A Taxonomy of Treemap Visualization Techniques

Willy Scheibel

, Matthias Trapp

, Daniel Limberger

and J

urgen D

ollner

Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Germany

Keywords:

Treemaps, Taxonomy.

Abstract:

A treemap is a visualization that has been speciﬁcally designed to facilitate the exploration of tree-structured

data and, more general, hierarchically structured data. The family of visualization techniques that use a

visual metaphor for parent-child relationships based “on the property of containment” (Johnson, 1993) is

commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes

increasingly important to distinguish clearly between techniques and their speciﬁc characteristics. This paper

proposes to discern between Space-ﬁlling Treemap

, Containment Treemap

, Implicit Edge Representation

Tree

, and Mapped Tree

for classiﬁcation of hierarchy visualization techniques and highlights their

respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one

another:

⊂ T

. With this proposal, we intend to stimulate a discussion on a more unambiguous

classiﬁcation of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.

1 INTRODUCTION

Treemaps are well established visualization techniques

for tree-structured data and hierarchically structured

data (McNabb and Laramee, 2017). Such data is

prevalent in almost all application domains, includ-

ing demographics (Jern et al., 2009), business intel-

ligence (Roberts and Laramee, 2018), health (Chaz-

ard et al., 2006), and software development (Merino

et al., 2018). Since a large part of today’s data

sets is tree structured—either inherently or otherwise

supplemented—the treemap is a versatile tool for in-

formation visualization. Within the past three decades,

over 300 hierarchy visualization techniques (including

traditional treemaps) and variations have been pro-

posed (Schulz, 2011). The main task of a treemap

is to map the structure of a data set and at the same

time to enable additional visual variables per data el-

ement (Carpendale, 2003) (see Figure 1). Thereby,

a treemap can be varied and adjusted using different

layout algorithms (Vernier et al., 2018) and additional

visual variables, visual metaphors, and interaction tech-

niques (Limberger et al., 2019).

Although there seems to be some consensus on

whether a visualization technique is called a treemap or

not (“property of containment”, Johnson 1993), there

are different views on how strictly to adhere to the con-

tainment metaphor. Bubble Treemaps (G

ortler et al.,

https://orcid.org/0000-0002-7885-9857

https://orcid.org/0000-0003-3861-5759

https://orcid.org/0000-0002-9111-4809

2017) and BeamTrees (van Ham and van Wijk, 2003)

are two examples. This vague use of the term treemap

and the lack of consolidation of the different concepts

makes it difﬁcult to deal with individual cases, com-

plicates summary statements, and might constrain a

deeper understanding.

We propose a taxonomy of four classes of visual-

ization techniques resulting mainly from the encoding

and representation of parent-child relationships. The

taxonomy considers typical properties of layout and

geometry representations and is itself a hyponymy.

Each class contains common hierarchy visualization

techniques that could be called treemaps. In a brief

discussion, we emphasize the applicability of and ex-

tension points to this taxonomy. Our goal is a more

unambiguous classiﬁcation of treemaps and a strength-

ened concept of treemap categories. Together, these

could form a basis for discussions of limitations, guide-

lines, and generalizations of techniques and, further-

more, facilitate the identiﬁcation and differentiation of

such techniques for domain-speciﬁc applications.

2 FOUNDATION OF TREEMAPS

For the last 30 years, the treemap visualization tech-

nique was extended, reﬁned, and used in different

contexts. This adaptation process extended the scope

of and expectations from treemaps for visualization

designers and users. Here, we want to recapitulate

the original forces and predecessors leading to the

Scheibel, W., Trapp, M., Limberger, D. and Döllner, J.

A Taxonomy of Treemap Visualization Techniques.

DOI: 10.5220/0009153902730280

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP, pages

273-280

ISBN: 978-989-758-402-2; ISSN: 2184-4321

273

Figure 1: An example of a 2D treemap depicting software

system data using area and color mapping as well as nesting.

ﬁrst treemap visualization technique and introduce a

conceptual visualization pipeline that seems common

to all hierarchy visualization techniques. Further, we

review other generalization approaches for treemaps.

Predecessors of Treemaps.

