Data-Driven Guides: Supporting Expressive Design

Data-Driven Guides: Supporting Expressive Design for Information
Nam Wook Kim, Eston Schweickart, Zhicheng Liu, Mira Dontcheva, Wilmot Li, Jovan Popovic, and Hanspeter Pfister
Fig. 1: Nigel Holmes’ Monstrous Costs chart, recreated by importing a monster graphic (left) and retargeting the teeth of the
monster with DDG (middle). Taking advantage of the data-binding capability of DDG, small multiples are easily created by
copying the chart and changing the data for each cloned chart (right).
Abstract—In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing
visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand,
freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper,
we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment.
Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw,
place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length,
area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data.
When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of
the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven
Index Terms—Information graphics, visualization, design tools, 2D graphics.
With the increased quantity and improved accessibility of data, people from a variety of backgrounds, including journalist, bloggers, and
designers, seek to effectively communicate messages found from complex data in an accessible graphical form. Unlike traditional visualizations (e.g. bar charts or scatterplots) that focus on data exploration and
analysis, communicative visualizations put more emphasis on presentation [31]. Commonly referred to as infographics, these visualizations are often embellished with unique representations to convey a
story or specific message. When creating such custom information
graphics, designers must consider various factors including not only
perceptual effectiveness, but also aesthetics, memorability, and engagement [37, 7, 17]. While embellishments in visualization design
have traditionally been considered harmful, thoughtfully crafted cus-
• Nam Wook Kim and Hanspeter Pfister are with the John A. Paulson School
of Engineering and Applied Sciences, Harvard University,
• Eston Schweickart is with the Computer Science department at Cornell
University, Email:
• Zhicheng Liu, Mira Dontcheva, Wilmot Li, and Jovan Popovic are with
Adobe Research,,,,
Manuscript received xx xxx. 201x; accepted xx xxx. 201x. Date of
Publication xx xxx. 201x; date of current version xx xxx. 201x.
For information on obtaining reprints of this article, please send
e-mail to:
Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx/
tom visualizations can be highly engaging and get the messages across
more effectively.
In recent years, many visualization creation tools have been developed to meet the growing demand for visually communicating
data [14]. To make visualization construction easier, most existing
tools automate the visual encoding process. For instance, chart templates ease the burden of manually encoding data by providing predefined palettes of chart types. However, they do not allow users to
create novel custom charts except changing a small number of style
parameters such as colors or fonts. More sophisticated tools improve
upon the template-based approach by enabling a wide range of specifications for data graphics including marks, scales, and layouts. While
these tools make complex visual encoding easy, they tend to limit the
design space or enforce a rigid order of operations in order to achieve
desired effects. As noted in the comprehensive user study by Bigelow
et al. [3], the lack of flexibility in existing visualization creation tools
reduces their applicability to designers. For this reason, designers still
rely on freeform illustration tools such as Adobe Illustrator1 to create
custom visualizations, which currently do not provide visualizationspecific abstractions. This results in time-consuming and error-prone
manual visual encoding that prevents designers from exploring diverse
design variations.
In this research, we addresses the question of how to reduce the gap
between easy-to-use visualization creation and flexible graphic design
tools. Informed by Bigelow et al. [3] who studied how designers work
with data, we focus on the less explored area of how designers manually encode data into custom graphics in a graphic design environ1
ment. To identify the challenges designers face in creating custom
visualizations of data, we conducted semi-structured interviews with
infographic designers. Findings reveal that tool flexibility is important
in infographic design for various reasons including designing custom
marks and adding annotations. Designers employ various tricks and
hacks to work around the lack of data-driven abstractions in graphic
design tools. More specifically, two common issues that emerged from
the manual encoding practice are 1) the laborious task of placing and
measuring graphics based on data using guides such as rulers or grids,
and 2) the absence of data binding in hand-crafted design or externally created charts. We conclude that there are unique opportunities
to improve current graphic design tools by providing support for datadriven design, instead of trying to make current visualization creation
tools more flexible by adding more parameters.
To alleviate the issues in the existing infographic practice, we contribute Data-Driven Guides(DDG), a technique for designing expressive data-driven graphics. Instead of being confined by predefined
templates or marks, designers can generate guides from data and use
the guides to accurately place and measure custom shapes. We provide
guides to encode three main visual channels: length, area, and position
following the principles of information encoding [2, 36]. Users can
combine more than one guide to construct a variety of visual structures that represent data. When the underlying data is changed, we
use a 2D deformation technique to transform the user-defined shapes
based on the guides.
To demonstrate how DDG can be integrated into a designer’s flexible workflow, we implement DDG in the context of a web-based vector
drawing tool to support infographic design. To evaluate DDG’s ability
to support the flexible, expressive, and accurate design of custom data
graphics, we demonstrate its use to create diverse example graphics
that are difficult to manually construct or that were inaccurately created with existing tools. We also conducted a user study to evaluate
the usability of DDG. Participants describe the interaction model for
DDG as intuitive and straightforward, suggesting DDG was more useful for data-driven drawing compared to traditional guides including
rulers and grids. Their feedback confirms that DDG would improve
their design practice of creating custom information graphics.
2.1 Visualization Design Tools & Environments
For the last few decades, there has been considerable effort to create easy-to-use interfaces for data visualization. Grammel et al. provide a survey on various types of visualization construction tools [14].
Among them, chart templates are most widely used in many applications including spreadsheets, presentation software, graphic design
tools, and online services (e.g., Many Eyes [49], RAW 2 , Plotly 3 ).
They facilitate the quick and easy construction of charts, though users
are limited to predefined chart types and are only allowed to change a
small number of configuration parameters.
More advanced tools enable more expressive design of data graphics by exposing low-level specification parameters such as scales and
marks. Some of these tools [45, 41] are based on formal graphical
specifications such as the grammar of graphics [54] or a declarative
model [19, 10]. Tableau 4 follows a similar formulation, and has been
designed to support rapid exploratory data analysis rather than custom
visualization design. On the other hand, Lyra [41], built on the Vega
grammar 5 , provides a more accessible interface to customize visual
encodings. Spritzer et al. [44] use CSS-like stylesheets to touch up the
look of nodes, edges, and presentation aids in node-link diagrams to
enhance their communicative power.
Some tools attempt to reduce the gulf of execution by appropriating
direct manipulation techniques [26, 40, 33] or demonstrational interfaces [38]. Other tools allow users to interactively construct multiple, coordinated visualizations [39, 52]. Although existing tools have
enabled the design of highly customized visualizations without programming, they are still confined to preset scales, layouts, and marks.
The ability for designers to directly manipulate visual structures on the
canvas is very limited compared to freeform drawing tools.
On the other hand, there are a variety of programming toolkits that
enable a high degree of control. They provide flexibility in creating custom representations with the help of the expressive power of
the underlying programming languages. Some are based on a formal
specification approach [8, 53], and others provide composable operators for different visual encodings [20] or extensible visualization widgets [12]. However, the flexibility of programming languages comes
with a steep learning curve for people who do not have programming
expertise, including many designers.
