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Tamara Munzner Department of Computer Science University of British Columbia Ch 1/2/3: Intro, Data, Tasks Paper: Design Study Methodology Tamara Munzner Department of Computer Science University of British Columbia CPSC 547, Information Visualization Week 2: 17 September 2019 http://www.cs.ubc.ca/~tmm/courses/547-19 News • Signup sheet round 2: check column (or add yourself) • Canvas comments/question discussion –one question/comment per reading required • everybody got this right, great! –responses to others required • a few of you did not do this • original requirement of 2, considering cutback to just 1: discuss • decision: cut back to just 1 –if you spot typo in book, let me know if it’s not already in errata list • http://www.cs.ubc.ca/~tmm/vadbook/errata.html • (but don’t count it as a question) • not useful to tell me about typos in published papers 2 Ch 1. What’s Vis, and Why Do It? 3 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 4 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.] 5 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 6 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 7 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 8 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 9 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 10 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 11 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? 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 FieldsVAD (Continuous) Ch 2: 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? Static Dynamic [VAD Fig 2.1] Why? 12 How? Ch 2. What: Data Abstraction 13 Three major datatypes DatasetDatasetDatasetDataset Types Types Types Types Dataset Types Tables Networks Fields (Continuous) Geometry (Spatial) TablesDatasetTablesTables Types NetworksTablesNetworksNetworks FieldsFieldsFields NetworksSpatial(Continuous) (Continuous)(Continuous) GeometryFieldsGeometryGeometry (Continuous) (Spatial) (Spatial)(Spatial) Geometry (Spatial) Attributes (columns) Grid of positionsGrid of positions TablesAttributesAttributesAttributes (columns) (columns) (columns) NetworksAttributes (columns) FieldsGridGrid (Continuous) of of positions positions GeometryGrid of positions (Spatial) ItemsItems Link Link ItemsItems Items LinkLink Cell Cell Grid of positionsLink (rows)(rows) Attributes (columns) CellCell Cell Position Position (rows)(rows) (rows) Node Node PositionPosition Position NodeNode Node Items (item)Link(item) Cell containingCell containing value value (item)(item) AttributesCell (columns)Attributes (columns)(item) (rows) CellCell containing containing value value Cell containing value AttributesAttributes (columns) (columns) Attributes (columns) Position Node (item) Value in cell Value in cell Cell containing value AttributesValueValue in in (columns)cell cell Value in cell MultidimensionalMultidimensional Table Table Trees Trees MultidimensionalMultidimensional Table Table MultidimensionalTreesTrees Table Trees Value in cell Multidimensional Table Trees Value in cell • visualization vs computer graphics Value in cell ValueValue in in cell cell Value in cell –geometry is design decision Value in cell 14 Attribute types Attributes Attribute Types Categorical Ordered Ordinal Quantitative Ordering Direction Sequential Diverging Cyclic 15 Dataset and data types Data and Dataset Types Tables Networks & Fields Geometry Clusters, Trees Sets, Lists Items Items (nodes) Grids Items Items Attributes Links Positions Positions Attributes Attributes Data Types Items Attributes Links Positions Grids Dataset Availability Static Dynamic 16 Further reading: Articles • Mathematics and the Internet: A Source
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