Upon looking back

from the ﬁrst mentioning of a treemap, the preced-

ing ingredients for this visualization technique can be

found in statistical cartograms, mosaic displays, and

nested depiction for hierarchical components. The

early treemaps are a skilled combination of those tech-

niques. One original building block is the statistical

cartogram (Raisz, 1934). These manually designed

cartograms depicted geographic and demographic data

by derivation of rectangular diagrams. Thereby, ad-

jacency of geographic locations were maintained and

the rectangle for a location could be varied in size de-

pending on the demographic data. These properties

are now backed by algorithms and attainable by IT

systems as well (van Kreveld and Speckmann, 2007).

A second building block, the mosaic display (Friendly,

2002), uses an algorithmic approach to dissect the

surface for depiction. The mosaic display appeared

around 1877 and gained scientiﬁc attention in 1981.

The technique uses a two-dimensional subdivision of

a rectangle based on categorical distribution for data

points in two attributes—or more using scatter-plot

techniques. The main insights from those diagrams

are the distribution of data items among different cate-

gories. That is, treemaps were anecdotally referred to

as “large weighted Venn diagrams” (Johnson, 1993).

A third building block is the depiction of hierarchi-

cal relations using nesting. For example, they can

be found with contour models (Johnston, 1969) and

Nassi-Shneiderman-Diagrams (Nassi and Shneider-

man, 1973), used for depiction of hierarchical com-

ponents of algorithms. A combination of these ap-

proaches resulted in the ﬁrst treemap visualization

technique (Johnson and Shneiderman, 1991).

Treemap Visualization Pipeline.

We assume that

the creation of visual representations from data, e.g.,

the transformation of tree-structured data into images

from tree maps, can be understood as a visualization

pipeline. This pipeline can be a part of an interactive

feedback loop of creating images from data, perceiving

these images, and gaining insights—the visualization

process (Figure 3). Within this process, the visualiza-

tion pipeline is represented using three phases, namely

Preprocessing, Mapping, and Image Synthesis. The

required algorithms to create a treemap visualization

from raw data are associated to one, or sometimes

multiple, phases of this pipeline. Thereby, some algo-

rithms are speciﬁc to treemap visualization and others

are more generally applicable in the ﬁelds of data pro-

cessing and information visualization. The speciﬁcs

for treemap visualization techniques are prominent

in the mapping phase and the geometry phase, i.e.,

these phases highly contribute to the appearance of a

treemap visualization (Figure 2).

Generalization on Treemaps.

The similarities of

treemap visualizations, as proposed by Shneiderman

and Johnson, to other hierarchy visualization tech-

niques were used before to provide means of general-

ization of hierarchy visualization techniques. As such,

Schulz et al. coined the term implicit hierarchy visual-

izations (Schulz et al., 2011a) as a category of visual-

ization techniques that uses implicit, i.e., non-drawn,

metaphors to represent the parent-child relationships.

Later, Schulz et al. presented a generative layout ap-

proach for rooted tree drawings that could be used to

create a wide range (and mixed use) of visualization

Preprocessing

Mapping

Image Synthesis

Analysis

Preprocessing & Filtering

Layouting

Geometry Creation

Rendering

Problem Data

Visualization Data

TopologyAttributes

Layout

Geometry

Image

Figure 2: Concept of a hierarchy visualization pipeline in-

cluding the tree structure (topology), per-node attributes, and

the spatialization process of this abstract data.

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

274

Visualization Pipeline

User

Preprocessing

Mapping Image Synthesis

Perception

& Cognition

Exploration

API

Problem Data Focus Data

Geometry

Image

KnowledgeConﬁguration

Figure 3: Illustration of the visualization process and the included visualization pipeline. Adapted from van Wijk (2005).

techniques with implicit and explicit edge represen-

tations (Schulz et al., 2013). Likewise, Baudel and

Broeksema discussed a design space for rectangular,

sequential, and space-ﬁlling treemap layouts (Baudel

and Broeksema, 2012), creating an own set of treemap

visualization techniques.

3 CATEGORIES

Throughout the community of information visualiza-

tion, there seems to be some consensus on the visual-

ization techniques that may be called a treemap. Disre-

garding their differences, the emerged set of common

characteristics is as follows:

•

First, a treemap is a visualization technique suit-

able to depict tree-structured data.

•

There is a representation of the parent-child rela-

tionships without use of explicit edge links, i.e.,

the containment metaphor (Schulz et al., 2011a).

•

The depiction of leaf nodes implies an area or

volume in the geometry space (Park et al., 2018).