Other generic tools can also be used to create custom visualizations.
Visual programming approach allows users to construct the underlying
data flow and visual structure of a visualization using node-link style
interfaces [13, 30]. However, they separate the construction interface
from the canvas area (i.e., poor ‘closeness of mapping’ between the
problem domain and the tool [15]). Constraint-based drawing tools
enable users to create a visualization purely based on geometric constraints on the properties of graphical primitives including position,
rotation, and scaling. They are not optimized for visualization construction (e.g., repetition of a mark)6 or require procedural thinking to
define loops to generate visual marks parametrically 7 .
Despite the wealth of tools available for creating visualizations,
Bigelow et al. [3] found in their study that designers, who are primary producers of many popular visualizations in recent years, do not
use most, if any, of those tools. They found that manual visual encoding using freeform illustration tools is tolerated in order to maintain
flexibility and richness in the design process. Similarly, previous studies investigated potential benefits (e.g., familiarity and expressivity) of
manual visual mapping in physical design environments where a visualization is constructed through hand-drawn sketching [51, 50] and
manipulation of tangible materials [25, 29, 46, 24, 55].
Inspired by these works, we attempt to take advantage of the benefits of manual encoding to support expressive design of infographics.
Instead of developing another visualization creation tool that enforces
a rigid order of operations and prevents users from engaging in manual
encoding, we provide guides generated from data that help designers
draw their own visual marks and layouts in a flexible graphic design
Benefits of Visual Embellishments
A common belief in the visualization community with regards to visualization design is that visual representations should maximize the
data-ink ratio and avoid unnecessary decoration as much as possible [48]. Most visualization systems today are based on these principles that inform perceptually effective visual encodings of data [11,
18, 47]. It is only recently that researchers have started exploring other
aspects of visualization design such as memorability [7, 6], aesthetics [37], and engagement [17, 9]. These metrics focus on communication and presentation rather than data exploration and analysis. Recent
studies looked at the benefits of embellishments on comprehension
and recall [1, 16, 23, 5]. Although embellishments can have negative impacts on visual search time [5] or certain analytic tasks [43],
it is now generally understood that embellishment is not equivalent
to chart junk. Judiciously embellished visual representations can help
communicate the context of data that makes it easier to remember and
recall. As a result, there has been active development of presentationoriented visualization techniques [31, 44] that are beginning to find
applications in visual storytelling [33, 32].
As noted by Bigelow et al. [3], designers are likely to continue to
use freeform graphic design tools for the sake of flexibility, but these
tools do not currently provide well-defined data-driven abstractions.
Our goal in this research is to provide appropriate tools to alleviate
error-prone manual operations required in the design of engaging and
memorable custom infographics.
Deformation for Vector Graphics
Deformation is a well-studied topic in the area of computer graphics
and has found many applications in various fields including computeraided design, fabrication, and computer animation. It can be used
to transform raster images, geometric models, and vector graphics
in a more flexible way compared to object-level affine transformations. The most common technique used for shape deformation is
linear blend skinning [28]. It applies a weighted sum of affine transformations to each point on the object, where weights are often chosen
manually by users. Recently, Jacobson et al. [27] developed bounded
biharmonic weights that can reduce tedious manual weight painting
and allow interactive deformation through convenient handles such as
points, bones, and cages. Liu et al. [34] extended this work to allow
for deforming vector graphics (e.g., Bézier splines).
In this work, we augment the deformation techniques listed above
to transform custom shapes based on data using guides as the backbone of the shapes. This approach is similar in intent (though not in
implementation) to the skeletal strokes by Hsu et al. [22]. While creating a data visualization usually involves simple linear transformations
applied to conventional marks such as rectangles and circles, flexible
shape deformation allows for more expressive design.
In order to understand the design practice employed by infographic
designers, we conducted semi-structured interviews with six design
professionals; we also analyzed examples of infographics from online
tutorials, books, and videos on hand-crafting infographics. The interviewees include two professional designers (P1, P2) hired through
UpWork 8 , three student designers (P3, P4, P5) enrolled in a master’s
program in information design and visualization, and a visualization
researcher (P6) who has experience in infographic design. Through
the interviews and analysis of existing design practices we hoped to
understand the infographic design process, why designers still rely on
graphic design tools, and what difficulties they face in creating infographics. Each interview session took about 45-60 minutes. The interviewees were asked to walk through their design processes as well
as to elaborate on how they manually encode data into graphics using
their own examples. While our findings in general are in line with the
the work by Bigelow et al. [3] we focused particularly on the challenges in manual visual encoding.
Lack of flexibility or data-encoding support in existing tools.
All the interviewees emphasized the importance of tool flexibility in
order to cope with many intricate factors in infographic design. This
was even true for those who have experience in programming or advanced visualization creation tools. They particularly valued the ability to make design decisions on their own (P2, P4), and disliked the
rigid and automatic design process enforced by visualization creation
tools (e.g., Tableau’s shelf configuration) (P3, P5). They said that these
tools are not necessarily designed with communication in mind, making it difficult to customize graphics through means such as adding
annotations and embellishments or designing new visual marks and
layouts (P1, P3, P6). For example, two of them complained that their
clients requested a specific aspect ratio or corporate theme, which is
impossible to handle using existing visualization creation tools (P1,
P3). These findings conform with the previous research [3].
However, an interviewee (P5) also suggested that graphic design
tools are also limited because of their lack of support for drawing datadriven graphics. In order to work around the limitation, designers often
employ various manual tricks and hacks (e.g., using brushes to create
arc bars 9 ). Interviewees acknowledged that they made mistakes due
to this manual encoding process, often generating inaccurate representations of data (P2, P3, P4) such as placing or scaling a visual mark
a few pixels off from its actual data value. Interestingly, they did not
consider this as an important issue, instead emphasizing the communication of the overall message.
Fig. 2: Excerpt from an online tutorial10 explaining how to measure a
custom visual mark using a hand-crafted scale.
Tedious manual encoding required in creating custom design.
To design visual representations of data, designers employ diverse
design methods. In the case of using a built-in charting tool (e.g.,
the graph tool in Adobe Illustrator) or importing external charts, the
degree to which designers perform customizations is marginal (e.g.,
changing basic style properties such as colors, sizes, or fonts). On
the other hand, when designing unique visual marks or layouts, design
methods are more involved and require laborious manual encoding.
The purpose of such methods all comes down to how to place and
measure custom shapes based on data. We found that designers typically rely on traditional guides such as rulers or grids, or even create
their own scales (Fig. 2). For example, an interviewee (P5) explained
the use of a bar chart as a measurement tool by juxtaposing the bar
chart with custom shapes. Other interviewees also showed other cases
such as measuring areas using the AutoCad software (P1) and finding the locations of visual marks on a map using a GIS software (P2).
