Presentation of Research Paper "Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps" at 21st International Conference Information Visualisation (IV 2017) in London, UK.

1. Mixed-Projection Treemaps
A Novel Approach Mixing 2D and 2.5D Treemaps
2D Treemap Mixed-projection Treemap
Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner
Hasso Plattner Institute, University of Potsdam, Germany

2. Contributions of this Talk
Introduction and Problem Statement
An introduction to 2D and 2.5D treemaps and issues, e.g., depicting unknown data.
Node-Local Tilt
A transformation that enables to dynamically mix different projections and seamless integration of
2.5D treemaps for region-of-interests into 2D treemaps.
Interactive Tilt Control
An interaction concept that enables users to control the projection by means of manual and
automated tilting, either providing explicit control or using animated state transitions, respectively.
2 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

3. Treemaps | Introduction
Treemaps [4] are used to visualize tree-structured data,
e.g., stock markets [9], sensor data [6], business data [8],
file systems [7], software system information [10]
1. non-spatial data is spatialized—it is given a gestalt
that preserves the data’s structure, e.g., rectangular
treemaps use nested rectangles to depict nodes,
2. and data (attributes) are mapped to visual variables,
i.e., properties of the rectangles (size and color).
3 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

4. Treemaps | Introduction
Treemaps [4] are used to visualize tree-structured data,
e.g., stock markets [9], sensor data [6], business data [8],
file systems [7], software system information [10]
1. non-spatial data is spatialized—it is given a gestalt
that preserves the data’s structure, e.g., rectangular
treemaps use nested rectangles to depict nodes,
2. and data (attributes) are mapped to visual variables,
i.e., properties of the rectangles (size and color).
4 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

5. Treemaps | Introduction
Treemaps [4] are used to visualize tree-structured data,
e.g., stock markets [9], sensor data [6], business data [8],
file systems [7], software system information [10]
1. non-spatial data is spatialized—it is given a gestalt
that preserves the data’s structure, e.g., rectangular
treemaps use nested rectangles to depict nodes,
2. and data (attributes) are mapped to visual variables,
i.e., properties of the rectangles (size and color).
5 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

6. Treemaps | Spatialization of Tree-structured Data
Leaf nodes are colored and have numerical weights, which can represent any associated attribute, e.g.,
file size. The weight of a parent node is defined by the sum of the weights of its child nodes.
Simple Graph Rectangular Treemap
100
100
10 70
25
8
12
2
2 3
1 5 40
2
6 8 11
6 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

7. Treemaps | Spatialization of Tree-structured Data
Leaf nodes are colored and have numerical weights, which can represent any associated attribute, e.g.,
file size. The weight of a parent node is defined by the sum of the weights of its child nodes.
Simple Graph Rectangular Treemap
8
12 10 70
100
10 70
25
8
12
2
2 3
1 5 40
2
6 8 11
7 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

8. Treemaps | Spatialization of Tree-structured Data
Leaf nodes are colored and have numerical weights, which can represent any associated attribute, e.g.,
file size. The weight of a parent node is defined by the sum of the weights of its child nodes.
Simple Graph Rectangular Treemap
100
10 70
25
8
12
2
2 3
1 5 40
2
6 8 11
8
12
2
2
2
3
1
5
40
25
8 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

9. Treemaps | Spatialization of Tree-structured Data
Leaf nodes are colored and have numerical weights, which can represent any associated attribute, e.g.,
file size. The weight of a parent node is defined by the sum of the weights of its child nodes.
Simple Graph Rectangular Treemap
8
12
2
2
2
3
1
5
40
8 11
6
100
10 70
25
8
12
2
2 3
1 5 40
2
6 8 11
9 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

10. Treemaps | Spatialization of Tree-structured Data
Leaf nodes are colored and have numerical weights, which can represent any associated attribute, e.g.,
file size. The weight of a parent node is defined by the sum of the weights of its child nodes.
Simple Graph Rectangular Treemap
100
10 70
25
8
12
2
2 3
1 5 40
2
6 8 11
8
12
2
2
2
3
1
5
40
8 11
6
10 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

11. Treemaps | Adding Height as Visual Variable
2.5D Treemap [1]
11 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

12. Software Maps | Basic Example
# complexity
nesting-level or McCabe
# developers
that touched this unit
# lines-of-code
12 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