•

The representation of sibling nodes should be guar-

anteed to be non-overlapping (Johnson and Shnei-

derman, 1991).

In other words, the prominent property for treemaps is

the visual containment in the depiction, which implies

a containment within the layout as well. Empirically,

the technique in question allows for at least one of

either area, volume, or color for attribute mapping

of leaf nodes. More effective treemap visualization

techniques allow for at least two visual variables for

attribute mapping of leaf nodes.

Based upon this, we believe the term treemap is

not only suitable for the ﬁrst treemap introduced by

Shneiderman and Johnson but for a broader range of

hierarchy visualization techniques. To contribute to

the discussion, we propose four different sets of char-

acteristics that could be used to distinguish between

treemaps, tree visualization techniques, and general

hierarchy visualization techniques (H ):

• the Space-ﬁlling Treemap T

• the Containment Treemap T

• the Implicit Edge Representation Tree T

• and the Mapped Tree T

These categories focus on the characteristics of the

spatialization process (resulting in the layout) and the

visual representation in geometry space. Thereby, each

category is a superset of the previous one, forming a

hierarchy of tree visualization techniques. This prop-

erty allows to categorize the taxonomy itself as a hy-

ponymy. The four categories are motivated and deﬁned

as follows.

Space-ﬁlling Treemap.

A space-ﬁlling treemap re-

sults from a recursive subdivision of a surface (John-

son and Shneiderman, 1991) or an

-dimensional

cube (Johnson, 1993). The main characteristic is the

full subdivision and distribution of a parents’ surface

or volume for its children, resulting in space-ﬁlling

depictions of the leaf nodes. This category of treemaps

includes not only subdivisions of rectangles but oth-

ers as general rectangular (Wattenberg, 2005), convex

polygonal (Balzer and Deussen, 2005; Wang et al.,

2019), and non-convex polygonal (Auber et al., 2013)

partitions as well (Figure 5).

Containment Treemap.

The category of Contain-

ment Treemaps extends Space-ﬁlling Treemaps by loos-

ening the space-ﬁlling property. This still requires that

child nodes are located within their parent surfaces

but they must not utilize all the space of the parent.

Mainly, hierarchy visualization techniques from this

category are currently considered treemap visualiza-

tion techniques. Typically, the corresponding layouts

are created using packing algorithms (Yamaguchi and

Itoh, 2003; Wettel and Lanza, 2008; Scheibel et al.,

2018) (Figure 6). Another approach is the transfor-

mation of the treemap layout, e.g. for Generalized

Treemaps (Vliegen et al., 2006). Similarly to space-

ﬁlling treemaps, containment treemaps allow for dif-

ferent geometrical shapes as well, e.g., circles (Wetzel,

2003; G

ortler et al., 2017) and ellipses (Collin et al.,

2007).

A Taxonomy of Treemap Visualization Techniques

275

Space-ﬁlling Treemap

Containment Treemap

Implicit Edge Rep-

resentation Tree

Mapped Tree

⊂ ⊂ ⊂

Figure 4: The four proposed treemap categories, namely Space-ﬁlling Treemap, Containment Treemap, Implicit Edge Represen-

tation Tree, and Mapped Tree. Each category includes their predecessors, building a more general category. The dotted line

highlights the observed current border, whether a visualization technique is called a treemap.

Implicit Edge Representation Tree.

When loosen-

ing the containment property and allowing all types of

implicit edge representations (Schulz et al., 2011a),

i.e., containment, adjacency, and overlap, an even

broader set of visualization techniques can be labeled

as treemaps. With this set of characteristics, visual-

ization techniques that were created using transforma-

tions on treemap layouts, e.g., Cascaded Treemaps (L

and Fogarty, 2008) and the BeamTree (van Ham and

van Wijk, 2003), can be considered treemaps as well.

In fact, their goal is to “convey the same contain-

ment relationship” (L

u and Fogarty, 2008) and effec-

tively do so. Applying the idea of layout postprocess-

ings to one-dimensional treemap layouts, the results

are icicle plots (Kruskal and Landwehr, 1983), and

with additional projection they can result in sunburst

views (Stasko et al., 2000), and other derived tech-

niques (Webber et al., 2006; Holten, 2006) (Figure 7).