Interviewees, particularly those who have experience in programming
(P3, P6), also said that it would be best to use external tools and environments for handling large data or more complex charts such as
graphs or networks.
Absence of data binding in custom or imported charts. When
designers use unconventional methods to craft custom visual encodings, data binding is usually not supported. Even though the final
design outcomes are static printed media, the lack of data binding interferes with the overall design process. For example, as noted in [3],
a complete redesign is often caused by the designers’ wrong assumptions about the data behavior and because they start sketching without
using actual data. Our interviewees also mentioned that they occasionally use wrong data or need to update existing data sets with new ones,
showing a potential benefit of data binding (P1, P5, P6). In a related
example, designers use the graph tool in Adobe Illustrator to create a
chart. To be able to freely manipulate the geometric primitives, they
ungroup the chart elements. However, the ungrouping operation results in a loss of data binding. An interviewee said that, for this reason, she usually adheres to the given chart even if she wants to further
customize its design such as deleting its axis lines (P5); we later found
that this was the case for many designers 11 . Another interviewee also
expressed frustration about the absence of data binding and the need
for externally created and imported charts (P6).
Our findings from the interviews imply that graphic design tools are
flexible but currently lack appropriate support for data-driven design.
To address this challenge, we decide to augment the current design
experience already common to designers, rather than develop a completely new visualization design tool. Based on our interviews and
analysis of how infographics are usually created we have identified
the following design goals for an infographic design tool. These design goals provide concrete guidelines to improve the process of constructing custom data graphics within the context of designers’ exist10
Fig. 3: Overview of the vector drawing tool in which DDG is implemented: (a) top menu bar providing conventional features such as undo,
redo, and SVG import & export operations. The menu item for DDG is the same as the context menu. (b) left toolbar providing the DDG tool
as well as other selection and drawing tools. (c) DDG tool panel for specifying a dataset and options to create data guides. It is also used for
updating the guides. (d) context menu for executing DDG related commands such as linking objects, repeating shapes, and generating labels. (e)
side panel for adjusting the various properties of selected objects such as the behavior or direction of the links as well as object styles including
color or opacity. The side panel currently shows link configurations for three selected data guides.
ing design practice.
1. Maintain flexibility in the design process. Our interviewees
listed the lack of freedom as one of the major factors that discourages
them from using existing visualization creation tools. Augmenting
current graphic design tools would be beneficial, since designers are
already familiar with these tools and their flexibility in dealing with
intricate design considerations. Instead of enforcing a rigid order of
operations to create visualizations, the tool should relax constraints on
the sequence of infographic construction enabling diverse workflows.
Designers often create infographics through a top-down graphical process in which they design the overall appearance before plotting the
real data [3]. The tool should be flexible enough to allow for the custom design of marks and layouts, satisfying the designers’ need to express their creativity and to design novel infographics from the ground
2. Provide methods for accurate data-driven drawing. The designers in our interviews raised concerns around the tedious and errorprone process of manually encoding data into custom graphics. Their
existing design practice demonstrated that the current support from
graphic design tools such as rulers or grids is not sufficient. Providing
advanced guides can be a potential solution as interviewees reported
that they relied on custom scales or traditional charts as data guides.
The advanced guides can be driven by data and designed to place and
measure custom shapes along any dimension in contrast to existing orthogonal axes, which work best with conventional marks such as rectangles, lines, or circles. Embedding data-driven drawing capabilities
into graphic design tools can significantly reduce the need for manual
and error-prone data encodings.
3. Support persistent data binding for freeform graphics. A
common challenge designers face is the absence of data binding support in custom charts and imported charts. Therefore, it would make
sense that graphic design tools support persistent data bindings for imported charts [4] or provide ways to bind data to freeform graphics. In
order to support the top-down design process, the ability to place data
on existing graphics can be a possible solution instead of generating
visualizations directly from data in a non-visual, symbolic manner via
a GUI [3]. The flexible data binding support will increase the reusability of custom charts and also help designers avoid tedious rework when
data is changed, allowing them to explore different design variations
more efficiently.
We now introduce DDG, a technique for designing expressive custom
infographics based on data. We draw inspiration from existing design
practices in areas such as architectural or user interface design, where
a guide is used as a reference (e.g., a ruler and grid) for precise drawing
or alignment. We explain DDG in the context of a vector drawing tool
we built to provide a flexible design environment. The technique can
be implemented in other graphic design tools such as Adobe Illustrator. The term DDG can refer to both the technique we are introducing
and the actual guides. To disambiguate, we use “DDG” to refer to the
technique and “data guides” when we talk about the actual guides in
the rest of this paper.
Fig. 4: Length and area guides that can also be used as a position
We follow the theoretical frameworks of visual encodings [2, 36]
that describe the most effective channels to encode information. We
use data guides to size the primary visual variables of length and area,
which in turn are represented as line- and circle-shaped guides, respectively (Fig. 4). The resulting guides can be used to encode positions as
well (See balloons in Fig. 12f). In addition, more than one guide can
be combined to create more expressive visual structures (Fig. 12f-i).
The visual variables—length, position, and area—are popularly used
in infographic design [7], though area encodings are frequently misused by designers using inaccurate scales (e.g., using the diameter of
circles to match data instead of the area 12 ). Other visual variables
such as color or angle are not as effective as length, area, and position
for encoding quantitative values, and are left for future work.
5.1 Providing Flexible and Familiar Interactions
DDG is designed to be fluidly integrated in a flexible graphic design
environment, favored by infographic designers. To this end, the visual appearance and interaction model of DDG follow those of regular
guides available in existing graphic design tools. The data guides always appear on top of other objects, have particular fill and stroke
styles (e.g., no fill color or stroke width), and are not printed in the
final design. Similar to regular guides, data guides do not impose a
specific design workflow, meaning that they can be used at any stage
of the design process (e.g., both top-down and bottom-design workflows).
A main difference from regular guides is that data guides are driven
by data. A group of data guides can be created from a tabular dataset
consisting of a series of numerical values and their category names;
depending on the encoding type, the length (line) or area (circle) of a
data guide represents a data value in the dataset. The relative sizes of
data guides within the parent group are preserved in order to be in sync
with the underlying dataset. For example, if a user scales a data guide,
the other guides in the same group (i.e., its siblings) are proportionally
scaled (Fig. 5).
Fig. 5: Changing a data guide will affect its siblings in order to preserve the relative size differences within the same group (blue colors
represent new guide states and red colors represent the direction of
user manipulation, e.g., grabbing and moving an anchor point.).
To provide familiar interactions used in graphic design tools, DDG
supports free manipulation (i.e.,move, rotate, scale etc) to create a custom layout. Users can also change the underlying dataset by manipulating data guides directly on the canvas. For instance, when a data
guide is copied and pasted, a new data guide is created within the same
group, which also adds a new data value to the dataset; likewise, if the
whole group of guides is copied and pasted, a whole new dataset is
created (Fig. 1 right). Also, combining different groups of guides has
the effect of combining different datasets graphically.
