0 Research theory/model Mara Solen and Tamara Munzner are with the University
of British Columbia, Department of Computer Science. E-mail:
<EMAIL_ADDRESS>
# A Design Space for Visualization with Large Scale-Item Ratios

Mara Solen0000-0002-5191-5193  Tamara Munzner0000-0002-3294-3869

###### Abstract

The scale-item ratio is the relationship between the largest scale and the
smallest item in a visualization. Designing visualizations when this ratio is
large can be challenging, and designers have developed many approaches to
overcome this challenge. We present a design space for visualization with
large scale-item ratios. The design space includes three dimensions, with
eight total subdimensions. We demonstrate its descriptive power by using it to
code approaches from a corpus we compiled of 54 examples, created by a mix of
academics and practitioners. We then partition these examples into five
strategies, which are shared approaches with respect to design space dimension
choices. We demonstrate generative power by analyzing missed opportunities
within the corpus of examples, identified through analysis of the design
space, where we note how certain examples could have benefited from different
choices.

Supplemental materials:
https://osf.io/wbrdm/?view_only=04389a2101a04e71a2c208a93bf2f7f2

###### keywords:

Visualization, design space, scale-item ratio, multiple scales.

The three dimensions and eight subdimensions of the design space, along with
the five strategies. Quantitative dimensions are shown with their components.
Categorical dimensions are shown with their choices.

##  1 Introduction and Background

Designing visualizations where the smallest data items can be clearly seen
from a high level is challenging. Visualizations typically have a limit to the
space they can use, and yet must encode items in the limited space while
retaining distinguishability. Sometimes, items may even be smaller than can be
represented, for example if they require subpixel positioning.

The challenge arises in a variety of disciplines. Gillmann et al. include the
challenge of designing multiscale visualizations in their ten open challenges
in medical visualization [23]. They comment on how multiple scales are often
not visualized simultaneously in medical imaging, and that the field needs to
find techniques for integrating the different scales together, suggesting
high-level ideas such as focus and context, zooming, and filtering. Similarly,
Ståhlbom et al. describe that working with multiscale data is a challenge
faced by those working on DNA sequencing, as they need to analyze the data at
varying levels [53]. In our own work, we describe the challenge of designing a
digital exhibit called DeLVE for educating museum visitors about the
geological and biological history across varying scales of time, where
visitors need to be able to relate the various scales to each other [5].

Visualization designers have used many techniques to address this challenge,
ranging from interactive zooming to multiple simultaneously-visible scales.
However, no framework exists to support designers with low-level design
decisions while facing this challenge. To meet this need, we present a design
space for these scenarios, motivated by the authors’ work on and challenges
with designing DeLVE.

First, we must carefully describe the visualization design scenarios that are
the target of this design space to ensure a feasible scope. We define value as
the magnitude of data items in data space. We can then construct a mapping, a
transformation from a value to a discretized display space position. A scale
is a region of the discretized display space that depicts a mapping, and it
ranges from minimum to maximum positions in display space. A size is the
difference between a scale or an item’s minimum and maximum positions in
display space. Finally, we define scale-item ratio to be the ratio between the
size of the largest scale and the size of the smallest item, both in display
space. This paper covers scenarios in which the scale-item ratio is large.

We identify large scale-item ratio scenarios by assessing whether we face one
or both of two challenges when visualizing the full dataset on a single linear
scale that fits fully within a human range of vision. The first challenge is
when the smallest item is not visible, for example if its size, as determined
by the linear scale, results in it being smaller than a pixel, or the smallest
manufacturable detail size for physicalizations. The second challenge is when
the smallest items are not clearly separable, for example when multiple items
must fit within a single pixel or smallest manufacturable detail size. If one
or both of these challenges arise under these conditions, then we consider the
visualization design scenario to be one with large scale-item ratio. This
approach for identifying large scale-item ratio scenarios depends heavily on
context, however; differences in factors like screen pixel density or physical
material used to display the visualization will directly impact the
quantitative limit between large and small scale-item ratios.

The two primary contributions of this paper are a design space for
visualization scenarios with large scale-item ratios, and strategies that
partition the example set according to shared approaches for design space
choices. We also provide the secondary contribution of a corpus of 54 examples
of visualizations developed by both researchers and practitioners, coded
according to the design space and the strategies.

##  2 Related Work

We now discuss related work, first covering work that analyzes collections of
large scale-item ratio visualizations and then discussing design spaces.

###  2.1 Existing Frameworks

Existing work has investigated visualizations to help users understand scales
and the use of multiscale visualization. In their work on concrete scales,
Chevalier et al. provide a framework for the varied use of the technique in
visualization [16]. While we consider the use of concrete scales as a
dimension of our design space, we do not further break down its use and the
other dimensions are independent from concrete scales.

Garrison et al. conduct a survey on and construct an overview of visualization
in physiology that focuses on multiscale problems [22]. While their work
covers many visualization examples, they focus on breaking down their findings
by different parts of physiology research and do not provide a model or
guidance for visualization design elements.

Jakobsen et al. investigated the relationship between display size,
information space, and scale. They conduct two user experiments, varying
visualization technique, display sizes, and the mapping between the
information space and the display size. They report their findings and discuss
how the varying factors impact a user’s ability to complete a task. In
contrast to our discussion of the scale-item ratio, they do not name any of
the relationships between factors. Further, they do not provide lower-level
guidance for visualization design as we aim to do.

The most closely related work to our own is the structured literature analysis
of design practices in multiscale visualization research by Cakmak et al.
[14]. Their work differs from ours, as where they describe the state of
research on multiscale visualization, we aim to provide a framework of lower-
level design components used in large scale-item ratio visualizations. Their
coding scheme is very different than the dimensions and choices in our own
design space, as where they describe high level idiom and interaction choices,
we provide lower-level design components which are independent from idiom.
Further, their scope is far larger than our own, covering many aspects of
multiscale visualization that extend beyond the narrower context of large
scale-item ratios that we investigate. However, their coverage is more narrow,
because their analysis covers only the academic literature; they exclude non-
academic examples of real-world use by practitioners, which we do include.
This difference leads to many of their dimensions and choices to be far
outside the scope of our work.

Several of their design considerations do touch on our concerns. The most
relevant are Understand relations across different scales, Guide users during
multiscale navigations, Visualize abstraction measurements across scales, and
Design tailored multiscale domain visualizations. Our design space can be seen
as a response to these calls for action, providing a structure for analysis to
address these very questions. Similarly, our design space responds to one of
the open research questions they identified, their call for further
quantification of visual scalability. We use their paper as a seed paper for
examples from academic literature, and we incorporate the search terms they
used in our systematic literature search.

