to an end-user, for use as a dashboard, in which case the connections elucidate the relationships between the views of the data, even when related charts are not side-by-side. Although ConnectedCharts can result in many line segments or curves being displayed (Figure 3.2), the same is also true of parallel coordinate plots, where the line segments or curves serve to depict distributions, relationships, correlations, clusters, and inverse correlations. ConnectedCharts can be thought of as a generalization of parallel coordinate plots, that allow the advantages of these connections to be leveraged not just for 1-dimensional axes, but for many
ConnectedCharts allow charts to be intermixed with parallel coordinate axes.
Our contributions include (1) a formal description of a design space of charts based on plots of 2D rectangles, general enough to subsume scatterplots, bar charts, Gantt charts, and variants; (2) an analysis of the types of connections that can be displayed between charts; (3) a demonstration of how ConnectedCharts can be used to produce SPLOMs, PCPs, and hybrid combinations of these and other charts; (4) a generalization of the Attribute Relation Graph of Interest (ARGOI) presented in (Claessen and van Wijk, 2011).
Background
Linking and coordination across views
Many visualizations involve multiple views of data that are somehow linked to convey a relationship between the views. Buja et al. (Buja et al., 1991) give several techniques for this: linking with color (e.g., drawing corresponding elements with the same color), linking “by drawing lines connecting [corresponding] points”, and linking “over time” with a “smooth animation” from one view to another. Wong and Bergeron (Wong and Bergeron, 1997) point out that linking can also be done by aligning axes in different views, as is done with the scatterplots in a scatterplot matrix (SPLOM) (Hartigan, 1975). At least some of these linking techniques can be performed interactively. For example, it may not be feasible to draw all data elements in colors that distinguish the corresponding subsets of points from each other, nor may it be useful to draw all line segments between corresponding points. However, if the mouse cursor hovers over an element, the corresponding elements could then be indicated with a highlight color or line segments. An example of such interactive linking is “brushing and linking” (Becker and Cleveland, 1987). Interactive linking of views is also called coordination (Wang Baldonado et al., 2000; Roberts, 2007), which is also very common in visualizations. North (North, 2000) presents a software framework for this, and distinguishes 3 types of coordination: selection ↔ selection (i.e., selection in one view causes a selection in another), selection ↔ navigation, and navigation ↔ navigation.
Of particular relevance to the current work is linking done using line segments or curves. Interactive linking across views with line segments dates back at least to work by Ted Nelson in the early 1970s (Nelson, 1999). Line segments between views have been used for metavisualization of the coordination of views (Weaver, 2005; Tobiasz et al., 2009). There are also several examples of line segments and curves drawn between corresponding data elements in different views. “M and N” plots (Diaconis and Friedman, 1980) are an early example: in a “2 and 2” plot, points in corresponding 2D scatterplots are connected to convey 4-dimensional tuples. Parallel coordinates (Inselberg, 1985; Wegman, 1990) use line segments to connect tuples across multiple axes, and each axis can be thought of as a 1D “view” of the data. VisLink (Collins and Carpendale, 2007) is a general framework for connecting data elements across views. More recent examples of connecting elements across views include (Aris and Shneiderman, 2007; Viau et al., 2010). There is also evidence that connecting elements allows a user to find elements faster than with simple highlighting (Steinberger et al., 2011). With ConnectedCharts, we can make use of interactive color highlighting, but also display static curves to link together charts. These curves are distinct from previous work in a few ways: first, rather than statically display all possible linking curves between corresponding elements, we only display those that the user has established through their interactions with the charts, avoiding excessive clutter; second, the connecting curves between charts are “anchored” to the axes or edges of charts, avoiding clutter or occlusion within each chart; third, we allow for several kinds of charts to be connected, and identify several kinds of connections that can be shown (see section 3.6). Note that we do not propose ConnectedCharts as a replacement to brushing and linking. Instead, we see these two approaches as having complementary advantages (Figure 3.4): brushing and linking is flexible and scales well to large data sets, but requires the user to invest time moving a pointing device over the data. ConnectedCharts, however, can reveal relationships at a glance prior to any interaction, and even without reading axis labels, just like how the connective lines in parallel coordinates reveal relationships between their axes.
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Table des matières
INTRODUCTION
Infovis
Scope of this research
Thesis organization
CHAPTER 1 LITERATURE SURVEY
1.1 Important models in Infovis
1.2 Graphical encoding
1.3 From data to graphics
1.4 Hybrid visualizations
1.5 Description of existing hybrid visualizations
CHAPTER 2 CHARACTERIZING HYBRID VISUALIZATIONS
2.1 Abstract
2.2 Introduction
2.3 Background
2.4 Visualization Pipeline
2.5 Methods for Combining Visualizations
2.5.1 Geometric Assembly
2.5.1.1 Side-by-Side Assembly
2.5.1.2 Overlaying Assembly
2.5.2 Heterogeneous Combinations of Glyphs
2.5.3 Nesting Visualizations
2.5.4 Hybrid Layout Operators
2.5.5 Hybrid Glyph Generation Operators
2.6 Example Design Studies
2.6.1 Example 1: Combining Scatterplots
2.6.2 Example 2: Scatterplots + Parallel Coordinates
2.7 Conclusion
2.8 Future Directions
CHAPTER 3 CONNECTEDCHARTS: A HYBRID VISUALIZATION BY EXPLICIT LINKING
3.1 Abstract
3.2 Introduction
3.3 Backgroun
3.3.1 Linking and coordination across views
3.3.2 Support for history in visualization
3.3.3 Multiple views of multidimensional multivariate data
3.4 Data model
3.5 A design space of charts
3.6 Types of connections
3.7 ConnectedCharts prototype
3.7.1 Data
3.7.2 Examples
3.8 Generalizing ARGOIs
3.9 Conclusions and future directions
CHAPTER 4 FLOWVIZMENU, P-SPLOM AND AD-LAYOUT: HYBRID VISUALIZATIONS FOR NETWORK EXPLORATION
4.1 Abstract
4.2 Introduction
4.3 Related Work
4.4 Metrics used
4.5 The FlowVizMenu
4.6 Attribute-Driven Layout
4.7 Scatterplot matrices (SPLOMs)
4.7.1 Ranked scatterplot matrix
4.7.2 Scatterplot staircase (SPLOS)
4.7.3 Parallel scatterplot matrix (P-SPLOM)
4.8 Initial user feedback
4.9 Example of use with real-world data
4.10 Conclusions and Future Directions
CONCLUSION
Summary and discussion
Hybrid visualizations
ConnectedCharts
FlowVizMenu, P-SPLOM and A-D layout
Contributions an evaluation
Future work
BIBLIOGRAPHY
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