This is valid as implicit edge representations fulﬁll the

containment property during layouting as well but the

depiction in geometry space may drop the visual cues

for containment (Johnson, 1993).

Mapped Tree.

While all previously proposed cat-

egories use an implicit edge representation, there

are tree visualization techniques that use other rela-

tion encoding techniques such as vicinity or explicit

links (Schulz et al., 2013). Most of them share the

invariant of overlap-free spatialization across node

siblings, resulting in an unambiguous bidirectional

mapping of the tree-structure to the depiction. Such a

depiction is provided if the underlying tree layout is

based on the property of containment, even if the re-

sulting visualization does not encode the parent-child

relationship using an implicit edge representation. Ex-

emplary visualization techniques for the Mapped Tree

category include space-optimized trees (Nguyen and

Huang, 2002), contour maps (Kubota et al., 2006),

and point-based tree depictions (Schulz et al., 2011b)

(Figure 8). Assuming a bidirectional mapping of lay-

out algorithms, “[c]ontainment based treemap algo-

rithms [. . . ] form the core of a powerful and extensible

grand uniﬁed theory of hierarchical visualization—the

treemap” (Johnson, 1993).

4 DISCUSSION

When we categorize techniques of hierarchy visual-

ization we aim at the most speciﬁc category. As an

example, the appropriate category of a CodeCity visu-

alization is a containment treemap

. Through the

taxonomy’s hyponymous nature, the more general cat-

egories

and

are valid as well, and so is the

category of general hierarchy visualization techniques

(Figure 9). However, we want to argue for a more

speciﬁc use of terms (Table 1).

The proposed taxonomy is derived from visual fea-

tures of the visualization techniques. Common to all

categories is the property of containment from their

underlying layouts. As alternatives to the focus on vi-

sual features, the categorization could be based on the

initial use of a containment-based layout. This would

render all techniques that are classiﬁed as mapped trees

as treemap.

The actual boundary between mapped trees

and general hierarchy visualization techniques

re-

mains to be determined. We argue that only with a

systematic top-down or bottom-up approach that ad-

heres to the containment metaphor during layouting,

the resulting visualization technique can result in a

treemap. That is, hierarchy visualization techniques

using other approaches may not ensure overlap-free

sibling nodes for the underlying layouts, e.g., Pythago-

ras Trees (Beck et al., 2014). For those techniques,

we observe their categorization as mapped tree

soon as they achieve overlap-free sibling nodes (Munz

et al., 2019).

Regarding the mixed use of treemap visualization

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

276

Hybrid Lay-

out Treemap

Treemap with

Bound Aspect Ratio

Voronoi Treemap

GosperMap

Jigsaw Treemap

Figure 5: Examples of treemaps from the category Space-Filling Treemap. Image courtesy from left to right: (Hahn and

ollner, 2017), (de Berg et al., 2011), (Balzer et al., 2005), (Auber et al., 2013), and (Wattenberg, 2005).

Data-Jewelry Box

EvoCells Treemap

CodeCity

Pebbles Treemap

Rectangular Hier-

archical Cartogram

Figure 6: Examples of treemaps from the category Containment Treemap. Image courtesy from left to right: (Itoh et al., 2004),

(Scheibel et al., 2018), (Wettel and Lanza, 2007), (Wetzel, 2003), and (Slingsby et al., 2010).

Sunburst View

BeamTree

Bundle View

Flame Graphs

Software Cities

Figure 7: Examples of treemaps from the category Implicit Edge-Representation Treemap. Image courtesy from left to right:

(Stasko et al., 2000), (van Ham and van Wijk, 2003), (Holten, 2006), (Gregg, 2016), and (Steinbr

uckner and Lewerentz, 2013).

Space-optimized Tree

RINGS

Information Slices

Tree Drawings

ENCCON

Figure 8: Examples of treemaps from the category Mapped Tree. Image courtesy from left to right: (Nguyen and Huang, 2002),

(Tee Teoh and Kwan-Liu, 2002), (Andrews and Heidegger, 1998), (Toosi and Nikolov, 2014), and (Nguyen and Huang, 2005).