###  2.2 Design Spaces

Design spaces impose systematic structure on a set of possibilities for a
specific problem, capturing the key variables at play. They provide an
actionable structure for systematically reasoning about solutions [18].
Describing and analyzing portions of a design space allows us to understand
differences among designs and suggest new possibilities [15]. They increase
cognitive efficiency and support inferences, by grouping similar instances
together to facilitate reasoning about classes rather than instances [49].

Visualization researchers have developed design spaces for a variety of
topics. Goffin et al. created a design space for word-scale visualizations
[25], which, similar to our design space, focused on the design components of
the visualizations within their scope. However, it is useful to consider
design spaces for concepts outside of design components. Schulz et al. use a
design space to describe abstract visualization tasks [51]. Elliot et al.
construct a design space to describe methods, specifically for vision science
research on visualizations [18]. Kim et al. provide a design space around
accessible visualization which encompasses a combination of these concepts,
including design components and abstract tasks as well types of users and
technologies [37]. No previous work provides a design space for visualization
with large scale-item rations; this paper addresses that gap.

##  3 Methods

The development and validation of our design space took place in three stages:
Initialize, Expand, Refine. We iteratively created a corpus of examples to
guide the creation and assess the value of the design space, adding new
relevant examples and removing those that were no longer in scope at each
stage. While collecting examples, we iteratively analyze the corpus to
construct the eight dimensions of the design space. We then analyze the
dimension choices these examples use to construct a set of five strategies,
and use the design space choices to identify missed opportunities in certain
examples.

###  3.1 Corpus Collection and Design Space Construction

We now discuss the process of each stage of corpus collection and dimension
iteration.

####  3.1.1 Initialize

In the Initialize stage, we constructed an initial set of 21 examples. We
began with a small number of examples that we were already aware of or were
provided to us by domain expert co-authors with whom we collaborated in other
projects. After collecting the initial set, we paused collection to construct
an initial version of the design space dimensions, so that we could collect
further examples in a later stage to validate those choices. Our initial
design space fully described the initial set.

####  3.1.2 Expand

In the Expand stage, we focused on increasing the coverage of the example
corpus to validate our initial design space. We collected 16 more examples,
called the expansion set, primarily through forward and backward chaining on
our existing academic examples but also from Google searches for practitioner
work, additional suggestions, and author memory, resulting in a total of 37
examples in the corpus. The additional examples led to refinements which
involved both introducing new dimensions to capture more differences and
eliminating or merging uninformative dimensions in an effort to improve
distinguishability between examples. After modifying the design space, we re-
coded the initial set in the new dimensions. Since this stage resulted in
changes to the design space, we sought out further collect examples, again to
validate the newly-refined dimensions.

####  3.1.3 Refine

By the final Refine stage, we had exhausted all examples from author memory
and domain expert suggestion, the majority of which were in real-world use
rather than from academic literature. We chose to conduct a systematic
literature search to collect relevant examples from academic literature,
validate the design space, and finalize the dimensions. We began by reviewing
each of the 122 articles included in Cakmak et al.’s collection [14], then
including only papers that discussed visualization systems which had large
scale-item ratio, leaving 19 papers. The result was 20 new examples, as one
paper contributed two systems. We then conducted a search for multiple scales
visualization examples since 2021, the year of Cakmak et al.’s submission. We
used the Bielefeld Academic Search Engine (BASE), which meets quality
requirements for academic literature searches [27]. We filtered the search to
only include journal and conference articles since 2021 (inclusive), and used
the same keywords used by Cakmak et al., namely the keyword “visualization”
paired with one of the following terms: “multiple scales”, “multiresolution”,
“multiple levels of detail”, “multi-level”, and “multiscale” [14]. This
initial search returned 112 results. We then reviewed each article and
included only ones that covered large scale-item ratio visualization systems
and removed duplicates, leaving six articles. In total, data collection in
this stage, including both the backward chaining from Cakmak et al. and the
keyword search, led to 26 new examples which we call the systematic set. We
coded these new examples in the design space. We also removed 9 previously-
collected examples as they no longer met our inclusion criteria of covering a
scenario with large scale-item ratio. At the end of the refine stage, our
corpus contained 54 examples; 14 examples remained from the initial set, 14
from the expansion set, and 26 from the systematic set.

We then coded all examples in the systematic set in our design space
dimensions. During this iteration, we did not identify any meaningful
differences between examples which were not already described by our design
space, validating it. However, our reflection on the entire corpus at the end
of this stage led to refinements to the design space to improve
understandability. Finally, we re-coded all 54 examples in the corpus into our
final set of dimensions and choices. We present the full and final design
space in detail in Section 4.

###  3.2 Strategies and Missed Opportunities Analysis

We also sought to identify implications for design using our design space. To
this end, we conducted analysis in two ways. First, we analyzed coded examples
within the design space to find meaningful groups through iterative coding in
three steps. We began by coding similar examples into groups that only
differed by one or two dimensions. Then, we determined which of the dimensions
remained consistent within each group. We used these sets of consistent
dimensions to give meaningful names to the groups and define precisely what
makes an example fit within a group. We would then begin this process again,
but in the first step would code the groups rather than the individual
examples, leading to us merging similar groups into single, larger groups.
Once we were no longer able to merge groups together, we applied the
definitions of the groups to the entire corpus again to ensure that we had the
correct examples in each group and that each example fit into exactly one
group each. This process led us to identify five groups, which we call
strategies, namely shared approaches with respect to design space choices. We
discuss the results of this analysis in Section 5.

We also analysed missed opportunities in our corpus that we identified through
usage of the design space in general, and the interaction between the choices
for strategy and for the dimension pertaining to purpose. We noted where there
were large discrepancies such as heavily used or underused choices across the
entire corpus and within specific strategies. We identified examples using the
discrepancies and analyzed them with respect to whether using alternative
choices could have improved the design. We discuss our findings from this
analysis in Section 6.1.

##  4 The Design Space

The design space contains 3 independent dimensions, each with between one and
four subdimensions, for a total of eight subdimensions. Figure A Design Space
for Visualization with Large Scale-Item Ratios shows an overview of the
dimension hierarchy. In this section, we describe each dimension in detail
(bold), and explain the choices within it (italics) by referring to examples
in the corpus. We also provide four synthetic illustrations that are
simplified and stylized evocations of examples found within the corpus, shown
in Figure 1. We designed these illustrations to, together, cover the most
complex visual concepts described in the design space’s dimensions, enabling
us to describe them more clearly. Table 1 shows all examples and their codes
for each dimension. See Table 2 for a description of each example in the
corpus.

