http://www.cs.ubc.ca/~tmm/courses/journ15

Week 6: Rules of Thumb, NetworksDiscussion: Bringing It All Together

Tamara MunznerDepartment of Computer ScienceUniversity of British Columbia

JRNL 520M, Special Topics in Contemporary Journalism: Visualization for JournalistsWeek 6: 20 October 2015

Now

• Rules of Thumb, Networks• Discussion: Vis in the News

– recent articles

• Break

• Evaluations– I’ll be outside room

• Lab– Start on final assignment– I’ll circulate to answer questions about any/all past stuff

• consolidation, not new material2

Structure: Revised plan

• 85% Assignments (6 of them)– Lab 1: 15%– Lab 2: 15%– Lab 3: 10%– Lab 4: 10%– Lab 5: 10%– Lab 6: 25% (two weeks to complete)

• 15% Participation

• The lowest of the first five lab marks will be dropped.

3

Rules of Thumb

• No unjustified 3D– Power of the plane– Disparity of depth– Occlusion hides information– Perspective distortion dangers– Tilted text isn’t legible

• No unjustified 2D• Eyes beat memory• Resolution over immersion• Overview first, zoom and filter, details on demand• Responsiveness is required• Function first, form next

4

No unjustified 3D: Power of the plane

5

• high-ranked spatial position channels: planar spatial position– not depth!

Magnitude Channels: Ordered Attributes

Position on common scale

Position on unaligned scale

Length (1D size)

Tilt/angle

Area (2D size)

Depth (3D position)

No unjustified 3D: Danger of depth

• we don’t really live in 3D: we see in 2.05D– acquire more info on image plane quickly from eye movements– acquire more info for depth slower, from head/body motion

6

TowardsAway

Up

Down

Right

Left

Thousands of points up/down and left/right

We can only see the outside shell of the world

Occlusion hides information

• occlusion• interaction complexity

7

[Distortion Viewing Techniques for 3D Data. Carpendale et al. InfoVis1996.]

Perspective distortion loses information

• perspective distortion– interferes with all size channel encodings– power of the plane is lost!

8

[Visualizing the Results of Multimedia Web Search Engines. Mukherjea, Hirata, and Hara. InfoVis 96]

3D vs 2D bar charts

• 3D bars never a good idea!

9[http://perceptualedge.com/files/GraphDesignIQ.html]

Tilted text isn’t legible

• text legibility– far worse when tilted from image plane

• further reading

[Exploring and Reducing the Effects of Orientation on Text Readability in Volumetric Displays.Grossman et al. CHI 2007]

10

[Visualizing the World-Wide Web with the Navigational View Builder.Mukherjea and Foley. Computer Networks and ISDN Systems, 1995.]

No unjustified 3D example: Time-series data

• extruded curves: detailed comparisons impossible

11[Cluster and Calendar based Visualization of Time Series Data. van Wijk and van Selow, Proc. InfoVis 99.]

No unjustified 3D example: Transform for new data abstraction

• derived data: cluster hierarchy • juxtapose multiple views: calendar, superimposed 2D curves

12[Cluster and Calendar based Visualization of Time Series Data. van Wijk and van Selow, Proc. InfoVis 99.]

Justified 3D: shape perception

• benefits outweigh costs when task is shape perception for 3D spatial data– interactive navigation supports

synthesis across many viewpoints

13[Image-Based Streamline Generation and Rendering. Li and Shen. IEEE Trans. Visualization and Computer Graphics (TVCG) 13:3 (2007), 630–640.]

No unjustified 3D

• 3D legitimate for true 3D spatial data• 3D needs very careful justification for abstract data

– enthusiasm in 1990s, but now skepticism– be especially careful with 3D for point clouds or networks

14

[WEBPATH-a three dimensional Web history. Frecon and Smith. Proc. InfoVis 1999]

No unjustified 2D

• consider whether network data requires 2D spatial layout– especially if reading text is central to task!– arranging as network means lower information density

and harder label lookup compared to text lists

• benefits outweigh costs when topological structure/context important for task– be especially careful for search results, document

collections, ontologies

15

Targets

Network Data

Topology

Paths

Eyes beat memory

• principle: external cognition vs. internal memory – easy to compare by moving eyes between side-by-side views– harder to compare visible item to memory of what you saw

• implications for animation– great for choreographed storytelling– great for transitions between two states– poor for many states with changes everywhere

• consider small multiples instead

16

literal abstract

show time with time show time with space

animation small multiples

17

Eyes beat memory example: Cerebral

• small multiples: one graph instance per experimental condition– same spatial layout

– color differently, by condition

[Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Barsky, Munzner, Gardy, and Kincaid. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2008) 14:6 (2008), 1253–1260.]