A Taxonomy of Treemap Visualization Techniques

277

Thematic Soft-

ware Maps

Bubble Hierarchies

BaobabView

Tree of Percentage

Pythagoras Tree

Figure 9: Examples of visualization techniques that does not relate to any proposed class and are therefore associated with

Image courtesy from left to right: (Kuhn et al., 2010), (Hlawatsch et al., 2014), (Andrews and Heidegger, 1998), (van den

Elzen and van Wijk, 2011), and (Kleiner and Hartigan, 1981), and (Beck et al., 2014).

with other approaches, this taxonomy is explicitly de-

signed ignorant. As a debatable example, the mixed-

projection treemap could be categorized as either

(Limberger et al., 2017). Another example is the

adjacent usage of treemaps by means of small multi-

ples. It may not inherently render the whole visualiza-

tion a treemap, although the composition could be la-

beled a space-ﬁlling treemap

(Scheibel et al., 2016).

A constrasting example thereto is the CodeSurveyor

visualization that uses an overlap-free distribution of

Table 1: An association of different hierarchy visualization

techniques to the proposed taxonomy. For direct comparison,

this table includes hierarchy visualization techniques

that

are not covered by this taxonomy as well. The

•

and

◦

dots

denote the most speciﬁc category and associated hyperonyms

(more general categories), respectively.

Visualization Technique T

BeamTree (van Ham and van Wijk, 2003) • ◦ ◦

Bundle View (Holten, 2006) • ◦ ◦

Cactus Tree (Dang and Forbes, 2017) •

CodeCity (Wettel and Lanza, 2008) • ◦ ◦ ◦

Contrast Treemaps (Tu and Shen, 2007) • ◦ ◦ ◦ ◦

Data Jewelry Box (Itoh et al., 2004) • ◦ ◦ ◦

Ellimaps (Collin et al., 2007) • ◦ ◦ ◦ ◦

EvoCells (Scheibel et al., 2018) • ◦ ◦ ◦

Flame Graph (Gregg, 2016) • ◦ ◦

Gosper Map (Auber et al., 2013) • ◦ ◦ ◦ ◦

Icicle Plot (Kruskal and Landwehr, 1983) • ◦ ◦

Information Slices (Andrews and Heidegger, 1998) • ◦

Overlap-free Pythagoras Tree (Munz et al., 2019) • ◦

Pebbles Treemap (Wetzel, 2003) • ◦ ◦ ◦

Pythagoras Tree (Beck et al., 2014) •

Quantum Treemaps (Bederson et al., 2002) • ◦ ◦ ◦

Slice’n’Dice Treemap (Johnson and Shneiderman, 1991) • ◦ ◦ ◦ ◦

Software Cities (Steinbr

uckner and Lewerentz, 2013) • ◦

Space-Optimized Tree (Nguyen and Huang, 2002) • ◦

Stable Voronoi Treemap (Hahn et al., 2014) • ◦ ◦ ◦ ◦

Sunburst View (Stasko et al., 2000) • ◦ ◦

Tree Drawings (Toosi and Nikolov, 2014) • ◦

Treemaps with Bound Aspect Ratio (de Berg et al., 2011) • ◦ ◦ ◦ ◦

Voronoi Treemap (Balzer et al., 2005) • ◦ ◦ ◦ ◦

the uppermost level and, in fact, should be labeled a

containment treemap T

(Hawes et al., 2015).

5 CONCLUSIONS

This paper proposes a taxonomy for treemap visual-

ization techniques, namely Space-ﬁlling Treemap

Containment Treemap

, Implicit Edge Representa-

tion Tree

, and Mapped Tree

. This is feasible

because all associated techniques adhere to the prop-

erty of containment, either in their visual representa-

tion or in their underlying layout. Furthermore, the

taxonomy should clarify the labeling of visualization

techniques as treemaps and improve communication

on their characteristics, visual metaphors, internally

used algorithms, and interaction techniques.

As future work, the classiﬁcation of existing hier-

archy visualization techniques by means of the pro-

posed taxonomy should be continued. We suggest

to integrate the conceptual model of these different

classes into hierarchy visualization systems and their

APIs (Scheibel et al., 2019). Right now, we stimu-

late this discussion on a classiﬁcation of treemaps and,

probably, a broadening of the concept of treemaps and

their role within the ﬁeld of hierarchy visualization.

ACKNOWLEDGEMENTS

This work was partially funded by the German Federal

Ministry for Economic Affairs and Energy (BMWi,

ZIM) within the project “TASAM”.