Figure 1: The four synthetic illustrations. a) Static Multilevel is a static visualiation with two scales, one higher-level and one lower-level. The two scales use different encodings, and the point of focus for the lower-level scale is determined by existing structure in the data. The two scales are connected by connection line marks. b) Interactive Multilevel is a visualization with two scales. The user chooses the zoom level by selecting one of four discrete options above to zoom in to below. c) Interactive Zoom is a line chart that the user can both zoom into using a scroll wheel and pan across by dragging with a mouse, based on Multiscale Trace [20]. d) Auto Zoom is a visualization that employs automatic zooming between objects at a constant rate, based on Powers of Ten [17]. |  |  |  | Scales | Navigation |  Familiarity
---|---|---|---|---|---|---
Example (short title) |  Citation |  Source |  Stage |  count |  step type |  encodings |  assoc. |  type |  mode |  visceral time |  concrete
Single-View Pan and Zoom
Zoom Line Chart |  [55] |  academic |  init. |  3:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
Cuttlefish (fig 6) |  [60] |  academic |  expand |  3:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
EVEVis |  [43] |  academic |  expand |  4:1:1 |  user mixed |  different |  |  both |  digital |  no |  no
Multilevel Poetry |  [44] |  academic |  expand |  4:1:1 |  user disc. |  different |  |  both |  digital |  no |  no
Multiscale Trace |  [20] |  academic |  expand |  5:1:1 |  user cont. |  different |  |  both |  digital |  no |  no
Europe OSM |  [66] |  academic |  refine |  3:1:1 |  user cont. |  different |  |  both |  digital |  no |  no
Zoomable Treemaps |  [10] |  academic |  refine |  3:1:1 |  user disc. |  same |  |  both |  digital |  no |  no
Chameleon |  [59] |  academic |  refine |  5:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
Hierarchical Route Maps |  [63] |  academic |  refine |  3:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
Large Viewing Vis |  [32] |  academic |  refine |  3:1:1 |  user cont. |  same |  |  both |  physical |  no |  no
Kyrix-S |  [54] |  academic |  refine |  15:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
Membrane Mapping |  [62] |  academic |  refine |  4:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
Execution Trace Vis |  [56] |  academic |  refine |  2:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
MuSE |  [21] |  academic |  refine |  3:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
ScaleTrotter |  [29] |  academic |  refine |  7:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
SpaceFold |  [13] |  academic |  refine |  2:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
TagNetLens |  [26] |  academic |  refine |  2:1:1 |  user disc. |  same |  |  both |  digital |  no |  no
Hierarchy Vis |  [30] |  academic |  refine |  2:1:1 |  user disc. |  same |  |  both |  digital |  no |  no
Chemical Vis |  [64] |  academic |  refine |  2:1:1 |  user cont. |  different |  |  both |  digital |  no |  no
Simultaneous Occluding Embed
Melange |  [19] |  practitioner |  init. |  2:2:1 |  user cont. |  same |  none |  both |  digital |  no |  no
FingerGlass |  [36] |  academic |  expand |  2:2:1 |  user cont. |  same |  none |  both |  digital |  no |  no
Tabletop Gestures |  [50] |  academic |  refine |  2:1:1 |  user cont. |  same |  marks |  both |  digital |  no |  no
Gimlenses |  [48] |  academic |  refine |  3:3:1 |  user cont. |  same |  marks |  both |  digital |  no |  no
GrouseFlocks |  [6] |  academic |  refine |  2:2:2 |  user cont. |  same |  marks |  both |  digital |  no |  no
Digital Earth |  [52] |  academic |  refine |  3:3:1 |  user cont. |  same |  marks |  both |  digital |  no |  no
AdvEx |  [65] |  academic |  refine |  2:2:1 |  user disc. |  same |  marks |  both |  digital |  no |  no
Scalable Insets |  [39] |  academic |  refine |  2:2:1 |  user cont. |  same |  marks |  both |  digital |  no |  no
Multi-Foci COVID Vis |  [41] |  academic |  refine |  2:2:1 |  user cont. |  different |  marks |  both |  digital |  no |  no
PhysicLenses |  [13] |  academic |  refine |  2:2:1 |  user cont. |  same |  marks |  both |  digital |  no |  no
TissUUmaps |  [47] |  academic |  refine |  2:2:1 |  user cont. |  same |  marks |  both |  digital |  no |  no
TrailMap |  [67] |  academic |  refine |  2:2:1 |  user cont. |  same |  none |  both |  digital |  no |  no
Simultaneous Separate Multilevel
Multiscale Unfolding |  [28] |  academic |  init. |  6:6:6 |  user cont. |  same |  none |  both |  digital |  no |  no
Rivet (MTSC) |  [11] |  academic |  init. |  6:3:3 |  user cont. |  same |  marks |  both |  digital |  no |  no
Temp Earth |  [12] |  practitioner |  init. |  7:7:7 |  data driven |  same |  none |  none |  |  |  no
TraXplorer (fig 2) |  [33] |  academic |  init. |  5:5:5 |  user cont. |  same |  channels |  both |  digital |  no |  no
Mandelbrot Explorer |  [8] |  practitioner |  expand |  inf:6:6 |  constant |  same |  none |  both |  digital |  no |  no
MizBee |  [42] |  academic |  expand |  3:3:3 |  user disc. |  different |  channels |  both |  digital |  no |  no
PolyZoom |  [34] |  academic |  expand |  4:3:3 |  user cont. |  same |  marks |  both |  digital |  no |  no
Chromoscope |  [40] |  academic |  refine |  3:2:2 |  user cont. |  different |  none |  both |  digital |  no |  no
TimeNotes |  [61] |  academic |  refine |  4:4:4 |  user cont. |  same |  both |  both |  digital |  no |  no
Familiar Zoom
DeLVE |  [5] |  academic |  init. |  10:10:10 |  data driven |  same |  both |  zoom |  digital |  no |  yes
Here is Today |  [57] |  practitioner |  init. |  11:1:1 |  data driven |  same |  |  zoom |  digital |  no |  yes
Powers of Ten |  [17] |  practitioner |  init. |  40:1:1 |  constant |  same |  |  zoom |  digital |  yes |  yes
XKCD Money |  [45] |  practitioner |  init. |  5:5:5 |  constant |  same |  marks |  none |  digital |  no |  yes
Cell Size and Scale |  [58] |  practitioner |  expand |  9:1:1 |  user cont. |  same |  |  zoom |  digital |  no |  yes
Scale of the Universe 2 |  [31] |  practitioner |  expand |  63:1:1 |  user cont. |  same |  |  zoom |  digital |  no |  yes
The Size of Space |  [2] |  practitioner |  expand |  35:1:1 |  data driven |  same |  |  zoom |  digital |  no |  yes
Universcale |  [46] |  practitioner |  expand |  42:1:1 |  user cont. |  same |  |  zoom |  digital |  no |  yes
US Debt |  [24] |  practitioner |  expand |  9:3:3 |  data driven |  same |  none |  zoom |  digital |  no |  yes
Lengthy Pan
Science Museum Timeline |  [3] |  practitioner |  init. |  1:1:1 |  |  |  |  pan |  physical |  yes |  no
The Deep Sea |  [1] |  practitioner |  init. |  1:1:1 |  |  |  |  pan |  digital |  yes |  yes
Trail of Time |  [35] |  academic |  init. |  1:1:1 |  |  |  |  pan |  physical |  yes |  no
University Timeline Walk |  [4] |  practitioner |  init. |  1:1:1 |  |  |  |  pan |  physical |  yes |  no
Wealth Shown to Scale |  [38] |  practitioner |  expand |  1:1:1 |  |  |  |  pan |  digital |  yes |  yes
Synthetic Illustrations Set
Static Multilevel |  |  |  |  2:2:2 |  data driven |  different |  marks |  none |  |  no |  no
Interactive Multilevel |  |  |  |  2:2:2 |  user disc. |  same |  channels |  both |  digital |  no |  no
Interactive Zoom |  |  |  |  2:1:1 |  user cont. |  same |  |  both |  digital |  no |  no
Auto Zoom |  |  |  |  4:1:1 |  constant |  same |  |  zoom |  digital |  no |  yes
Table 1: The 54 corpus examples grouped according to the 5 strategies, and the
4 synthetic illustrations, coded by the hierarchical dimensions of the design
space. The Count subdimension uses the format total:simultaneous:separate to
display its three components. Abbreviations: init. = initialize; assoc. =
association; cont. = continuous; disc. = discrete; inf. = infinite.