Fig. 6: Users can specify the direction in which a length guide change
its length: either endpoint (left) or both endpoints (right) of the line
Fig. 7: Drawing a shape directly on top of data guides will link the
shape to the guide (left). Otherwise, users can explicitly link the shape
with the guides (right).
5.2 Data-Driven Drawing with DDG
A data guide serves as a ruler backed up by data to minimize designer’s
effort to manually place and measure graphics; its size and shape indicate where a data value lies on the canvas. Users can draw custom shapes from scratch directly on top of data guides (Fig. 7 left).
The overall drawing experience is closer to drawing with a pen and
ruler (i.e., bottom-up design process). Alternatively users can use data
guides to repurpose existing artworks by matching the artworks to the
size of the guides (Fig. 1); this workflow is the top-down, graphical
process of placing data on existing graphics. A data guide supports
snapping to its anchor points and path segments for precision.
To keep track of the correspondence between guides and shapes,
DDG introduces the notion of linking. To reduce the effort of explicitly creating links, we automatically create the links in certain cases.
For example, when a shape’s anchor point is placed directly on a guide
(e.g., drawing a shape on top of DDG), or if a guide is adjusted so that
its own anchor point is placed on a shape (e.g., retargeting existing
shape), then the shape is automatically linked to the guide. Data labels
are generated only if requested (Fig. 10), and automatically linked to
related guides on creation.
To ensure flexibility in measuring custom shapes, a length guide
can be a curved line, if necessary, by adding additional anchor points
along its path and adjusting the handles of the anchor points (Fig. 12e).
The length guide has one additional option indicating the direction in
which it increases or decreases when its data value is changed (Length
change direction option in Fig. 3c); i.e., both endpoints or either endpoint of the line segment (Fig. 6). An area guide always remains a
circle shape for accurate perception of the area; a squared rectangle
would provide a comparable area perception, although the perception
judgement may be impaired by varying orientations [11].
Users can combine multiple data guides in order to construct more
expressive structures (Fig. 12c, f-i). This has the same effect as combining different visual variables, in our case length, area and position.
To help constructing a visual structure, we provide two simple layout
functions including linear and radial layouts (
) in addition to
conventional alignment functions (e.g., align to left, distribute vertically etc).
Fig. 8: Selecting and repeating a shape will duplicate the shape over
the sibling guides of its linked guides.
Fig. 9: Selecting and repeating a guide (area) will reposition its sibling
guides based on its position relative to the linked guide (length).
To further assist in drawing a data-driven graphic, we provide a
number of visualization-specific features. The repeat feature in DDG
allows an associated shape to be repeated over its sibling guides in
the same group (Fig. 8, 9). If a pair of guides from two groups is
supposed to encode a single shape, we only repeat the shape once (e.g.,
a pregnant women figure in which her belly and height is associated
with area and length guides respectively, in 12i). Our repeat command
is optional and unobtrusive, meaning users can use a different mark
for each guide. They can also customize each shape after executing
the repeat command.
In addition, creating a visualization often involves the generation of
many repeated visual elements in a small area on the canvas. In order
to assist in making edits to the visualization, we provide a number of
DDG currently works in limited cases (e.g., a single group with linear
and radial layouts), and needs to be improved in the future.
Fig. 10: Labels are generated only when requested and are automatically linked to data guides when created.
selection methods. 1) selecting objects associated with data guides , 2)
selecting sibling guides within the same group, and 3) selecting intersecting objects. These selection methods operate based on the current
selection and can be progressively applied to expand the current selection. This selection mechanism resembles D3 [8]’s selectors or the
magic wand tools in graphic design applications that select objects of
the same color, stroke weight, or opacity.
5.3 Supporting Data Binding with Custom Graphics
DDG is basically an intermediate layer for associating data with any
objects including shapes, texts, or guides. When data is changed, related guides will consequently change their forms, which in turn transforms the objects that are linked to the guides (Fig. 11).
To address the challenge of binding data to freeform shapes, we
employ a deformation technique for vector graphics [27, 34] as it
enables a shape to change its form along any dimension (i.e., using the
data guide as the backbone of the shape). The deformation technique
applies a linear combination of affine transformations to each anchor
point on the shape in order to adapt to the new state of the associated
guide. Occasionally, we would find that the change of a backbone
guide would cause associated shapes to extend beyond the end of the
backbone, or not extend far enough. This is a particularly important
scenario to avoid in DDG, as it can lead to misleading data graphics.
If we identify points on a shape that should exactly follow the end of
its backbone (i.e., the end-points of a length guide), we set a stronger
weight for a transformation handle at the end of the backbone.
We also provide a translation-only behavior in case the user wants
to use a guide to position custom graphics. In this case, the shape is
not deformed but translated along the direction in which the associated
guide is changed (e.g., along the path of a length guide or the direction
from the center to the perimeter of an area guide). The translation
behavior is the only option for the links to data guides and text labels
as they are not deformable.
Figure 3 shows our vector drawing tool that combines all the features
of DDG in a unified application. The overall interface is similar to
other graphic design tools. In the left toolbar, basic drawing and selection tools are supported along with the DataGuide tool with which
users can create and update data guides (Figure 3c). Using the Select
tool, users can manipulate objects including guides (i.e., moving, rotating, and scaling). Initially, guides generated from the same dataset
belong to a group. To select an individual data guide, the user must
double-click the group first. Using the Control tool, users can select
anchor points, handles, as well as individual paths. They can use the
Pen tool to draw custom shapes, made of spline curves, with the help
of the data guides. Users can link the shapes with the data guides
through the context menu or top toolbar, in which they can also enable
other features such as repeat, label generation, and selection (Figure
3a, d).
In the right panel (Figure 3e), users can change various properties
of selected objects such as fill color, stroke width, or text size through
Style and Text tabs. When a data guide is selected, a linear or radial
layout configuration option is activated on the Style tab. Users can
also inspect the underlying scene graph through the Layer tab. On the
Link tab, users can inspect all the links associated with selected objects
(Figure 3e). They can also change the desired behavior of the link (i.e.,
deformation or translation).
In the menu bar, we provide common drawing tool features such as
aligning, arranging, and grouping objects, undo/redo operations, and
other features designed for data guides. Guide-specific features are
also available through the context menu. The tool has the ability to
import SVGs and export the canvas area, which is useful for repurposing existing artworks using data guides as well as leveraging other
full-fledged design tools to draw more complex shapes. When the canvas area is exported, our tool-specific properties such as links between
guides and objects are preserved in the exported SVG file.
The tool is web-based and heavily depends on Paper.js 13 , a vector graphics framework that runs on top of the HTML5 Canvas. The
tool runs on a Python web server that interfaces with our deformation
framework written in C++. The deformation framework accepts three
inputs that include a list of shapes to be deformed, a list of guides before a deformation, and a list of guides after deformation. We plan to
make the tool and all our code open-source available upon publication
of this paper.
