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Network Visualization CMSC828O Principles of Visualization Design Network Visualization CMSC828O Principles of Visualization Design –Tamara Munzner. Visualization Analysis and Design. AK Peters Visualization Series. CRC Press, 2014. • http://www.cs.ubc.ca/~tmm/vadbook/ 2 Why have a human in the loop? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods. • don’t need vis when fully automatic solution exists and is trusted • many analysis problems ill-specified – don’t know exactly what questions to ask in advance • possibilities – long-term use for end users (e.g. exploratory analysis of scientific data) – presentation of known results – stepping stone to better understanding of requirements before developing models – help developers of automatic solution refine/debug, determine parameters – help end users of automatic solutions verify, build trust 3 Why use an external representation? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. • external representation: replace cognition with perception [Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Barsky, Munzner, Gardy, and Kincaid. IEEE TVCG (Proc. InfoVis) 14(6):1253-1260, 2008.] 4 Anscombe’s Quartet: Raw Data 1 2 3 4 X Y X Y X Y X Y Why represent all the data? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. Mean • summaries lose information, details matterVariance – confirm expected and find unexpected patternsCorrelation – assess validity of statistical model Anscombe’s Quartet Identical statistics x mean 9 x variance 10 y mean 7.5 y variance 3.75 x/y correlation 0.816 https://www.youtube.com/watch?v=DbJyPELmhJc Same Stats, Different Graphs 5 Why focus on tasks and effectiveness? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. • tasks serve as constraint on design (as does data) – idioms do not serve all tasks equally! – challenge: recast tasks from domain-specific vocabulary to abstract forms • most possibilities ineffective – validation is necessary, but tricky – increases chance of finding good solutions if you understand full space of possibilities • what counts as effective? – novel: enable entirely new kinds of analysis – faster: speed up existing workflows 6 Why are there resource limitations? Vis designers must take into account three very different kinds of resource limitations: those of computers, of humans, and of displays. • computational limits – processing time – system memory • human limits – human attention and memory • display limits – pixels are precious resource, the most constrained resource – information density: ratio of space used to encode info vs unused whitespace • tradeoff between clutter and wasting space, find sweet spot between dense and sparse 7 Analysis: What, why, and how • what is shown? – data abstraction • why is the user looking at it? – task abstraction • how is it shown? – idiom: visual encoding and interaction • abstract vocabulary avoids domain-specific terms – translation process iterative, tricky • what-why-how analysis framework as scaffold to think systematically about design space 8 Why analyze? SpaceTree TreeJuxtaposer • imposes structure on huge design space –scaffold to help you think systematically about choices –analyzing existing as stepping stone to designing new –most possibilities ineffective for [SpaceTree: Supporting Exploration in Large [TreeJuxtaposer: Scalable Tree Comparison Using particular task/data combination Node Link Tree, Design Evolution and Empirical Focus+Context With Guaranteed Visibility. ACM Trans. on Evaluation. Grosjean, Plaisant, and Bederson. Graphics (Proc. SIGGRAPH) 22:453– 462, 2003.] What? Why? How? Proc. InfoVis 2002, p 57–64.] Tree Actions SpaceTree Present Locate Identify Encode Navigate Select Filter Aggregate Targets TreeJuxtaposer Path between two nodes Encode Navigate Select Arrange 9 What? Datasets Attributes Data Types Attribute Types Items Attributes Links Positions Grids Categorical Data and Dataset Types Tables Networks & Fields Geometry Clusters, Ordered Trees sets, lists Ordinal Items Items (nodes) Grids Items Items Attributes Links Positions Positions Attributes Attributes Quantitative Dataset Types Ordering Direction Tables Networks Fields (Continuous)Data Abstraction Sequential Attributes (columns) Grid of positions What? Datasets Attributes Link Items Cell (rows) Node Data Types Diverging Attribute Types (item) Items Attributes Links Positions Grids Categorical Cell containing value Attributes (columns) Data and Dataset Types Value in cell Cyclic Multidimensional Table Trees Tables Networks & Fields Geometry Clusters, Ordered