###  4.1 Scales

The Scales dimension describes a visualization’s encodings in terms of the
number of different scales and how any different scales differ in mapping,
whether they share low-level encoding choices, and how they are associated
with each other. It includes four subdimensions: count, step type, encodings,
association.

####  4.1.1 Count

The count subdimension includes three quantitative components: total,
simultaneous, and separate.

Total. The total component represents the total number of scales, using the
definition from Section 1, accessible in a visualization. In the case that the
number of possible unique scales is discrete, it is straightforward to count.
For example, in the synthetic illustration Static Multilevel shown in Figure
1a, we can simply count the two scales. In the corpus example Multiscale
Unfolding [28], which is a visualization of multiple levels of DNA, there are
six unique scales so the total component is six.

In the case of multiple scales being chosen from a continuous scale, we count
the orders of magnitude using the expression
$round(log_{10}(max)-log_{10}(min)+1)$. In the synthetic illustration Auto
Zoom, shown in Figure 1d, the visualization automatically zooms out through
different objects encoded by coloured shapes. In this synthetic illustration,
there are four different orders of magnitude, so the total component is four.
An example of the continuous case from our example corpus is Scale of the
Universe 2 [31], a visualization of various objects in the universe from beach
balls to the universe itself to quantum foam the width of one planck. In this
example, the user navigates through scales from $10^{27}$ to $10^{-35}$ of the
original scale. Using these values, we calculate 63 orders of magnitude.

The corpus example Mandelbrot Explorer, a web page where users can explore
Mandelbrot sets by zooming in to areas of their choosing, stands out from
other examples as the user can zoom an infinite number of times due to its
recursive nature [8]. We code this example as having an infinite total count.

Simultaneous. The simultaneous component represents the number of different
scales that are visible from a viewpoint or on a screen at once. We define
visible as where the user can still see the finest detail, whether that is an
individual item, the smallest trend, or something else. Auto Zoom only shows a
single scale at a time, and the user has to wait for it to zoom out to show a
different scale. A corpus example with only one scale visible at once is the
large physical timeline of Earth’s biological history at science museum local
to the authors (Science Museum Timeline) [3].

Static Multilevel shows two different scales on screen at once. MizBee [42], a
visualization tool for analysing genomic data at the genome, chromosome, and
block levels, shows three different scales on screen simultaneously.

The number of simultaneous scales cannot be larger than the total component,
as any scales which are different and visualized simultaneously must be
counted towards the total count.

Separate. The separate component represents the number of simultaneously
visible scales that do not occlude each other in any way. The corpus example
FingerGlass, which is a technique for zooming on multitouch screens, creates a
zoomed lens on top of the original view rather than in a separate area of the
screen when a user zooms [36].

Both synthetic illustrations of Static Multilevel and Interactive Multilevel,
shown in Figures 1a and b, have two separate scales as in both cases neither
of the scales occludes another. The corpus example Rivet, which includes a
multitier strip chart that shows multiple zoomed levels of computer systems
data, shows three separate scales at once without occlusion, so its separate
component is three.

The number of separate scales cannot be larger than the simultaneous
component, as any scales which are different and visualized simultaneously
without occlusion must be counted towards the simultaneous count.

####  4.1.2 Step Size Type

Step size type is a subdimension that describes the relationship between steps
from scale to scale in multiscale examples, and it has five choices: constant,
data driven, user continuous, user discrete, and user mixed. In order for a
visualization to have steps between scales, it must have multiple scales, so
examples with a total count of one were left with blank step size type cells.

Constant. When this dimension is constant, it means that the difference
between scales, which is typically multiplicative, does not change. Auto Zoom
zooms out at a constant rate, so the step size is constant. A corpus example
is XKCD’s Money webcomic (XKCD Money) [45], a unit chart of different
quantities of money on different scales with multiplicative increases of 1000
between each scale.

Data driven. The step size type being data driven means that the scales’
mappings change by a data-based amount. Static Multilevel uses a data driven
step size as it is not chosen by the user and it is based on an aspect of the
data. Multiscale Unfolding [28] visualizes genomic data on multiple scales,
and the scales are based on different levels of structure from the chromosome
to the base pair. The differences between these scales, then, come directly
from that pre-existing structure.

User continuous. The user continuous option describes when the user chooses
the step size from a range. The synthetic illustration Interactive Zoom, shown
in Figure 1c, uses this choice as the user decides on the specific zoom level
themself. An example is the zoomable timeline view (Multiscale Trace) from
Ezzati-Jivan et al. [20], a visualization of large quantities of trace data
that the user can zoom in to.