13. Problem Statement | Exploration of Unknown Data
Exploration of unknown data is highly iterative and hinders use of well-tuned map themes.
In order to reduce cognitive load, the following iterative approach is applied:
13 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

14. Problem Statement | Exploration of Unknown Data
Exploration of unknown data is highly iterative and hinders use of well-tuned map themes.
In order to reduce cognitive load, the following iterative approach is applied:
Topology, Weight
14 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

15. Problem Statement | Exploration of Unknown Data
Exploration of unknown data is highly iterative and hinders use of well-tuned map themes.
In order to reduce cognitive load, the following iterative approach is applied:
Topology, Weight Topology, Weight, Color
15 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

16. Problem Statement | Exploration of Unknown Data
Exploration of unknown data is highly iterative and hinders use of well-tuned map themes.
In order to reduce cognitive load, the following iterative approach is applied:
Topology, Weight Topology, Weight, Color Topology, Weight, Color, Height
16 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

17. Problem Statement | Exploration of Unknown Data
Exploration of unknown data is highly iterative and hinders use of well-tuned map themes.
In order to reduce cognitive load, the following iterative approach is applied:
Topology, Weight Topology, Weight, Color Topology, Weight, Color, Height
17 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

18. Problem Statement | Exploration of Unknown Data
Exploration of unknown data is highly iterative and hinders use of well-tuned map themes.
In order to reduce cognitive load, the following iterative approach is applied:
Topology, Weight Topology, Weight, Color Mixed-Projection Full 2.5D (optional)
18 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

19. Approach | Node-local Tilt
Tilt
The rotation of an inner node (and its children) to enable exploration of additional attributes using
visual variables common for 2.5D treemaps (e.g., height, transparency, sketchiness).
Any tilt comprises the following two node-local operations:
• a tilt transformation Λ = [TC] TAT−1
R RTR
• and a tilt projection Γ = (1 − t) P0 + tP1.
19 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

20. Approach | Node-local Tilt
Tilt
The rotation of an inner node (and its children) to enable exploration of additional attributes using
visual variables common for 2.5D treemaps (e.g., height, transparency, sketchiness).
Any tilt comprises the following two node-local operations:
• a tilt transformation Λ = [TC] TAT−1
R RTR
• and a tilt projection Γ = (1 − t) P0 + tP1.
20 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

21. Approach | Node-local Tilt | Transformation
Λ = [TC] TAT−1
R RTR
TR shifts the node’s rotation axis using a relative offset
τ ∈ [−1, +1]1
R rotates the node by the tilt angle α
TA anchors the node to a preferred relative location υ ∈ [−1, +1]1
TC reduces occlusion introduced when using a perspective
projection (optional).
0
1−1 at bottom and +1 at top, 0 at center.
21 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

22. Approach | Node-local Tilt | Transformation
Λ = [TC] TAT−1
R RTR
TR shifts the node’s rotation axis using a relative offset
τ ∈ [−1, +1]1
R rotates the node by the tilt angle α
TA anchors the node to a preferred relative location υ ∈ [−1, +1]1
TC reduces occlusion introduced when using a perspective
projection (optional).
TR
1
1−1 at bottom and +1 at top, 0 at center.
22 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

23. Approach | Node-local Tilt | Transformation
Λ = [TC] TAT−1
R RTR
TR shifts the node’s rotation axis using a relative offset
τ ∈ [−1, +1]1
R rotates the node by the tilt angle α
TA anchors the node to a preferred relative location υ ∈ [−1, +1]1
TC reduces occlusion introduced when using a perspective
projection (optional).
α
R
2
1−1 at bottom and +1 at top, 0 at center.
23 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

24. Approach | Node-local Tilt | Transformation
Λ = [TC] TAT−1
R RTR
TR shifts the node’s rotation axis using a relative offset
τ ∈ [−1, +1]1
R rotates the node by the tilt angle α
TA anchors the node to a preferred relative location υ ∈ [−1, +1]1
TC reduces occlusion introduced when using a perspective
projection (optional).
3
TR
-1
1−1 at bottom and +1 at top, 0 at center.
24 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