18

Why not animation?

• disparate frames and regions: comparison difficult– vs contiguous frames– vs small region– vs coherent motion of group

• safe special case– animated transitions

Change blindness

• if attention is directed elsewhere, even drastic changes not noticeable – door experiment

• change blindness demos– mask in between images

19

Resolution beats immersion

• immersion typically not helpful for abstract data– do not need sense of presence or stereoscopic 3D

• resolution much more important– pixels are the scarcest resource– desktop also better for workflow integration

• virtual reality for abstract data very difficult to justify

20

[Development of an information visualization tool using virtual reality. Kirner and Martins. Proc. Symp. Applied Computing 2000]

Overview first, zoom and filter, details on demand• influential mantra from Shneiderman

• overview = summary– microcosm of full vis design problem

21

[The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Shneiderman. Proc. IEEE Visual Languages, pp. 336–343, 1996.]

Query

Identify Compare Summarise

Responsiveness is required

• three major categories– 0.1 seconds: perceptual processing– 1 second: immediate response– 10 seconds: brief tasks

• importance of visual feedback

22

Function first, form next

• start with focus on functionality– straightforward to improve aesthetics later on, as refinement– if no expertise in-house, find good graphic designer to work with

• dangerous to start with aesthetics– usually impossible to add function retroactively

23

Further reading• Visualization Analysis and Design. Tamara Munzner. CRC Press, 2014.

– Chap 6: Rules of Thumb

• Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules. Jeff Johnson. Morgan Kaufmann, 2010.– Chap 12: We Have Time Requirements

24

25

Arrange networks and trees

Arrange Networks and Trees

Node–Link Diagrams

Enclosure

Adjacency Matrix

TREESNETWORKS

Connection Marks

TREESNETWORKS

Derived Table

TREESNETWORKS

Containment Marks

Idiom: force-directed placement• visual encoding

– link connection marks, node point marks

• considerations– spatial position: no meaning directly encoded

• left free to minimize crossings

– proximity semantics?• sometimes meaningful

• sometimes arbitrary, artifact of layout algorithm

• tension with length– long edges more visually salient than short

• tasks– explore topology; locate paths, clusters

• scalability– node/edge density E < 4N

26http://mbostock.github.com/d3/ex/force.html

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Idiom: sfdp (multi-level force-directed placement)

• data– original: network– derived: cluster hierarchy atop it

• considerations– better algorithm for same encoding technique

• same: fundamental use of space• hierarchy used for algorithm speed/quality but

not shown explicitly• (more on algorithm vs encoding in afternoon)

• scalability– nodes, edges: 1K-10K– hairball problem eventually hits

27

[Efficient and high quality force-directed graph drawing. Hu. The Mathematica Journal 10:37–71, 2005.]

http://www.research.att.com/yifanhu/GALLERY/GRAPHS/index1.html

Idiom: adjacency matrix view• data: network

– transform into same data/encoding as heatmap

• derived data: table from network– 1 quant attrib

• weighted edge between nodes

– 2 categ attribs: node list x 2

• visual encoding– cell shows presence/absence of edge

• scalability– 1K nodes, 1M edges

28

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7.1. Using Space 135

Figure 7.5: Comparing matrix and node-link views of a five-node network.a) Matrix view. b) Node-link view. From [Henry et al. 07], Figure 3b and3a. (Permission needed.)

the number of available pixels per cell; typically only a few levels wouldbe distinguishable between the largest and the smallest cell size. Networkmatrix views can also show weighted networks, where each link has an as-sociated quantitative value attribute, by encoding with an ordered channelsuch as color luminance or size.

For undirected networks where links are symmetric, only half of thematrix needs to be shown, above or below the diagonal, because a linkfrom node A to node B necessarily implies a link from B to A. For directednetworks, the full square matrix has meaning, because links can be asym-metric. Figure 7.5 shows a simple example of an undirected network, witha matrix view of the five-node dataset in Figure 7.5a and a correspondingnode-link view in Figure 7.5b.

