Data Visualization (CIS 468)

Transcription

1 Data Visualization (CIS 468) Multiple Views Dr. David Koop

2 Multiple Views [Improvise, Weaver, 24] 2

3 Multiple Views Why have just one visualization? Sometimes data is best examined in more than one view - Clutter/visual overload - Different attributes (cannot show all attributes in one view) - Different scales (task requires overview or detail) - Different encodings (no single encoding is optimal for all tasks) Eyes Beat Memory (Ch. 6) - Aiding working memory: side-by-side/layers > animated > jump cuts - Showing all visual elements at once don't need to remember 3

4 Summary of Choices (Scatterplot + Bar Chart) (a) Juxtaposed views. (b) Integrated views. (c) Superimposed views abcde fgh (d) Overloaded views. (e) Nested views. [W. Javed and N. Elmqvist, 212] 4

5 Juxtaposition [ComVis, K. Matkovic et al., 28] 5

6 Integration "best statistical graphic ever" (later known as a Sankey Diagram) [Napoleon's March to Moscow, C. J. Minard, 1869] 6

7 Multiple Views Juxtapose and Coordinate Multiple Side-by-Side Views Linked Highlighting Share Data: All/Subset/None Share Navigation [Munzner (ill. Maguire), 214] 7

8 Multiple Views All Subset None Same Redundant Overview/ ail Small Multiples Multiform Multiform, Overview/ ail No Linkage [Munzner (ill. Maguire), 214] 8

9 Multiform [Improvise, Weaver, 24] 9

10 Overview-ail View [Wikipedia] 1

11 Small Multiples Same encoding, but different data in each view (e.g. SPLOM) 7 sepal length sepal width petal length petal width [M. Bostock] 11

12 Multiple Views Partition into Side-by-Side Views Superimpose Layers [Munzner (ill. Maguire), 214] 12

13 Partitioning: Matrix Alignment Manchuria No. 462 Manchuria No. 462 Manchuna No. 462 Manchuria No Waseca Waseca Crookston Crookston Morris Morris.i :31 University Farm University Farm Duluth Duluth Grand Raoids Grand Raoids Manchuria No. 462 Manchuria No. 462 Manchuria No. 462 Manchuria No. 462 Manchuria No. 462 Manchuria No o 1931 I I I I Waseca Crookston Morris University Farm Duluth Grand Rapids I:) Barley Yield (bushels/acre) [Becker et al., 1996] 13

14 F Islin Ter City offlat London Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Harrow Barnet Enfield Haringey Waltham Forest Redbridge Havering Sem F Barking Flat Ter Ter Partitioning: Recursive Subdivision A Hammer Flat Flat Mert Havering T Newham Ter Se Flat Ter Flat Westminster Ter Flat Kingston M Ter Flat Ter Fl Harrow B Se Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Hillingdon Brent Camden Westminster Hackney Newham Barking Flat Ter Fl Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Fl Hounslow Ealing Hammersmith Kensington Islington Tower Hamlets Greenwich Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Richmond Kingston Merton Wandsworth City of London Southwark Bexley B Hillingdon Se E Hounslow Se Flat Ter Fl K Richmond Se Flat Ter Flat Ter Flat Ter Flat Ter Flat Ter Fl Sutton S Croydon Lambeth C Lewisham Bromley Se [Slingsby et al., 29] 14

15 Assignment 4 assignment4.html Interaction, Network, and Multiple Views Due before Thanksgiving, but remember Quiz 2! 15

16 Superimposition [Mapgets, A. Voisard, 1995] 16

17 Superimposed Layers Put different layers in the same spatial region, overlay information Usually each layer spans the entire view Must be identifiable: visually distinguishable Cartography has to deal with this a lot May be static or dynamic (user controls which layers are shown) 17

18 Example: Superimposed Line Charts 8 Temperature (ºF) 7 Austin 6 New York San Francisco October November December 212 February March April May June July August September [M. Bostock] 18

19 Example: Hierarchical Edge Bundles [D. Holten, 26] 19

20 D3 Multiple Views and Interaction Process mouseover and mouseout events - Get selected element - Provide feedback (e.g. highlighting) Find matching items in other view(s) - Can use filter for this - Highlight them - Make sure that if they overlap, the highlighted item is on top 2