Fig. 11: Updating data will transform shapes using the guides as the
backbones of the shapes.
To enable more expressive design, we attempt to relax the constraints on how links are constructed. More than one shape can be
linked to a data guide (e.g., a composite balloon shape is associated
with an area guide in Fig. 12g), and likewise multiple guides can
be associated with a single shape (e.g., a cloud has four area guides
attached in Fig. 12h). In the latter case, all the guides are used to
transform the shape. In addition, data guides can be linked to themselves, allowing more complex structures (Fig. 12c, f-i). For example,
a length guide can be used to position an area guide (in Fig. 12g, i) or
a group of guides (in Fig. 12f, h).
When a new guide is added or an existing guide is removed (i.e.,
data cardinality change), we need to rearrange data guides accordingly.
To achieve this, we heuristically estimate the categorical scale and orientation of the guides. This problem is difficult because the layout of
the guides is not fixed, and it becomes more complicated when the visual structure of an infographic combines multiple groups of guides.
Example Infographics
To evaluate the expressivity of DDG, we created a diverse set of example data-driven infographics. The examples include simple basic
charts as well as more expressive data graphics that are difficult to create using existing tools or with programming; they are only a subset
of infographics that users can create with DDG. We also found that
some existing infographics that were not created with DDG contained
inaccurate data mapping when compared to DDG generated from the
same datasets.
As DDG does not enforce predefined palettes, users have the freedom to create custom designs. They can freely manipulate the guides
and design their own visual marks to create different styles of charts
as shown in Fig. 12. For example, they can create visual marks from
a single stroke (Fig. 12b), using the repeat command (Fig. 12d), or
by drawing different marks for individual guides (Fig. 12a). They
also can create a layout using simple alignment and distribution features (e.g., aligned to bottom in 12c) that are typically available in any
graphic design tool or using the layout functions (e.g., radial layout in
Fig. 12d).
In addition to changing the basic styles of charts, users can create
further embellished data graphics. We recreated two Nigel Holmes’
829.8 m
Crowdfunded Projects on Kickstarter in 2012
Tallest Buildings in the World.
632 m
Source: Company Reports, Economist.
Data Source: Wikipedia.
American’s Uninsured Rate Dips Below 10%
527 m
Money pledged, $m (Total: 319.8)
Source: CDC/NCHS, National Health Interview Survey, 2010-2015.
% of uninsured rate in the U.S.
431 m
333 m
Film and video
Technology Publishing
300 m
184 m
147 m
Burj Khalifa
Willis Tower
Tokyo Tower
Space Needle
Shanghai Center Empire State Bldg.
Eiffel Tower
Employment Costs
for a steel worker
per hour.
Source: OECD Regional Well-Bing (10 point scale).
Civic Engagement
Access to service
Source: OECD better life index and World Bank Open Data.
Civic Engagement
Work-Life Balance
Life Satisfaction
Data Source: Pay Scale, Inc.
GDP to CO2 Emissions, G5 Countries
The effect of age on fertility.
Pregnancy rate (%) per age group
The Walt Disney
CEO to Worker Pay Ratios
Success rate, %.
21St Century Fox
OECD Better Life Index by GDP of G10 Countries
average of first
nine months
Film and video
Well-Being: California vs Massachusetts
Good Year
Size of Balloon
Compensation($) for CEO, 2013.
Height of balloon basket
Median Salary ($) for Workers, 2013.
South Africa
CO2 emissions
(metric tons per capita)
Source: World Bank Open Data (2011-2015).
GDP per capita ($)
Source:BabyCenter Medical Advisory Board, 2015.
Fig. 12: Examples created with DDG. (a) An isotype chart using data guides to measure the heights of imported building icons. (b) An area chart
using a single stroke to draw the area and to encode slopes in the declining trend. (c) A sankey-style diagram where two DDG are juxtaposed to
compare the rankings of two different metrics. (d) A radial chart created using the radial layout function we provide. (e) Nigel Holmes’ factory
worker chart using curved DDG to encode data. The incorrect representation in the original chart is fixed in our version. (f) A flower chart
using a parent DDG to encode stems, while multiple child DDG are used for flowers (i.e., hierarchical dataset). (g) A balloon chart using area
DDG for the size of balloons and position DDG for the location of the balloons. (h) A cloud and chimney chart where four DDG created from
the same dataset are used to encode each cloud. (i) A customized isotype chart using both length and area DDG to encode a pregnant woman’s
height and belly respectively.
infographics [21] that were used in a previous evaluation study investigating the effects of visual embellishment [1]. For the factory worker
chart (Fig. 12e), data guides were adjusted to be curved lines on which
gushed lava was drawn; however, the curved lines may not be desirable in most other cases. The monster chart was created differently
(Fig. 1). Instead of drawing it from scratch, we imported a monster
graphic into the tool and repurposed it with data guides by adjusting
the teeth to match the size of the guides. We can then reuse the chart
for different datasets by simply copying and pasting the chart (Fig. 1).
More complex visual structures can be constructed by combining
multiple guides. In Fig. 12i, two groups of data guides were combined
side-by-side (i.e., each area guide was linked to each length guide). A
human shape was then linked to an area and length guide and repeated
for sibling area and length guides; i.e., both the area and length guides
act as the backbone of the shape. Another example is the flower chart
(Fig. 12f) where all guides in each child group (flower) are linked to
each guides in the parent group (stem). The guides in the child group
were laid out using the radial layout function.
The graphics will be dynamically updated based on the changes in
the underlying dataset. For example, when the dataset for the bottom
guide group in Fig. 12f is changed, the positions of the flowers as
well as the sizes of the stems will be updated accordingly. Likewise,
changing the dataset for the length guide group in Fig. 12i will update
the positions of the heads and the heights of the bodies, while the sizes
of the bellies will remain the same. However, the current version of
DDG does not handle the cardinality change in the dataset well and we
discuss this problem in the limitation section (e.g., inserting additional
data values in Fig. 12c).
We also found that some existing infographics were potentially inaccurately designed. For example, when we juxtaposed data guides
on top of the original image we found that the factory worker chart
(Fig. 13a) by Nigel Holmes may have an incorrect representation of
the data. We also found a similar case in the balloon chart (Fig. 13b);
that is, the radius of the balloon instead of the area was used to represent the data value. This case is actually a commonly found mistake
in existing infographic design practice.
South Africa
$ 7,590
South Africa
South Africa
Fig. 14: Participant-generated graphics in the third task in the user
study, further embellished by the first author.
Fig. 13: (a) When recreating the factory worker chart, we found that
the lengths of three lava marks representing France, Japan, and Britain
do not match the size of data guides; the baseline is not clear however.
(b) With DDG, we found that the radius of balloons was used instead
of the area.