25. Approach | Node-local Tilt | Transformation
Λ = [TC] TAT−1
R RTR
TR shifts the node’s rotation axis using a relative offset
τ ∈ [−1, +1]1
R rotates the node by the tilt angle α
TA anchors the node to a preferred relative location υ ∈ [−1, +1]1
TC reduces occlusion introduced when using a perspective
projection (optional).
TA
4
1−1 at bottom and +1 at top, 0 at center.
25 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

26. Approach | Node-local Tilt | Transformation
Λ = [TC] TAT−1
R RTR
TR shifts the node’s rotation axis using a relative offset
τ ∈ [−1, +1]1
R rotates the node by the tilt angle α
TA anchors the node to a preferred relative location υ ∈ [−1, +1]1
TC reduces occlusion introduced when using a perspective
projection (optional).
TC
5
1−1 at bottom and +1 at top, 0 at center.
26 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

27. Approach | Node-local Tilt | Transformation
Tilt transformation Λ of an inner-node n with perspective projection:
0
TR
1
α
R
2 3
TR
-1
TA
4
TC
5
n is
(1) translated by TR for (2) rotation by R with α = 60◦
at the local tilt offset τ = 0.5. After (3) translating n (T −1
R ) it is (4) moved to the bottom
anchor by TA with υ = −1 and, (5) shifted up again by TC in order to reduce the occlusion caused by the perspective projection.
27 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

28. Approach | Node-local Tilt | Projection
Γ = (1 − t) P0 + tP1
mixes any two given projections P0 and P1 with respect to the
node local tilt angle α and a global angular threshold β with
P0 as orthographic projection,
P1 as perspective projection that cover the exact same treemap
region, and
t = αβ−1
, clamped to [0, 1].
28 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

29. Approach | Node-local Tilt | Projection
Γ = (1 − t) P0 + tP1
mixes any two given projections P0 and P1 with respect to the
node local tilt angle α and a global angular threshold β with
P0 as orthographic projection,
P1 as perspective projection that cover the exact same treemap
region, and
t = αβ−1
, clamped to [0, 1].
29 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

30. Approach | Node-local Tilt | Projection
Γ = (1 − t) P0 + tP1
mixes any two given projections P0 and P1 with respect to the
node local tilt angle α and a global angular threshold β with
P0 as orthographic projection,
P1 as perspective projection that cover the exact same treemap
region, and
t = αβ−1
, clamped to [0, 1].
30 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

31. Approach | Node-local Tilt | Projection
Γ = (1 − t) P0 + tP1
mixes any two given projections P0 and P1 with respect to the
node local tilt angle α and a global angular threshold β with
P0 as orthographic projection,
P1 as perspective projection that cover the exact same treemap
region, and
t = αβ−1
, clamped to [0, 1].
31 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

32. Approach | Node-local Tilt | Parametrization for α = 60◦
Ortho Persp, τ = −1 Persp, τ = 0 Persp, τ = +1
υ = −1 (bottom)
υ = 0 (center)
υ = +1 (top)
32 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

33. Approach | Interactive Tilt Control
Interaction
Users can interact directly with treemap nodes based on direct manipulation metaphors.
For it we suggest two similar tilt modes:
• automated tilt enabling users to invoke a preset tilt angle or un-tilt any node by a single input event,
e.g., click/touch to an inner node,
• manual tilt enabling users to increase and decrease the tilt angle of any node seamlessly,
e.g., via click/touch to an inner node and vertical drag down.
33 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

34. Approach | Interactive Tilt Control
Interaction
Users can interact directly with treemap nodes based on direct manipulation metaphors.
For it we suggest two similar tilt modes:
• automated tilt enabling users to invoke a preset tilt angle or un-tilt any node by a single input event,
e.g., click/touch to an inner node,
• manual tilt enabling users to increase and decrease the tilt angle of any node seamlessly,
e.g., via click/touch to an inner node and vertical drag down.
34 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

35. Approach | Automated Tilt (local)
35 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

36. Approach | Manual Tilt (global)
36 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

37. Future Work
Additional Rotation Non-rectangular Treemaps [3] Available Screen Space
37 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

38. Conclusion and Contact Information
This talk presented
• a node-local tilt transformation and
• interactive tilt control
in order to facilitate exploration of unknown data
using 2D and 2.5D treemaps.
Author email addresses:
• daniel.limberger@hpi.de
• willy.scheibel@hpi.de
• matthias.trapp@hpi.de
• juergen-doellner@hpi.de
38 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