User discrete. In some visualizations, designers allow users to choose how to
navigate from a discrete set of options, a choice we call user discrete. The
synthetic illustration of Interactive Multilevel, shown in Figure 1b, uses
this choice as the user chooses the step size by selecting an area to zoom in
to from a set of four discrete options. Figure 6 of Waldin et al.’s Cuttlefish
paper [60] illustrates this idea with a hierarchical treemap where the user
can click on a node and have it expanded out, effectively zooming in.

User mixed. Finally, some visualizations use both user continuous and user
discrete, which together form the final option for this dimension: user mixed.
EVEVis from Miller et al. [43], a visualization for evolution data at multiple
scales, uses user mixed as the user can first select a region from a
continuous space, then further zooming is done via discrete selections.

####  4.1.3 Encodings

Encodings is a subdimension that describes whether different scales use the
same or different visual encodings. This subdimension relies on a
visualization using more than one scale, so it is left blank for corpus
examples with a total of one.

Different. Static Multilevel uses different encodings on each scale, with a
line chart at the higher level and a bar chart at the lower level. Mittman et
al.’s multi-level visualization scheme for poetry (Multilevel Poetry) [44],
which visualizes poetry at the four levels of phoneme, full poem, small set of
poems, and large set of poems, uses different encodings on each of these four
different scales.

Same. In contrast, an example that uses the same encoding on every scale is
Auto Zoom. The same is true for Zoom Line Chart from FusionCharts [55], a line
chart where the user can choose regions to zoom in to.

####  4.1.4 Association

Association is a subdimension that describes how marks representing the same
item can be visually linked across simultaneously visible scales. It relies on
a visualization including multiple scales visible at once, and was left blank
for examples with a simultaneous count of one.

None. Chromoscope, a visualization of genomic data at multiple scales [40],
does not include any marks or channels to show association between its
different scales, so we code this subdimension as none.

Marks. Association can be done by marks, often using connection marks to show
association. An example of this approach is Static Multilevel, where there are
lines showing how the lower level and the focused point of the higher level
are associated.

Channels. The other option we identified in our corpus was association by
visual channels such as colour. Interactive Multilevel uses the colour channel
to show that the lower-level scale is the zoomed in to the blue part of the
higher-level scale. Colour is used to show association in TraXplorer [33], an
implementation of stack zooming where there are multiple levels and branches
of zoom, as the colour of a selected region’s background matches the colour of
its zoomed-in counterpart’s border.

Both. TimeNotes [5], a multiscale visualization technique for time-oriented
data inspired by stack zooming, used both marks and channels to show
association.

###  4.2 Navigation

The Navigation dimension covers the interaction capabilities of the design. It
includes three subdimensions: type, mode, and visceral time.

####  4.2.1 Type

Type is a subdimension that describes the ways in which users can navigate
between and within scales.

None. Some visualizations, such as Static Multilevel, are none, meaning they
do not have any interaction. Temp Earth, a corpus example, visualizes the
Earth’s temperature on a set of increasingly-zoomed scales towards the more
recent past [12], and also has no user navigation.

Zooming. Zooming is navigation that changes the mapping of a scale or adds
another scale with a different mapping without changing the point of focus.
Auto Zoom uses zooming to gradually change the mapping. Cell Size and Scale
[58], a digital visualization similar to Scale of the Universe 2 [31] where
the user zooms through objects of different scales, is an example of zooming,
as the scale changes but the point of focus does not.

Panning. Panning is navigation that changes the point of focus without
changing the mapping. The Deep Sea [1] is a digital visualization where the
user gradually pans to traverse the range of ocean depths from the surface to
the bottom of the ocean. The mapping of the scale does not change during the
traversal, but the point of focus on the scale does.

Both. Some visualizations incorporate both zooming and panning. Rivet [11], a
visualization tool for the analysis of computer system data, has a multi-tier
strip chart where the user can select point of focus to zoom in on,
effectively panning and zooming at once. The synthetic illustration
Interactive Zoom also uses both, but rather than having the user drag the
mouse to select the new zoom window in one step, it has the user place the
mouse on the desired point of focus and uses the scroll wheel to zoom until
they are satisfied.

When visualizations incorporated both panning and zooming, we found that they
were intended to have open-ended navigation, where the navigation is driven
fully by the user. When only one of zooming or panning were in use, the
designs had an intended path to follow through the data, either by panning
along a large scale or by zooming along many scales.

####  4.2.2 Mode

Mode is a subdimension that describes how navigation is done, whether through
physically moving oneself or by digitally navigating via computer input
devices. If there is no navigation in an example, we left the mode cell blank.

Physical. In some navigable visualizations, users control the navigation by
physically moving their body. Visitors to the University Timeline Walk [4], a
timeline of Earth’s history embedded in the ground at a university local to
the authors, pan across the timeline by physically moving so they can view the
next pieces of information. Large Viewing Vis, a technique that uses a large
screen with visible features at many levels and that requires users to move
closer to to see smaller features, has users both pan and zoom physically.

Digital. Virtual visualizations are controlled by an input device such as
computer mice, keyboards, or touch screens. Interactive Multilevel is an
example of a visualization where navigation is controlled by a mouse.
FingerGlass [36], a technique for digital geographic maps with pinch to zoom
mechanics, used touch screens for navigation. Universcale [46], a
visualization where the user scrolls through different objects on different
scales, relies on a device that can scroll, such as a mouse with a scroll
wheel.

####  4.2.3 Visceral Time

The visceral time subdimension describes whether a visualization relies on the
user’s experience of time passing while navigating. If there is no navigation
in an example, we left the visceral time cell blank.

No. Most of our corpus examples do not rely on the experience of time. For
example, in Here is Today, a timeline visualization with a single total scale
that starts with a single day and zooms out to the age of the universe [57],
the user navigates by clicking and the animations are fast, meaning that users
can fully navigate through the visualization very quickly.

Yes. Some visualizations of fully unfamiliar datasets do rely on visceral
time. Trail of Time, a large physical timeline that people hike along in the
Grand Canyon where each meter represents one million years [35], is an example
of using the significant amount of time it takes for a visitor to complete the
hike to help them conceptualize the multi-billion-year timeline. Another
corpus example of this is Powers of Ten [17], a video documentary that
gradually zooms between different scales. Video playback at a standard speed
takes nearly ten minutes, so the sense of the time required to zoom at a
constant rate between the scales aids the watcher’s conceptualization of the
difference between the scales.