Matrix views of networks can achieve very high information density, upto a limit of one thousand nodes and one million edges, just like clusterheatmaps and all other matrix views that uses small area marks.

Technique network matrix viewData Types networkDerived Data table: network nodes as keys, link status between two

nodes as valuesView Comp. space: area marks in 2D matrix alignmentScalability nodes: 1K

edges: 1M

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7.1.3.3 Multiple Keys: Partition and Subdivide When a dataset has onlyone key, then it is straightforward to use that key to separate into one region

[NodeTrix: a Hybrid Visualization of Social Networks. Henry, Fekete, and McGuffin. IEEE TVCG (Proc. InfoVis) 13(6):1302-1309, 2007.]

[Points of view: Networks. Gehlenborg and Wong. Nature Methods 9:115.]

Connection vs. adjacency comparison

• adjacency matrix strengths– predictability, scalability, supports reordering– some topology tasks trainable

• node-link diagram strengths– topology understanding, path tracing– intuitive, no training needed

• empirical study– node-link best for small networks– matrix best for large networks

• if tasks don’t involve topological structure!

29

[On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Ghoniem, Fekete, and Castagliola. Information Visualization 4:2 (2005), 114–135.]

http://www.michaelmcguffin.com/courses/vis/patternsInAdjacencyMatrix.png

Idiom: radial node-link tree• data

– tree

• encoding– link connection marks– point node marks– radial axis orientation

• angular proximity: siblings• distance from center: depth in tree

• tasks– understanding topology, following paths

• scalability– 1K - 10K nodes

30http://mbostock.github.com/d3/ex/tree.html

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Idiom: treemap• data

– tree– 1 quant attrib at leaf nodes

• encoding– area containment marks for hierarchical structure– rectilinear orientation– size encodes quant attrib

• tasks– query attribute at leaf nodes

• scalability– 1M leaf nodes

31

http://tulip.labri.fr/Documentation/3_7/userHandbook/html/ch06.html

Link marks: Connection and Containment

• marks as links (vs. nodes)– common case in network drawing– 1D case: connection

• ex: all node-link diagrams• emphasizes topology, path tracing• networks and trees

– 2D case: containment• ex: all treemap variants• emphasizes attribute values at leaves (size coding)• only trees

32

Node–Link Diagram Treemap Elastic Hierarchy

Node-Link Containment

[Elastic Hierarchies: Combining Treemaps and Node-Link Diagrams. Dong, McGuffin, and Chignell. Proc. InfoVis 2005, p. 57-64.]

Containment Connection

Tree drawing idioms comparison

• data shown– link relationships

– tree depth– sibling order

• design choices– connection vs containment link marks– rectilinear vs radial layout

– spatial position channels

• considerations– redundant? arbitrary?

– information density?• avoid wasting space

33

[Quantifying the Space-Efficiency of 2D Graphical Representations of Trees. McGuffin and Robert. Information Visualization 9:2 (2010), 115–140.]

Further reading• Visualization Analysis and Design. Munzner. AK Peters / CRC Press, Oct 2014.

– Chap 9: Arrange Networks and Trees

• Treevis.net: A Tree Visualization Reference. Schulz. IEEE Computer Graphics and Applications 31:6 (2011), 11–15. http://www.treevis.net

34

Further reading

• The Functional Art. Alberto Cairo. Peachpit Press, 2012– http://www.thefunctionalart.com/

• great blog– coming soon: The Truthful Art– great data journalism visualization resources

• Communicating Data with Tableau. Ben Jones. O’Reilly 2014– for more on Tableau

– (also, LAVA Hackathon Oct 24-25

35

Discussion

• 156 families– analysis vs presentation

• chicken/coffee maps• Canadian elections• what else?

36

• Break

• Evals

37

Lab/Assignment 6• putting it all together

– find, or create, a newsworthy dataset• don’t reuse one you used in a past lab

– create Tableau visualization(s) visualizing it• at least one static• at least one linked/interactive

– write up story suitable for public consumption, featuring your vis at its heart– upload your viz to Tableau public so that you can embed the interactive material in your story– in separate document, write up design rationale and reflections

– note that you have two weeks• due Tue Nov 3 9am

38