REFERENCES

Andrews, K. and Heidegger, H. (1998). Information slices:

Visualising and exploring large hierarchies using cas-

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

278

cading, semi-circular discs. In Proc. InfoVis, pages

9–11. IEEE.

Auber, D., Huet, C., Lambert, A., Renoust, B., Sallaberry, A.,

and Saulnier, A. (2013). Gospermap: Using a gosper

curve for laying out hierarchical data. Transactions on

Visualization and Computer Graphics, 19(11):1820–

1832. IEEE.

Balzer, M. and Deussen, O. (2005). Voronoi treemaps. In

Proc. InfoVis, pages 49–56. IEEE.

Balzer, M., Deussen, O., and Lewerentz, C. (2005). Voronoi

treemaps for the visualization of software metrics. In

Proc. SoftVis, pages 165–172. ACM.

Baudel, T. and Broeksema, B. (2012). Capturing the design

space of sequential space-ﬁlling layouts. Transactions

on Visualization and Computer Graphics, 18(12):2593–

2602. IEEE.

Beck, F., Burch, M., Munz, T., Di Silvestro, L., and

Weiskopf, D. (2014). Generalized pythagoras trees

for visualizing hierarchies. In Proc. IVAPP, pages

17–28. ScitePress.

Bederson, B. B., Shneiderman, B., and Wattenberg, M.

(2002). Ordered and quantum treemaps: Making effec-

tive use of 2d space to display hierarchies. Transactions

on Graphics, 21(4):833–854. ACM.

Carpendale, M. S. T. (2003). Considering visual variables as

a basis for information visualization. Technical report,

University of Calgary, Canada. Nr. 2001-693-14.

Chazard, E., Puech, P., Gregoire, M., and Beuscart, R. (2006).

Using treemaps to represent medical data. Studies in

Health Technology and Informatics, 124:522–527.

Collin, P., Otjacques, B., Gobert, X., Noirhomme, M., and

Feltz, F. (2007). Visualizing the activity of a web-based

collaborative platform. In Proc. iV, pages 251–256.

IEEE.

Dang, T. and Forbes, A. (2017). CactusTree: A tree draw-

ing approach for hierarchical edge bundling. In Proc.

PaciﬁcVis, pages 210–214. IEEE.

de Berg, M., Speckmann, B., and van der Weele, V. (2011).

Treemaps with bounded aspect ratio. In Proc. ISAAC,

pages 260–270. Springer.

Friendly, M. (2002). A brief history of the mosaic display.

Journal of Computational and Graphical Statistics,

11(1):89–107. Taylor & Francis.

ortler, J., Schulz, C., Weiskopf, D., and Deussen, O.

(2017). Bubble treemaps for uncertainty visualization.

Transactions on Visualization and Computer Graphics,

24(1):719–728. IEEE.

Gregg, B. (2016). The ﬂame graph. Communications,

59(6):48–57. ACM.

Hahn, S. and D

ollner, J. (2017). Hybrid-treemap layouting.

In Proc. EuroVis – Short Papers, pages 79–83. EG.

Hahn, S., Tr

umper, J., Moritz, D., and D

ollner, J. (2014).

Visualization of varying hierarchies by stable layout

of voronoi treemaps. In Proc. IVAPP, pages 50–58.

SciTePress.

Hawes, N., Marshall, S., and Anslow, C. (2015). Codesur-

veyor: Mapping large-scale software to aid in code

comprehension. In Proc. VISSOFT, pages 96–105.

IEEE.

Hlawatsch, M., Burch, M., and Weiskopf, D. (2014). Bubble

hierarchies. In Proc. CAe, pages 77–80. ACM.

Holten, D. (2006). Hierarchical edge bundles: Visual-

ization of adjacency relations in hierarchical data.

Transactions on Visualization and Computer Graphics,

12(5):741–748. IEEE.

Itoh, T., Kajinaga, Y., Yamaguchi, Y., and Ikehata, Y. (2004).

Hierarchical data visualization using a fast rectangle-

packing algorithm. Transactions on Visualization and

Computer Graphics, 10:302–313. IEEE.

Jern, M., Rogstadius, J., and

Astr

om, T. (2009). Treemaps

and choropleth maps applied to regional hierarchical

statistical data. In Proc. iV, pages 403–410. IEEE.

Johnson, B. S. (1993). Treemaps: Visualizing Hierarchi-

cal and Categorical Data. PhD thesis, University of

Maryland. UMI Order No. GAX94-25057.