###  4.3 Familiarity

The familiarity dimension is a dimension with a single subdimension: concrete.
It describes whether visualizations compare familiar objects to unfamiliar one
to help users understand the scale of the unfamiliar ones, and is related to
the concept of concrete scales [16]. Examples of familiar objects from our
corpus are meters, days, and dollars.

No. Execution Trace Vis [56], a visualization of complex program traces, is an
example of a visualization that does not incorporate familiar objects as all
items in the dataset are events in a trace log which occur during time spans
that are much smaller than those humans are familiar with.

Yes. Universcale [46] begins focused on familiar objects near a meter in size,
such as turtles or dogs which are familiar to many, but zooms to show very
small or very large objects which have sizes that humans do not directly
interact with.

##  5 Strategies

We identified five groups of examples, which we call strategies, from our
iterative coding of examples with respect to their dimension choices: Single-
View Pan and Zoom, Simultaneous Occluding Embed, Simultaneous Separate
Multilevel, and Familiar Zoom, and Lengthy Pan. The strategies are both
concise, in that they are simple to reason about due to their lack of
complexity, and disjoint, in that they do not overlap. While each strategy
includes a set of dimensions which must use certain choices, other choices,
unmentioned below, are unrestricted. We now describe and discuss each
strategy.

###  5.1 Single-View Pan and Zoom

19 of our corpus examples use the Single-View Pan and Zoom strategy, which
involves multiple total scales but only one simultaneous scale and both
zooming and panning. Given the large number of examples using this strategy,
there are many variations. While all of the examples which use this strategy
have only a single simultaneous scale, the number of total scales can vary
significantly depending on the number of scales or levels within the data.

Some examples that use this strategy rely on pointing and scrolling with the
mouse, while others allow the user to click and drag to choose the next
viewing window. Others force the user to choose a discrete zoom option from a
set, rather than allowing continuous zooming, such as Multilevel Poetry [44].
Also related to navigation, the Large Viewing Vis stands out within this group
for its use of physical navigation, although the overall strategy is the same.

Many of these visualizations use the same encodings at each scale, but some
have different encodings at different scales. Europe OSM, a visualization of
map data that the user can view at different levels [66], uses pie charts to
summarize data when zoomed out but encodes finer-grain detail using overlaid
grids and pointmarks when zoomed in.

###  5.2 Simultaneous Occluding Embed

12 of our corpus examples use the Simultaneous Occluding Embed strategy,
meaning that they have multiple simultaneous scales but only one separate
scale as the simultaneous scales occlude each other in some way. They also
need both zooming and panning.

Many of these examples use the inset zoom or lens zoom techniques, where a
zoomed-in area appears in a window on of a visualization. This strategy limits
the zoomed-in window to be smaller than the rest of the visualization, as full
occlusion would result in only a single simultaneous scale. Sometimes this
window occludes the area being zoomed into, like in Melange [19], a technique
that folds a visualization to make the zoomed-in area appear physically closer
to the user. In other examples, such as FingerGlass [36], the window occludes
a separate part of the visualization, sometimes chosen by the user.

Similar to the Single-View Pan and Zoom strategy, step size type can vary.
However, our corpus does not include any examples of this strategy where the
user must use physical navigation. Many of these examples use marks to show
association between the scales, but three include no association. Multi-Foci
COVID Vis, a visualization of COVID data in selected geographic areas [41], is
the only example using this strategy to also use different encodings on
different scales.

###  5.3 Simultaneous Separate Multilevel

9 of our corpus examples use the Simultaneous Separate Multilevel strategy,
which describes visualizations where there are multiple separate scales,
meaning also that there are multiple simultaneous and total scales, but which
do not rely on familiarity. The difference between this strategy and
Simultaneous Occluding Embed is that multiple scales appear without occluding
one another, meaning that all scales can be of equal size.

Similar to both Single-View Pan and Zoom and Simultaneous Occluding Embed,
examples using this strategy can both use different encodings or the same
encodings on the varying scales. We find that all options for association are
in use by at least one example in this group.

We found that when encoding choices across the separate scales of examples
which use this strategy are the same, the different scales are aligned and
either stacked on top of each other or placed beside each other with most of
them using some form of association between them. In contrast, when the
encoding choices are different, the scales are unaligned and placed in
different completely separate views with no association.

All but one example that used this strategy employed both panning and zooming,
and all of these examples with navigation relied on digital navigation. The
one example that did not use both panning and zooming was Temp Earth [12],
which divides the axis into pieces, each of which has a different scale which
is a multiple smaller than the one on its left.

###  5.4 Familiar Zoom

9 of our corpus examples use the Familiar Zoom strategy, which relies on
zooming through a series of scales that include at least one familiar scale.
All Familiar Zoom examples must also incorporate familiarity. We also
categorize an example that has no navigation but shows multiple separate
scales with association by marks between them, XKCD Money [45], as Familiar
Zoom. Some of these examples focus on helping users conceptualize large
scales, some with small scales, and some with both.

These examples rely on the user making comparisons between the different
scales with varying step size type, beginning with a familiar scale. Most of
these examples only show one simultaneous scale, with the zooming modifying
the scale of the single view. However, three of these examples have multiple
separate scales. Two of the examples with multiple separate scales include a
form of association between the scales, likely to aid the comparison between
them. While most of these examples do not rely on visceral time, one of them
does: Powers of Ten [17].

###  5.5 Lengthy Pan

5 of the examples in our corpus use the Lengthy Pan strategy, which involves
only a single total scale that the user pans along and which relies on
visceral time. All of these examples focused on helping users conceptualize
large scales.

These examples can incorporate both physical navigation, where the user
physically moves their body along it, or digital navigation, where the user
scrolls along it. All of the examples that use this strategy take a
significant amount of time to fully navigate through, as intended by the
designer. While many visualizations are designed to reduce human feelings of
boredom or exhaustion by making information analysis and communication fast,
we conjecture that Lengthy Pan examples rely on these feelings to help convey
a sense of scale. Two of these examples additionally include familiar items in
their datasets for comparison against the unfamiliar items, showing use of
familiarity.

##  6 Discussion

We now discuss missed opportunities in our corpus that we identified through
usage of the design space, and the strengths and limitations of our design
space and strategies.

###  6.1 Missed Opportunities

Here, we discuss benefits and limitations of different choices within
dimensions and how some examples may have benefited from alternate choices.