39. Bibliography I
[1] BLADH, T., CARR, D. A., AND SCHOLL, J.
Extending tree-maps to three dimensions: A comparative study.
In Proc. APCHI (2004), pp. 50–59.
[2] BOHNET, J., AND DÖLLNER, J.
Monitoring code quality and development activity by software maps.
In Proc. ACM MTD (2011), pp. 9–16.
[3] HAHN, S., AND DÖLLNER, J.
Hybrid-treemap layouting.
In Proceedings of EuroVis 2017 - Short Papers (2017).
[4] JOHNSON, B., AND SHNEIDERMAN, B.
Treemaps: A space-filling approach to the visualization of hierarchical information structures.
In Proc. IEEE VIS (1991), pp. 284–291.
39 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

40. Bibliography II
[5] MCCABE, T. J.
A complexity measure.
vol. SE-2, pp. 308–320.
[6] MITCHELL, W., SHOOK, D., AND SHAH, S. L.
A picture worth a thousand control loops: An innovative way of visualizing controller performance
data.
In Invited Plenary Presentation, Control Systems (2004).
[7] SHNEIDERMAN, B.
Tree visualization with treemaps: A 2D space-filling approach.
ACM Trans. Graph. 11, 1 (1992), 92–99.
[8] VLIEGEN, R., VAN WIJK, J. J., AND VAN DER LINDEN, E.-J.
Visualizing business data with generalized treemaps.
IEEE Trans. Vis. Comput. Graph. 12, 5 (2006), 789–796.
40 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

41. Bibliography III
[9] WATTENBERG, M.
Visualizing the stock market.
In Proc. ACM CHI EA (1999), pp. 188–189.
[10] WETTEL, R., AND LANZA, M.
CodeCity: 3d visualization of large-scale software.
In Proc. ACM ICSE Companion (2008), pp. 921–922.
41 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

42. Software Maps | Map Theme
For a given task, a map theme specifies the
mapping of attributes to visual variables.
Technical Depth maps logic lines-of-code to weight, a nesting-level metric to color, and McCabe
complexity [5]. It is used to reveal and monitor the ’technical debts’ inherent to a
software system’s implementation.
Risk of Knowledge Drain maps logic lines-of-code to weight, the number of active developers to color,
and a composite, nesting-level or McCabe based complexity measure indicating
difficult-to-comprehend code to height. It is used to identify complex code units
known only by few developers and reveal knowledge distribution.
42 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

43. Software Maps | Map Theme
For a given task, a map theme specifies the
mapping of attributes to visual variables.
Technical Depth maps logic lines-of-code to weight, a nesting-level metric to color, and McCabe
complexity [5]. It is used to reveal and monitor the ’technical debts’ inherent to a
software system’s implementation.
Risk of Knowledge Drain maps logic lines-of-code to weight, the number of active developers to color,
and a composite, nesting-level or McCabe based complexity measure indicating
difficult-to-comprehend code to height. It is used to identify complex code units
known only by few developers and reveal knowledge distribution.
43 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

44. Software Maps | Map Theme
For a given task, a map theme specifies the
mapping of attributes to visual variables.
Technical Depth maps logic lines-of-code to weight, a nesting-level metric to color, and McCabe
complexity [5]. It is used to reveal and monitor the ’technical debts’ inherent to a
software system’s implementation.
Risk of Knowledge Drain maps logic lines-of-code to weight, the number of active developers to color,
and a composite, nesting-level or McCabe based complexity measure indicating
difficult-to-comprehend code to height. It is used to identify complex code units
known only by few developers and reveal knowledge distribution.
44 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12

45. Software Maps | Goals
Make code quality of software systems visible to “stakeholders in
the development process, particularly, to the management” [2] by
means of depicting, e.g., metrics and activity data.
Depending on the applied map theme Software Maps facilitate
• exploring structures,
• monitoring development processes,
• monitoring software quality, and
• identifying areas that require attention in the ongoing
development process.
45 Mixed-Projection Treemaps: A Novel Approach Mixing 2D and 2.5D Treemaps Daniel Limberger, Willy Scheibel, Matthias Trapp, and Jürgen Döllner| iV2017 London 2017-07-12