Johnson, B. S. and Shneiderman, B. (1991). Tree-maps:

A space-ﬁlling approach to the visualization of hier-

archical information structures. In Proc. VIS, pages

284–291. IEEE.

Johnston, J. B. (1969). Structure of multiple activity algo-

rithms. In Proc. SOSP, pages 80–82. ACM.

Kleiner, B. and Hartigan, J. A. (1981). Representing points

in many dimensions by trees and castles. Journal of the

American Statistical Association, 76(374):260–269.

Kruskal, J. B. and Landwehr, J. M. (1983). Icicle plots: Bet-

ter displays for hierarchical clustering. The American

Statistician, 37(2):162–168. Taylor & Francis.

Kubota, H., Nishida, T., and Sumi, Y. (2006). Visualization

of contents archive by contour map representation. In

Proc. Annual Conference of the Japanese Society for

Artiﬁcial Intelligence, pages 19–32. Springer.

Kuhn, A., Erni, D., Loretan, P., and Nierstrasz, O. (2010).

Software cartography: thematic software visualiza-

tion with consistent layout. Journal of Software

Maintenance and Evolution: Research and Practice,

22(3):191–210. Wiley.

Limberger, D., Scheibel, W., D

ollner, J., and Trapp, M.

(2019). Advanced visual metaphors and techniques

for software maps. In Proc. VINCI, pages 1–8. ACM.

Limberger, D., Scheibel, W., Trapp, M., and D

ollner, J.

(2017). Mixed-projection treemaps: A novel approach

mixing 2d and 2.5d treemaps. In Proc. iV, pages 164–

169. IEEE.

u, H. and Fogarty, J. (2008). Cascaded treemaps: Examin-

ing the visibility and stability of structure in treemaps.

In Proc. Graphics Interface, pages 259–266. Canadian

Information Processing Society.

McNabb, L. and Laramee, R. S. (2017). Survey of Surveys

(SoS) - mapping the landscape of survey papers in

information visualization. Computer Graphics Forum,

36(3):589–617. EG.

Merino, L., Ghafari, M., Anslow, C., and Nierstrasz, O.

(2018). A systematic literature review of software visu-

alization evaluation. Journal of Systems and Software,

144:165–180. Elsevier.

Munz, T., Burch, M., van Benthem, T., Poels, Y., Beck,

F., and Weiskopf, D. (2019). Overlap-free draw-

ing of generalized pythagoras trees for hierarchy vi-

sualization. arXiv Computing Research Repository,

abs/1907.12845.

A Taxonomy of Treemap Visualization Techniques

279

Nassi, I. and Shneiderman, B. (1973). Flowchart tech-

niques for structured programming. SIGPLAN Notices,

8(8):12–26. ACM.

Nguyen, Q. V. and Huang, M. L. (2002). Improvements of

space-optimized tree for visualizing and manipulating

very large hierarchies. In Selected Papers Pan-Sydney

Workshop on Visualisation, VIP ’02, pages 75–81. Aus-

tralian Computer Society, Inc.

Nguyen, Q. V. and Huang, M. L. (2005). EncCon: An ap-

proach to constructing interactive visualization of large

hierarchical data. Information Visualization, 4(1):1–21.

Palgrave.

Park, D., Drucker, S. M., Fernandez, R., and Elmqvist, N.

(2018). ATOM: A grammar for unit visualizations.

Transactions on Visualization and Computer Graphics,

24(12):3032–3043. IEEE.

Raisz, E. (1934). The rectangular statistical cartogram. Ge-

ographical Review, 24(2):292–296. American Geo-

graphical Society, Wiley.

Roberts, R. C. and Laramee, R. S. (2018). Visualising busi-

ness data: A survey. Information, 9(11):285:1–54.

MDPI.

Scheibel, W., Hartmann, J., and D

ollner, J. (2019). De-

sign and implementation of web-based hierarchy vi-

sualization services. In Proc. IVAPP, pages 141–152.

SciTePress.

Scheibel, W., Trapp, M., and D

ollner, J. (2016). Interactive

revision exploration using small multiples of software

maps. In Proc. IVAPP, pages 131–138. SciTePress.

Scheibel, W., Weyand, C., and D

ollner, J. (2018). EvoCells –

a treemap layout algorithm for evolving tree data. In

Proc. IVAPP, pages 273–280. SciTePress.