####  6.1.1 Employing Physical Navigation

Dynamic navigation is well-used in multi-scale visualizations with large
scale-item ratio. Navigation is useful for analysis in these visualizations as
it allows the user to choose regions to analyze in greater detail. We also see
it used in examples intended for presentation, possibly to increase engagement
through interactivity or to avoid overwhelming users by gradually revealing
information. Most the corpus examples were digital and had to use input
devices to control navigation, but a few examples relied on physical movement.
Ball et al. found that physical navigation is beneficial and preferred by
users in the right conditions [7], so we encourage design teams with
sufficient resources, particularly space and materials, to consider employing
some use of physical movement by the user.

####  6.1.2 Simultaneous and Separate Scales

The majority of the corpus examples used small total, simultaneous, and
separate counts. Using a single separate scale or a small number of separate
scales can allow for more space to be dedicated to detail within the scale,
reduce the complexity of the visual representation, and reduce the complexity
of the navigation required. However, increasing the number of total scales
that the user can navigate to can help to show more detail, and increasing the
number that are simultaneous and separate can be beneficial for comparison
across scales or drill-down navigation. Here is Today [57] may have benefited
from the use of multiple simultaneously visible scales, as its navigation
between scales was sometimes hard to follow due to multiple changes of
direction in the direction of motion of the timeline. Many of the examples
using the Single-View Pan and Zoom strategy, such as Hierarchical Route Maps,
a visualization of routes with zooming and panning capabilities to help
navigators in both dense and sparse areas [63], may have benefitted from
additional separate scales, as they can support the user in navigation by
allowing the user to skip to a different point of focus without needing to pan
around or zoom out and back in.

####  6.1.3 Use of Differing Scale Encodings

Most examples with multiple scales use the same visual encoding on each scale.
In our corpus, most examples which use different encodings on different scales
have pre-existing, real-world structure. However, we believe this design
choice may also be beneficial for other scenarios. If the visualization
encodes large quantities of data across multiple scales, then some scales will
likely have much more data to encode than others, which may change what
encoding is most effective. Multiscale Trace [20] uses different encodings on
different scales for this reason.

In a similar scenario, higher-level scales may be used simply for navigation,
to find smaller regions to analyse in more detail. In this case, the user is
using different scales for different tasks, and the designer should consider
this difference when making visual encoding choices for the different scales.
Europe OSM [66] uses different encodings on different scales for this reason.

An example where using different encodings on different scales may have been
beneficial is Rivet [11], which encodes larger quantities of data on its
highest-level scale than its lowest-level scale and uses different scales for
different tasks.

####  6.1.4 Explicit Association Between Scales

While association by marks or channels is not underused in our corpus,
examples which did not use it may have benefitted from it. Using association
is helpful for comparison across multiple scales as it can explicitly show a
change in mapping by relating the same item across scales. However,
association is also helpful for user tasks other than across-scale comparison.
For example, if the user is intended to use the multiple scales to navigate to
a smaller region for analysis, using association can help the user keep track
of the zoomed-in location on the larger-scale landscape. One example that uses
the Simultaneous Separate Multilevel strategy and also incorporate different
encodings on different scales, Chromoscope [40], lacks any association, but
some association may have helped users to navigate and to align the associated
marks on different scales.

####  6.1.5 Visceral Time and Familiarity

In our example corpus, the only examples that used visceral time or
familiarity were examples intended to help users conceptualize large or small
scales or the relationships between them. When designing a visualization for
other tasks, the incorporation of visceral time may just slow down the
analysis without improving the user’s takeaways, and artificially adding
familiar items to allow familiarity would likely require the addition of
different scales to navigate through but not aid the user in any way.

However, when designing a visualization for these tasks of conceptualizing and
comparing scales, visceral time and familiarity may be beneficial to consider.
In particular, three of the examples that use the Lengthy Pan strategy do not
have familiarity, but beginning with a familiar object may help to convey the
size of the scale.

###  6.2 Strengths and Limitations

We assess the design space and the strategies in terms of descriptive,
generative, and evaluative power [9].

The design space has strong descriptive power, as all meaningful differences
from our analysis are distinguishable in the final version. We have further
confidence in its completeness and descriptive power because we have evidence
of saturation: no example in the systematic set, which we found and coded
during the final Refine stage, required any changes to the design space to
describe. (Although we further reflected and refined the dimensions, choices,
and strategies after adding the systematic set, we were able to fully describe
all examples in the systematic set with the design space prior to the final
modifications.) The five strategies also demonstrate the descriptive power of
the design space, because it is defined in terms of choices within the design
space dimensions. The strategies themselves also have descriptive power, in
that they provide a disjoint partition of the set of examples. One limitation
of our work is that the set of strategies may not be complete: although they
do fully describe our example corpus, future designs may use new strategies.

The design space also has generative power. Analysing it revealed missed
opportunities within our example corpus which, if available during their
design, could have resulted in changes to some examples. In the future, it
could inform designers about design possibilities that they may not have
considered without this specific prompting. Our strategies also demonstrate
strong generative power, because the set of five strategies is a very concise
set of options. Choosing one of these strategies can inform and speed design
by immediately constraining some of the design choices. We note that the
concise set of just five strategies may be useful for quickly choosing which
out-of-the-box solutions to use when novelty is not required; in contrast, the
more detailed design space may be useful for generating custom visualizations.

One limitation of this paper is that we have not yet validated this design
space or strategies in terms of evaluative power, an effort we leave for
future work.

Another limitation of the design space and strategies is that our collection
of examples from real-world use for the corpus was opportunistic rather than
systematic. While we did systematically search academic literature, a
systematic search for practitioner examples would not be straightforward to
conduct. In particular, finding examples used for education and communication
by practitioners is challenging using visualization search terms; some are not
even posted publicly online.

##  7 Conclusion

In this paper, we present a design space for visualization scenarios with
large scale-item ratios: large disparities between the size of the smallest
item and the largest scale. The design space has three dimensions: Scales,
Navigation, and Familiarity. These dimensions are split into eight
subdimensions: count, step type, encodings, and association for Scales; type,
mode, and visceral time for Navigation; and concrete for Familiarity. We also
present a set of five strategies, which are shared approaches with respect to
design space choices and are a partition such that each example fits into
exactly one strategy. We collect and analyze a corpus of 54 examples from both
research and practice, and code them according to the hierarchical dimensions
of this design space and the five strategies. We used these coded examples to
develop, validate, and illustrate the design space through three rounds of
data collection. We present an analysis of missed opportunities for several
examples that considers alternative dimension choices and strategies. Finally,
we evaluate the strengths and limitations of the design space and the
strategies.

##  8 Appendix

We include a short description of each corpus example in Table 2.