Trees Sets, Lists Ordinal Items Items (nodes) Grids Items Items Attributes Links Positions Positions Attributes Attributes Quantitative Value in cell Dataset Types Ordering Direction Tables Networks Fields (Continuous) Sequential Attributes (columns) Grid of positions Link (Spatial) Items Cell Geometry (rows) Node Diverging (item) Cell containing value Attributes (columns) Value in cell Cyclic Multidimensional Table Trees Position Value in cell Dataset Availability What? Geometry (Spatial) Static Dynamic Why? Position How? Dataset Availability What? [VAD Fig 2.1] Static Dynamic Why? 10 How? VAD Ch 3: Task Abstraction Why? Actions Targets Analyze All Data Consume Trends Outliers Features Discover Present Enjoy Attributes Produce Annotate Record Derive One Many tag Distribution Dependency Correlation Similarity Extremes Search Target known Target unknown Location • {action, target} pairs Lookup Browse Network Data known Location Locate Explore Topology –discover distribution unknown –compare trends Query Paths –locate outliers Identify Compare Summarize What? –browse topology Spatial Data Why? Shape How? [VAD Fig 3.1] 11 How? Encode Manipulate Facet Reduce Arrange Change Juxtapose Filter Express Separate Order Align Select Partition Aggregate Use How? Navigate Superimpose Embed How? How? EncodeEncode ManipulEnatceode ManipulFacatete ManipulatRedue Facecet Facet ReduReducece ArArangerrange MapArrCangehange ChangeJuxtapose ChangeFilJuterxtaposeJuxtapose FilterFilter Express Separate from cExpategoressrical and oSrepaderredat e Express Separaattteributes Color Hue Saturation Luminance Order Align OSelerderct Align Partition Select Aggregate Partition Aggregate Order Align Size, Angle, CurvatureS, ele... ct Partition Aggregate Use Use Navigate Superimpose NavigatEmbede Superimpose Embed Shape What? Use Map Map Navigate Superimpose Embed Motionfrom categorical and ordered Why? from categorical and ordered Direction, Rate, Frequency, ... attributes attributes How? Color Color Hue Saturation Luminance Hue Saturation Luminance Map fSirzome, Angl caet,ego Curvraicalture, and... ordered Size, Angle, Curvature, ... attributes Color 12 Shape Shape What? Hue Saturation Luminance What? Motion Why? Motion Direction, Rate, Frequency, ... Why? DirSiection,ze, R Aatengl, Frequene, Ccy,u ...rvature, ... How? How? Shape What? Motion Why? Direction, Rate, Frequency, ... How? Actions High-level actions: Analyze • consume Analyze –discover vs present Consume Discover Present Enjoy • classic split • aka explore vs explain –enjoy • newcomer Produce • aka casual, social Annotate Record Derive tag • produce –annotate, record –derive Search • crucial design choice Target known Target unknown Location 13 Lookup Browse known Location Locate Explore unknown Query Identify Compare Summarise Derive • don’t just draw what you’re given! –decide what the right thing to show is –create it with a series of transformations from the original dataset –draw that • one of the four major strategies for handling complexity exports imports trade balance trade balance = exports −imports Derived Data Original Data 14 Actions Analyze Consume Discover Present Enjoy Produce Annotate Record Derive tag Actions: Mid-level search, low-level query • what does user know? Search –target, location Target known Target unknown Location Lookup Browse • how much of the data known Location matters? Locate Explore unknown –one, some, all Query • independent choices, Identify Compare Summarize mix & match –analyze, query, search 15 Why? Actions Targets Analyze All Data Consume Trends Outliers Features Discover Present Enjoy Attributes Produce Annotate Record Derive One Many tag Distribution Dependency Correlation Similarity Why? Extremes Targets Search Actions Targets Target known Target unknown Location Analyze All Data Lookup Browse Network Data known Consume Trends Outliers Features Location Locate Explore Topology Discover Present Enjoy unknown Query Paths Attributes Identify Compare Summarize Produce What? Annotate Record Derive One Many Spatial Data tag Distribution Dependency Correlation Similarity Why? Shape Extremes How? Search Target known Target unknown Location 16 Lookup Browse Network Data known Location Locate Explore Topology unknown Query Paths Identify Compare Summarize What? Spatial Data Why? Shape How? Analysis example: Compare idioms SpaceTree TreeJuxtaposer [SpaceTree: Supporting Exploration in Large Node Link Tree, Design Evolution and Empirical Evaluation. Grosjean, Plaisant, and Bederson. Proc. InfoVis 2002, p 57–64.] [TreeJuxtaposer:
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