Schulz, H.-J. (2011). Treevis.net: A tree visualization refer-

ence. Computer Graphics and Applications, 31(6):11–

15. IEEE.

Schulz, H.-J., Akbar, Z., and Maurer, F. (2013). A generative

layout approach for rooted tree drawings. In Proc.

PaciﬁcVis, pages 225–232. IEEE.

Schulz, H.-J., Hadlak, S., and Schumann, H. (2011a). The

design space of implicit hierarchy visualization: A

survey. Transactions on Visualization and Computer

Graphics, 17(4):393–411. IEEE.

Schulz, H.-J., Hadlak, S., and Schumann, H. (2011b). Point-

based visualization for large hierarchies. Transactions

on Visualization and Computer Graphics, 17(5):598–

611. IEEE.

Slingsby, A., Dykes, J., and Wood, J. (2010). Rectangu-

lar hierarchical cartograms for socio-economic data.

Journal of Maps, 6(1):330–345. Taylor & Francis.

Stasko, J., Catrambone, R., Guzdial, M., and McDonald, K.

(2000). An evaluation of space-ﬁlling information visu-

alizations for depicting hierarchical structures. Techni-

cal Report 5, Georgia Institute of Technology, Atlanta,

GA, USA. GVU Technical Report;GIT-GVU-00-03.

Steinbr

uckner, F. and Lewerentz, C. (2013). Understanding

software evolution with software cities. Information

Visualization, 12(2):200–216. SAGE.

Tee Teoh, S. and Kwan-Liu, M. (2002). Rings: A tech-

nique for visualizing large hierarchies. In Proc. Graph

Drawing, pages 268–275. Springer.

Toosi, F. G. and Nikolov, N. S. (2014). Circular tree drawing

by simulating network synchronisation dynamics and

scaling. In Graph Drawing, pages 511–512. Springer.

Tu, Y. and Shen, H. W. (2007). Visualizing changes of

hierarchical data using treemaps. Transactions on Vi-

sualization and Computer Graphics, 13(6):1286–1293.

IEEE.

van den Elzen, S. and van Wijk, J. J. (2011). BaobabView:

Interactive construction and analysis of decision trees.

In Proc. VAST, pages 151–160. IEEE.

van Ham, F. and van Wijk, J. J. (2003). Beamtrees: Com-

pact visualization of large hierarchies. Information

Visualization, 2(1):31–39. Palgrave.

van Kreveld, M. and Speckmann, B. (2007). On rectangular

cartograms. Computational Geometry, 37(3):175–187.

Elsevier.

Vernier, E. F., Telea, A. C., and Comba, J. (2018). Quanti-

tative comparison of dynamic treemaps for software

evolution visualization. In Proc. VISSOFT, pages 96–

106. IEEE.

Vliegen, R., van Wijk, J. J., and van der Linden, E. J. (2006).

Visualizing business data with generalized treemaps.

Transactions on Visualization and Computer Graphics,

12(5):789–796. IEEE.

Wang, Y.-C., Lin, F., and Seah, H.-S. (2019). Orthogonal

voronoi diagram and treemap. arXiv Computing Re-

search Repository, abs/1904.02348.

Wattenberg, M. (2005). A note on space-ﬁlling visualizations

and space-ﬁlling curves. In Proc. InfoVis, pages 181–

186. IEEE.

Webber, R., Herbert, R. D., and Jiang, W. (2006). Space-

ﬁlling techniques in visualizing output from computer

based economic models. In Proc. Computing in Eco-

nomics and Finance, volume 67. Soc. for Computa-

tional Economics.

Wettel, R. and Lanza, M. (2007). Visualizing software sys-

tems as cities. In Proc. VISSOFT, pages 92–99. IEEE.

Wettel, R. and Lanza, M. (2008). Visual exploration of

large-scale system evolution. In Proc. WCRE, pages

219–228. IEEE.

Wetzel, K. (2003). pebbles - using circu-

lar treemaps to visualize disk usage.

http://lip.sourceforge.net/ctreemap.html.

Yamaguchi, Y. and Itoh, T. (2003). Visualization of dis-

tributed processes using “Data Jewelry Box” algorithm.

In Proc. CGI, pages 162–169. IEEE.

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

280