## Supplemental Materials

The supplemental material is available on OSF at
https://osf.io/wbrdm/?view_only=04389a2101a04e71a2c208a93bf2f7f2, released
under a CC-BY-4.0 license. We provide a CSV file containing the example
corpus, coded by the dimensions and by the strategies. In addition to the
information shown in Table 1, it contains the full titles of examples, the
specific figure we coded for academic examples, and how we found the example.

Example |  Cit. |  Description
---|---|---
Single-View Pan and Zoom
Zoom Line Chart |  [55] |  a technique for zooming where the user drags with the mouse to select an area to zoom in to
Cuttlefish (fig 6) |  [60] |  a hierarchical treemap where the user can click on a node and have it expanded out, effectively zooming in
EVEVis |  [43] |  a visualization for evolution data at multiple scales
Multilevel Poetry |  [44] |  a tool for visualizing poetry at the four levels of phoneme, full poem, small set of poems, and large set of poems
Multiscale Trace |  [20] |  a visualization of large quantities of trace data that the user can zoom in to
Europe OSM |  [66] |  a visualization of map data that the user can view at different levels
Zoomable Treemaps |  [10] |  a technique for navigating through complex treemaps, including zooming in to user-chosen areas
Chameleon |  [59] |  a technique for adjusting colour scheme based on zoom level, implemented into an interactive multi-scale visualization of HIV
Hierarchical Route Maps |  [63] |  a visualization of routes with zooming and panning capabilities to help navigators in both dense and sparse areas
Large Viewing Vis |  [32] |  a type of visualization that uses a very large screen to display visualizations which users can navigate by physically moving their bodies
Kyrix-S |  [54] |  a system for creating scalable scatterplots where the user can navigate, including selecting areas to zoom in to
Membrane Mapping |  [62] |  a visualization tool for zooming in to different scales of cell, from mesoscopic to molecular
Execution Trace Vis |  [56] |  a visualization of complex program traces
MuSE |  [21] |  a tool that utilizes infinite pan and zoom for creating visualizations at multiple scales
ScaleTrotter |  [29] |  an interactive, multi-scale visualization of genome data
SpaceFold |  [13] |  a technique for zooming where users fold the visualization
TagNetLens |  [26] |  an interactive tool for exploring tag data with zooming and panning
Hierarchy Vis |  [30] |  a visualization technique for hierarchical data with zooming by selecting links between nodes
Chemical Vis |  [64] |  a technique for visualizing bioactive chemical data at multiple levels of detail
Simultaneous Occluding Embed
Melange |  [19] |  a technique that folds a visualization to make the zoomed-in area appear physically closer to the user
FingerGlass |  [36] |  a technique for zooming on multitouch screens
Tabletop Gestures |  [50] |  a set of techniques for multiple users to zoom in on tabletop devices
Gimlenses |  [48] |  a technique for navigating 3D models by creating a tree of zoomed insets
GrouseFlocks |  [6] |  a system for navigating hierarchical graphs with zooming
Digital Earth |  [52] |  a system for visualizing geospatial data with a tree of zoomed insets
AdvEx |  [65] |  a visualization of adversarial attacks with inset zooming
Scalable Insets |  [39] |  a technique for interactively zooming through the use of zoomed insets
Multi-Foci COVID Vis |  [41] |  a visualization of COVID data in selected geographic areas
PhysicLenses |  [13] |  a technique for zooming where users use two fingers to create a magnification lens
TissUUmaps |  [47] |  a visualization of spatial omics data with inset zooming
TrailMap |  [67] |  a visualization of map data with a zoomed view anchored in the corner to show the bookmarked region
Simultaneous Separate Multilevel
Multiscale Unfolding |  [28] |  a visualization of multiple levels of DNA
Rivet (MTSC) |  [11] |  a visualization tool for the analysis of computer system data
Temp Earth |  [12] |  a visualization of the Earth’s temperature on a set of increasingly-zoomed scales towards the more recent past
TraXplorer (fig 2) |  [33] |  an implementation of stack zooming where there are multiple levels and branches of zoom
Mandelbrot Explorer |  [8] |  a website for exploring the Mandelbrot set where the user recursively selects a spot to zoom in
MizBee |  [42] |  a visualization tool for analysing genomic data at the genome, chromosome, and block levels
PolyZoom |  [34] |  a visualization technique for constructing a tree of zoomed views
Chromoscope |  [40] |  a visualization of genomic data at multiple scales
TimeNotes |  [61] |  a multiscale visualization technique for time-oriented data inspired by stack zooming
Familiar Zoom
DeLVE |  [5] |  a visualization of historical events across many scales, showing all the scales simultaneously
Here is Today |  [57] |  a timeline visualization with a single total scale that starts with a single day and zooms out to the age of the universe
Powers of Ten |  [17] |  a video documentary that gradually zooms between different scales
XKCD Money |  [45] |  a unit chart of different quantities of money on different scales with multiplicative increases of 1000 between each scale
Cell Size and Scale |  [58] |  a digital visualization where the user zooms through objects of different scales
Scale of the Universe 2 |  [31] |  a visualization of various objects in the universe from beach balls to the universe itself to quantum foam the width of one planck
The Size of Space |  [2] |  a visualization were the user steps through a series of objects of exponentially increasing size, comparing them with those that came before
Universcale |  [46] |  a visualization where the user scrolls through different objects on different scales
US Debt |  [24] |  a visualization where the user scrolls through increasing quantities of money visualized by referring to the previous quantity
Lengthy Pan
Science Museum Timeline |  [3] |  a large physical timeline of Earth’s biological history at science museum local to the authors
The Deep Sea |  [1] |  a digital visualization where the user gradually pans to traverse the range of ocean depths from the surface to the bottom of the ocean
Trail of Time |  [35] |  a large physical timeline that people hike along in the Grand Canyon where each meter represents one million years
University Timeline Walk |  [4] |  a timeline of Earth’s history embedded in the ground at a university local to the authors
Wealth Shown to Scale |  [38] |  a visualization where the user scrolls along an extremely long axis which shows monetary values from one thousand to trillions of dollars
Table 2: Brief text descriptions for each of the 54 examples in the corpus.
Abbreviations: Cit. = Citation.

## References

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  * [2] N. Agarwal.  The size of space.  https://neal.fun/size-of-space/, 2019.  Accessed: 2023-11-21.
  * [3] Anonymized.  Science museum earth timeline.  URL anonymized, 2021.  Accessed: 2023-11-21.
  * [4] Anonymized.  University timeline walk.  URL anonymized, 2022.  Accessed: 2023-11-21.
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