Usability Study
To evaluate the usability of DDG for designing information graphics, we conducted a user study with 13 designers currently enrolled in
professional schools of various design disciplines (e.g., architecture,
design technology, urban planning, information design). Based on the
pre-study survey, 5 participants (E1, E3, E4, E7, E11) had more than
six years of experience in graphic design as well as information design and visualization, while 4 participants (E9, E12, E13) had less
than two years of experience in both fields (i.e., both fields had similar participant distributions); other participants (E2, E5, E6, E8, E10),
lie in-between. When specifically asked, all participants mentioned
that they mostly create charts and graphs as side work. This is in
line with real-world designers such as those found on freelancing platforms (e.g., UpWork), who create not only infographics but also other
graphic design works such as logos or posters. In terms of frequently
used design tools, participants specified that they used vector drawing
tools, image editing tools, presentation software, and spreadsheets in
the order listed. Only two participants had experience with programming. Each participant received a $20 gift card for their time.
Procedure. A 60-minute study session started with a 15 minute tutorial introducing the tool interface and a handful of examples demonstrating how to work with DDG (e.g., drawing data graphics from
scratch and repurposing existing artworks). Using the datasets for
the CO2 emissions and GDP of G10 countries, participants were first
asked to recreate two graphics (similar to shown in Fig. 3). In the
third task, they were asked to create their own graphic using a smaller
dataset of G5 countries. All the datasets were extracted from the World
Bank Open Data 14 . The first two tasks were intended to make sure
that participants experienced all aspects of DDG, while the last task
was to see whether DDG enabled expressive infographic design. The
study setup was informal, allowing participants to interrupt at any time
to ask questions during the tasks. They were asked to complete a poststudy survey and were debriefed at the end.
All participants completed the two replication tasks with minimal
guidance, taking roughly 15 minutes in total. The third task was
open-ended and often involved an interview-like conversation between
participants and the study moderator to understand their thought processes and derive useful feedback for the tool. The tasks were not
strictly timed. In 5-point Likert scale questions during the post-study
survey (1-strongly disagree, 5-strongly agree), participants highly
rated their experience with DDG: interactions with DDG were intuitive (μ=4.0, σ=0.71), DDG is useful for positioning and measuring custom shapes based on data compared to rulers or grids (μ=4.7,
σ=0.63), DDG is useful for designing creative and expressive infographics (μ=4.9, σ=0.38), and DDG would improve their current design practice of creating custom data graphics (μ=4.5, σ=0.81).
The overall reactions from the participants were very promising as
well. Although we did not specifically ask during the study, almost all
participants said that drawing with DDG was fun and enjoyable (e.g.,
E6: “I am having so much fun with this!”). In addition, most participants asked us whether the tool was available for use and said they
would like to use it when available (e.g., E4: “I’m looking forward
to a live release some day.”). Some participants suggested that DDG
could be implemented in existing graphic design tools (e.g., E8: “I
like the idea of data guide, and it could even be a function or a plug in
in other programs potentially.”). Another participant (E3) who studies
architecture said that “I think that this would be a wonderful aid in
creating graphics for architectural representations as well.”.
They also made other positive comments in the post-study survey. Two participants commented regarding the tool interface that
“data guides provide a simple and straightforward interface”(E1),
and “overall it was very intuitive, and it has a wonderfully simple
and pleasing interface.”(E2). Similarly, two participants also commented on the usefulness of DDG: “even though the tool has some
bugs, it would already be a huge improvement to the infographic workflow.”(E6) and “this would be an incredibly useful tool for making infographics, there’s almost no learning curve.”(E8). Others expressed
more specific use cases that “data guides would allow me to represent data in a much more compelling and highly consumable way. I
need this in my life!”(E9) and “currently, I need a calculator to make
graphics that respond to data in Illustrator and you have to modify
each element individually, which is pretty arduous. This tool makes it
much easier to try things out and experiment with the graphics.”.
We also learned lessons that imply further necessary improvements
to DDG. First of all, although participants found the interface was
easy-to-use, we observed that they often struggled with keeping tracking of the links with data guides. Although we highlight linked items
and provide a panel for inspecting the links (Figure 3e), the learning
curve for this seems to be steep for beginners. This was especially
noticeable in the third task where participants tried to create complex
structures. During the study, we also observed instances where participants attempt to create a cohesive layout using two groups of guides
while we do not have an appropriate support. Another shortcoming
was that the result of shape deformation was not always perfect as it
is still in the development phase. Other minor issues included missing
features that were available in other full-fledged graphic design tools
such as Adobe Illustrator. Since our focus was not to recreate an existing design tool, we ignore these issues in this work. One participant
(E12) made the interesting suggestion that it would be useful to have
a variety of templates while also allowing users to create custom visual structures. Lastly, one participant also expressed her concern that
“I usually do very analytical infographics, using traditional forms like
bars or circles. Because of that, I’m not quite sure if data guides might
be very useful” (E5).
Benefits and Challenges of Manual Visual Encoding
An inherent benefit of allowing manual visual encoding is creative
freedom in design, which makes it possible to create a wide variety
of visual representations of data. In the third design task in the user
study, we observed that none of the designs created by participants
were the same although they were not fully embellished due to the
time constraint. While our study is limited to a graphic design environment, previous research similarly found that tool flexibility led to
more expressive designs, in which pencil & paper sketching [51] and
tangible blocks [25] were used to construct a visualization. A potential advantage of having flexibility in design is the ability to develop
context-specific visual representations. In a sense, this characteristic differentiates infographics from traditional visualization techniques
that are generalizable across different datasets but often impose a loss
of data context at the same time.
A main downside of manual encoding of data is that the process
is tedious and time-consuming especially when dealing with precision
or large data. While existing visualization creation tools address this
problem by automating visual mapping process, we approach it in a
very different way by providing helper guides driven by data. The
guides are inherently different from construction user interfaces used
in the existing tools, which usually enforce a rigid order of operations
and do not give the feeling of directness. While we focused on supporting three visual variables—length, area, and position, it would be
interesting to think about how to generalize the concept to other variables such as color, slope, or angle.
The flexibility of DDG comes with a caveat, however. It is still
possible for users to create inaccurate representations as DDG does not
prevent it. It would be beneficial to have intelligent agents or systems
that provide design critiques based on design principles established
in the visualization community [35] (e.g., calculating areas of visual
marks to check whether they match actual data represented through
data guides).
Opportunities for new visualization design tools
There is still a much unexplored gap in how designers create innovative visualizations and how currently available tools mandate the process of generating visualizations. Most existing visualization creation
tools are based on formal specifications for rapidly generating traditional statistical graphics. However, designers still engage in manual
encoding in order to design unique visual representations of data that
are often found to be more attractive, engaging and easier to remember.
In the formative study, we only investigated a subset of infographic authoring processes in the wild, which informed our design goals and led
to the development of DDG. Novel infographics often involve a wide
variety of different authoring techniques, most of which are still unknown to the visualization research community. Further investigations
will be necessary to address the challenges and unearth the benefits of
such visual mapping techniques, which will also provide insights on
new visualization design tools.
DDG can be considered as a constraint-based drawing technique in
a sense that the form and size of a data guide constrain the appearance
of an associated shape. The use of a deformation technique enabling
fine-grained constraint behavior differentiates our work from existing
constraint-based drawing tools that mostly provide object-level constraint transformations (i.e., translate, rotate, scale constrained objects). In the same vein, there are still opportunities incorporating
different design paradigms such as parametric drawing 15 into visualization design environments. Directly manipulating geometry using
data as parametric constraints might be a possible solution in this direction.
The most common reaction we observed during the user study was
that the participants seemed to enjoy working with DDG particularly
in the third creative design task regardless whether they succeeded or
not. This suggests that there may be an interesting avenue for developing creativity support tools for data graphic design [42]. For ex15
ample, how can we develop computational tools to support freeform
data sketching 16 or tangible visualization construction 17 whose inherent expressivity makes it appropriate for creative activities? Although
such tools may not serve analytic purposes, they may find their usefulness in casual or personal contexts. For instance, a digital alternative
to the use of tangible tokens [25] can make use of a large database of
icons 18 as tokens to create diverse isotype charts.
DDG has inherent limitations. First, DDG currently operates on a simple tabular dataset. Therefore, it is impossible to create certain types
of charts that work on multivariate data (e.g., scatter plots) and graph
data (e.g., networks). For such complex data structures, it may make
more sense to create visualizations automatically, and then manually
embellish them for communication [44]. Second, data guides allow
freeform manipulations for flexible layouts, meaning that they may
not be always axis-aligned. This makes it difficult to not only generate
guide elements such as axes but also determine the position and orientation of a new guide when a new column is added to the dataset. In
the similar vein, because of the flexibility in constructing novel visual
structures, DDG is currently limited in supporting the data cardinality
change (i.e., ambiguities in whether it is necessary to automatically
generate marks and links for new guides). Lastly, DDG currently only
supports length, area and position visual variables, requiring manually
encoding other frequently used variables such as color.
Most of the limitations we found through the user study were related to the tool maturity. First, the current interaction model for linking shapes to a data guide through the context menu and keeping track
of the links on the inspection panel needs further refinements. That
is, the direct manipulation of data guides successfully reduced the gulf
of execution, but there is still room for narrowing the gulf of evaluation in assessing the link states. Second, our tool does not provide
advanced layouts of data guides except the simple linear and radial
layouts. Currently, manually manipulating a large number of guides is
cumbersome especially if it involves more than one group of guides.
Lastly, the user study was rather limited, calling for more focused and
task-oriented studies to better evaluate the effectiveness of DDG.
In this paper, we introduce DDG, an interaction technique for designing custom data-driven graphics. DDG is designed to addresses issues
in the current design practice where designers manually encode data
into custom graphics. Unlike traditional guides such as rulers or grids,
data guides are generated from data and enable direct manipulation
for intuitive interaction. DDG maintain a flexible design process by
allowing users to draw custom shapes on top of guides or to use the
guides to repurpose existing artworks. DDG’s data binding support
for freeform shapes further improves the design process by alleviating manual encoding when data is changed as well as increasing the
reusability of custom charts. We demonstrate the expressiveness and
usability of DDG through example graphics and a user study.
For future work, we plan to improve the accessibility of DDG by
addressing the limitations we learned from the user study, including
creating links and assessing the link states, coordinating the layouts of
multiple groups of data guides, and providing predefined guide structures for novice users. We believe that there are still many opportunities in this less-studied area as we have outlined in the discussion
and limitation section. We plan to further investigate the existing infographic design practice, focusing on the visual mapping process, in order to inform the design of next generation visualization design tools.
The authors wish to thank Jean-Daniel Fekete, Jeremy Boy, Johanna
Beyer, Kasper Dinkla, Hendrik Strobelt, and James Tompkin for valuable feedback on this project. This work was supported in part by a
grant from the Kwanjeong Educational Foundation.
[1] S. Bateman, R. L. Mandryk, C. Gutwin, A. Genest, D. McDine, and
C. Brooks. Useful junk?: the effects of visual embellishment on comprehension and memorability of charts. In Proc. of CHI, pages 2573–2582.
ACM, 2010.
[2] J. Bertin. Semiology of graphics: diagrams, networks, maps. 1983.
[3] A. Bigelow, S. Drucker, D. Fisher, and M. Meyer. Reflections on how
designers design with data. In Proc. of AVI, pages 17–24. ACM, 2014.
[4] A. Bigelow, S. Drucker, D. Fisher, and M. Meyer. Iterating between tools
to create and edit visualizations. IEEE TVCG, page in press, 2016.
[5] R. Borgo, A. Abdul-Rahman, F. Mohamed, P. W. Grant, I. Reppa,
L. Floridi, and M. Chen. An empirical study on using visual embellishments in visualization. IEEE TVCG, 18(12):2759–2768, 2012.
[6] M. A. Borkin, Z. Bylinskii, N. W. Kim, C. M. Bainbridge, C. S. Yeh,
D. Borkin, H. Pfister, and A. Oliva. Beyond memorability: Visualization
recognition and recall. IEEE TVCG, 22(1):519–528, 2016.
[7] M. A. Borkin, A. A. Vo, Z. Bylinskii, P. Isola, S. Sunkavalli, A. Oliva,
and H. Pfister. What makes a visualization memorable? IEEE TVCG,
19(12):2306–2315, 2013.
[8] M. Bostock, V. Ogievetsky, and J. Heer. D3 data-driven documents. IEEE
TVCG, 17(12):2301–2309, 2011.
[9] J. Boy, F. Detienne, and J.-D. Fekete. Storytelling in information visualizations: Does it engage users to explore data? In Proc. of CHI, pages
1449–1458. ACM, 2015.
[10] P. Castells, P. Szekely, and E. Salcher. Declarative models of presentation.
In Proc. of IUI, pages 137–144. ACM, 1997.
[11] W. S. Cleveland and R. McGill. Graphical perception: Theory, experimentation, and application to the development of graphical methods.
Journal of the American statistical association, 79(387):531–554, 1984.
[12] J.-D. Fekete. The infovis toolkit. In Prof. IEEE InfoVis, pages 167–174,
[13] I. Fujishiro, Y. Ichikawa, R. Furuhata, and Y. Takeshima. Gadget/iv: a
taxonomic approach to semi-automatic design of information visualization applications using modular visualization environment. In Proc. of
IEEE InfoVis, pages 77–83, 2000.
[14] L. Grammel, C. Bennett, M. Tory, and M.-A. Storey. A Survey of Visualization Construction User Interfaces. In EuroVis - Short Papers, pages
19–23. The Eurographics Association, 2013.
[15] T. R. G. Green and M. Petre. Usability analysis of visual programming
environments: a cognitive dimensions framework. Journal of Visual Languages & Computing, 7(2):131–174, 1996.
[16] S. Haroz, R. Kosara, and S. L. Franconeri. Isotype visualization: Working
memory, performance, and engagement with pictographs. In Proc. of
CHI, pages 1191–1200. ACM, 2015.
[17] L. Harrison, K. Reinecke, and R. Chang. Infographic aesthetics: Designing for the first impression. In Proc. of CHI, pages 1187–1190. ACM,
[18] L. Harrison, F. Yang, S. Franconeri, and R. Chang. Ranking visualizations of correlation using weber’s law. IEEE TVCG, 20(12):1943–1952,
[19] J. Heer and M. Bostock. Declarative language design for interactive visualization. IEEE TVCG, 16(6):1149–1156, 2010.
[20] J. Heer, S. K. Card, and J. A. Landay. Prefuse: a toolkit for interactive
information visualization. In Proc. of CHI, pages 421–430. ACM, 2005.
[21] N. Holmes. Designer’s guide to creating charts & diagrams. WatsonGuptill, 1984.
[22] S. C. Hsu and I. H. Lee. Drawing and animation using skeletal strokes.
In Proc. of ACM SIGGRAPH, pages 109–118. ACM, 1994.
[23] J. Hullman, E. Adar, and P. Shah. Benefitting infovis with visual difficulties. IEEE TVCG, 17(12):2213–2222, 2011.
[24] S. Huron, S. Carpendale, A. Thudt, A. Tang, and M. Mauerer. Constructive visualization. In Proc. of DIS, pages 433–442. ACM, 2014.
[25] S. Huron, Y. Jansen, and S. Carpendale. Constructing visual representations: Investigating the use of tangible tokens. IEEE TVCG, 20(12):2102–
2111, 2014.
[26] E. L. Hutchins, J. D. Hollan, and D. A. Norman. Direct manipulation
interfaces. Human–Computer Interaction, 1(4):311–338, 1985.
[27] A. Jacobson, I. Baran, J. Popović, and O. Sorkine. Bounded biharmonic
weights for real-time deformation. ACM Trans. Graph., 30(4):78:1–78:8,
July 2011.
[28] A. Jacobson, Z. Deng, L. Kavan, and J. Lewis. Skinning: Real-time shape
deformation. In ACM SIGGRAPH 2014 Courses, 2014.
[29] Y. Jansen and P. Dragicevic. An interaction model for visualizations beyond the desktop. IEEE TVCG, 19(12):2396–2405, 2013.
[30] R. Kazman and J. Carriere. Rapid prototyping of information visualizations using vanish. In Proc. of IEEE InfoVis, pages 21–28, 1996.
[31] R. Kosara. Presentation-oriented visualization techniques. IEEE CG&A,
36(1):80–85, 2016.
[32] R. Kosara and J. Mackinlay. Storytelling: The next step for visualization.
Computer, (5):44–50, 2013.
[33] B. Lee, R. H. Kazi, and G. Smith. Sketchstory: Telling more engaging
stories with data through freeform sketching. IEEE TVCG, 19(12):2416–
2425, Dec. 2013.
[34] S. Liu, A. Jacobson, and Y. Gingold. Skinning cubic bézier splines and
catmull-clark subdivision surfaces. ACM Trans. Graph., 33(6):190:1–
190:9, Nov. 2014.
[35] K. Luther, J.-L. Tolentino, W. Wu, A. Pavel, B. P. Bailey, M. Agrawala,
B. Hartmann, and S. P. Dow. Structuring, aggregating, and evaluating
crowdsourced design critique. In Proc. of CSCW, pages 473–485. ACM,
[36] J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Trans. Graph., 5(2):110–141, 1986.
[37] A. V. Moere and H. Purchase. On the role of design in information visualization. Information Visualization, 10(4):356–371, 2011.
[38] B. A. Myers, J. Goldstein, and M. A. Goldberg. Creating charts by
demonstration. In Proc. of CHI, pages 106–111. ACM, 1994.
[39] D. Ren, T. Hollerer, and X. Yuan. ivisdesigner: Expressive interactive
design of information visualizations. IEEE TVCG, 20(12):2092–2101,
[40] S. F. Roth, J. Kolojejchick, J. Mattis, and J. Goldstein. Interactive graphic
design using automatic presentation knowledge. In Proc. of CHI, pages
112–117. ACM, 1994.
[41] A. Satyanarayan and J. Heer. Lyra: An interactive visualization design
environment. Computer Graphics Forum (Proc. EuroVis), 33(3):351–
360, 2014.
[42] B. Shneiderman. Creativity support tools: Accelerating discovery and
innovation. Communications of the ACM, 50(12):20–32, 2007.
[43] D. Skau, L. Harrison, and R. Kosara. An evaluation of the impact of
visual embellishments in bar charts. Computer Graphics Forum (Proc.
EuroVis), 34(3):221–230, 2015.
[44] A. S. Spritzer, J. Boy, P. Dragicevic, J.-D. Fekete, and C. M. Dal
Sasso Freitas. Towards a smooth design process for static communicative node-link diagrams. Computer Graphics Forum (Proc. EuroVis),
34(3):461–470, 2015.
[45] C. Stolte, D. Tang, and P. Hanrahan. Polaris: A system for query, analysis,
and visualization of multidimensional relational databases. IEEE TVCG,
8(1):52–65, 2002.
[46] S. Swaminathan, C. Shi, Y. Jansen, P. Dragicevic, L. A. Oehlberg, and
J.-D. Fekete. Supporting the design and fabrication of physical visualizations. In Proc. of CHI, pages 3845–3854. ACM, 2014.
[47] J. Talbot, V. Setlur, and A. Anand. Four experiments on the perception of
bar charts. IEEE TVCG, 20(12):2152–2160, 2014.
[48] E. R. Tufte. The Visual Display of Quantitative Information. Graphics
Press, Cheshire, CT, USA, 1986.
[49] F. B. Viegas, M. Wattenberg, F. Van Ham, J. Kriss, and M. McKeon. Manyeyes: a site for visualization at internet scale. IEEE TVCG,
13(6):1121–1128, 2007.
[50] J. Walny, S. Carpendale, N. H. Riche, G. Venolia, and P. Fawcett. Visual
thinking in action: Visualizations as used on whiteboards. IEEE TVCG,
17(12):2508–2517, 2011.
[51] J. Walny, S. Huron, and S. Carpendale. An exploratory study of data
sketching for visual representation. Computer Graphics Forum (Proc.
EuroVis), 34(3):231–240, 2015.
[52] C. Weaver. Building highly-coordinated visualizations in improvise. In
Proc. of IEEE InfoVis, pages 159–166.
[53] H. Wickham. ggplot2: elegant graphics for data analysis. Springer Science & Business Media, 2009.
[54] L. Wilkinson. The Grammar of Graphics (Statistics and Computing).
Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2005.
[55] T. Wun, J. Payne, S. Huron, and S. Carpendale. Comparing Bar Chart
Authoring with Microsoft Excel and Tangible Tiles. Computer Graphics
Forum (Proc. EuroVis), 2016.